MultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification.

Understanding the structural and functional diversity of toxin proteins is critical for elucidating macromolecular behavior, mechanistic variability, and structure-driven bioactivity. Traditional approaches have primarily focused on binary toxicity prediction, offering limited resolution into distinct modes of action of toxins. Here, we present MultiTox, an ensemble stacking framework for the classification of toxin proteins based on their molecular mode of action: neurotoxins, cytotoxins, hemotoxins, and enterotoxins.

Protocol for cellular age prediction in yeast and human single cells using transfer learning.

Here, we present a protocol for predicting cellular age via computer vision analysis of cellular morphology and aging-related bioactivities from phase contrast microscopy images. We describe the steps for cultivating yeast cells, performing phase contrast microscopy of drug-treated yeast cells, and inducing senescence in human dermal fibroblasts. We detail the process of using the scCamAge Docker container, running the scCamAge model, applying the yeast-trained model to senescent human fibroblasts, and performing transfer learning to adapt scCamAge using human fibroblast data.

Cross-species comparison of rodent and human decision-making in the Iowa Gambling Task in select neurological and psychiatric disorders: translational approach to examine age- and sex-specific effects of stress and corticolimbic perturbations.

Introduction: Rodent models are widely used to understand brain pathologies and address cognitive deficits experienced by humans diagnosed with clinical disorders. However, stark differences in the nervous system and in the environmental demands of rodents and humans make it difficult to translate insights from rodents to humans. Age and sex further increase vulnerability to disorders via experiences marked by neglect, deprivation, threat, and constraining environments instead of care, nutrition, safety, and enriching environment.

Understanding 30-Day Mortality After First STEMI Through DAGs: Unravelling Epidemiological Cause-Effect Links.

Background And Aim: Traditional statistical tests have limitations in analyzing cause-and-effect relationships. Directed acyclic graphs (DAGs) offer a structured representation of causality. This study aimed to utilize DAGs to explore the causal impact of epidemiological factors on 30-day mortality among patients following their first acute ST-elevation myocardial infarction (STEMI).

HAIRpred: Prediction of human antibody interacting residues in an antigen from its primary structure.

In the past, several methods have been developed for predicting conformational B-cell epitopes in antigens that are not specific to any host. Our primary analysis of antibody-antigen complexes indicated a need to develop host-specific B-cell epitopes. In this study, we present a novel approach to predict conformational B-cell epitopes specific to human hosts by focusing on human antibody interacting residues in antigens.

Synergistic Mn-Cr Co-doping in NaV(PO)OF Promising Cathode: Unlocking Superior Performance for Next-Generation Sodium-Ion Batteries.

Sodium vanadium fluorophosphate (NaVO(PO)F, NVOPF), with a NASICON framework, is a promising cathode material due to its robust 3D structure, high operating potential (∼3.8 V), and theoretical energy density (≈494 Wh kg). However, its commercial viability is limited by low electronic conductivity and a reduced practical energy density.

A comprehensive targeted panel of 295 genes: Unveiling key disease initiating and transformative biomarkers in multiple myeloma.

Background: Multiple myeloma (MM) is a hematological malignancy that progresses from a benign precursor stage known as Monoclonal Gammopathy of Undetermined Significance (MGUS). Distinguishing MM from MGUS at the molecular level by identifying key biomarkers, genomic alterations, and gene interactions is critical for early detection and deeper insight into MM pathogenesis.

Methods: We have developed an advanced genomics domain-rooted AI-workflow that combines the traditional statistical mutation profiling methods with the proposed BIO-DGI (Bio-Inspired Graph Network Learning-based Gene-Gene Interaction) attention-based deep learning architecture exploiting gene-gene interaction.

Maternal health education and social support needs across the perinatal continuum of care: a thematic analysis of interviews with postpartum women in Punjab, India.

Background: Perinatal social support and maternal education throughout the antenatal, childbirth, and postpartum phases contribute to the optimization of health and well-being of mothers and infants. Understanding deficits among support and educational resources can contribute to improved public health decision-making and maternal and child healthcare and wellbeing. This study aimed to explore health and social experiences and resources to characterize and unmet needs across the perinatal period.

Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data.

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies.

The role of anxiety in modulating temporal processing and sensory hyperresponsiveness in autism spectrum disorder: an fMRI study.

The atypical sensory features and high comorbidity of anxiety disorders in individuals with autism spectrum disorder (ASD) are attracting increasing attention. Among individuals with ASD, those who exhibit heightened sensory hyperresponsiveness tend to show enhanced temporal processing of sensory stimuli, despite no observed differences in stimulus detection thresholds. A previous study reported the role of anxiety in modulating emotion-cued changes of visual temporal resolution in ASD.

Inter-host diversity associated with age, sex, and menstrual cycle modulates clinical manifestations in DENV-2 patients.

Dengue virus (DENV-2) remains a global threat, yet the influence of age, sex, and menstrual status on its epidemiology and genetic diversity is underexplored. We analyzed 2136 hospitalized DENV-2 patients (ages 0-86) using whole-genome sequencing (WGS) to examine how these host factors shape interhost viral diversity and clinical manifestations. Young adult males (19-35 years) had the highest prevalence with sex-based clinical differences where females exhibited severe hematological changes, while males showed increased hepatic injury.

