Development and content validation of yoga module for the mental health of NICU mothers.

BACKGROUND: Postnatal yoga has been found to be effective for maternal mental health management. But a validated yoga module for the mental health of early postpartum mothers with infants admitted to the Neonatal Intensive Care Unit (NICU) is lacking. AIM: To design and validate a yoga module for the mental health of early postpartum mothers having infants admitted to the NICU. MATERIALS AND METHODS: First phase: A yoga module was designed through a review of published research articles and yogic texts for NICU mothers. Second phase: thirty-eight yoga experts validated the yoga module. Lawshe's formula was used to calculate each item's content validity ratio (CVR). The intra-class correlation coefficient was determined for the validated yoga module. Third phase: The validated yoga module was pilot-tested with a sample size of 20 NICU mothers. RESULTS: Thirty-eight yoga experts validated the yoga module for NICU mothers. Thirteen practices included in the module indicated good content validity (cutoff value: 0.316). The module's content validity index (CVI) and intra-class correlation coefficient were 0.672 and 0.924, respectively. Ten days of practicing the yoga module resulted in a significant reduction in maternal stress levels in the yoga group (p < 0.001) compared to the control group (p = 0.427). CONCLUSION: The present study suggests good content validity of the yoga module for the mental health of NICU mothers. However, future randomized controlled trials must be carried out to determine both the feasibility and clinical efficacy of the Yoga Module for NICU mothers.

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.

Effect of Yoga Therapy on Health Outcomes in Women With Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis.

The purpose of this study was to assess the effect of yoga therapy (YT) on health outcomes of women suffering from polycystic ovary syndrome (PCOS). Interventional studies, with postmenarchal and premenopausal females with PCOS who received YT, with any health outcome reported, were included. Scopus, Cochrane, PubMed, Embase, and Medline databases were electronically searched. Systematic review included 11 experimental studies, representing 515 participants with PCOS, out of which 2 randomized controlled trials (RCTs) were included for meta-analysis. Random effects model was applied using Review Manager Software version 5.4.1 and strength of evidence was assessed using GRADEpro Guideline Development Tool, 2020. Meta-analysis showed that YT may significantly decrease menstrual irregularity (MD -.41, 95% CI -.74 to -.08), clinical hyperandrogenism (MD -.70, 95% CI -1.15 to -.26), fasting blood glucose (MD -.22 mmol/L, 95% CI -.44 to -.01), fasting insulin (MD -28.21 pmol/L, 95% CI -43.79 to -12.63), and homeostatic model assessment-insulin resistance value (MD -.86, 95% CI -1.29 to -.43). Strength of evidence was "low." In conclusion, YT may have beneficial effects on health outcomes in women suffering from PCOS. However, low strength of evidence suggests need of conducting well-designed RCTs to assess the efficacy of YT for PCOS.

Development and validation of 20-min yoga module for reducing burnout among healthcare worker(s).

BACKGROUND: A high prevalence of burnout has been reported among healthcare worker(s). During the current pandemic, such burnout has increased due to excessive load of patient care, use of personal protective equipment (PPE) kits, working in long shifts, staying away from family due to isolation norms, and disrupted social life. Existing yoga techniques used for reducing burnout include 45 min to hour-long sessions, which may not be feasible for regular practice by the healthcare worker(s). OBJECTIVE: The proposed study aimed to develop a 20-min yoga module to reduce burnout among healthcare worker(s). METHODS: To develop a 20-min yoga module, we reviewed yoga texts and relevant scientific research articles. Components of the 20-min yoga module include sukshma vyayama (loosening exercises), pranayama (regulated breathing), and dhyana (meditation). Nineteen yoga experts validated the 20-min yoga module with an average (SD) of 11.47 (6.77) years of research and clinical experience in yoga. Content validity ratio (CVR) was calculated according to Lawshe's method. Items having a CVR of 0.47 and above were retained in the module. RESULTS AND CONCLUSION: The content validity index (CVI) of the entire module was 0.83. CVR results of the elements of the 20-min yoga module indicated that experts consider these practices to be essential for reducing burnout among the healthcare worker(s). The strength of the 20-min yoga module lies in its short duration and easy-to-learn practices. 20-min yoga module can be implemented in practice by the healthcare worker(s) for reducing burnout following efficacy studies through further clinical trials.

