MicroRNA­mediated regulation of muscular atrophy: Exploring molecular pathways and therapeutics (Review).

Muscular atrophy, which results in loss of muscle mass and strength, is a significant concern for patients with various diseases. It is crucial to comprehend the molecular mechanisms underlying this condition to devise targeted treatments. MicroRNAs (miRNAs) have emerged as key regulators of gene expression, serving vital roles in numerous cellular processes, including the maintenance of muscle stability. An intricate network of miRNAs finely regulates gene expression, influencing pathways related to muscle protein production, and muscle breakdown and regeneration. Dysregulation of specific miRNAs has been linked to the development of muscular atrophy, affecting important signaling pathways including the protein kinase B/mTOR and ubiquitin­proteasome systems. The present review summarizes recent work on miRNA patterns associated with muscular atrophy under various physiological and pathological conditions, elucidating its intricate regulatory networks. In conclusion, the present review lays a foundation for the development of novel treatment options for individuals affected by muscular atrophy, and explores other regulatory pathways, such as autophagy and inflammatory signaling, to ensure a comprehensive overview of the multifarious nature of muscular atrophy. The objective of the present review was to elucidate the complex molecular pathways involved in muscular atrophy, and to facilitate the development of innovative and specific therapeutic strategies for the prevention or reversal of muscular atrophy in diverse clinical scenarios.

Interventions to Prevent and Respond to Violence Against Justice-Involved Young Women: A Scoping Review.

Young women who have had contact with the criminal justice system (justice-involved young women) have an increased risk of being a victim of violence. However, no reviews have synthesized the evidence on interventions to prevent or respond to violence against justice-involved young women. We conducted a scoping review to identify interventions designed to prevent or respond to violence against justice-involved young women. We searched Medline, Criminal Justice Abstracts, Web of Science, and Google Scholar for peer-reviewed and gray literature published in English from January 1, 2000 until March 23, 2021. Consistent with the public health approach to violence, we included primary, secondary, and tertiary interventions. Excluding duplicates, our search returned 5,603 records, 14 of which met our inclusion criteria. We narratively synthesized the included studies, all of which were conducted in the United States. Most included studies examined a tertiary intervention (n = 10), and few examined a primary (n = 2) or secondary (n = 2) intervention. Across the Joanna Briggs Institute Critical Appraisal Tools, the percentage of items met ranged from 0% to 78%. There was some limited evidence that tertiary interventions that included cognitive behavioral therapy reduced the mental health impacts of violence victimization among justice-involved young women. There was little evidence on primary and secondary interventions. Effective and evidence-based interventions to prevent violence victimization and revictimization against justice-involved young women remains a critical gap in knowledge.

Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms.

Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set. They also provide a concise tutorial on machine learning to illustrate predictive models trained on complex and diverse structures in the MLORD data set.

Strain-tunable Berry curvature in quasi-two-dimensional chromium telluride.

Magnetic transition metal chalcogenides form an emerging platform for exploring spin-orbit driven Berry phase phenomena owing to the nontrivial interplay between topology and magnetism. Here we show that the anomalous Hall effect in pristine Cr2Te3 thin films manifests a unique temperature-dependent sign reversal at nonzero magnetization, resulting from the momentum-space Berry curvature as established by first-principles simulations. The sign change is strain tunable, enabled by the sharp and well-defined substrate/film interface in the quasi-two-dimensional Cr2Te3 epitaxial films, revealed by scanning transmission electron microscopy and depth-sensitive polarized neutron reflectometry. This Berry phase effect further introduces hump-shaped Hall peaks in pristine Cr2Te3 near the coercive field during the magnetization switching process, owing to the presence of strain-modulated magnetic layers/domains. The versatile interface tunability of Berry curvature in Cr2Te3 thin films offers new opportunities for topological electronics.

GalaxyDock2-HEME: Protein-ligand docking for heme proteins.

Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2-HEME, a new protein-ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation-dependent scoring term to describe heme iron-ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein-ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein-ligand complexes in which ligands do not bind iron show that GalaxyDock2-HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins.

Structural basis for Y2 receptor-mediated neuropeptide Y and peptide YY signaling.