Molecular Modeling of Non-Canonical Intramolecular RNA Triple Helix Structures Predicted from TRIPinRNA and Their In Vitro Biophysical Structure Validation.

RNA triple helices have traditionally been characterized by pyrimidine-type U·A-U or C·G-C triplets, with other base triplets considered to be destabilizing. However, the presence of non-canonical triplets in riboswitches and self-splicing introns suggests that triplexes containing longer stretches of such triplets may exist in the human genome too. Using molecular modeling, we investigated a chimeric triple helix derived from the FLRT2-AS1 lncRNA, confirming its stability over a 500 ns simulation.

Preliminary impact of an mHealth education and social support intervention on maternal health knowledge and outcomes among postpartum mothers in Punjab, India.

Background: Significant disruptions in the perinatal continuum of care occur postpartum in India, despite it being a critical time to optimize maternal health and wellbeing. Group-oriented mHealth approaches may help mitigate the impact of limited access to care and the lack of social support that characterize this period. Our team developed and pilot tested a provider-moderated group intervention to increase education, communication with providers, to refer participants to in-person care, and to connect them with a virtual social support group of other mothers with similarly aged infants through weekly calls and text chat.

Analysis of frequency domain features for the classification of evoked emotions using EEG signals.

Emotion is a natural instinctive state of mind that greatly influences human physiological activities and daily life decisions. Electroencephalogram (EEG) signals created from the central nervous system are very useful for emotion recognition and classification. In this study, EEG signals of individuals are analyzed by the variational mode decomposition (VMD) for frequency domain features to recognize visual stimuli-based evoked emotions (happy, sad, fear).

scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics.

Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models.

Critical behavioral traits foster peer engagement in Online Mental Health Communities.

Online Mental Health Communities (OMHCs), such as Reddit, have witnessed a surge in popularity as go-to platforms for seeking information and support in managing mental health needs. Platforms like Reddit offer immediate interactions with peers, granting users a vital space for seeking mental health assistance. However, the largely unregulated nature of these platforms introduces intricate challenges for both users and society at large.

Role of Gut and Urinary Microbiome in Children with Urinary Tract Infections: A Systematic Review.

: The complex interaction between the gut and urinary microbiota underscores the importance of understanding microbial dysbiosis in pediatric urinary tract infection (UTI). However, the literature on the gut-urinary axis in pediatric UTIs is limited. This systematic review aims to summarize the current literature on the roles of gut and urinary dysbiosis in pediatric UTIs.

Exploring the influence of age and diet on gut microbiota development in children during the first 5 years: a study from Yaoundé, Cameroon.

Introduction: The development of the human gut microbiota is shaped by factors like delivery mode, infant feeding practices, maternal diet, and environmental conditions. Diet plays a pivotal role in determining the diversity and composition of the gut microbiome, which in turn impacts immune development and overall health during this critical period. The early years, which are vital for microbial shaping, highlight a gap in understanding how the shift from milk-based diets to solid foods influences gut microbiota development in infants and young children, particularly in Yaoundé, Cameroon.

Alkhumra Hemorrhagic Fever Virus (AHFV): A Concise Overview.

Alkhumra fever is a viral disease caused by the Alkhumra hemorrhagic fever virus (AHFV). It belongs to family , genus . AHFV is primarily transmitted to humans through the bite of infected ticks, for example, .

ToxinPredictor: Computational models to predict the toxicity of molecules.

Predicting the toxicity of molecules is essential in fields like drug discovery, environmental protection, and industrial chemical management. While traditional experimental methods are time-consuming and costly, computational models offer an efficient alternative. In this study, we introduce ToxinPredictor, a machine learning-based model to predict the toxicity of small molecules using their structural properties.

In Silico Strategies for Characterizing Inner Cavities of Lipid-Binding Proteins.

Cavities in proteins perform diverse functions such as substrate binding, enzyme catalysis, passage for transportation of small molecules, and protein oligomerization. Often, the physical properties of these cavities are closely linked to the protein function; such as the hydrophobic lipid-binding cavities in lipid-binding proteins (LBPs) that protect lipid substrates from the larger aqueous milieu. Therefore, the characterization of protein cavities can provide valuable insights into protein structure-function relationships, hinting toward their mechanism of action while aiding in the identification of ligand binding sites that are essential for drug discovery approaches.

Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.

Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and aids in identifying additional geroprotectors. Here we present AgeXtend, an artificial intelligence (AI)-based multimodal geroprotector prediction platform that leverages bioactivity data of known geroprotectors.

Normal Gut Microbiomes in Diverse Populations: Clinical Implications.

The human microbiome is a sensor and modulator of physiology and homeostasis. Remarkable tractability underpins the promise of therapeutic manipulation of the microbiome. However, the definition of a normal or healthy microbiome has been elusive.

Integrative Functions of the Hypothalamus: Linking Cognition, Emotion and Physiology for Well-being and Adaptability.

Background: The hypothalamus, a small yet crucial neuroanatomical structure, integrates external (e.g., environmental) and internal (e.