text
string | predicted_class
string | confidence
float16 |
|---|---|---|
We conclude that the common approach of prioritizing loci with established genome-wide significant association signals without further discrimination for G×E interaction analyses might be useful, but the efficiency of such analyses could be substantially improved by focusing on variants with low P-values for both variance heterogeneity and marginal effects. We provide these rankings here to facilitate this approach.
|
study
| 100.0 |
Three sources of genome-wide results were used: i) meta-analysis of Levene’s test results for between-genotype heterogeneity of phenotypic variances; ii) published results for marginal effects genome-wide association studies undertaken by the GIANT and GLGC consortia; iii) published results for SNP × physical activity and SNP × smoking in BMI (from the GIANT consortium).
|
study
| 100.0 |
We performed a genome-wide search for SNPs whose associations with the following traits are characterized by high between-genotype variance heterogeneity: BMI, TC, TG, HDL-C and LDL-C. The variance heterogeneity analyses were performed using Levene’s test in up to 44,211 participants of European descent from seven population-based cohorts. Descriptions of these cohorts are presented in S2 Table. To minimize bias that might result from unequal sample sizes between SNPs when calculating the correlations between the P-values from the marginal (Pm) and variance heterogeneity (Pv) meta-analyses, we restricted the sample size for analyses to 26,000 participants for BMI and to 24,000 participants for lipid traits (S4 Fig).
|
study
| 100.0 |
A detailed summary of sample sizes, genotyping platforms, genotype calling algorithms, sample and SNP quality control filters, and analysis software for all participating cohorts are provided in S2 and S3 Tables. For each individual, SNPs were imputed using the CEU reference panel of HapMap II (S2 Table). We excluded SNPs with low imputation quality (below 0.3 for MACH, 0.4 for IMPUTE, and 0.8 for PLINK imputed data), Hardy-Weinberg equilibrium P <10−6, directly genotyped SNP call rate < 95%, and minor allele frequency (MAF) < 1%.
|
study
| 100.0 |
We identified SNPs that have been robustly associated (P<5x10-8) with the five cardiometabolic traits in European ancestry populations: 77 SNPs associated with BMI discovered by GIANT ; and 58 SNPs associated with LDL-C, 71 SNPs associated with HDL-C, 74 SNPs associated with TC, and 40 SNPs associated with TG discovered by GLGC.
|
study
| 100.0 |
We used Levene’s test to identify SNPs that show heterogeneity of phenotypic variances (σi2) across the three genotype groups at each SNP locus (i = 0, 1, or 2). We first log10 transformed all five traits followed by a z-score transformation by subtracting the sample mean and dividing by the sample standard deviation (SD), and further Winsorized the z-score values at 4 SD. The transformed phenotype Y was then used to calculate Z, defined by the absolute deviation of each participant’s phenotype from the sample mean of his or her respective genotype group at a given SNP locus. For each trait, participating cohorts provided the necessary summary statistics for each genotype at each marker . Specifically, the per genotype group counts (n0s, n1s, n2s), per genotype means (Z¯0s,Z¯1s,Z¯2s), and per genotype group variances of Z (σ0s2,σ1s2,σ2s2) were centrally collected and meta-analyzed. The minimum number of observations per genotype group required is 30 participants per cohort.
|
study
| 100.0 |
Where N is the combined sample size, Z¯is and σZis2 are the sample mean and variance of Z in the ith genotype group of the sth study, respectively. When combining summary-level data to calculate the Levene’s test statistics L, the following natural weights ωis and γi were calculated: ωis=nis∑snis and γi=niN, where ni the sum of genotype counts in the ith genotype group across all participating cohorts. These weights are determined by the frequency of the marker amongst the cohorts, such that the sum of both weights is equal to 1, i.e. ∑sωis=1 and ∑iγi=1. The meta-analysis Levene’s test P-value is obtained by comparing L to an F-distribution with df1 = 2 and df2 = N-3.
|
study
| 100.0 |
Marginal effects P-values for BMI and the relevant lipid traits were obtained from publically available GWAS summary data from the GIANT and GLGC consortia, respectively (all cohorts included here in the Levene’s meta-analysis were also included in the GIANT and GLGC datasets).
|
study
| 100.0 |
To illustrate our findings, we rank-ordered the P-values (from lowest to highest) from both marginal effects and variance effects analyses for all 1,927,671 SNPs so that the lowest P-value for a given trait was assigned a rank equal to the lowest 100th centile. These rank-scaled distributions for Pm for all five traits are presented in Fig 1.
|
study
| 100.0 |
We calculated Spearman’s correlations for each of the five cardiometabolic traits between Pm and Pv. This was done using a pruned set of SNPs. Pruning was performed in the TwinGene cohort using the--indep-pairwise 50 5 0.1 command in PLINK by calculating LD (r2) for each pair of SNPs within a window of 50 SNPs, removing one of a pair of SNPs if r2>0.1; we proceeded by shifting the window 5 SNPs forwards and repeating the procedure. Spearman’s correlations were computed for categories of SNPs: i) all pruned SNPs, ii) the subset of SNPs that was nominally significant (Pm<0.05) in the marginal effects analysis, iii) the subset of SNPs with Pm<10−4 in the marginal effects analysis, and iv) SNPs that were previously established in conventional marginal effects GWAS meta-analyses (Pm<5×10−8). We also compared Spearman’s correlations between these categories of SNPs using the test for equality of two correlations .
|
study
| 100.0 |
We also tested if there is a difference in Pv ranks for SNPs from the lowest 100th centile of the Pm rank-ordered distribution for all five traits and the rest of SNPs in the pruned set of SNPs using the Mann–Whitney U test, including and excluding established SNPs (or SNPs that were +/-500kb from the reported lead SNP). This analysis was repeated for SNPs from the 99th centile vs SNPs from 1st to 98th centiles of the Pm rank-ordered distribution. The same Mann–Whitney U tests were used to study differences in Pm ranks for SNPs from the lowest 100th and 99th centiles of the Pv rank-ordered distribution and the rest of SNPs in the pruned set of SNPs.
|
study
| 100.0 |
We used now published data from 210,316 European-ancestry adults (from the GIANT consortium) pertaining to marginal effects meta-analyses for BMI that had been performed separately by strata of smoking (45,968 smokers vs. 164,355 non-smokers) . The genetic marginal effect estimates, calculated separately within each of the two strata, were compared using a heterogeneity test to infer the presence or absence of SNP × smoking interaction effects. The same analyses were performed using physical activity as a binary stratifying variable in up to 180,287 European-ancestry adults (42,065 physically active vs. 138,222 physically inactive) . We calculated Spearman correlations between the P-values derived from the marginal effects meta-analysis and the Pint from the interaction effects meta-analysis (i.e., the between-strata heterogeneity test for SNP × smoking and SNP × physical activity interactions from the GIANT consortium); these tests were undertaken for all SNPs and those SNPs that were nominally significant (Pm<0.05) in the marginal effects analysis. We then performed enrichment analyses to test if the numbers of nominally significant (Pint<0.05) GWAS-derived SNPs from both SNP × physical activity and SNP × smoking analyses were greater than expected by chance under the binomial distribution. We further calculated the OR of having Pint<0.05 given Pv<0.05 versus Pv≥0.05 both SNP × physical activity and SNP × smoking interaction analyses in a pruned set of TwinGene SNPs produced using the—indep-pairwise 50 5 0.8 command in PLINK .
|
study
| 100.0 |
Thereafter, we calculated the average rank for each SNP’s ranking on the Pint rank-ordered distributions from the SNP × smoking and SNP × physical activity interaction analyses and performed enrichment analysis using these average ranks with >95th centile instead of Pint<0.05 as the cut-off.
|
study
| 100.0 |
We simulated genetic data for 44,000 individuals from a pruned set of 50,335 SNPs with allele frequencies, effect estimates and Pm values drawn from the GIANT consortium. We generated an outcome trait by summing the products of the simulated allele counts and effect estimates over all SNPs for each individual, and subsequently added a randomly generated non-normal error term such that the trait resembles the observed distribution of the transformed BMI trait used in the main (real data) analyses. We also simulated a fixed binary interacting factor with 30% prevalence. Using this simulated dataset, we calculated Pm, Pv and Pint values for each SNP and undertook i) pairwise Spearman correlation analyses between Pm, Pv and Pint values (5,000 simulations), ii) enrichment analysis using binomial tests (2,500 simulations) and iii) Mann-Whitney U tests to determine systematic differences in Pv and Pm ranks (2,500 simulations). Following the same pipeline, we created additional simulated datasets narrowing down SNPs to i) those with Pm values from the lowest percentile (n = 504; highest Pm = 5×10−3) and to ii) genome-wide significant SNPs (n = 71; Pm<5×10−8), and tested the pairwise Spearman correlation for Pm, Pv and Pint values (1,000 simulations for both sets). Simulations were run using the statistical software R (v. 3.3.2).
|
study
| 100.0 |
A: Quantile-quantile plot of Spearman correlation test P-values for ranks of Pm and Pv. Quantile-quantile plot of Spearman correlation test P-values for ranks of Pm and Pv. The figure illustrates 5,000 Spearman correlation P values testing for correlation between Pm and and Pv values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines. The dashed red line represents the correlation P value obtained from the “real data” analysis presented in the main text. B. Quantile-quantile plot of Spearman correlation test P-values for ranks of Pm and Pint. Quantile-quantile plot of Spearman correlation test P-values for ranks of Pm and Pint. The figure illustrates 5,000 Spearman correlation P values testing for correlation between Pm and and Pint values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines. C. Quantile-quantile plot of Spearman correlation test P-values for ranks of Pint and Pv. Quantile-quantile plot of Spearman correlation test P-values for ranks of Pint and Pv. The figure illustrates 5,000 Spearman correlation P values testing for correlation between Pint and and Pv values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines.
|
study
| 100.0 |
A. Quantile-quantile plot of binomial test P-values for enrichment of variants with Pv<0.05 among variants with Pm<0.05. Quantile-quantile plot of binomial test P-values for enrichment of variants with Pv<0.05 among variants with Pm<0.05. The figure illustrates 2,500 binomial P values testing for enrichment of variants with Pv<0.05 among all variants with Pm<0.05. Pv and and Pm values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines. B. Quantile-quantile plot of binomial test P-values for enrichment of variants with Pv<0.05 among variants with Pint<0.05. Quantile-quantile plot of binomial test P-values for enrichment of variants with Pv<0.05 among variants with Pint<0.05. The figure illustrates 2,500 binomial P values testing for enrichment of variants with Pv<0.05 among all variants with Pint<0.05. Pv and and Pint values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, the distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines. The dashed red line represents the correlation P value obtained from the “real data” analysis presented in the main text.
|
study
| 100.0 |
A. Quantile-quantile plot of Mann-Whitney U test P-values for systematic differences in Pv ranks among variants with top ranking and lower ranking Pm values. Quantile-quantile plot of Mann-Whitney U test P-values for systematic differences in Pv ranks among variants with top ranking and lower ranking Pm values. The figure illustrates 2,500 Mann-Whitney U P values testing for systematic differences in Pv ranks among those variants with the most significant Pm values (100th percentile of Pm distribution) and the remaining variants (1–99 percentile of Pm distribution). Pv and and Pm values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines. The dashed red line represents the correlation P value obtained from the “real data” analysis presented in the main text. B. Quantile-quantile plot of Mann-Whitney U test P-values for systematic differences in Pm ranks among variants with top ranking and lower ranking Pv values. Quantile-quantile plot of Mann-Whitney U test P-values for systematic differences in Pm ranks among variants with top ranking and lower ranking Pv values. The figure illustrates 2,500 Mann-Whitney U P values testing for systematic differences in Pm ranks among those variants with the most significant Pv values (100th percentile of Pv distribution) and the remaining variants (1–99 percentile of Pv distribution). Pv and and Pm values drawn from a simulated dataset of 44,000 individuals and 50,335 SNPs. In the figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines. The dashed red line represents the correlation P value obtained from the “real data” analysis presented in the main text.
|
study
| 100.0 |
Associations between SNPs and BMI (A), LDL (B), HDL (C), TG (D), TC (E) are presented. Only SNPs with N ≥ 26,000 samples for BMI and N ≥ 24,000 for lipid traits are shown. In each sub-figure, distribution under the null hypothesis is represented as a black line while its 95% confidence interval is represented as dashed gray lines.
