A regulatory T cell Notch4-GDF15 axis licenses tissue inflammation in asthma.

Elucidating the mechanisms that sustain asthmatic inflammation is critical for precision therapies. We found that interleukin-6- and STAT3 transcription factor-dependent upregulation of Notch4 receptor on lung tissue regulatory T (Treg) cells is necessary for allergens and particulate matter pollutants to promote airway inflammation. Notch4 subverted Treg cells into the type 2 and type 17 helper (TH2 and TH17) effector T cells by Wnt and Hippo pathway-dependent mechanisms. Wnt activation induced growth and differentiation factor 15 expression in Treg cells, which activated group 2 innate lymphoid cells to provide a feed-forward mechanism for aggravated inflammation. Notch4, Wnt and Hippo were upregulated in circulating Treg cells of individuals with asthma as a function of disease severity, in association with reduced Treg cell-mediated suppression. Our studies thus identify Notch4-mediated immune tolerance subversion as a fundamental mechanism that licenses tissue inflammation in asthma.

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Statistical simulations show that scientists need not increase overall sample size by default when including both sexes in in vivo studies.

In recent years, there has been a strong drive to improve the inclusion of animals of both sexes in the design of in vivo research studies, driven by a need to increase sex representation in fundamental biology and drug development. This has resulted in inclusion mandates by funding bodies and journals, alongside numerous published manuscripts highlighting the issue and providing guidance to scientists. However, progress is slow and barriers to the routine use of both sexes remain. A frequent, major concern is the perceived need for a higher overall sample size to achieve an equivalent level of statistical power, which would result in an increased ethical and resource burden. This perception arises from either the belief that sex inclusion will increase variability in the data (either through a baseline difference or a treatment effect that depends on sex), thus reducing the sensitivity of statistical tests, or from misapprehensions about the correct way to analyse the data, including disaggregation or pooling by sex. Here, we conduct an in-depth examination of the consequences of including both sexes on statistical power. We performed simulations by constructing artificial datasets that encompass a range of outcomes that may occur in studies studying a treatment effect in the context of both sexes. This includes both baseline sex differences and situations in which the size of the treatment effect depends on sex in both the same and opposite directions. The data were then analysed using either a factorial analysis approach, which is appropriate for the design, or a t test approach following pooling or disaggregation of the data, which are common but erroneous strategies. The results demonstrate that there is no loss of power to detect treatment effects when splitting the sample size across sexes in most scenarios, providing that the data are analysed using an appropriate factorial analysis method (e.g., two-way ANOVA). In the rare situations where power is lost, the benefit of understanding the role of sex outweighs the power considerations. Additionally, use of the inappropriate analysis pipelines results in a loss of statistical power. Therefore, we recommend analysing data collected from both sexes using factorial analysis and splitting the sample size across male and female mice as a standard strategy.

Reverse and forward engineering of Drosophila corneal nanocoatings.

Insect eyes have an anti-reflective coating, owing to nanostructures on the corneal surface creating a gradient of refractive index between that of air and that of the lens material1,2. These nanocoatings have also been shown to provide anti-adhesive functionality3. The morphology of corneal nanocoatings are very diverse in arthropods, with nipple-like structures that can be organized into arrays or fused into ridge-like structures4. This diversity can be attributed to a reaction-diffusion mechanism4 and patterning principles developed by Alan Turing5, which have applications in numerous biological settings6. The nanocoatings on insect corneas are one example of such Turing patterns, and the first known example of nanoscale Turing patterns4. Here we demonstrate a clear link between the morphology and function of the nanocoatings on Drosophila corneas. We find that nanocoatings that consist of individual protrusions have better anti-reflective properties, whereas partially merged structures have better anti-adhesion properties. We use biochemical analysis and genetic modification techniques to reverse engineer the protein Retinin and corneal waxes as the building blocks of the nanostructures. In the context of Turing patterns, these building blocks fulfil the roles of activator and inhibitor, respectively. We then establish low-cost production of Retinin, and mix this synthetic protein with waxes to forward engineer various artificial nanocoatings with insect-like morphology and anti-adhesive or anti-reflective function. Our combined reverse- and forward-engineering approach thus provides a way to economically produce functional nanostructured coatings from biodegradable materials.

netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA.

Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these issues, we developed an unsupervised workflow to identify groups of graphs from reliable network-based statistics. In particular, we first compute the similarity between networks via appropriate distance measures between graphs and use them in an unsupervised hierarchical algorithm to identify classes of similar networks. Then, to determine the optimal number of clusters, we recursively test for distances between two groups of networks. The test itself finds its inspiration in distance-wise ANOVA algorithms. Finally, we assess significance via the permutation of between-object distance matrices. Notably, the approach, which we will call netANOVA, is flexible since users can choose multiple options to adapt to specific contexts and network types. We demonstrate the benefits and pitfalls of our approach via extensive simulations and an application to two real-life datasets. NetANOVA achieved high performance in many simulation scenarios while controlling type I error. On non-synthetic data, comparison against state-of-the-art methods showed that netANOVA is often among the top performers. There are many application fields, including precision medicine, for which identifying disease subtypes via individual-level biological networks improves prevention programs, diagnosis and disease monitoring.

Individuals with adverse childhood experiences explore less and underweight reward feedback.

Adverse childhood experiences (ACEs) are extreme stressors that lead to negative psychosocial outcomes in adulthood. Nonhuman animals explore less after exposure to early stress. Therefore, in this preregistered study, we hypothesized that reduced exploration following ACEs would also be evident in human adults. Further, we predicted that adults with ACEs, in a foraging task, would adopt a decision-making policy that relies on the most-recent reward feedback, a rational strategy for unstable environments. We analyzed data from 145 adult participants, 47 with four or more ACEs and 98 with fewer than four ACEs. In the foraging task, participants evaluated the trade-off between exploiting a known patch with diminishing rewards and exploring a novel one with a fresh distribution of rewards. Using computational modeling, we quantified the degree to which participants' decisions weighted recent feedback. As predicted, participants with ACEs explored less. However, contrary to our hypothesis, they underweighted recent feedback. These unexpected findings indicate that early adversity may dampen reward sensitivity. Our results may help to identify cognitive mechanisms that link childhood trauma to the onset of psychopathology.

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study.

BACKGROUND: Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC. METHODS: In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables. RESULTS: The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC. CONCLUSIONS: The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Variable-selection ANOVA Simultaneous Component Analysis (VASCA).

MOTIVATION: ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the 'holistic' testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers). RESULTS: We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart. AVAILABILITY AND IMPLEMENTATION: The code for VASCA is available in the MEDA Toolbox at https://github.com/josecamachop/MEDA-Toolbox (release v1.3). The simulation results and motivating example can be reproduced using the repository at https://github.com/josecamachop/VASCA/tree/v1.0.0 (DOI 10.5281/zenodo.7410623). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene-expression measurement: variance-modeling considerations for robust data analysis.

System-wide measurements of gene expression by DNA microarray and, more recently, RNA-sequencing strategies have become de facto tools of modern biology and have led to deep understanding of biological mechanisms and pathways. However, analyses of the measurements have often ignored statistically robust methods that account for variance, resulting in misleading biological interpretations.

Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.

Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the "Methods" and "Results" sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.

Robustness of response-adaptive randomization.

Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

Clinical variables influencing the perception of fatigue in people with multiple sclerosis: a cross-sectional study using FSIQ-RMS.

BACKGROUND: Physical fatigue is one of the most disabling symptoms in people with Multiple Sclerosis (PwMS). Several factors might influence the development of fatigue, such as gender, education, body mass index (BMI), Expanded Disability Status Scale (EDSS), disease duration, working status (Ws), physiotherapy (Ph), and disease-modifying therapies (DMTs). Fatigue Symptoms and Impacts Questionnaire-Relapsing Multiple Sclerosis (FSIQ-RMS) is a patient-reported outcome (PRO) that allows one to define the impact of fatigue in PwMS clearly. This study aimed to assess fatigue impact on PwMS by using FSIQ-RMS. METHODS: The participants were enrolled from May to July 2021 in MS Centers of Sant'Andrea Hospital and Policlinico Umberto I Hospital in Rome. Fatigue was evaluated using the FSIQ-RMS, validated, and culturally adapted in Italian. Clinical and demographic data were collected at the same time. RESULTS: We enrolled 178 PwMS [Female 74.16%; RMS 82.58%, SPMS 17.52%]. FSIQ-RMS scores were significantly correlated with EDSS (p-value < 0.01). Analysis of variance between means showed a statistically significant difference between the BMI groups at the 24hours_FSIQ-RMS score and the 7days_FSIQ-RMS score (p < 0.01), with the lower BMI group having the highest scores. Furthermore, perceived fatigue significantly improved both in subjects performing Ph (p < 0.05) and in those who actively work (p < 0.01). CONCLUSIONS: The use of FSIQ-RMS in a real-world setting confirmed that underweight and high levels of disability are closely related to fatigue. In addition, Ph and active Ws are strongly correlated with fatigue in PwMS.

