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.

Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn's disease and intestinal tuberculosis: insights from an integrated multi-cohort analysis.

Background/aims: Crohn's disease (CD) and intestinal tuberculosis (ITB) are gastrointestinal (GI) inflammatory disorders with overlapping clinical presentations but diverging etiologies. The study aims to decipher CD and ITB-associated gut dysbiosis signatures and identify disease-associated co-occurring modules to evaluate whether this dysbiosis signature is a disease-specific trait or is a shared feature across diseases of diverging etiologies.

Methods: Disease-associated gut microbial modules were identified using statistical machine learning and co-abundance network analysis in controls, CD and ITB patients recruited as part of this study.

FlavorDB2: An updated database of flavor molecules.

Flavor is expressed through the interaction of molecules via gustatory and olfactory mechanisms. Knowing the pivotal role of flavor molecules in food and fragrances, the existence of a comprehensive repository becomes valuable. Such a repository encompasses the information of flavor molecules characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis.

Machine learning based classification of presence utilizing psychophysiological signals in immersive virtual environments.

In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased.

Non-canonical NF-κB signaling limits the tolerogenic β-catenin-Raldh2 axis in gut dendritic cells to exacerbate intestinal pathologies.

Dendritic cell (DC) dysfunction is known to exacerbate intestinal pathologies, but the mechanisms compromising DC-mediated immune regulation in this context remain unclear. Here, we show that intestinal dendritic cells from a mouse model of experimental colitis exhibit significant levels of noncanonical NF-κB signaling, which activates the RelB:p52 heterodimer. Genetic inactivation of this pathway in DCs alleviates intestinal pathologies in mice suffering from colitis.

Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study.

Background: Comprehensive session summaries enable effective continuity in mental health counseling, facilitating informed therapy planning. However, manual summarization presents a significant challenge, diverting experts' attention from the core counseling process. Leveraging advances in automatic summarization to streamline the summarization process addresses this issue because this enables mental health professionals to access concise summaries of lengthy therapy sessions, thereby increasing their efficiency.

Computational gastronomy: capturing culinary creativity by making food computable.

Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culinary and cultural heritage, food systems play a pivotal role in sustainability and the survival of life on our planet.

Emerging trends in antimicrobial resistance in bloodstream infections: multicentric longitudinal study in India (2017-2022).

Background: Antimicrobial resistance (AMR) has escalated to pandemic levels, posing a significant global health threat. This study examines the patterns and trends of AMR in Bloodstream Infections (BSIs) across India, aiming to inform better surveillance and intervention strategies.

Methods: Six-year data from 21 tertiary care centers in the Indian Council of Medical Research's AMR Surveillance Network (IAMRSN) were retrospectively analyzed to estimate cluster-robust trends in resistance.

Exploring the current dynamics of preprints.

Preprints are non-peer-reviewed and publicly available articles for open and transparent research communication. Preprint servers host the submission of such manuscripts, and despite the presence of established preprint servers, their numbers have continued to rise in recent times. A steep increasing pattern in posted preprints and their accommodating servers has been observed over the last decade.

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization.

The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients.

Inflammatory Protein Signatures as Predictive Disease-Specific Markers for Non-Alcoholic Steatohepatitis (NASH).

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic disease worldwide, consisting of a broad spectrum of diseases such as simple steatosis (NAFL), non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Hepatic inflammation plays a key role in the pathophysiology of NAFLD. Inflammatory mediators such as cytokines and chemokines are considered as contributing factors to NAFLD development and progression.

Literature mining discerns latent disease-gene relationships.

Motivation: Dysregulation of a gene's function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape project, researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells.

Corrigendum to 'Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics' [Genes & Diseases 10 (2023) 1367-1401].

[This corrects the article DOI: 10.1016/j.gendis.

The multimodality cell segmentation challenge: toward universal solutions.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments.

Tracking an untracked space debris after an inelastic collision using physics informed neural network.

With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris' trajectory is crucial to avoid future collisions.