|
study
| 99.94 |
Walking is a complex task involving the coordination of multiple body segments over multiple cycles (steps) [1–3]. As one of the most practised of all motor skills , variability allows us to continuously construct adaptive coordination patterns as we move through the environment, resisting expected and unexpected perturbations that may occur causing instability . Studies of variability are thus central to understanding gait stability [7–9]. Control of speed during walking is a coordinated task driven by the interactions of the nervous and musculoskeletal systems and the environment . Motor tasks such as a step cycle are provided a window of optimal variability that enables accurate, stable completion of that task , despite internal and external perturbations.
|
review
| 89.3 |
Variability in kinematic parameters is not always considered beneficial, but equally some degree of variability is not always considered detrimental . High variability in kinetic and kinematic variables can increase energetic costs , and increase the risk of falls , but equally it can facilitate an increase in motor performance . Both younger (less than 65 years) and older (more than 65 years) adults show increases in the variability in step parameters when walking at speeds faster, or slower than their comfortable walking speed ; only there is a slightly wider distribution in the magnitude of variability in older adults . Thus, the nature of the relationship between motor control and biomechanical variability is not simple, and is not yet completely understood; for example, we do not understand how variability functions at the body–ground interface. Fortunately, plantar pressure records capture the summation of kinetic and kinematic forces produced by the moving body against the ground. Fluctuations in the centre of mass over the base of support, and perturbations that challenge stability and balance during adaptive coordination, occur here at the foot–ground interface. It is therefore timely for a systematic study characterizing variability in plantar pressure, and this is the goal of this contribution.
|
study
| 99.94 |
The variable nature of motor patterns and the potential for a change in substrate or speed with each step imply that each pressure record will be slightly different . A recent study of plantar pressure at a single walking speed using approximately 500 records per subject, demonstrated large inter- and intra-subject (i.e. step-to-step) pressure variation in the midfoot . However, discussion of variability across the whole plantar surface and the influence of speed were not considered as part of that contribution.
|
study
| 100.0 |
Here therefore, we compare the magnitude and spatial variability in plantar pressure records across a wide range of controlled walking speeds (1.1–1.9 m s−1) using a large dataset. Most previous investigations of the effects of speed on peak plantar pressure distribution use self-selected walking speeds, described as ‘slow’, ‘preferred’ and ‘fast’ [19–30]. Here, we control for speed in order to standardize the comparative analysis between walking speeds. We applied two calculators of variability to this dataset: mean square error (m.s.e.) and coefficient of variation (CV) at a pixel level across the whole plantar surface, using pedobarographic statistical parametric mapping (pSPM). The aims of this study were to assess the effect of walking speed (1.1–1.9 m s−1) on the: (i) magnitude and (ii) spatial distribution of variability across the whole plantar surface of the foot and (iii) to describe the spatial distribution of variability using these two commonly used metrics in biomechanical studies, m.s.e. and CV.
|
study
| 100.0 |
A total of 16 subjects (11 male, 5 female, aged 21–47 years; table 1), without pathologies, abnormalities or injuries, walked barefoot on a Zebris FDM-THM plantar pressure sensing treadmill at controlled speeds of 1.1 m s−1, 1.3 m s−1, 1.5 m s−1, 1.7 m s−1 and 1.9 m s−1 for 5 min in a randomized trial order. The slowest speed (1.1 m s−1) was selected as slower than a ‘comfortable’ walking speed for healthy, young cohorts. We are conscious that comfortable speed is relative to different ages and abilities; however, 1.1 m s−1 may generally be considered slow for healthy cohorts. The fastest speed, 1.9 m s−1, was chosen as the closest speed to the accepted walk/run transition (defined as 1.88 m s−1 ). 1.3 m s−1, 1.5 m s−1 and 1.7 m s−1 were chosen for intuitive ease of analysis. Table 1.Summary of subjects' anthropomorphic measurements and N pressure records collected at each walking speed. Leg length was measured as the distance from the greater trochanter to the plantar foot surface.weightheightleg lengthNNNNNtotalsubjectgenderage(kg)(m)(m)(1.1 m s−1)(1.3 m s−1)(1.5 m s−1)(1.7 m s−1)(1.9 m s−1)NAM21751.740.9055285836266686943099BM2878.61.9115265445776136222882CF24591.650.835746006286877213210DM2783.31.850.9255165585936416882996EM2282.71.960.9654755335626086462824FM26861.751.34865205625906222780GM29701.750.8855335916076447023077HM30871.70.9055265685686546652981IF3152.71.580.855606126516947463263JM31741.730.925025535866517072999KM44801.9315045735976306602964LF37631.630.945295775946226492971MF3752.71.560.836116636927518183535NM24811.750.895235636146466863032OF2364.41.580.855786146877087653352PM20731.850.965135715996246592966total N48 931
|
study
| 100.0 |
Peak pressure values from each sensor contacted on the treadmill, within the boundaries of each foot, were extracted using a custom-written C program , yielding between 2780 and 3535 peak pressure images (p-images) per subject across the five speed trials (table 1). This exceeds the required sample size of 400 steps suggested for reliability in studies of variability in lower extremity kinematic parameters . All p-images within each speed trial were registered to each other in a vertical stack using a two-stage rigid body transformation via an algorithm that minimized the m.s.e. between the images, such that homologous structures optimally overlapped . The first step recorded during each subject's walking trials was used as the registration template to which all subsequent images in each speed trial were registered. The mean image from each speed trial was calculated from the stack and used as the registration template for a second iteration of the same dataset .
|
study
| 100.0 |
Intra-subject analyses using pSPM were conducted on all pressure records to quantify spatial variability at the five controlled walking speeds . To quantitatively compare variability in peak pressure at different speeds, we used two different measures of variability. The algorithm used to register p-images in pSPM is based on minimizing the m.s.e. between pixels globally, across each image. Previous studies of walking speed using pSPM have sought to test if the mean pressure across the foot differs across walking speeds . Thus, in the context of previous studies using pSPM that examine changes in pressure with speed it is logical to assess variability relative to the mean pressure in each pixel at each speed. The m.s.e. reflects the absolute variation of each pixel value from the same pixel value in the mean p-image. In addition, we chose to calculate the CV as a widely used metric to quantify variability in clinical [1,7,33–35] and sports biomechanics [36–40]. CV calculates the variance of the entire sample about the mean but cannot take into consideration the error that arises from differences in pixel vector values. However, the widespread use of CV to quantify variability in kinematic parameters during gait allows us to compare levels of variability in plantar pressure to other biomechanical parameters (e.g. step length, width, time and impulse).
|
study
| 100.0 |
The m.s.e. was calculated over non-zero pixels in each p-image within a subject's total sample according to m.s.e.=1N∑(I0k−Ikk)2, where N is the total number of non-zero pixels in the mean image, I0 is the mean of the subject's overall sample and Ik is an individual pedobarographic record. The CV was calculated over non-zero pixels in each pedobarographic image within a subject's total sample according to CV=ms.d.×100, where m is the mean value of each non-zero pixel over all pressure images in the sample, and s.d. is the standard deviation of the sample across all p-images in each subject's dataset. Each value is multiplied by 100 to produce a percentage classed as high (more than 5%) or low (less than 5%) . Very low levels of variability are considered less than 3% .
|
study
| 100.0 |
The m.s.e. and CV of each non-zero pixel is summed to produce a total m.s.e. and CV value for each individual pressure record, about the subject's overall mean pressure image. Each speed trial produces a different number of prints as more steps are generally taken at faster speeds. To standardize our comparisons we used a downsampling approach to extract random sub-samples of 400 prints from the overall n, a total of 100 times for each speed and calculated the mean m.s.e. and CV of these 100 samples of 400 prints. This value was retained as representative for each speed. To assess changes in variability with speed, we plotted m.s.e. and CV against speed using reduced major axis (RMA) regression, and tested for linear changes with speed. In addition, we conducted topological regression analysis on the m.s.e. analysis, to test for linear changes in variability across the plantar surface with speed. All image processing and analysis described above was conducted using Matlab (MathWorks, USA).
|
study
| 100.0 |
The main results of this study can be summarized as follows: (i) The magnitude of variability in plantar pressure assessed by m.s.e. is disparate and observed between speeds, and between subjects (figure 1). All subjects expressed CV less than 2.5% (figure 1), which is less than that reported as ‘very low’ by standard measures of CV in biomechanics literature (less than 3%).(ii) RMA regression suggested positive linear trends in m.s.e. across walking speeds in 11 out of 16 subjects, and CV and speed in 15 out of 16 subjects (figure 1). R2 and p-values strongly support a linear increase in plantar pressure variability with speed when assessed by m.s.e. in 4 subjects, and in 11 subjects when assessed by CV (R2 > 0.5, p = <0.05) (table 2) (figure 1). Topological regression analysis of m.s.e. and speed confirmed no statistical support for a systematic linear increase in variability with speed (figure 2).(iii) RMA regression suggested positive linear relationships between m.s.e. and Froude number in 11 out of 16 subjects, and CV and Froude number in 15 out of 16 subjects (figure 3). However, R2 and p-values (R2 > 0.5, p = <0.05) only strongly support linear increases in variability with Froude number when assessed by m.s.e. in 4 subjects, but in 11 subjects when assessed by CV (table 2) (figure 3).(iv) Qualitative analysis of topological variation maps suggests that spatial distribution of m.s.e. is highest in the forefoot, specifically the lateral and medial margins, under metatarsal heads five and one, respectively, in all subjects, and is independent of speed (figure 4).(v) Qualitative analysis of topological variation maps suggests that the spatial distribution of CV is highest in the midfoot, medial phalanges, big toe and lateral margins of the heel in all subjects, and is independent of speed (figure 5). Figure 1.(a–p) RMA regression suggested positive linear trends in m.s.e. (y axis) across all walking speeds in 11 out of 16 subjects, and CV (z axis) and speed in 15 out of 16 subjects. However r2 and p-values only strongly support a linear increase in plantar pressure variability with speed in four subjects when assessed by m.s.e., but in 10 subjects when assessed by CV (r2 > 0.75, p = <0.05) (see table 2). Figure 2.(a–p) Linear regression by pSPM (left panel) reveals no statistical support for linear changes in variability by m.s.e. with speed. The left image is the inference map, showing areas of increasing or decreasing variability in pressure across the plantar surface. The red pixels indicate that variability is increasing with speed around the periphery of the heel and forefoot only. The blue pixels indicate that variability is decreasing with speed around periphery of the midfoot and occasionally the hallux. Statistically significant pixels are concentrated almost exclusively around the periphery of mechanically distinct areas of the foot, thus they are most probably artefacts of small changes in foot contact area, size and shape with speed. The mechanically distinct areas of the plantar surface, heel, mid- and forefoot, remain white with no statistically significant relationships evident between variability and speed. Figure 3.(a–p) RMA regression suggested positive linear relationships between m.s.e. (y axis) and Froude number in 11 out of 16 subjects, and CV (z axis) and Froude number in 15 out of 16 subjects. However, R2 and p-values (r2 > 0.75, p = <0.05) strongly support linear increases in variability with Froude in only four subjects when assessed by m.s.e. in four subjects, and in 11 subjects when assessed by CV (table 3). Froude number is (Fr = v2/g × LL), where v2 is speed squared, g is gravity (9.81 m s−2) and LL is each subject's leg length measured from the superior apex of the iliac crest to the where the heel meets the floor. Figure 4.