Factors affecting subsequent dose of COVID-19 vaccine uptake based on BASNEF model among older adults.

BACKGROUND: Vaccination is a primary prevention approach to preventing disease by disconnecting the transmission chain. The current study utilized a BASNEF model framework to identify factors influencing subsequent doses of COVID-19 vaccination among older adults. METHODS: This cross-sectional study was performed in the west of Iran in May 2022. The participants were selected via multi-stage sampling. Finally, 1120 participants contributed to the present study. The questionnaire consisted of three sections: a) Socio-demographic characteristics, b) cognitive impairments tests, and c) Questionnaire about the subsequent dose of COVID-19 vaccine uptake based on the BASNEF model. Data were analyzed using the software IBM AMOS-20 and SPSS-23 via one-way analysis of variance (ANOVA) and independent sample T-tests were used, too. The significance level of statistical tests was regarded as less than 0.05. RESULTS: The presented results of analyzing 50% of the variance of vaccination intention as the dependent variable (R square = 0.497) and 10% of the behavior variance as the dependent variable (R square = 0.104) can be explained based on the BASNEF model. The enabling factors (ß = 0.636, p < 0.001) and the intention (ß = 0.322, p < 0.001) were important factors for subsequent doses of COVID-19 vaccine uptake in older adults. CONCLUSION: So, planning and implementing promotional intervention programs for older people (over 65; 80), females, illiterate, widows and divorced, good economic status, and urban areas is essential. It seems that enabling factors such as free vaccinations, vaccination inaccessible places such as public social security agencies, social supports such as involvement of the government and physicians, and improving information by the medium or knowledge-sharing experience, which can be further used to enhance the acceptance of subsequent doses of COVID-19 uptake in older adults.

A causal relationship between panic disorder and risk of alzheimer disease: a two-sample mendelian randomization analysis.

BACKGROUND: Observational studies have suggested a link between panic disorder (PD) and Alzheimer disease (AD). This study aimed to identify the underlying association of PD with the risk of AD using Mendelian randomization. METHODS: Genetic instrumental variables (IVs) were retrieved in the genome-wide association study between PD and AD. Then, five different models, namely inverse variance weighting (IVW), weighted median, weighted mode, MR-Egger and MR-robust adjusted profile scores (MR-RAPS), were used for MR Analysis. Finally, the heterogeneity and pleiotropy of identified IVs were verified by multiple sensitivity tests. RESULTS: The Cochran's Q test based on MR Egger and IVW showed that no evidence of heterogeneity was found in the effects of instrumental variables, so a fixed-effect model was used. IVW analysis (OR 1.000479, 95% CI [1.000147056, 1.000811539], p = 0.005) indicated that PD was associated with an increased risk of AD, and a causal association existed between them. Meanwhile, weighted median (OR 1.000513373, 95% CI [1.000052145, 1.000974814], p = 0.029) and MR-RAPS (OR 1.000510118, 95% CI [1.000148046, 1.00087232], p = 0.006) also showed the similar findings. In addition, extensive sensitivity analyses confirmed the robustness and accuracy of these results. CONCLUSION: This investigation provides evidence of a potential causal relationship between PD and the increased risk of AD. Based on our MR results, when diagnosing and treating patients with PD, clinicians should pay more attention to their AD-related symptoms to choose therapeutic measures or minimize comorbidities. Furthermore, the development of drugs that improve both PD and AD may better treat patients with these comorbidities.

Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure.