Neuropeptide Y (NPY) and its receptors are expressed in various human tissues including the brain where they regulate appetite and emotion. Upon NPY stimulation, the neuropeptide Y1 and Y2 receptors (Y1R and Y2R, respectively) activate GI signaling, but their physiological responses to food intake are different. In addition, deletion of the two N-terminal amino acids of peptide YY (PYY(3-36)), the endogenous form found in circulation, can stimulate Y2R but not Y1R, suggesting that Y1R and Y2R may have distinct ligand-binding modes. Here, we report the cryo-electron microscopy structures of the PYY(3-36)‒Y2R‒Gi and NPY‒Y2R‒Gi complexes. Using cell-based assays, molecular dynamics simulations, and structural analysis, we revealed the molecular basis of the exclusive binding of PYY(3-36) to Y2R. Furthermore, we demonstrated that Y2R favors G protein signaling over ß-arrestin signaling upon activation, whereas Y1R does not show a preference between these two pathways.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking.

Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

Sex differences in suicide, suicidal ideation, and self-harm after release from incarceration: a systematic review and meta-analysis.

PURPOSE: People released from incarceration are at increased risk of suicide compared to the general population. We aimed to synthesise evidence on the incidence of and sex differences in suicide, suicidal ideation, and self-harm after release from incarceration. METHODS: We searched MEDLINE, EMBASE, PsycINFO, Web of Science and PubMed between 1 January 1970 and 14 October 2021 for suicide, suicidal ideation, and self-harm after release from incarceration (PROSPERO registration: CRD42020208885). We calculated pooled crude mortality rates (CMRs) and standardised mortality ratios (SMRs) for suicide, overall and by sex, using random-effects models. We calculated a pooled incidence rate ratio (IRR) comparing rates of suicide by sex. RESULTS: Twenty-nine studies were included. The pooled suicide CMR per 100,000 person years was 114.5 (95%CI 97.0, 132.0, I2 = 99.2%) for non-sex stratified samples, 139.5 (95% CI 91.3, 187.8, I2 = 88.6%) for women, and 121.8 (95% CI 82.4, 161.2, I2 = 99.1%) for men. The suicide SMR was 7.4 (95% CI 5.4, 9.4, I2 = 98.3%) for non-sex stratified samples, 14.9 for women (95% CI 6.7, 23.1, I2 = 88.3%), and 4.6 for men (95% CI 1.3, 7.8, I2 = 98.8%). The pooled suicide IRR comparing women to men was 1.1 (95% CI 0.9, 1.4, I2 = 82.2%). No studies reporting self-harm or suicidal ideation after incarceration reported sex differences. CONCLUSION: People released from incarceration are greater than seven times more likely to die by suicide than the general population. The rate of suicide is higher after release than during incarceration, with the elevation in suicide risk (compared with the general population) three times higher for women than for men. Greater effort to prevent suicide after incarceration, particularly among women, is urgently needed.

Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction.

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.

HProteome-BSite: predicted binding sites and ligands in human 3D proteome.

Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.

Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom.

Worldwide, racial and ethnic minorities have been disproportionately impacted by COVID-19 with increased risk of infection, its related complications, and death. In the initial phase of population-based vaccination in the United States (U.S.) and United Kingdom (U.K.), vaccine hesitancy may result in differences in uptake. We performed a cohort study among U.S. and U.K. participants who volunteered to take part in the smartphone-based COVID Symptom Study (March 2020-February 2021) and used logistic regression to estimate odds ratios of vaccine hesitancy and uptake. In the U.S. (n = 87,388), compared to white participants, vaccine hesitancy was greater for Black and Hispanic participants and those reporting more than one or other race. In the U.K. (n = 1,254,294), racial and ethnic minority participants showed similar levels of vaccine hesitancy to the U.S. However, associations between participant race and ethnicity and levels of vaccine uptake were observed to be different in the U.S. and the U.K. studies. Among U.S. participants, vaccine uptake was significantly lower among Black participants, which persisted among participants that self-reported being vaccine-willing. In contrast, statistically significant racial and ethnic disparities in vaccine uptake were not observed in the U.K sample. In this study of self-reported vaccine hesitancy and uptake, lower levels of vaccine uptake in Black participants in the U.S. during the initial vaccine rollout may be attributable to both hesitancy and disparities in access.

Association Between Aspirin Use and Gastric Adenocarcinoma: A Prospective Cohort Study.