(a–p) m.s.e. variation maps represent the distribution and magnitude of the combined mean m.s.e. in each pixel across the plantar surface of the foot at each speed trial in all subjects. Intra-subject spatial variability measured by the mean m.s.e. is highest and confined almost exclusively to the lateral and medial forefoot in all subjects; however, there is no consistent increasing or decreasing relationship between mean m.s.e. and speed. Figure 5.(a–p) CV variation maps represent the distribution and magnitude of the combined CV in each pixel across the plantar surface of the foot at each speed trial in all subjects. Intra-subject spatial variability measured by the CV is highest under the big toe and medial phalanges, as well as in the midfoot. There is no consistent increasing or decreasing relationship between mean CV and speed. Table 2.Regression statistics reveal a linear increase in m.s.e. with speed in only four subjects (subjects B, E, L, N). When assessed by CV, regression statistics reveal a linear increase in CV with speed in 10 subjects (subjects B, D, E, H, I, K, L, M, N, P).subjectcalculationR2p-valuesslopeinterceptAm.s.e.0.0101810.87175−0.439552.8989CV0.0843090.635560.358290.7358Bm.s.e.0.853920.0248251.24250.77694CV0.962160.0248251.5003−0.58675Cm.s.e.0.424120.233872.470.49312CV0.260870.379191.2834−0.17Dm.s.e.0.173870.484912.9502−0.10196CV0.739860.0614460.96520.05617Em.s.e.0.547970.152570.802831.0234CV0.731530.0646250.798050.51636Fm.s.e.0.0497470.718392.15862.1586CV0.276770.362490.736740.43294Gm.s.e.0.0516140.713242.77440.63995CV0.363760.281570.372310.89058Hm.s.e.0.107840.589523.4058−0.64398CV0.643910.102230.860140.10809Im.s.e.0.0817860.081786−1.33334.711CV0.736810.0626010.641070.21239Jm.s.e.0.332370.30894−1.33714.742CV0.200080.450081.0392−0.068518Km.s.e.0.40880.24542−1.54955.3533CV0.802210.0398170.710560.13506Lm.s.e.5.84 × 10−50.990270.712870.29741CV0.830650.0312330.785580.35856Mm.s.e.0.245190.396340.67866−0.29176CV0.910410.0117030.856540.067362Nm.s.e.0.748690.0581510.94132−0.086887CV0.925050.00891251.2525−0.53741Om.s.e.0.21920.426440.447260.65783CV0.256580.38381−1.2194−1.2194Pm.s.e.0.113620.57909−0.740772.5079CV0.763030.0529630.946360.94636 Table 3.Regression statistics reveal a linear increase in m.s.e. with Froude number in only four subjects (subjects B, E, M, N). When assessed by CV, regression statistics reveal a linear increase in CV with speed in 10 subjects (subjects B, D, E, F, H, I, K, L, M, N, P).subjectcalculationR2p-valuesslopeinterceptAm.s.e.0.00461520.91357−1.58682.5799CV0.128920.128921.29340.99583Bm.s.e.0.814870.0358974.05031.6787CV0.959110.00355394.89060.50204Cm.s.e.0.381290.267099.70092.2857CV0.252650.388095.04050.76138Dm.s.e.0.159010.5060810.3972.0391CV0.680140.0857473.40163.4016Em.s.e.0.590740.590742.71211.6061CV0.721490.0685452.69592.6959Fm.s.e.0.0522310.711575.41285.4128CV0.956260.237010.405580.96763Gm.s.e.0.121110.121110.566019.7704CV0.299580.299581.31121.1867Hm.s.e.0.0659150.06591512.2681.8277CV0.704060.0755893.09830.73232Im.s.e.0.0925190.618780.618783.759CV0.794260.0423632.45860.67764Jm.s.e.0.267840.26784−6.08783.9844CV0.238480.40394.73160.5203Km.s.e.0.360640.360640.28421−5.0511CV0.752010.0569342.31632.3163Lm.s.e.0.000387220.974952.00990.85545CV0.782170.0463522.72430.92868Mm.s.e.0.928680.432052.79450.12158CV0.90310.90313.36410.68899Nm.s.e.0.789120.789122.87450.6905CV0.69050.0044444.58780.37161Om.s.e.0.174440.484121.71530.98243CV0.291520.34758−4.67652.8734Pm.s.e.0.10530.5942−2.51541.9703CV0.744930.0595463.21360.77527
|
study
| 99.94 |
(i) The magnitude of variability in plantar pressure assessed by m.s.e. is disparate and observed between speeds, and between subjects (figure 1). All subjects expressed CV less than 2.5% (figure 1), which is less than that reported as ‘very low’ by standard measures of CV in biomechanics literature (less than 3%).
|
study
| 100.0 |
(ii) RMA regression suggested positive linear trends in m.s.e. across walking speeds in 11 out of 16 subjects, and CV and speed in 15 out of 16 subjects (figure 1). R2 and p-values strongly support a linear increase in plantar pressure variability with speed when assessed by m.s.e. in 4 subjects, and in 11 subjects when assessed by CV (R2 > 0.5, p = <0.05) (table 2) (figure 1). Topological regression analysis of m.s.e. and speed confirmed no statistical support for a systematic linear increase in variability with speed (figure 2).
|
study
| 100.0 |
(iii) RMA regression suggested positive linear relationships between m.s.e. and Froude number in 11 out of 16 subjects, and CV and Froude number in 15 out of 16 subjects (figure 3). However, R2 and p-values (R2 > 0.5, p = <0.05) only strongly support linear increases in variability with Froude number when assessed by m.s.e. in 4 subjects, but in 11 subjects when assessed by CV (table 2) (figure 3).
|
study
| 100.0 |
(iv) Qualitative analysis of topological variation maps suggests that spatial distribution of m.s.e. is highest in the forefoot, specifically the lateral and medial margins, under metatarsal heads five and one, respectively, in all subjects, and is independent of speed (figure 4).
|
study
| 99.7 |
(a–p) RMA regression suggested positive linear trends in m.s.e. (y axis) across all walking speeds in 11 out of 16 subjects, and CV (z axis) and speed in 15 out of 16 subjects. However r2 and p-values only strongly support a linear increase in plantar pressure variability with speed in four subjects when assessed by m.s.e., but in 10 subjects when assessed by CV (r2 > 0.75, p = <0.05) (see table 2).
|
study
| 100.0 |
(a–p) Linear regression by pSPM (left panel) reveals no statistical support for linear changes in variability by m.s.e. with speed. The left image is the inference map, showing areas of increasing or decreasing variability in pressure across the plantar surface. The red pixels indicate that variability is increasing with speed around the periphery of the heel and forefoot only. The blue pixels indicate that variability is decreasing with speed around periphery of the midfoot and occasionally the hallux. Statistically significant pixels are concentrated almost exclusively around the periphery of mechanically distinct areas of the foot, thus they are most probably artefacts of small changes in foot contact area, size and shape with speed. The mechanically distinct areas of the plantar surface, heel, mid- and forefoot, remain white with no statistically significant relationships evident between variability and speed.
|
study
| 100.0 |
(a–p) RMA regression suggested positive linear relationships between m.s.e. (y axis) and Froude number in 11 out of 16 subjects, and CV (z axis) and Froude number in 15 out of 16 subjects. However, R2 and p-values (r2 > 0.75, p = <0.05) strongly support linear increases in variability with Froude in only four subjects when assessed by m.s.e. in four subjects, and in 11 subjects when assessed by CV (table 3). Froude number is (Fr = v2/g × LL), where v2 is speed squared, g is gravity (9.81 m s−2) and LL is each subject's leg length measured from the superior apex of the iliac crest to the where the heel meets the floor.
|
study
| 100.0 |
(a–p) m.s.e. variation maps represent the distribution and magnitude of the combined mean m.s.e. in each pixel across the plantar surface of the foot at each speed trial in all subjects. Intra-subject spatial variability measured by the mean m.s.e. is highest and confined almost exclusively to the lateral and medial forefoot in all subjects; however, there is no consistent increasing or decreasing relationship between mean m.s.e. and speed.
|
study
| 100.0 |
(a–p) CV variation maps represent the distribution and magnitude of the combined CV in each pixel across the plantar surface of the foot at each speed trial in all subjects. Intra-subject spatial variability measured by the CV is highest under the big toe and medial phalanges, as well as in the midfoot. There is no consistent increasing or decreasing relationship between mean CV and speed.
|
study
| 99.94 |
Regression statistics reveal a linear increase in m.s.e. with Froude number in only four subjects (subjects B, E, M, N). When assessed by CV, regression statistics reveal a linear increase in CV with speed in 10 subjects (subjects B, D, E, F, H, I, K, L, M, N, P).
|
study
| 99.9 |
Variability is a fundamental feature of all biological systems and, to our knowledge; this is the first attempt to understand the nature of variability in plantar pressure, and to quantify its relationship with walking speed. Our overall findings are that: both within and between subjects: (i) the absolute magnitude of variability in plantar pressure step-to-step is widely disparate across speeds, when calculated by m.s.e. (figures 1, 3 and 4), but low in absolute terms (less than 2.5%) when calculated by CV (figures 1, 3 and 5), (ii) RMA regression showed a linear increase in variability in plantar pressure by m.s.e. with speed and Froude number in only 4 subjects (R2 > 0.5, p = <0.05) (table 2) (figure 1), but in 11 subjects when assessed by CV with speed and Froude number (R2 > 0.5, p = <0.05) (table 3) (figure 3). Topological pixel-by-pixel analysis by pSPM did not provide any statistical support for an increasing or decreasing linear relationship between m.s.e. and walking speed (figure 2); and finally (iii) two commonly used metrics for quantifying variability provide directly contrasting pictures of how variability in plantar pressure step-to-step is spatially distributed across the plantar surface of the foot (figures 4 and 5). The main implication for these results is that when assessed by m.s.e., the relationship between variability in plantar pressure and speed, does not follow the commonly reported biomechanical paradigm of variability measured in other lower limb kinematic parameters, that is, becoming more consistent at speeds faster or slower than comfortable walking speed.
|
study
| 100.0 |
The inter-subject range of variability in m.s.e. is high. For example, the subject with the highest m.s.e. was 9.8× more variable in this parameter than the subject with the lowest m.s.e. at 1.5 m s−1 (figure 1). The subject with the highest CV was 2.3× more variable than the subject with the lowest CV at 1.5 m s−1 (figure 1). The m.s.e. is not used as a common calculator of variability in gait studies, and thus we cannot directly compare magnitudes derived here to values measured for other gait parameters. However, the large inter-subject variation in m.s.e. and CV is striking given our relatively homogeneous cohort of healthy adults, who ranged from 21 to 44 years old with no pathology or pre-existing injuries and a low BMI. CV less than 5% in kinematic step parameters is considered low [1,12,33,41,46–49], and less than 3% very low. Here, we found variability in the kinetic step-parameter of plantar pressure assessed by CV was less than 2.5% across all subjects.
|
study
| 100.0 |
Low variability in plantar pressure can be understood by Bernstein's dynamic systems theory. Coordination is defined by mastering redundant degrees of freedom to produce a controllable movement outcome . The anatomical complexity of the foot provides multiple degrees of freedom, and optimal performance is achieved by exploiting available high levels of redundancy . Variability in plantar pressure is low in order to coordinate the multiple available degrees of freedom in the foot complex, accounting for potential changes in substrate compliance, direction, speed and slope: all inevitable features of locomotion . Controlled speed on a treadmill and thus, full sensorimotor awareness that there are no expected or unexpected changes in speed, substrate or direction, increases the pattern coordination of redundant degrees of freedom during normal walking, maintaining low variability. Each step cycle is thus assembled temporarily, but flexibly to facilitate adaptability, and maintain balance and stability . It has been shown that when the sensorimotor system adopts functionally preferred states of coordination between soft and hard tissues it is ordered and stable, reflecting consistency in motor patterns and thus low variability.
|
study
| 99.94 |
These results do not tend to support Todorov & Jordan's theory that variability is higher in kinetic than kinematic parameters . In their study of motor pattern behaviour using a computer controller, variability was higher in kinetic (specifically force and control signal variability) than kinematic features (joint angles), during a variety of hitting, trajectory and manipulative tasks. They suggested that higher variability in kinetics than kinematics is an underlying natural property of the mechanical system being controlled, rather than variability in the controller facilitating the movement . However, in our study, variability in plantar pressure (kinetics) was consistently lower (less than 2.5%) than values considered very low (less than 3%) for variability of lower extremity kinematics. This difference might be attributed to the fact that our data reflect functional specifics of gait, whereas Todorov & Jordan's data were derived from fine motor movements measured from a controller. Further investigation of this dataset is required to understand this specific relationship.