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the "Normics" algorithm: Proteins are ranked based on their expression level-corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE likelihood of a protein in a given dataset. Subsequent normalization is based on a subset of non-DE proteins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demonstrated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard variance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected subsets of high DE likelihood.

Effects of virtual reality versus motor imagery versus routine physical therapy in patients with parkinson's disease: a randomized controlled trial.

BACKGROUND: Parkinson's Disease (PD) is the second most common progressive neurodegenerative disorder, mostly affecting balance and motor function caused mainly by a lack of dopamine in the brain. The use of virtual reality (VR) and motor imagery (MI) is emerging as an effective method of rehabilitation for people with Parkinson's disease. Motor imagery and virtual reality have not been compared in patients with Parkinson's disease. This randomized clinical trial is unique to compare the effects of virtual reality with routine physical therapy, motor imagery with routine physical therapy, and routine physical therapy alone on balance, motor function, and activities of daily living in patients with Parkinson's disease. METHODS: A total of sixty patients with Parkinson's disease were randomized into three groups using lottery method; twenty with virtual reality therapy in addition to physical therapy (group A = VR + RPT), twenty with imagery therapy in addition to physical therapy (group B = MI + RPT), and twenty were treated with only routine physical therapy (group C = RPT). All patients were evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS) for motor function and activities of daily living, the Berg balance scale (BBS) for balance, and the Activities-specific Balance Confidence Scale (ABCs) for balance confidence at baseline, six and twelve weeks, and one month after treatment discontinuation. The one-way ANOVA was used to compare the outcomes between three groups, and the repeated measures ANOVA was used to compare the outcomes within each of the three groups at a significance level of p-value = 0.05. RESULTS: According to UPDRS III, the VR + RPT group showed significant improvement in motor function, compared to the MI + RPT and RPT groups, as the Mean ± SD at baseline was 33.95 ± 3.501 and at the 12-week assessment was 17.20 ± 9.451 with a p-value = 0.001. In the VR + RPT group, the BBS score at baseline was 37.15 ± 3.437 and at 12th week was 50.10 ± 4.897 with a p-value = 0.019. Among the VR + RPT group, the ABCS score showed significant improvement as the M ± SD at baseline was 57.95 ± 4.629, and at the 12th week was 78.59 ± 6.386 with a p-value = 0.010. At baseline, the UPDRS II for activities of daily living in the VR + RPT group was 25.20 ± 3.036 and at 12th week it was 15.30 ± 2.364 with p-value of 0.000. CONCLUSION: The current study found that the combination of VR and RPT proved to be the most effective treatment method for improving balance, motor function, and activities of daily living in patients with Parkinson's disease when compared to MI + RPT or RPT alone.

Different sedentary behavior domains present distinct associations with eating-related indicators.

BACKGROUND: Unhealthful dietary patterns have been consistently associated with low levels of physical activity (PA), but studies dedicated to sedentary behavior (SB) are scarce, especially in adults. The few studies that investigated the association between SB and dietary patterns focused mostly on specific types of SB, such as TV-watching or screen time. SB can be accumulated in distinct domains (i.e., work, transport, and leisure-time), thus, it is key to investigate in depth the impact that different domains of SB can have on eating-related indicators. We aimed to investigate the associations between different SB domains and eating-related indicators, in a sample of adults. METHODS: Cross-sectional data from students, teachers, and staff from a Portuguese University was collected in November/2021 through an anonymous online survey. Data analyses were performed using the IBM SPSS software (version 28.0) and included descriptive statistics, partial correlations, and group comparisons using one-way ANOVA. Daily average SB at work/study, transport, and in leisure-time were self-reported and eating-related indicators were measured with several items from the Mediterranean Diet Score. Specific eating-related behaviors reflecting a protective eating pattern (e.g., eating breakfast regularly), and eating behavior traits (e.g., external eating) were also assessed. Body mass index (BMI) was calculated as weight (kg)/height(m)2. The International Physical Activity Questionnaire/Short-Form was used to assess PA. RESULTS: The sample included 301 adults (60.1% women), with a mean age of 34.5 years. Overall, leisure-time SB was inversely associated with adherence to the Mediterranean diet (r = -0.20; p < 0.001) and with a protective eating profile (r = -0.31; p < 0.001). Higher transport SB was also related to lower adherence to the Mediterranean diet (r = -0.20; p < 0.001) and to an unhealthier eating profile (r = -0.22; p < 0.001), but no associations were found for work-related SB (p > 0.05). These results persisted after the adjustment for BMI, sex, and self-reported PA. These results were impacted by the age tertile. CONCLUSIONS: Our findings suggest that adults with higher levels of SB in leisure-time and transport domains tend to report less healthy eating-related behaviors, irrespective of BMI, sex, and PA level. However, some differences in these associations were found according to the age tertile. This information may assist public health authorities in focusing their efforts in augmenting literacy on SB, namely on how SB can be accumulated via different settings. Furthermore, public health literacy efforts need to extend besides the more known deleterious effects of SB on health (e.g., diabetes, cardiovascular disease), to also include the interplay with eating indicators. Strategies to reduce SB and unhealthy eating should be particularly focused on promoting physically active forms of commuting and reducing SB in the leisure setting.