Prospective data examining the association of aspirin use, according to dose and duration, with long-term risk of gastric adenocarcinoma in non-Asian cohorts are lacking. We evaluated the association between aspirin use and risk of gastric adenocarcinoma in two large prospective U.S. cohort studies, the Nurses' Health Study and the Health Professionals Follow-up Study. Cox proportional hazards regression models were used to calculate multivariable adjusted HRs and 95% confidence intervals (CI). Among the 159,116 participants, we documented 316 gastric adenocarcinoma cases (176 women, 140 men) over 34 years encompassing 4.5 million person-years. Among women, regular aspirin use (at least two times or more per week) was significantly associated with lower risk of gastric adenocarcinoma (multivariable HR, 0.52; 95% CI, 0.37-0.73) compared with nonregular use. However, regular aspirin use was not associated with gastric adenocarcinoma risk among men (multivariable HR, 1.08; 95% CI, 0.77-1.52; Pheterogeneity for sex = 0.003). Among women, the lower risk of gastric adenocarcinoma was more apparent with increasing duration of aspirin use (Ptrend < 0.001) and more than five tablets per week (multivariable HR, 0.51; 95% CI, 0.31-0.84). Regular, long-term aspirin use was associated with lower risk of gastric adenocarcinoma among women, but not men. The benefit appeared after at least 10 years of use and was maximized at higher doses among women. The heterogeneity by sex in the association of aspirin use with risk of gastric adenocarcinoma requires further investigation. PREVENTION RELEVANCE: Novel prevention is urgently needed to reduce incidence and mortality of gastric cancer. We found that regular aspirin use was associated with lower risk of gastric adenocarcinoma among women, but not men. The benefit appeared after at least 10 years of use and was maximized at higher doses among women. See related Spotlight, p. 213.

A pGpG-specific phosphodiesterase regulates cyclic di-GMP signaling in Vibrio cholerae.

The bacterial second messenger bis-(3'-5')-cyclic diguanylate monophosphate (c-di-GMP) controls various cellular processes, including motility, toxin production, and biofilm formation. c-di-GMP is enzymatically synthesized by GGDEF domain-containing diguanylate cyclases and degraded by HD-GYP domain-containing phosphodiesterases (PDEs) to 2 GMP or by EAL domain-containing PDE-As to 5'-phosphoguanylyl-(3',5')-guanosine (pGpG). Since excess pGpG feedback inhibits PDE-A activity and thereby can lead to the uncontrolled accumulation of c-di-GMP, a PDE that degrades pGpG to 2 GMP (PDE-B) has been presumed to exist. To date, the only enzyme known to hydrolyze pGpG is oligoribonuclease Orn, which degrades all kinds of oligoribonucleotides. Here, we identified a pGpG-specific PDE, which we named PggH, using biochemical approaches in the gram-negative bacteria Vibrio cholerae. Biochemical experiments revealed that PggH exhibited specific PDE activity only toward pGpG, thus differing from the previously reported Orn. Furthermore, the high-resolution structure of PggH revealed the basis for its PDE activity and narrow substrate specificity. Finally, we propose that PggH could modulate the activities of PDE-As and the intracellular concentration of c-di-GMP, resulting in phenotypic changes including in biofilm formation.

Diet quality and risk and severity of COVID-19: a prospective cohort study.

OBJECTIVE: Poor metabolic health and unhealthy lifestyle factors have been associated with risk and severity of COVID-19, but data for diet are lacking. We aimed to investigate the association of diet quality with risk and severity of COVID-19 and its interaction with socioeconomic deprivation. DESIGN: We used data from 592 571 participants of the smartphone-based COVID-19 Symptom Study. Diet information was collected for the prepandemic period using a short food frequency questionnaire, and diet quality was assessed using a healthful Plant-Based Diet Score, which emphasises healthy plant foods such as fruits or vegetables. Multivariable Cox models were fitted to calculate HRs and 95% CIs for COVID-19 risk and severity defined using a validated symptom-based algorithm or hospitalisation with oxygen support, respectively. RESULTS: Over 3 886 274 person-months of follow-up, 31 815 COVID-19 cases were documented. Compared with individuals in the lowest quartile of the diet score, high diet quality was associated with lower risk of COVID-19 (HR 0.91; 95% CI 0.88 to 0.94) and severe COVID-19 (HR 0.59; 95% CI 0.47 to 0.74). The joint association of low diet quality and increased deprivation on COVID-19 risk was higher than the sum of the risk associated with each factor alone (Pinteraction=0.005). The corresponding absolute excess rate per 10 000 person/months for lowest vs highest quartile of diet score was 22.5 (95% CI 18.8 to 26.3) among persons living in areas with low deprivation and 40.8 (95% CI 31.7 to 49.8) among persons living in areas with high deprivation. CONCLUSIONS: A diet characterised by healthy plant-based foods was associated with lower risk and severity of COVID-19. This association may be particularly evident among individuals living in areas with higher socioeconomic deprivation.