|
study
| 100.0 |
We found that variability in plantar pressure does not follow the same U-shaped curve relationship with speed, as do other lower limb step kinematic parameters. The strength of long-range correlations for each gait pattern consistently follows U-shaped curves, centred on the subjects preferred speed . RMA regression statistics support an increasing variability in plantar pressure with speed when assessed by m.s.e. in only 4 out of 16 subjects, but in 10 out of 16 subjects when assessed by CV (r2 > 0.75, p ≤ 0.05] (table 2) (figure 1). When speed was normalized by Froude number, we found support for a linear increase in plantar pressure variability when assessed by m.s.e. in 4 subjects, and in 11 out of 16 subjects when assessed by CV (r2 > 0.75, p ≤ 0.05) (table 3) (figure 3). Almost all the same subjects had strong statistical support between comparisons (tables 2 and 3); however, subjects exhibiting strong statistical support were not consistently the same by speed or Froude number. All four subjects who exemplify strong supporting statistics showed an increasing linear relationship between variability in plantar pressure and speed when assessed by m.s.e. (tables 2 and 3) (figures 1 and 3).
|
study
| 100.0 |
The general lack of linear relationship was confirmed by topological pixel-by-pixel regression analysis of m.s.e. versus speed using pSPM, that revealed no statistical support for a linear increase or decrease in m.s.e. with speed (figure 2). Only pixels at the outer margin of the foot showed strong statistical linear trends, as a direct result of small changes in foot area contact size and shape that occurs step-to-step and at different speeds. Specifically, the red margins of the left inference prints in figure 2 indicate an increase in m.s.e. with speed around the periphery of the heel and forefoot, while the blue margins indicate a decrease in m.s.e. with speed around the periphery of the midfoot. It is likely that this significant relationship reflects changes in pressure or contact area increasing with speed in the forefoot and heel, and decreasing in pressure or contact area in the midfoot as speed increases. These artefacts could be removed by use of a non-rigid body image registration approach.
|
study
| 100.0 |
As discussed, walking speed has previously been shown to influence variability in lower limb step kinematics. Near the walk–run–walk transitions, stride duration increases before and after the transition , and increases at slower walking speeds (0.2–0.6 m s−1) compared with speeds of 0.8–1.4 m s−1. A linear increase in variability is present in joint angles , step length , step time interval and step impulse . Irrespective of the variable being measured, the data most commonly follow a U-shaped function at speeds faster and slower than comfortable walking speed, [1,53,54,56–58]. When younger and older adults are compared, stride time variability, and variability in frontal hip and knee motions, knee internal and external rotation, trunk motions and step width increased with speed in older adults. While the magnitude of variability is greater in older adults (more than 65), the variability–speed relationship is consistent between younger and older adults . The observed difference here could be due to the fact that plantar pressure is not kinematic but kinetic, and this warrants further investigation.
|
study
| 100.0 |
Finally, we presented in this contribution a novel means of analysis of quantifying the spatial distribution of step-to-step variation across the plantar surface using both m.s.e. and CV metrics. Using pSPM, we visualized variability in plantar pressure distribution in variation maps that plotted the mean m.s.e. and CV of each pixel from within the sample across the whole plantar surface of the foot, and combined the means to represent one print for each speed (figures 4 and 5). The m.s.e. variation maps show centres of highest variation to be highly localized under the lateral and medial forefoot, under metatarsal heads five and one (figure 4). However, equally, by the same qualitative assessment, CV variation maps show that the centres of highest variation lay more generally under the midfoot and phalanges (figure 5). This disparity may be largely explained by the differences between the two metrics and the ‘typical’ distribution of peak pressure across anatomical regions of the foot. The m.s.e. directly reflects the tendency for absolute peak pressure values to vary about the sample's absolute mean value, and may therefore be slightly susceptible towards bias indicating higher variation in areas of high absolute pressure. By contrast, CV represents a normalized measure of variability and shows a strong preference towards highlighting areas of low mean pressure as being highly variable. This generally explains why high CV values (red) are evident around the periphery of the foot and within the midfoot, and lower CV values (blue) are clustered where areas of high pressure exist, namely at the heel and forefoot (figure 5).
|
study
| 100.0 |
Although m.s.e. and CV appear qualitatively to paint opposing pictures of spatial variation, it is possible that the step-to-step variations they highlight are not biomechanically contradictory. A recent study found that pressure in the lateral forefoot was higher in steps where midfoot pressure was also elevated; and conversely that the same subjects exhibited statistically significant increases in pressure in the medial forefoot and hallux in steps where midfoot pressure was low . Consistent with the latter finding, Stolwijk and co-workers found that Malawian subjects with anatomically and/or functionally flatter feet also exhibited a more laterally placed centre of pressure in late stance. It is therefore possible that the variability in the midfoot highlighted by CV maps (figure 5) is functionally correlated to the forefoot variation seen in m.s.e. variation maps (figure 4), in terms of varying paths of the centre-of-pressure in mid- to late stance. Such a relationship would imply that the function of the midfoot and metatarsals one and five are highly interdependent.
|
study
| 100.0 |
Until we can obtain large samples of coronal transverse ground reaction force curves, we cannot draw firm conclusions. We suggest, however, that theoretically such a scheme of variability in mid- and forefoot pressure might reflect a biomechanical function for controlling perturbations in balance in late stance in the coronal plane, through internal/external foot rotation. It is further possible that variability in mid- and forefoot pressure relates to the mechanical influence of a tunable gearing ratio within the foot : that is, the changing relative lengths of the muscle lever arm, and the load lever arm measured from the centre of pressure. Pre-tensioning, the effect of dorsiflexion at the talocrural joint on the plantar aponeurosis (PA) prior to heel strike , followed by a stretching of the PA around the metatarsal heads during toe-off (the windlass mechanism) , contributes to increased stiffening of the plantar soft tissues, and thus, increases the gear ratio at late stance : when the centre of mass is over the forefoot. Hypothetically, it may be possible that the combined variation in gearing and local stiffness —with a potentially substantial contribution of varying tension of the transverse and oblique heads of abductor hallucis —could contribute to the observed step-to-step variation in forefoot peak pressure, and to the extent to which medio-lateral transfer of pressure in late stance is achieved (figure 4).
|
study
| 100.0 |
Using topological analysis, the spatial distribution of variability across the foot remains constant across walking speeds: areas of highest variability appear consistently confined to the lateral and medial forefoot (as calculated by m.s.e.; figure 4 and under the forefoot and heel (as calculated by CV; figure 5). This is perhaps surprising given that systematic changes in peak pressure distribution with speed have been consistently noted in the literature: that is, that peak plantar pressure is found to positively correlate with absolute and normalized walking speed in the heel and forefoot [25–30,66]. In addition to an overall increase in peak pressure, some have also noted decreasing pressure in the lateral midfoot, as a function of walking speed [18,22,66–68]; some suggesting a greater medial shift in centre of pressure as walking speed increases .
|
study
| 99.94 |
This study offers the first quantitative and qualitative description of not only the observed variability in peak plantar pressure, but also the effect that speed has on the magnitude and spatial distribution of variability at the body–environment interface. Future work should consider comparable and quantifiable ways to compare kinetic and kinematic parameters, and the effects of sample size and step-to-step variation on statistical comparisons of foot pressure. Analysis of variability in plantar pressure in older people and people at risk of foot pathology such as diabetics, could assist in determining the impact of neuromuscular and sensorimotor decline on variability in plantar pressure distribution and its relationship to speed across ontogeny. Systematic changes in peak pressure across ontogeny have been demonstrated in a number of studies, and further insights into foot function may be gleaned if relatable changes in variability were found to exist. Tunable gearing in children has been shown to mature quite late: consistently low forefoot plantar pressures compared with adults are recorded in children under 5 years old, and there is late development of the medial-to-lateral transfer of the centre of pressure . Functionally, the forefoot delivers propulsive force from the hind- to the forefoot, facilitating toe-off; however, before 7–8 years of age, accelerative power is driven from the hind- and midfoot . That we found variability in peak pressure is highest in the lateral and medial forefoot (assessed by m.s.e.), and more generally in the forefoot (assessed by CV), is consistent with the findings of Li and co-workers . Finally, a similar analysis should be completed during non-treadmill walking at comfortable walking speeds, and on uneven terrain, to ascertain whether variability increases or decreases when speed is not controlled, thus testing dynamic system theory.
|
study
| 100.0 |
Two measures of variability were used in this paper: one, a standard mathematical formula to assess variability (m.s.e.), and another, a measure commonly applied to clinical questions (CV). We conducted experiments solely on healthy young subjects, observing disparate levels of variability consistently confined to the medial and lateral forefoot as assessed by m.s.e., and low levels of variability (less than 2.5%) in the forefoot and heel as assessed by CV. From these results, we determine that the magnitude of variability assessed by CV is generally dependent on speed, while the spatial distribution of variability in plantar pressure when assessed by m.s.e. is independent of speed. This paper presents the first attempt to understand not only the nature of variability in plantar pressure, but also the influence that speed has over the magnitude and spatial distribution of variability in plantar pressure. Functional variability and the exploitation of functional redundancy are products of an adaptive biomechanical system driven by motor control. Given the tendency for degradation of biomechanical systems, the study of variability and its contribution to balance and stability in gait across ontogeny, is an essential feature when understanding how to prevent, resist and recover from instability events during gait.
|
study
| 100.0 |
Molecular machines are molecules that are able to absorb energy and convert it into non-Brownian motion.1–10 Their significance has been acknowledged by the award of the 2016 Chemistry Nobel Prize to Jean-Pierre Sauvage, Sir J. Fraser Stoddart and Bernard L. Feringa for “the design and synthesis of molecular machines”. An important class of molecular machines are light-driven molecular rotary motors, first developed by Feringa and co-workers in 1999.11 These rotary molecular motors are chiral overcrowded alkenes in which one part of the molecule (rotor) undergoes unidirectional rotary motion with respect to another part of the molecule (stator) around a central carbon–carbon double bond (axle).
|
review
| 97.1 |
The rotary cycle of a fluorene-based molecular motor is illustrated in Fig. 1. In its lowest energy conformation (1a), the methyl substituent at the stereogenic centre adopts an axial orientation in which steric hindrance is minimised. Following absorption of ultraviolet (UV) light, excited state photoisomerisation causes the molecule to rotate around the axle (1b) and in this conformation the methyl substituent is forced to adopt an equatorial orientation. This step is sometimes referred to as the photochemical power stroke and determines the photochemical conversion efficiency of the motor. Following relaxation back to the ground electronic state, increased steric interactions between the rotor and the stator create tension in the molecule that is subsequently released as the rotor and stator blades snap over one another in a thermal helix inversion (1b–1c) and the methyl group re-adopts its thermodynamically favourable axial orientation. Importantly, the steric barrier to reverse rotation on the ground electronic state locks the molecular motor in this conformation until it can absorb another photon. A full unidirectional rotary cycle is then completed following a second UV photoisomerisation and ground-state thermal helix inversion (1c–1d–1a).
|
study
| 100.0 |
The rate determining step in the rotary cycle is the thermal helix inversion on the ground electronic state. This has been confirmed by time-resolved transient absorption spectroscopy measurements of molecular motors dissolved in hexane that found the light-induced isomerisation step to be completed in 20 ps.12 A great deal of synthetic effort has been focussed on designing and synthesising rotary motors with lower barriers to thermal helix inversion and higher rotation frequencies;13–21 however, there has been relatively little interest in optimising the photochemical power stroke. Meech and co-workers reported a series of very detailed time-resolved fluorescence and transient absorption spectroscopy measurements probing the initial photoisomerisation step of rotary motors dissolved in a range of solvents.22,23 They found that following photoexcitation, rapid structural relaxation out of the Franck–Condon region occurred on a timescale of a few 100 fs to populate a ‘dark state’. This ‘dark state’ remained coupled to the Franck–Condon region and relaxed through a conical intersection back to the ground state on a picosecond timescale, completing the light-induced isomerisation step. They found that attaching electron withdrawing and electron donating substituents in direct conjugation with the central carbon–carbon double bond modified the structure and energy of the ‘dark state’ and thus the efficiency of operation of these molecular motors, without affecting the rotation frequency. Recently, Amirjalayer et al. reported a detailed time-resolved infrared spectroscopy study, supported by quantum mechanical calculations, which hypothesised that electronic relaxation proceeds on the potential energy surfaces of two different electronic states and thus indicates that the efficiency of the motor is determined by the conical intersection between the ‘dark state’ and the ground state.24
|
study
| 99.9 |
From a theoretical perspective, there have been numerous studies of molecular rotary motors aimed at understanding how both the molecular structure and electronic structure influence the excited state and ground state dynamics. Kazaryan et al. used ensemble DFT (density functional theory) and OM2/MRCI molecular dynamics calculations to explore the potential energy surface of a fluorene based motor.25–27 They found barriers to rotation along the ground electronic state, S0, as well as the excitation energy to the first excited state, S1, using ensemble DFT and PM3 methods and, in agreement with the later experimental observations,22,23 they predicted that a conical intersection between the S0 and S1 states played a key role in the light-induced isomerisation step. Their MD simulations found an average excited state lifetime of 1.4 ps. Torras et al. employed QM/MM (quantum-mechanics/molecular mechanics) calculations to investigate another fluorene based overcrowded chiral alkene molecular motor and its rotation.28 They optimised structures using unrestricted DFT and the perturbative MP2 method and included solvent effects in their QM/MM calculations. They calculated barriers to rotation which were comparable to experiment and also found that the hybridisation of the ethylene axle evolved from being sp2 to sp3 and back again, as the motor rotated, to minimise steric repulsions. CASSCF (complete active space self consistent field) calculations were carried out by Amatatsu using a 2-orbital, 2-electron active space.29,30 Again it was found that a conical intersection between the S1 and S0 states was a key factor in the light-induced isomerisation step. Recently, Pang et al. have reported the results of trajectory surface-hopping dynamics at the semi-empirical OM2/MRCI level investigating the photoisomerisation dynamics;31 they found timescales in reasonable agreement with those measured by Meech and coworkers,22,23 although they suggest that the ‘dark state’ is not a separate electronic state, as suggested by Amirjalayer et al.,24 but a ‘dark region’ of the S1 state.