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor's ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor).

Statistical modeling of cell-to-cell variability in viral infection during passaging in suspension cell culture: Application in Monte-Carlo simulation.

Packaging during the passaging of viruses in cell cultures yields various phenotypes and is regulated by viral protein expression in infected cells. Although such a packaging mechanism has a profound effect in controlling the virus yield, little is known about the underlying statistical models followed by virus packaging and protein expression among cells infected with the virus. A predictive framework combining identification of the probability density function (PDF) based on log-likelihood and using the PDF for Monte-Carlo simulations is developed. The Birnbaum-Saunders distribution was found to be consistent with all three-virus packaging levels, including nucleocapsids/occlusion-derived virus (ODV), ODVs/polyhedra, and polyhedra/cell for both wild-type and genetically modified AcMNPV. Next, it was demonstrated that PDF fitting could be used to compare two viruses having distinctly different genetic configurations. Finally, the identified PDF can be incorporated in RNA synthesis parameters for baculovirus infection to predict the cell-to-cell variability in protein expression using Monte-Carlo simulations. The proposed tool can be used for the estimation of uncertainty in the kinetic parameter and prediction of cell-to-cell variability for other biological systems.

Quality of Life in Yoga Experienced and Yoga Naïve Asian Indian Adults with Obesity.

Background: Obesity adversely affects quality of life which then acts as a barrier to weight loss and weight loss maintenance. Hence, those interventions which positively influence the quality of life along with weight reduction are considered useful for sustained weight loss in persons with obesity. An earlier study showed better quality of life in obese adults who had experience of yoga compared to yoga naïve obese adults. However, the main limitation of the study was the small sample size (n=20 in each group). Objective: The present study aimed to determine whether with larger sample sizes the quality of life would differ in yoga experienced compared to yoga naïve adults with obesity. Methods: There were 596 Asian Indian obese adults (age range 20 to 59 years; group mean age ± SD; 43.9 ± 9.9 years): of whom (i) 298 were yoga experienced (154 females; group mean age ± SD; 44.0 ± 9.8 years) with a minimum of 1 month of experience in yoga practice and (ii) 298 were yoga naïve (154 females; group mean age ± SD; 43.8 ± 10.0 years). All the participants were assessed for quality of life using the Moorehead-Ardelt quality of life questionnaire II. Data were drawn from a larger nationwide trial which assessed the effects of yoga compared to nutritional advice on obesity over a one-year follow-up period (CTRI/2018/05/014077). Results: There were higher participant-reported outcomes for four out of six aspects of quality of life in the yoga experienced compared to the yoga naïve (p < 0.008, based on t values of the least squares linear regression analyses, Bonferroni adjusted, and adjusted for age, gender, and BMI as covariates). These were enjoyment in physical activities, ability to work, self-esteem, and social satisfaction. Conclusion: Obese adults with yoga experience appear to have better quality of life in specific aspects, compared to yoga naïve persons with a comparable degree of obesity.

Cell-to-Cell Variability in Protein Expression during Viral Infection: Monte-Carlo Simulation and Validation based on Confocal Imaging.

One of the major challenges is to identify the statistical model underlying the heterogeneity in viral protein expression in single cells. In this endeavor, we propose a computational tool to address the cell-to-cell variability in protein expression by random variate generation following probability distributions. Here, we show that statistical modeling using the probability density function of various distribution offers considerable potential for providing stochastic inputs to Monte Carlo simulation. Specifically, we present the ranking between three distribution families including gamma, normal and Weibull distribution using a comparison of cumulative frequency obtained from experiment and simulation. The major contribution of the proposed simulation method is to identify the underlying statistical model in kinetic parameters that capture the variability in protein expression in single cells obtained through imaging using confocal microscopy.