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.

Prediction of Anti-Freezing Proteins From Their Evolutionary Profile.

Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates, proposed and existing methods on an independent dataset containing 80 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewed by experts.

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.

A hybrid approach for predicting transcription factors.

Transcription factors are essential DNA-binding proteins that regulate the transcription rate of several genes and control the expression of genes inside a cell. The prediction of transcription factors with high precision is important for understanding biological processes such as cell differentiation, intracellular signaling, and cell-cycle control. In this study, we developed a hybrid method that combines alignment-based and alignment-free methods for predicting transcription factors with higher accuracy.

Co-localization and co-expression of Olfml3 with Iba1 in brain of mice.

Olfml3 is a microglia-specific protein whose role in neuroinflammation is elusive. In silico analysis was conducted to characterize the Olfml3 protein, followed by molecular docking and MD simulation to check possible interaction with Iba1. Further, expression and co-localization analysis was performed in the LPS-induced neuroinflammatory mice brains.

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.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides.

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity.

Pilot study to test the feasibility and clinical efficacy of a psychosocial care programme for patients with psychosis in low-resource settings.

Background: Home-based psychosocial care has the potential to improving outcomes in patients with schizophrenia and related disorders (SCZ). There is lack of India data for such care in early psychosis. We developed the "Saksham" programme, a bespoke self-managed home-based psychosocial care model, available in two formats: manual-based and mobile-application based.

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.

Comparing Analogy-Based Methods-Bio-Inspiration and Engineering-Domain Inspiration for Domain Selection and Novelty.

This study aims to support designers in developing transformative solutions in the engineering discipline using the Design-by-Analogy ideation method. Design-by-Analogy involves drawing inspiration from the source domain and applying it to the target domain. Based on the conceptual distance between the two domains, analogies are classified as biological-(natural), cross-(distant-engineering), and within-(near-engineering) domain analogies.

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics.

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature.

THPdb2: compilation of FDA approved therapeutic peptides and proteins.

During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides.

Dual functionality of the BiN monolayer: unraveling its photocatalytic and piezocatalytic water splitting properties.

To achieve scalable and economically viable green hydrogen (H) production, the photocatalytic and piezocatalytic processes are promising methods. The key to successful overall water splitting (OWS) for H production in these processes is using suitable semiconductor catalysts with appropriate band edge potentials, efficient optical absorption, higher mechanical flexibility, and piezoelectric coefficients. Thus, we explore the bismuth nitride (BiN) monolayer using density functional theory simulations, revealing intriguing catalytic properties.

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods.

Peptide hormones serve as genome-encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non-hormonal peptide sequences.

Protocol to decode the role of transcriptionally active microbes in SARS-CoV-2-positive patients using an RNA-seq-based approach.

The elucidation of the role of microorganisms in human infections has been hindered by difficulties using conventional culture-based techniques. Here, we present a protocol for the investigation of transcriptionally active microbes (TAMs) using an RNA sequencing (RNA-seq)-based approach. We describe the steps for RNA isolation, viral genome sequencing, RNA-seq library preparation, and metatranscriptomic and transcriptomic analysis.

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.

Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study.

Background: Opioid and substance misuse has become a widespread problem in the United States, leading to the "opioid crisis." The relationship between substance misuse and mental health has been extensively studied, with one possible relationship being that substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.

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.

Role of Amyloidogenic and Non-Amyloidogenic Protein Spaces in Neurodegenerative Diseases and their Mitigation Using Theranostic Agents.

Neurodegenerative diseases (NDDs) refer to a complex heterogeneous group of diseases which are associated with the accumulation of amyloid fibrils or plaques in the brain leading to progressive loss of neuronal functions. Alzheimer's disease is one of the major NDD responsible for 60-80 % of all dementia cases. Currently, there are no curative or disease-reversing/modifying molecules for many of the NDDs except a few such as donepezil, rivastigmine, galantamine, carbidopa and levodopa which treat the disease-associated symptoms.

Exploring the feasibility, acceptability and preliminary effects of a nurse delivered mhealth intervention for women living with HIV in South India: a pilot randomized controlled trial.

Purpose: We evaluated the feasibility, acceptability and preliminary efficacy of a standardized nurse delivered mobile phone intervention to improve adherence to antiretroviral treatment and clinical outcomes.

Methods: Feasibility and acceptability of the phone intervention was assessed with rates of eligibility, completed visits, and attritions. Intervention fidelity was assessed by checking recorded calls and feedback.