|
review
| 96.3 |
To gain a complete understanding of the intrinsic dynamics of a molecular rotary motor requires gas-phase measurements of isolated motor molecules, free from interactions with solvent molecules. Although weakly interacting solvents are generally considered to have little influence on the shapes of molecular potential energy surfaces, differential solvation of electronic states can modify the structure and energy of conical intersections. Solvent molecules also provide an effective sink for vibrational energy relaxation, so they may modify the dynamics on the potential energy surfaces. However, there have not been any reports of gas-phase measurements of the electronic structure and dynamics of molecular rotary motors to date, most likely due to the difficulties associated with generating molecular beams of such large molecules. Electrospray ionisation has proved a very effective method for transferring large molecules into the gas-phase in deprotonated anionic or protonated cationic forms. Here, we employ electrospray-ionisation to transfer a molecular rotary motor into the gas-phase, using a carboxylic acid group attached to the stator to provide a spectator group that is easy to deprotonate. We then use anion photoelectron spectroscopy to measure the electronic structure and dynamics of the molecular rotary motor and high-level CASSCF and MS-CASPT2 (multiconfigurational second-order perturbation theory) calculations to guide the interpretation of our experimental measurements. Interestingly, we find that the initial dynamics in the gas-phase are similar to those reported in solution:22–24 they involve relaxation away from the Franck–Condon region to a rotated conformer on the excited state and internal conversion back to the ground state. Gas-phase studies such as these, alongside solution phase studies, have the potential to improve our fundamental understanding of light-activated molecular rotary motors and inform the design of photoactivated nanoscale devices.
|
study
| 100.0 |
Photoelectron spectra were recorded using our anion photoelectron imaging apparatus.32–39 Briefly, deprotonated molecular motor anions were generated by electrospray ionisation of solutions of 1a (R = COOH) in methanol with a couple of drops of ammonia added. The anions, 1a (R = COO–), were mass selected by a quadrupole and passed into a collision cell, which also acted as a hexapole ion trap to generate packets of anions at a frequency of 20 Hz to match the repetition rate of the laser system. The ions were then transported via a potential switch to the interaction region of collinear velocity map imaging optics, where they interacted with nanosecond laser pulses with wavelengths in the range 320–230 nm. Photoelectrons generated in the interaction region were accelerated towards a position sensitive detector consisting of a stack of multichannel plates, a phosphor screen and a CCD camera. Laser-only images were subtracted from images recorded following the interaction of laser light with the anions, to eliminate background counts arising from scattered light and ionisation of residual gas. The photoelectron images were inverted using the pBASEX method40 and the energy scale was calibrated by recording the photoelectron spectrum of I–. The energy resolution is around 5% and the error in electron kinetic energy is ±0.05 eV.
|
study
| 100.0 |
Vertical excitation energies (VEEs) of the first few singlet excited states of anions 1a–1d (R = COO–) were calculated at DFT-optimised geometries using the state-averaged complete active space self consistent field (SA-CASSCF) method45 with equal weighting given to the first four states (SA(4)). The active space consisted of six pairs of π and π* orbitals (12, 12) on the stator, rotor and axle, selected by inspection of the natural orbital occupations (see ESI†). Orbitals localised predominantly on the carboxylic acid group were excluded, since this group was included for experimental reasons and does not contribute significantly to the photochemistry (Section 3.2).
|
study
| 100.0 |
We used the atomic natural orbital (ANO-L) basis set46 of polarised triple-zeta (double-zeta for hydrogen) quality (TZVP (14s9p4d3f)/[4s2p2d1f] for carbon and oxygen, DZVP (8s4p3d)/[2s1p] for hydrogen). To reduce computational time, we used the Cholesky decomposition47 with the default parameters for computation of two-electron integrals.
|
study
| 99.94 |
To obtain more accurate VEEs, dynamic correlation was included by using second order pertubation theory (PT2) with the multi state formalism (MS-CASPT2),48 with the SA(4)-CASSCF wave functions as a reference. We used the standard value of 0.25 for the ionisation potential-electron affinity (IPEA) shift as this has been shown to give more accurate dissociation and excitation energies.49 To minimise the effect of intruder states, an imaginary level shift of 0.2 a.u. was used.50
|
study
| 99.94 |
Vertical detachment energies (VDEs) of 1a–1d (R = COO–) were calculated as the differences between the MS-CASPT2 energies of the anions and their corresponding neutral radicals, at the DFT optimised geometries of the anions. Adiabatic detachment energies (ADEs) of 1a and 1b (R = COO–) were calculated as the energy differences between the MS-CASPT2 energies of the DFT optimised geometries of the anions and the MS-CASPT2 energies of the DFT optimised geometries of the corresponding neutral radicals.
|
study
| 100.0 |
All the photoelectron spectra have an intense, low eKE feature that decays exponentially with increasing eKE, characteristic of thermionic emission from the hot ground state of the molecular motor anion. For the 320–398 nm spectra, the low eKE distributions can be fit to I(eKE) = I 0 exp(–eKE/k B T) to obtain an estimate of the vibrational temperature of the ground state of the anion formed after internal conversion. We found k B T ≈ 0.2 eV. Although the value of k B T is not particularly meaningful because there are competing decay mechanisms, such as autodetachment, the observation of thermionic emission is very significant because it shows that internal conversion back to the ground state occurs, following excitation in the range 320–230 nm.
|
study
| 100.0 |
In addition to the exponentially decaying low eKE feature, the 230 nm (5.38 eV) photoelectron spectrum also has a broad, unresolved feature centered around 0.60 eV, corresponding to an electron binding energy, eBE = hν – eKE ≈ 4.8 eV. If these photoelectrons arise from direct detachment from the ground electronic state of the anion, the eBE of the peak maximum would correspond approximately to the VDE, although we note that the VDE and maximum may be offset in unresolved photodetachment spectra of relatively large molecular anions.55 Such a value for the VDE (4.8 eV) would also explain why direct detachment is not observed at the three lower photon energies (3.87–4.16 eV) used in this work.
|
study
| 100.0 |
Interestingly, there are also weak, continuous photoelectron distributions in the range 1.0–2.0 eV eKE, most noticeably in the 320 nm photoelectron spectrum. We attribute these to multiphoton processes or loss of CO2, followed by autodetachment from the decarboxylated motor anion which is expected to have a lower binding energy. We have identified CO2 loss as a competing decay channel in protein chromophore anions containing COO– using MS-MS measurements.56
|
study
| 100.0 |
The MS-CASPT2 calculated ground-state energies, VEEs, VDEs and ADEs of conformers 1a–1d, relative to the ground electronic state of conformer 1a, are listed in Table 1 and plotted in Fig. 3. Notably, although the VDE is more or less constant for all conformers, the ADE is lowered by approximately 0.7 eV for 1b compared to 1a.
|
study
| 100.0 |
The key natural molecular orbitals (NMOs) involved in the photoexcitation and subsequent rotation of the motor from 1a to 1b, i.e. the NMOs in which there is a significant change in the natural orbital occupation (NOO) between the ground state and the first electronically excited singlet state are listed in Table 2 and in the ESI.† Note that we only consider S1 in our discussions below because for the lowest energy 1a conformer, the oscillator strength of S2 is significantly lower than that of S1 and the VEE of S2 is higher than the photon energies corresponding to 320–298 nm, suggesting that it does not play a significant role in the excited state dynamics following excitation in this range.
|
study
| 100.0 |
To illustrate the redistribution of electron density that occurs during the S0 → S1 transition, the electron density differences between the NMOs involved in this transition are also plotted for each conformer 1a–1d, in Fig. 4. The initial S0 → S1 excitation of conformer 1a has π → π* character on the axle of the motor, consistent with the idea that the initial dynamics are governed by softening of the axle double bond followed by rotation.22 This transition also involves considerable change in electron density on the stator, which is consistent with the narrow and well-defined absorption spectra reported for neutral molecular motors based on substituted fluorenes.22,57 The characteristics of this charge redistribution are maintained when considering conformers 1b–1c. In terms of key NMOs, the S0 → S1 transition is dominated by a transfer of population from NMO 98, which is localised on the stator, to NMO 103, which is also localised on the stator but has significant π* anti-bonding character on the axle double bond (Table 2 and ESI†).
|
study
| 100.0 |
To benchmark our calculations, we have compared the MS-CASPT2 calculated energies of the S0 and S1 potential energy surfaces at some of the key conformations with those for a related, neutral, fluorene-based molecular motor, 9-(2,4,7-trimethyl-2,3-dihydro-1H-inden-1-ylidene)-9H-fluorene.26 On the S0 potential energy surface, conformer 1b of our molecular motor is 0.31 eV higher in energy than conformer 1a. This energy difference is slightly larger than the equivalent energy difference calculated for the neutral, fluorene-based molecular motor, using DFT with the B3LYP exchange correlation functional and 6-31G* basis set (0.15 eV).26
|
study
| 100.0 |
On the S1 potential energy surface, conformer 1b of our molecular motor is 0.23 eV lower in energy than conformer 1a. This lowering in energy can be rationalised in terms of the increased delocalisation of electron density across both the rotor and the stator (Fig. 4; this delocalisation is also present in 1c and 1d) and is the driving force for the initial rotation around the axle of the molecular motor following S0 to S1 photoexcitation. The energy difference between the 1a and 1b conformers on the S1 potential energy surface is almost identical to the equivalent energy difference calculated for the neutral, fluorene-based molecular motor, using SA-REBH&HLYP and TD-BH&HLYP methods.26
|
study
| 100.0 |
We also perfomed an unrestricted DFT (B3LYP/6-311G++(2df,2pd)) relaxed scan on the S0 potential energy surface by rotating around the double bond and, employing a broken symmetry methodology, found a barrier between 1a and 1b with height 2.0 eV, which is substantially higher than that found in the neutral fluorene based molecular motor (1.4 eV).26 The increase in relative energy of conformer 1b compared to 1a and increased barrier height could be attributed to the increased bulk of the rotor and the COO– substituent on the stator of our molecular motor, but it is worth noting that we only performed a single relaxed scan because we used a large basis set, rather than a multidimensional relaxed scan. We also performed a DFT (B3LYP/6-311G++(2df,2pd)) relaxed scan rotating around the double bond from conformer 1b to 1c and found a low (0.2 eV) barrier, which is consistent with this step being a thermal process on the ground electronic state.
|
study
| 100.0 |
The similarity between the S0 and S1 potential energy landscapes of our deprotonated molecular motor and the related, neutral, fluorene-based molecular motor,26 confirms our suggestion that the COO– substituent, included for experimental reasons, does not contribute significantly to the photochemistry of the molecular motor.
|
study
| 100.0 |
The electronic relaxation and electron emission processes possible following photoexcitation are illustrated on a Jablonski diagram in Fig. 5. The S1 state has shape resonance character with respect to the D0 continuum, implying a strong coupling between vibrationally excited states of S1 that lie above the ADE of the D0 continuum.