Geographic disparities in telemedicine mental health use by applying three way ANOVA on Medicaid claims population data.

BACKGROUND: Utilization of telemedicine care for vulnerable and low income populations, especially individuals with mental health conditions, is not well understood. The goal is to describe the utilization and regional disparities of telehealth care by mental health status in Texas. Texas Medicaid claims data were analyzed from September 1, 2012, to August 31, 2018 for Medicaid patients enrolled due to a disability. METHODS: We analyzed the growth in telemedicine care based on urban, suburban, and rural, and mental health status. We used t-tests to test for differences in sociodemographic characteristics across patients and performed a three-way Analyses of Variance (ANOVA) to evaluate whether the growth rates from 2013 to 2018 were different based on geography and patient type. We then estimated patient level multivariable ordinary least square regression models to estimate the relationship between the use of telemedicine and patient characteristics in 2013 and separately in 2018. Outcome was a binary variable of telemedicine use or not. Independent variables of interest include geography, age, gender, race, ethnicity, plan type, Medicare eligibility, diagnosed mental health condition, and ECI score. RESULTS: Overall, Medicaid patients with a telemedicine visit grew at 81%, with rural patients growing the fastest (181%). Patients with a telemedicine visit for a mental health condition grew by 77%. Telemedicine patients with mental health diagnoses tended to have 2 to 3 more visits per year compared to non-telemedicine patients with mental health diagnoses. In 2013, multivariable regressions display that urban and suburban patients, those that had a mental health diagnosis were more likely to use telemedicine, while patients that were younger, women, Hispanics, and those dual eligible were less likely to use telemedicine. By 2018, urban and suburban patients were less likely to use telemedicine. CONCLUSIONS: Growth in telemedicine care was strong in urban and rural areas between 2013 and 2018 even before the COVID-19 pandemic. Those with a mental health condition who received telemedicine care had a higher number of total mental health visits compared to those without telemedicine care. These findings hold across all geographic groups and suggest that mental health telemedicine visits did not substitute for face-to-face mental health visits.

Personal experiences bridge moral and political divides better than facts.

Both liberals and conservatives believe that using facts in political discussions helps to foster mutual respect, but 15 studies-across multiple methodologies and issues-show that these beliefs are mistaken. Political opponents respect moral beliefs more when they are supported by personal experiences, not facts. The respect-inducing power of personal experiences is revealed by survey studies across various political topics, a field study of conversations about guns, an analysis of YouTube comments from abortion opinion videos, and an archival analysis of 137 interview transcripts from Fox News and CNN. The personal experiences most likely to encourage respect from opponents are issue-relevant and involve harm. Mediation analyses reveal that these harm-related personal experiences increase respect by increasing perceptions of rationality: everyone can appreciate that avoiding harm is rational, even in people who hold different beliefs about guns, taxes, immigration, and the environment. Studies show that people believe in the truth of both facts and personal experiences in nonmoral disagreement; however, in moral disagreements, subjective experiences seem truer (i.e., are doubted less) than objective facts. These results provide a concrete demonstration of how to bridge moral divides while also revealing how our intuitions can lead us astray. Stretching back to the Enlightenment, philosophers and scientists have privileged objective facts over experiences in the pursuit of truth. However, furnishing perceptions of truth within moral disagreements is better accomplished by sharing subjective experiences, not by providing facts.