Protein oligomer structure prediction using GALAXY in CASP14.

Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo- or hetero-oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins. To this end, computational prediction of oligomer structures may be a useful tool in the absence of experimentally resolved structures. Here, we describe our server and human-expert methods used to predict oligomer structures in the CASP14 experiment. Examples are provided for cases in which manual domain-splitting led to improved oligomeric domain structures by ab initio docking, automated oligomer structure refinement led to improved subunit orientation and terminal structure, and manual oligomer modeling utilizing literature information generated a reasonable oligomer model. We also discussed the results of post-prediction docking calculations with AlphaFold2 monomers as input in comparison to our blind prediction results. Overall, ab initio docking of AlphaFold2 models did not lead to better oligomer structure prediction, which may be attributed to the interfacial structural difference between the AlphaFold2 monomer structures and the crystal oligomer structures. This result poses a next-stage challenge in oligomer structure prediction after the success of AlphaFold2. For successful protein assembly structure prediction, a different approach that exploits further evolutionary information on the interface and/or flexible docking taking the interfacial conformational flexibilities of subunit structures into account is needed.

Modeling SARS-CoV-2 proteins in the CASP-commons experiment.

Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).

Race, ethnicity, community-level socioeconomic factors, and risk of COVID-19 in the United States and the United Kingdom.

BACKGROUND: There is limited prior investigation of the combined influence of personal and community-level socioeconomic factors on racial/ethnic disparities in individual risk of coronavirus disease 2019 (COVID-19). METHODS: We performed a cross-sectional analysis nested within a prospective cohort of 2,102,364 participants from March 29, 2020 in the United States (US) and March 24, 2020 in the United Kingdom (UK) through December 02, 2020 via the COVID Symptom Study smartphone application. We examined the contribution of community-level deprivation using the Neighborhood Deprivation Index (NDI) and the Index of Multiple Deprivation (IMD) to observe racial/ethnic disparities in COVID-19 incidence. ClinicalTrials.gov registration: NCT04331509. FINDINGS: Compared with non-Hispanic White participants, the risk for a positive COVID-19 test was increased in the US for non-Hispanic Black (multivariable-adjusted odds ratio [OR], 1.32; 95% confidence interval [CI], 1.18-1.47) and Hispanic participants (OR, 1.42; 95% CI, 1.33-1.52) and in the UK for Black (OR, 1.17; 95% CI, 1.02-1.34), South Asian (OR, 1.39; 95% CI, 1.30-1.49), and Middle Eastern participants (OR, 1.38; 95% CI, 1.18-1.61). This elevated risk was associated with living in more deprived communities according to the NDI/IMD. After accounting for downstream mediators of COVID-19 risk, community-level deprivation still mediated 16.6% and 7.7% of the excess risk in Black compared to White participants in the US and the UK, respectively. INTERPRETATION: Our results illustrate the critical role of social determinants of health in the disproportionate COVID-19 risk experienced by racial and ethnic minorities.

Assessment of protein model structure accuracy estimation in CASP14: Old and new challenges.

In CASP, blind testing of model accuracy estimation methods has been conducted on models submitted by tertiary structure prediction servers. In CASP14, model accuracy estimation results were evaluated in terms of both global and local structure accuracy, as in the previous CASPs. Unlike the previous CASPs that did not show pronounced improvements in performance, the best single-model method (from the Baker group) showed an improved performance in CASP14, particularly in evaluating global structure accuracy when compared to both the best single-model methods in previous CASPs and the best multi-model methods in the current CASP. Although the CASP14 experiment on model accuracy estimation did not deal with the structures generated by AlphaFold2, new challenges that have arisen due to the success of AlphaFold2 are discussed.

Association of social distancing and face mask use with risk of COVID-19.

Given the continued burden of COVID-19 worldwide, there is a high unmet need for data on the effect of social distancing and face mask use to mitigate the risk of COVID-19. We examined the association of community-level social distancing measures and individual face mask use with risk of predicted COVID-19 in a large prospective U.S. cohort study of 198,077 participants. Individuals living in communities with the greatest social distancing had a 31% lower risk of predicted COVID-19 compared with those living in communities with poor social distancing. Self-reported 'always' use of face mask was associated with a 62% reduced risk of predicted COVID-19 even among individuals living in a community with poor social distancing. These findings provide support for the efficacy of mask-wearing even in settings of poor social distancing in reducing COVID-19 transmission. Despite mass vaccination campaigns in many parts of the world, continued efforts at social distancing and face mask use remain critically important in reducing the spread of COVID-19.