|
study
| 100.0 |
Following photoexcitation at 320–298 nm, population is transferred from the S0 state of conformer 1a to the S1 state of conformer 1a, below the ADE. Since excitation is below the ADE, direct electron detachment is not possible. The molecule rotates around its axle as it relaxes along the S1 potential energy surface. Population then either remains on the S1 potential surface or it undergoes internal conversion to S0, returning to 1a or forming the rotated conformer 1b. Thermionic emission from the ground electronic state of conformer 1a is not possible because the internal energy is lower than the ADE; however, the ADE of conformer 1b is approximately 0.7 eV lower than that of conformer 1a, and thermionic emission from the ground electronic state of conformer 1b is possible. Thus, we assign the low eKE features in the photoelectron spectra recorded at 320–298 nm to thermionic emission from conformer 1b. Importantly, this indicates that the initial dynamical steps of the isolated deprotonated rotary motor are similar to those observed for rotary motors in solution, i.e. the motor rotates on the excited electronic state and undergoes internal conversion back to the electronic ground state.
|
study
| 100.0 |
Following photoexcitation at 230 nm, population is transferred from the S0 state of conformer 1a to the S1 state of conformer 1a, above the ADE. The 230 nm photoelectron spectrum is reproduced in Fig. 6 and plotted as a function of electron binding energy, eBE = hν – eKE (bottom axis) and eKE (top axis). The MS-CASPT2 calculated VDEs and ADEs for motor conformers 1a and 1b and the VEE for conformer 1a are also marked on this plot. The calculated VDE for conformer 1a (4.91 eV) is very close to the maximum of the broad feature of the photoelectron spectrum (4.8 eV), supporting our assignment of this feature to direct detachment to the electronic continuum associated with the ground electronic state of the neutral radical, D0. However, the ADE for conformer 1a is very close to the VDE, so the photoelectrons observed with eBEs lower than the ADE must either result from direct detachment from vibrationally hot S0 to the D0 continuum associated with the 1a conformer, or to indirect detachment from S1 to D0 of the 1b conformer. Since the low eBE edge of the photoelectron spectrum is very close to the calculated ADE of the 1b conformer, it seems most likely that these lower eBE photoelectrons arise from autodetachment from the S1 state of the 1b conformer. Importantly, the observation of autodetachment from the S1 state of the 1b conformer as well as thermionic emission is indicative of competing relaxation processes. Thus, we can deduce that following excitation at 230 nm and relaxation on the S1 potential energy surface some population remains on the S1 potential energy surface and some population undergoes internal conversion back to S0. Thermionic emission is possible from both 1a and 1b conformers with the 5.39 eV of internal energy in S0 that comes from excitation at 230 nm. Our observations are consistent with recent time-resolved studies of second generation molecular rotary motors in solution which observed an initial relaxation out of the Franck–Condon region to a ‘dark state’, which subsequently decays back to the ground electronic state.22–24 The similarity between the gas and solution phases suggests that the mechanical motion associated with the initial photoisomerisation step of the rotary cycle is independent of the solvent environment.
|
study
| 100.0 |
We have shown that the combination of electrospray-ionisation, anion photoelectron spectroscopy and high-level computational chemistry calculations provides a powerful toolkit for gaining insight into the primary events that occur following photoexcitation of a unidirectional molecular motor in the gas-phase, free from solvent effects. Interestingly, the dynamics of the fluorene-based molecular motor anion that is the subject of this gas-phase study are similar to those of fluorene-based molecular motors in solution. That is, the initial dynamics are found to involve relaxation away from the Franck–Condon region to a rotated conformer on the excited state and internal conversion to a rotated conformer on the ground electronic state. It will be interesting to extend our photoelectron spectroscopy studies of rotary molecular motor anions in the gas-phase to the time-domain to measure the timescales of the dynamics and compare them with those obtained for analogous molecular rotary motors in solution. Calculations of the electronic structure and excited state dynamics of molecular rotary motors are non-trivial, so gas-phase studies provide important benchmarks for theory. Improving our fundamental understanding of photoactivated molecular rotary motors in this way is important for the future design of photoactivated nanoscale devices.
|
study
| 100.0 |
Each field of medicine has a defined scope; however, interactions between systems covered by different fields exist. A link between findings from psychiatry, neurology, immunology and endocrinology has been noted for many years. From the functional, anatomical and physiological perspectives, the correlation between the central nervous system (CNS) and the endocrine system is complex and involves several actors, such as cytokines, receptors and neurotransmitters. The immune system is connected to the endocrine and neural systems via a number of pathways that integrate the functions of the hypothalamus, pituitary glands, adrenal glands, thyroid glands, gonads and autonomic nervous system. Major clinical implications and a vast amount of pathologies are related to the relationships between the systems covered by the science of psychoneuroimmunoendocrinology.
|
review
| 99.9 |
Many studies have described the negative effects of stress on health. Ader and Cohen in 1975 studied the effects of stress on the immune system. These previous findings are currently accepted, and a new area of study focusing on inflammation, autoimmunity and secondary hypersensitivity to stress has been developed.
|
review
| 99.75 |
Socioeconomic status as well as chronic illnesses such as autoimmune and rheumatic diseases, asthma, allergic rhinitis, atopic dermatitis, urticaria, cardiovascular disease, hypertension and diabetes mellitus affect mood by generating stress, anxiety and depression, all of which negatively influence immune system function and regulation. For example, asthma was historically referred to as “nervous asthma” in relation to living with a histrionic mother, and atopic dermatitis was referred to as “neurodermatitis.”
|
review
| 84.1 |
Psychoneuroimmunoendocrinology is the study of the interaction between the psyche, neural function, endocrine function and immune responses. These systems can interact through two pathways: changes in neural and endocrine functions that alter immune responses or stimulation of immune responses that modify the functionality of the endocrine system and the CNS. Behavioral processes are able to initiate both pathways, which leads to altered behavior in an individual . Interactions between these various systems regulate a variety of physiological processes, and their normal interaction helps to reduce the vulnerability of individuals to certain diseases [2, 3]. One aim of psychoneuroimmunoendocrinology is to apply medical knowledge to different psychological disorders (e.g., depression), neurological conditions (e.g., dementia), immune disorders (e.g., autoimmune diseases) and neoplastic diseases .
|
review
| 99.9 |
The theory of “general adaptation syndrome” proposed in 1936 relates stress with cortico-adrenal secretion and accounts for the protective effects of adrenal extracts on stress . Subsequently, Solomon et al., introduced a “speculative theory”, later known as psychoimmunology, and following the introduction of this theory, Ader and Cohen published their work on classical conditioning of immune functions entitled “Behavioral conditioning of immunosuppression”, in which they proposed a functional link between the immune system and the nervous system such that the immune response generates cytokines that stimulate the CNS [6, 7]. This stimulation of the brain activates the hypothalamic-pituitary-adrenal axis, which in turn suppresses the immune response through the secretion of glucocorticoids . Accumulating evidence since 1980 has established the mechanism by which thoughts, emotions and behavior modulate and mediate endocrine and immune functions .
|
review
| 99.9 |
The presence of cellular receptors in the nervous, endocrine and immune systems allows the reception of information from other systems via chemical messengers. Under normal conditions, these three systems interact to establish a homeostatic balance [4, 9] that promotes adequate health and prepares the body for constant struggle against various diseases. The loss of this balance represents an interruption in the processes of interaction among these four systems, resulting in the onset of symptoms that characterize a pathogenic state. Many factors, such as heredity, environment, personality traits, emotions and lifestyles, influence these interactions. Whether the stress generated by psychiatric disorders such as depression and anxiety, behavior disorders, daily hassles, and changes in the environment helps or impairs the control of chronic inflammatory diseases remains under debate. It is common to observe people who thrive in environments or situations of high stress as well as people whose health is negatively affected by such stress .
|
review
| 99.8 |
Although no specific atopic personality profile has been established, growing knowledge of the nervous system supports new findings concerning the interaction between neuroimmunological and epigenetic factors. The skin and nervous system share a common origin: the ectoderm. Any factor that plays a neurological role can be observed in keratinocytes, fibroblasts, wandering or resident cells (mast cells, Langerhans cells), or stem or transient cells (lymphocytes, neutrophils and monocytes) of the skin. Thus, primitive defense responses such as inflammation and stress may modulate endocrine, dermatological and neurological responses [9, 10].
|
review
| 99.7 |
Stress modulates the immune response through the psychoneuroimmunoendocrine pathway and the hypothalamic-pituitary-adrenal axis via the release of cortisol, norepinephrine, epinephrine and interferon-gamma (IFN-γ) by T lymphocytes. Increased levels of proinflammatory cytokines such as IFN-γ (T helper cell type 1 (Th1) cytokine) and a rapid but tissue-damaging cellular immune response constitute the immune system response . Cortisol and catecholamines decrease the production of tumor necrosis factor-alpha (TNF-α) by antigen-presenting cells and promote Th2 responses via the release of interleukin (IL)10, IL13 and IL4 . This process permits the immune system to halt acute responses but also favors allergic diseases [14, 15]. It has recently been found that epigenetic factors encourage the development of inadequate stress responses, paving the way for a chronic stress response instead of an acute stress response [10, 16, 17].
