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The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framework named PTB-DDI for drug–drug interaction prediction. The PTB-DDI framework consists of four key modules: (1) ChemBerta tokenizer for molecular representation, (2) Bidirectional Long Short-Term Memory (BiLSTM) to capture the bidirectional context-aware features of drugs, (3) Multilayer Perceptron (MLP) for mining the nonlinear relationship of drug features, and (4) interaction predictor to perform an affine transformation and final prediction. In addition, we investigate the effect of dual-mode on parameter-sharing and parameter-independent within the PTB-DDI framework. Furthermore, we conducted comprehensive experiments on the two real-world datasets (i.e., BIOSNAP and DrugBank) to evaluate PTB-DDI framework performance. The results show that our proposed framework has significant improvements over the baselines based on both datasets. Based on the BIOSNAP dataset, the AUC-ROC, PR-AUC, and F1 scores are 0.997, 0.995, and 0.984, respectively. These metrics are 0.896, 0.873, and 0.826 based on the DrugBank dataset. Then, we conduct the case studies on the three newly approved drugs by the Food and Drug Administration (FDA) in 2024 using the PTB-DDI framework in dual modes. The obtained results indicate that our proposed framework has advantages for predicting drug–drug interactions and that the dual modes of the framework complement each other. Furthermore, a free website is developed to enhance accessibility and user experience. [ABSTRACT FROM AUTHOR]

Mycobacterial membrane protein Large 3 (MmpL3) of Mycobacterium tuberculosis (Mtb) is crucial for the translocation of trehalose monomycolate (TMM) across the inner bacterial cell membrane, making it a promising target for anti‐tuberculosis (TB) drug development. While several structural, microbiological, and in vitro studies have provided significant insights, the precise mechanisms underlying TMM transport by MmpL3 and its inhibition remain incompletely understood at the atomic level. In this study, molecular dynamic (MD) simulations for the apo form and seven inhibitor‐bound forms of Mtb MmpL3 were carried out to obtain a thorough comprehension of the protein's dynamics and function. MD simulations revealed that the seven inhibitors in this work stably bind to the central channel of the transmembrane domain and primarily forming hydrogen bonds with ASP251, ASP640, or both residues. Through dynamical cross‐correlation matrix and principal component analysis analyses, several types of coupled motions between different domains were observed in the apo state, and distinct conformational states were identified using Markov state model analysis. These coupled motions and varied conformational states likely contribute to the transport of TMM. However, simulations of inhibitor‐bound MmpL3 showed an enlargement of the proton channel, potentially disrupting coupled motions. This indicates that inhibitors may impair MmpL3's transport function by directly blocking the proton channel, thereby hindering coordinated domain movements and indirectly affecting TMM translocation. [ABSTRACT FROM AUTHOR]

In the realm of multiferroicity in 2D magnets, whether magnetic and polar skyrmions can coexist within a single topological entity has emerged as an important question. Here, we study Janus 2D magnets CrInX3 (X=Se, Te) for a comprehensive investigation of the magnetic ground state, magnetic excited state, and corresponding ferroelectric polarization by first-principles electronic structure calculations and Monte Carlo simulations. Specifically, we have thoroughly elucidated the magnetic exchange mechanisms, and have fully exemplified the magnetic field dependence of the magnon spectrum. More importantly, our study reveals a previously unrecognized, remarkably large spin-spiral-induced ferroelectric polarization (up to 194.9 μC/m2) in both compounds. We propose an approach to identify polar skyrmions within magnetic skyrmions, based on the observed direct correlation between spin texture and polarization density. Elucidating this correlation not only deepens our understanding of magnetic skyrmions but also paves the way for innovative research in the realm of multiferroic skyrmions. [ABSTRACT FROM AUTHOR]

Critical illness leads to millions of deaths worldwide each year, with a significant surge due to the COVID-19 pandemic. Patients with critical illness are frequently associated with systemic metabolic disorders and malnutrition. The idea of intervention for critically ill patients through enteral and parenteral nutrition has been paid more and more attention gradually. However, current nutritional therapies focus on evidence-based practice, and there have been lacking holistic approaches for nutritional support assessment. Metabolomics is a well-established omics technique in system biology that enables comprehensive profiling of metabolites in a biological system and thus provides the underlying information expressed and modulated by all other omics layers. In recent years, with the development of high-resolution and accurate mass spectrometry, metabolomics entered a new "generation", promoting its broader applications in critical care nutrition. In this review, we first described the technological development and milestones of next-generation metabolomics in the past 20 years. We then discussed the emerging roles of next-generation metabolomics in advancing our understanding of critical care nutrition, such as nutritional deficiency risk evaluation, metabolic mechanisms of nutritional therapies, and novel nutrition target identification. [ABSTRACT FROM AUTHOR]

Purpose: While observational studies have identified obesity as a potential risk factor for gastric cancer, the causality remains uncertain. This study aimed to evaluate the causal relationship between obesity and gastric cancer and identify the shared molecular signatures linking obesity to gastric cancer. Methods: A two-sample Mendelian randomization (MR) analysis was conducted using the GWAS data of body fat percentage (exposure, n = 331,117) and gastric cancer (outcome, n = 202,308). Bioinformatics and meta-analysis of multiomics data were performed to identify key molecules mediating the causality. The meta-analysis of the plasma/serum proteome included 1,662 obese and 3,153 gastric cancer patients. Obesity and gastric cancer-associated genes were identified using seven common gene ontology databases. The transcriptomic data were obtained from TCGA and GEO databases. The Bioinformatic findings were clinically validated in plasma from 220 obese and 400 gastric cancer patients across two hospitals. Finally, structural-based virtual screening (SBVS) was performed to explore the potential FDA-approved drugs targeting the identified mediating molecules. Results: The MR analysis revealed a significant causal association between obesity and gastric cancer (IVW, OR = 1.37, 95% CI:1.12-1.69, P = 0.0028), without pleiotropy or heterogeneity. Bioinformatic and meta-analysis of multi-omics data revealed shared TNF, PI3K-AKT, and cytokine signaling dysregulation, with significant upregulation of AKT1, IL-6, and TNF. The clinical study confirmed widespread upregulation of systemic inflammatory markers in the plasma of both diseases. SBVS identified six novel potent AKT1 inhibitors, including the dietary supplement adenosine, representing a potentially preventive drug with low toxicity. Conclusion: Obesity causally increases gastric cancer, likely mediated by persistent AKT1/IL-6/TNF upregulation. As a potential AKT1 inhibitor, adenosine may mitigate the obesity-to-gastric cancer transition. These findings could inform preventive drug development to reduce gastric cancer risk in obesity. [ABSTRACT FROM AUTHOR]