|
review
| 98.75 |
Table 1 shows a summary of different studies providing scientific relevance regarding the effect of stress on the immune system.Table 1Relevant studies about the influence of psychological stress on the immune systemReferencesMain conclusionsOrtega M. 2006 Stress is a risk factor for health in all systems of the body; even though a certain level of stress is essential to boost productivity, once a limit is exceeded by an intense stressor, the body becomes depleted, causing stress-associated diseases.Rosenthal S. 2002 Ray O. 2004 Sierra R et al. 2006 Sandín B. 2008 McEwen B.S. 2008 An altered immune system caused by stressful events increases the body’s vulnerability (to infectious diseases, cancer and autoimmune diseases).Borysenko J, Borysenko M. 1983 Gidron Y et al. 2003 Ho W, Evans D, Douglas S. 2002 Social, physical, and biological factors that cause stress may induce immunosuppression, including inability to adapt to the environment, trauma, major surgical interventions, radiation, infection, and cancer.Monjan A, Collector M. 1977 Exposure to chronic stress induced proliferation of T and B lymphocytes similar to or to an even greater than a control treatment. Additionally, exposure of mice to acute noise stress for more than 2 to 3 h a day for fewer than 2 consecutive days reduced B and T lymphocyte proliferation in response to the mitogens lipopolysaccharide and concanavalin A, compared to non-exposure to stress.Fillion L et al. 1994 Weiss J et al. 1989 Hucklebridge F, Clow A, Evans P. 1998 Stress is related to increased viral retention in tissues, along with a decrease in the number of circulating lymphocytes and in the mitogenic response in vitro. In addition, stress diminishes the activity of natural killer (NK) cells, a powerful mechanism for the elimination of tumor cells and the production of interferon-γ.Moynihan J, Ader R, Crota L et al. 1990 Most immune responses are suppressed by stress, but moderately intense stress conditions can increase them.Croset G et al. 1987 Rat immune system reactivity was tested by determining the proliferative response after mitogenic stimulation in vitro as well as the capacity to generate a primary antibody response after immunization with red blood cells from sheep. An increase in the immune response in vitro and in vivo was demonstrated following exposure to a single shock. Thus, it was concluded that emotional stimuli facilitate immune responses. However, when a rat was confronted with a conflict situation, there was a decrease in the reactivity of the immune system. These findings led to the final conclusion that the immune system specifically and immediately reacts to different psychological stimuli.Shirinsky I, Shirinsky V. 2001 Belova T et al. 1988 Immune reaction initiation may be strongly affected by stress-induced cerebrovascular damage.Churin A et al. 2003 Immobilization stress induces different immune system reactions in distinct strains of rodents. These reactions can be classified according to the intensity of the humoral immune response for thymus-dependent antigens into categories such as high, moderate and low responders. High and moderate responders are characterized by high sensitivity of the productive phase of the humoral immune response and the phagocytic activity of macrophages. In low responders, stress only slightly affected the productive phase of the humoral immune response, but peritoneal macrophage activity decreased. This evidence reflects the different reactions of the immune system.De Groot J et al. 2002 This study of stress revealed the consequences of stress on the quality and quantity of immunological memory in the long term. Mice were subjected to social stress after herpes simplex virus infection. Stressed mice were shown to exhibit suppressed antibody response and impaired memory for the production of IL4 and IL10 as a specific response to the virus, whereas non-stressed mice showed intact immune responses and immune memory.Guayerbas N et al. 2002 This study found that on standard behavioral tests, rodents with high levels of anxiety had less competition in their immune system (premature immunosenescence), as demonstrated by certain functional alterations of peritoneal macrophages, such as substrate adherence, chemotaxis, phagocytosis, and superoxide anion production.Zelena D et al. 2003 In rats subjected to stress by repeated trapping, chronic stress signs including decreased thymus size and weight, increased adrenal gland weight, and increased basal corticosterone levels were observed.Molina P. 2001 Studies in rats subjected to hemorrhagic shock stress showed a suppressive role of noradrenergic innervation in the increase in tissue TNF-α levels initiated by hemorrhage in vivo. Therefore, it was concluded that norepinephrine protects against tissue damage but may contribute to generalized immunosuppression following trauma.Wonnacott K, Bonneau R. 2002 In a murine model, stress reduced the ability of specific cytotoxic memory T lymphocytes to protect against lethal intranasal or intravaginal infection with a herpes simplex virus. Stress also restricted the ability of these lymphocytes to limit virus levels at the site of the infected mucosa.Paltrinieri S et al. 2002 The efficiency of granulocytes was studied in sheep subjected to acute stress, and the results demonstrated that acute stress significantly increased the adhesion of these cells. This mechanism could be responsible for the depression of innate immunity observed in stressed animals.Sánchez M, Cruz C. 1991 Human studies revealed that IgA class antibodies, which are important in the defense against viruses and bacteria, had reduced abundance in individuals with a particular personality type.Stowell J. 2003 In humans, certain academic examinations can have a noteworthy impact on mental and physical health.Matalka K. 2003 A review of mental stress models (short and written examinations as subacute and acute types of stressors) was conducted to understand the effects of stress on the neuroendocrine and immune systems. In stressed students, a short period (minutes) of preparation for a written exam induced the production of proinflammatory cytokines, which could be related to a Th1 response. Nevertheless, prolonged mental stress (of several days) caused deregulation of immune function, with a change in the cytokine response to a Th2 response.Anyanwu E et al. 2003 Abnormal NK cell activity was found in patients exposed to toxigenic materials, leading to adverse health conditions, including a wide range of neuroimmune and behavioral consequences.Ho C et al. 2001 Measurable changes in dendritic cell abundance were observed in patients undergoing surgery. These cells were rapidly mobilized in the circulation in response to surgical stress, and this activity may prepare host immune defenses against trauma.Woiciechowsky C et al. 1998 In patients with sympathetic activation due to acute accidental brain trauma, rapid systemic release of the anti-inflammatory cytokine IL10 from non-stimulated monocytes occurs. The rapid release of this cytokine may signify a common pathway for stress-induced immunosuppression.Dhabhar F, McEwen B. 1999 Divergent from the concept that stress impairs immunity, human studies showed that short-term stressors pointedly increase delayed hypersensitivity reactions of skin.Glaser R. 2005 Individuals exposed to chronic diseases are more likely to present deleterious health and hygiene habits compared to individuals who do not have stress, such as sleep disturbances, malnutrition, physical inactivity and drug and tobacco abuse; thus enhancing the adverse effects of stress on the immune system and overall health.Levitina E. 2001 Immunological studies in infants who suffered from perinatal hypoxic stress demonstrated impaired cellular immunity (lymphocyte subpopulations) and humoral immunity (immunoglobulin concentrations). Acute hypoxia led to transient immunodeficiency due to stress.Ramos, V et al. 2008 Chronic and excessive stimulation of the hypothalamic-pituitary-adrenal axis induces the production of glucocorticoids, the final products of this axis, altering the levels of white blood cells, decreasing the activity of NK cells and inhibiting the production and secretion of ILs that are important in mediating the immune response.Mohr D, Pelletier D. 2004 Stress in individuals with multiple sclerosis increases the permeability of the blood–brain barrier to immune cells circulating in the blood. As a result, there is an increase in the infiltration of leukocytes into the CNS.Selye H. 1936 Hypotrophy of the thymus and lymph nodes was demonstrated after exposure to stress. The immunomodulatory effect of glucocorticoids is essential to this effect.Kay G et al. 1998 Prenatal stress from maternal isolation and exposure to noise and intense light during the last week of gestation in rats reduced the proliferative response of B lymphocytes and decreased the cytotoxic activity of NK cells in peripheral blood.Spitzer et al. 2010 People diagnosed with post-traumatic stress disorder were found to be significantly more likely to have elevated C-reactive protein levels.Gill J, Page G 2008 Gola et al. 2013 Sutherland, A., Alexander, D. Hutchison, J.2003 von Kanel et al. 2006 von Kanel et al. 2007 Baker et al. 2001 Maes et al. 1999 Newport D, Nemeroff C. 2000 Research showed that people with post-traumatic stress disorder have elevated levels of proinflammatory cytokines, especially IL6, which has been considered a potential prognostic biomarker for this pathology.Gotovac et al. 2010 Pace et al. 2012 Evidence was found for cytotoxic changes to NK cells in people with post-traumatic stress disorder, as well as an increase in the number of glucocorticoid receptors on lymphocytes and a decrease in the sensitivity to glucocorticoids.Cohen et al. 2001 Herbert T, Cohen S. 1993 Reduced NK cell cytotoxicity, suppressed lymphocyte proliferative responses, and blunted humoral responses to immunization were found in chronic stress models.Montoro J et al. 2009 Activation of the neuroendocrine and sympathetic nervous systems through catecholamine and cortisol secretion influences the immune system, modifying the balance between Th1 and Th2 responses in favor of the Th2 response.
|
review
| 99.9 |
In 1936, the concept of general adaptation syndrome and its phases of alarm, resistance and exhaustion in response to an aggression were first proposed . Currently, consensus continues to be sought concerning the definition of stress, for which terms such as homeostatic imbalance, a discrepancy between expectations and perceptions of the environment, and allostasis are used. Allostasis is the ability to maintain a stable internal environment despite the influence of external elements, i.e., adaptation. Adaptation is not achieved when the response is ineffective or inadequate or when exposure to the agent that induces the response is prolonged, resulting in allostatic load, which is defined as wear and tear from the under- or overactivity of allostatic systems . Allostatic load is increased by an overreaction of the adaptive mechanisms capable of generating a disease, transforming a protective mechanism that maintains systemic homeostasis when faced with an aggression into a highly pathogenic mechanism with a prolonged effect. Stress is defined as a real or interpreted threat to the physiological or psychological integrity of an individual that results in specific, physiological or behavioral responses seeking to restore homeostasis and whose chronicity is potentially pathogenic . Castrillón et al. defined psychological stress as a pathophysiological process that occurs when an individual is faced with environmental demands that exceed his or her resources, inducing a response that involves physiological and cognitive activation of the body (CNS, endocrine system and immune system) in order to quickly and forcefully meet the demands of the situation. Therefore, the response to psychological stress is systemic in nature and has several metabolic consequences, such as increased steroid synthesis and a state of chronic inflammation . The response of the body to stress involves the participation of different homeostatic regulatory systems, causing functional alterations that lead to chronic stress, which forms the basis for the development of cardiovascular, metabolic, immunologic, allergic, oncologic and psychiatric disease. An individual’s response to stress is provoked by genetic and psychological factors, which explains the large interindividual variability in the response to similar stimuli . Different stressors cause distinct responses through the activation of specific neuroendocrine systems .
|
review
| 99.9 |
The following points are taken into account when explaining pathophysiological stress: first, the emotional, behavioral and physiological components of a stress reaction are controlled by corticotropin-releasing hormone; second, the intensity and duration of the reaction of the hypothalamic-pituitary-adrenal axis to stress are modulated by the release of glucocorticoids from the hippocampus, which is very sensitive to hippocampal neuronal activity and glucocorticoid insufficiency, and variation in the effectiveness of the brake system for hypothalamic-pituitary-adrenal axis activity likely accounts for interindividual differences in stress responses; and third, through a combination of cytokines and glucocorticoids, the reciprocal interactions between the immune system and the CNS constitute another regulatory element, and altered function of these interactions can be the origin of a pathology . Chronic stress produces alterations in hippocampal neurons, resulting in memory problems. Similarly, chronic stress can suppress immune system defenses and produce a range of psychophysiological symptoms such as adrenal fatigue caused by reduced cortisol levels. Emotional distress has a direct influence on inflammatory processes due to the chronic upregulation of proinflammatory cytokines, which are direct causes of respiratory allergies, rheumatoid arthritis, fibromyalgia, obesity, metabolic syndrome, type 2 diabetes, cancer and cardiovascular diseases. In addition, depression, insomnia, and chronic fatigue syndrome are caused by a reduction in cortisol levels . Such diseases are the result of a continuous process of multidirectional interactions among the frontal lobe of the brain (which perceives stress), the autonomic nervous system, the endocrine system and the immune system . A better understanding of the molecular actions of cortisol in the processes of memory and learning or in sleep disorders such as insomnia would facilitate progress in the prevention and both pharmacological and psychological treatment of stress disorders for those who are predisposed to such conditions . Figure 1 shows the impact of stress on the psychoneuroimmunoendocrine axis.Fig. 1Stress and psychoneuroimmunoendocrinology axis. ANS: autonomic nervous system; HPA: hypothalamic-pituitary-adrenal; IL-6: interleukin 6; RAA: renin-angiotensin-aldosterone
|
review
| 99.9 |
Epigenetics is the study of all non-genetic factors that interfere in the determination of ontogeny or in the development of an organism from fertilization to senescence. Such factors are involved in the heritable regulation of gene expression via methylation, acetylation and phosphorylation of DNA rather than alteration of the nucleotide sequence. The concept of epigenetics was coined by Conrad Waddington in 1953 and gained importance after the human genome project in 2003 [23, 24]. Living in urban areas, resulting in greater exposure to chemicals, reduced green spaces, and the consequent limited presence of flora, fauna and microbial life, is associated with immune dysfunction in humans. Reduced contact with nature and environmental microbiota appears to be related to a range of diseases including allergy and type 1 diabetes [25, 26]. Alterations in intestinal flora influence the development of not only asthma and allergies but also other chronic and recurring inflammatory disorders, such as type 1 diabetes, inflammatory bowel disease, obesity, and even psychiatric disorders . Epigenetics has transformed our understanding of the impact of the environment on our genes and health, which in turn will potentially streamline many lines of research in psychoneuroimmunology seeking to explain how environmental cues are transduced into the genome [28, 29]. In turn, the psychosocial environment can substantially change behavior and alter nervous, endocrine and immune functions.
|
review
| 99.9 |
A close relationship between allergic diseases and psychoneuroimmunoendocrinology exists [10, 30]. Stress negatively impacts patient quality of life, leading to development of depression, anxiety and unhealthy lifestyles along with secondary problems such as overweight and obesity, which negatively impact the control of atopic diseases . Studies support this association and urge further investigation. A study of adolescents in the United States documented that atopic diseases such as asthma, allergic rhinitis and atopic dermatitis correlate with increased risk of anxiety and depression and that this risk is further increased when asthma and allergic rhinitis are concomitantly present . Further research found that depressive disorders are common in individuals with asthma but that there is no correlation between the severity of asthma and the severity of depression . The quality of life of adolescent patients with atopic dermatitis and underlying psychiatric disorders was also assessed: a high prevalence of anxiety and depression was found in these individuals. Studies have shown a link between quality of life and sleep loss and depression induced by atopic dermatitis . Recently, the association between allergic rhinitis and psychiatric diseases such as depression and anxiety has been determined. In nine out of 11 studies, there was an association between allergic rhinitis and anxiety, and in ten out of 12 studies, there was an association of depressive disorder with allergic rhinitis . Patients with attention deficit/hyperactivity disorder have a higher incidence of asthma, allergic rhinitis and atopic dermatitis than the general population. Children with atopic diseases are exposed to higher levels of inflammatory cytokines that are released due to an allergic response, and these cytokines can cross the blood–brain barrier and activate neuroimmunological mechanisms involved in emotions and behavior. Furthermore, activation in regions of the prefrontal cortex, potentially due to exaggerated and sustained release of inflammatory mediators, has been found. Another possible hypothesis explaining the relationship between these two diseases is based on the finding that allergic rhinitis is often associated with sleep disturbance, which may cause symptoms of daytime fatigue, inattention, irritability and impulsivity, which are in turn components of clinical attention deficit/hyperactivity disorder and its associated pathologies . Urticaria occurs frequently in patients with psychiatric problems and emotional distress. Staubach P et al., found that 48 percent of patients with chronic spontaneous urticaria have at least one mental disorder; anxiety was the primary associated pathology, but depression and somatization disorders were also found [33, 36].
|
review
| 99.9 |
There is a relationship between obesity and allergic diseases. Adipokines, which are fat proteins that function as cytokines, chemokines and cytokine receptors have an important role in that relationship. At present, most studies on obesity, allergic diseases and asthma are based on the inflammatory and metabolic roles of leptin and adiponectin. Adiponectin is an anti-inflammatory protein that inhibits IL6, the transcription factor NFκB and TNF-α and that increases the concentration of IL1 and IL10. Adiponectin levels are decreased in obese people due to necrosis of fat tissue resulting from hypoxia, which causes infiltration of polymorphonuclear cells and macrophages that secrete IL6 and TNF-α and inhibit the synthesis of adiponectin . Lectin is a proinflammatory protein that promotes the release of IL6 and TNF-α, decreases the activity of regulatory T cells, promotes Th1 lymphocyte activity and increases the levels of IFN-γ [38, 39].
|
review
| 99.8 |
There is a strong positive association between asthma incidence and lectin levels in prepubescent males and postmenopausal women. Obesity reduces progesterone levels in women, which lowers the levels of β2 adrenergic receptors, decreasing the relaxation of muscle in the respiratory tract . The concentrations of both total and specific IgE in children and adolescents with allergic symptoms are higher among those who are overweight or obese .
|
study
| 99.94 |
Vitamin D deficiency is more common in the obese population, in whom there is an inverse relationship between serum vitamin D levels and the degree of obesity. Vitamin D has also been shown to skew T cells toward a less inflammatory state. For instance, 1,25(OH)2D3 decreases T cell-mediated IFN-γ production while increasing IL4 production [42, 43]. Both the generation and immunosuppressive capacity of Foxp3 + CD4+ regulatory T cells are increased by 1,25(OH)2D3 . Moreover, recent studies showed that production of the inflammatory cytokine IL17 by T cells is prevented by 1,25(OH)2D3 [43, 45]. In line with these results, other groups have documented that the development of Th17 cells is negatively modulated by 1,25(OH)2D3 . Production of IL21, IL22 and IL17 is also inhibited by physiologically relevant doses of 1,25(OH)2D3 in Th17-skewed T cells; this evidence suggests that principal changes in transcription are driven by the vitamin D receptor-transcription factor complex .
|
study
| 99.9 |
At the same time, vitamin D deficiency is associated with a decrease in immune cell proliferation as well as synthesis of cytokines, including IL1, IL2, IL6 and IL12, TNF-α and IFN-γ. These cytokines, which are upregulated in patients with obesity and metabolic syndrome, decrease the serum concentrations of vitamin D. Thus, it has been presumed that in overweight patients, as the amount of visceral adipose tissue increases, the kidnapping of vitamin D by adipose tissue increases. Secondarily, it is proposed that vitamin D deficiency or insufficiency is responsible for insulin resistance and thereby promotes metabolic syndrome . Vitamin D deficiency has been associated with increased airway hyperresponsiveness, decreased lung function, reduced asthma control, and resistance to steroids . A recently conducted study of treated asthmatic children showed that 84.2% of children with asthma had low levels of vitamin D. In that study, overweight was an important risk factor for vitamin D deficiency and insufficiency . Another study found that vitamin D deficiency is associated with an increased risk of severe asthma in asthmatic adults (odds ratio [OR], 5.04; 95% confidence interval [CI]: 1.23 to 20.72; p = 0.02) and that high levels of vitamin D are related to a lower risk of hospitalization or emergency department visitation in the past year (OR, 0.90; 95% CI, 0.84 to 0.98; p = 0.04) . Obesity and overweight have also been associated with increased residual capacity and increased risk of asthma. It has been observed that children with asthma are at an increased risk of exacerbations as well as uncontrolled asthma [51, 52]. It has also been shown that there is an association between low vitamin D levels, physical inactivity and high BMI . Alternatively, vitamin D may reduce asthma severity and improve asthma control .
|
review
| 99.9 |
Based on the pathophysiological mechanisms described above, the psychoneuroimmunoendocrine axis has been related to neuropsychiatric diseases such as depression [4, 55–57] and schizophrenia [56, 58–61], metabolic syndrome [62, 63], rheumatologic and autoimmune diseases [64–72], irritable bowel syndrome [73, 74], periodontal disease [75, 76] and neoplastic diseases [62, 77], and these relationships warrant significant attention. Psychoneuroimmunology represents the challenge of health professionals to achieve multidisciplinary management of each of these pathologies.
|
review
| 99.9 |
We propose a new medical model that has been described to be based on the concept of holistic medicine, in which biological, psychological, social and environmental aspects of the health-disease process are taken into account in the recommendation of a revised lifestyle. Through allostasis, the autonomic nervous system, the hypothalamic-pituitary-adrenal axis, the cardiovascular system, the immune system, the endocrine system and metabolism protect the body by preparing these systems to address both internal and external stress. This concept of allostasis complements the concept of stress. Allostasis represents the active adaptation process involving the production of mediators such as adrenal steroids, catecholamines, cytokines, neurotransmitters and other factors. After suffering chronic stress, adaptation responses or allostatic responses are initiated in the body. Inadequate or excessive responses following repeated stressful situations lead to allostatic load, which is the “price paid by the organism” for being forced to adapt to psychosocial or physical adversity. Thus, allostatic load constitutes the cumulative wear and tear resulting from chronic hyperactivity as an adaptation to the constant demands of life. The response to stress is physical, mental and behavioral and depends on basic personality as well as social, cultural, environmental and genetic factors. A new medical paradigm of health promotion and disease prevention is very important, as this paradigm supports lifestyle changes that increase resilience to stress and augment immune system defenses .
|
review
| 99.9 |
Health education must consider the need to educate people regarding their potential and shortcomings in assuming their own identity. Another contribution of health education is to orient people regarding the management of emotions in order to facilitate the appropriate channeling and expression of emotions, which is a form of disease prevention and, consequently, a reflection of health and wellbeing.
|
other
| 99.94 |
The discipline of health education has the major challenge of establishing principles and methodologies that enable people to learn healthy practices and lifestyles so as to enhance their capacity for resilience. In addition, health education seeks to develop and promote the process of addressing struggles or mishaps of life and of resisting, overcoming and transforming adversity in order to emerge strengthened or even renewed. The development of fundamental strategies for the prevention of disease and the recovery of health through health education interventions results in positive adaptation in contexts of great adversity. In addition, health education interventions should help people learn to take measures that enhance their ability to combat disease and that properly harmonize and balance mind-body function. Health promotion strategies should be directed toward prevention and resolution of health problems and toward improving quality of life .
|
review
| 99.8 |
As mentioned above, lifestyle changes that increase resilience to stress and enhance immune system defenses are indispensable. Adequate daily rest; a diet that decreases oxidative stress, including daily consumption of fruits, vegetables, legumes, essential fatty acids and trace elements; and physical exercises that activate the immune system, such as breathing exercises that increase breathing capacity, and elimination of cigarette, drug and alcohol use are among the lifestyle changes to be considered. In addition, focusing on psychological aspects such as tracing life goals, being flexible, maintaining harmonious communication with others, having a consistent attitude in life, optimism and proper management of emotions, can also help with stress management .
|
review
| 98.5 |
Diet appears to play an important role in stress management. Relations of multivitamins and minerals with stress have been described: the main identified antistress drugs contain vitamins E, B1, B2, B3, B5, B6, B12, and C, folic acid and the minerals zinc and iron . Omega-3 fatty acids are very important for the functioning of the human brain. Poor intake of these acids induces several alterations in neurotransmission that can cause diverse psychiatric disorders, including schizophrenia and major depression. It has been observed that patients with psychiatric disorders who use fatty acid supplements exhibit a significant improvement in their symptoms. In addition, omega-3 fatty acids have been shown to be useful in decreasing antisocial behavior, hostility and aggressiveness in patients who are exposed to a psychologically stressful environment. Therefore, supplements containing omega-3 fatty acids can reduce such behaviors .
|
review
| 99.9 |
Exercise can be an effective stress management strategy and should be recommended for addressing acute, episodic acute, and chronic stress. One advantage of incorporating exercise with other stress management techniques is the psychological and physical beneficial effects of exercise. However, it is important to remember that exercise is only one component of a stress management program. Even though exercise may be effective in helping a person feel calmer, this change will not resolve the main triggers of chronic stress. It may be necessary to refer people suffering from chronic stress to professionals who can help them cope with their stressors . Research on exercise and stress has typically focused on aerobic exercise. For instance, it has been reported that patients feel calmer after 20 to 30 min of aerobic exercise and that the calming effect of exercise can last for several hours afterwards. Recently, there has been an increase in the amount of research examining the role of body-mind types of exercise, such as yoga and Tai Chi in reducing stress. Nevertheless, there is limited research on the role of resistance exercise in managing stress . Studies of humans and animal models have shown that being physically active improves the ability of the body to handle stress due to changes in hormonal responses and that exercise results in actions of brain neurotransmitters, such as dopamine and serotonin, that affect the body, state of mind and behavior. Additionally, exercise may serve as a time away or release from stressors. In a study of women attending a university who reported that studying was their main stressor, performing a constant exercise activity without performing a study activity and resting while exercising had a greater calming effect than quiet rest . Recent publications on yoga or Tai Chi indicate that these types of mental exercise can be effective in reducing stress. Authors have suggested that the results should be viewed with caution because the quality of the studies varied [85, 86]. The decrease in stress reported in one review was similar to or greater than the reduction in other types of commonly used stress management strategies . Lack of time is the limitation to performing exercise most commonly expressed by individuals. Lack of motivation, tiredness, and poor sleep and eating habits are additional factors associated with stress that can negatively affect compliance with an exercise regime .
|
review
| 99.9 |
Acupuncture may be effective in the treatment of chronic stress symptoms . The main investigative findings concerning the effects of acupuncture on stress are outlined in Table 2.Table 2Acupuncture as a stress management strategy: randomized controlled trialsWu, Y, Yuan, J, Feng, X. (2011) Acupuncture as an adjunct to anesthesia was found to help maintain stable hemodynamics and reduce stress responses during laparoscopic cholecystectomy surgery.Kwong E, Yiu E. A. (2010) Acupuncture did not reduce salivary cortisol concentrations (and therefore could not reduce emotional stress) in female patients with dysphonia.Middlekauff H, Hui K, Yu J. (2002) Acute acupuncture appeared to control excessive sympathetic arousal during mental stress in individuals with advanced heart failure.Balk J, Catov J, Horn B. (2009) Acupuncture is associated with reduced stress during embryo transfer and increased pregnancy rates in women receiving in vitro fertilization.Hui K, Marina O, Liu J et al. (2010) Acupuncture affects areas of the brain known to reduce sensitivity to pain and stress, promotes relaxation, and deactivates the analytical brain, which is responsible for anxiety.Erickson K, Voss M, Prakash R et al. (2011) Kim H, Park H, Shim H et al. (2011) Acupuncture improves stress-induced memory impairment and increases acetylcholinesterase reactivity in the hippocampus.Park H, Kim H, Hahm D. (2010) Acupuncture reduces serum levels of corticosterone and the number of tyrosine hydroxylase-immunoreactive cells.Lee A, Fan L. (2009) Cheng K. (2009) Acupuncture regulates levels of neurotransmitters and hormones such as serotonin, noradrenaline, dopamine, neuropeptide Y and ACTH, thus altering the mood chemistry of the brain to help combat negative affective states.Arranz L, Guayerbas N, Siboni L et al. (2007) Acupuncture reverses pathological changes in levels of inflammatory cytokines that are associated with stress reactions.Kavoussi B, Ross B. (2007) Acupuncture reduces inflammation by promoting the release of vascular and immunomodulatory factors.
|
review
| 99.9 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.