Oral bioavailability property prediction based on task similarity transfer learning.

Drug absorption significantly influences pharmacokinetics. Accurately predicting human oral bioavailability (HOB) is essential for optimizing drug candidates and improving clinical success rates. The traditional method based on experiment is a common way to obtain HOB, but the experimental method is time-consuming and costly.

Pathogen virulence genes: Advances, challenges and future directions in infectious disease research (Review).

Pathogens, including bacteria, viruses and fungi, employ virulence genes to invade their hosts, circumvent immunity and induce diseases. The present review examines the categorization and regulatory mechanisms of virulence genes and their co‑evolution with antimicrobial resistance. The present review focused on the fimbrial adhesion H adhesion gene of , the spike protein gene of severe acute respiratory syndrome coronavirus 2 and the enhanced filamentous growth protein 1 (EFG1) morphological transition gene of , as well as their roles in host adhesion, immune evasion and tissue damage.

Integrating Generative Pretrained Transformer and Genetic Algorithms for Efficient and Diverse Molecular Generation.

In computer-aided drug design, molecular generation models play a crucial role in accelerating the drug development process. Current models mainly fall into two categories: deep learning models with high performance but poor interpretability and heuristic algorithms with better interpretability but limited performance. In this study, we introduce an innovative molecular generation model, the compound construction model (CCMol), which integrates the powerful generative capabilities of the generative pretrained transformer (GPT) and the efficient optimization mechanisms of genetic algorithms (GA) to achieve effective and innovative molecular structures.

High-sensitivity detection of brilliant blue by disposable chitosan-biochar electrochemical sensors.

This research created a disposable electrochemical sensor utilizing a chitosan-biochar (CS-H500) composite-modified glassy carbon electrode (GCE) for the very sensitive detection of brilliant blue (BB) in food products. The CS-H500 composite dispersion was synthesized using the ultrasonic dispersion method, while quantum chemical simulations clarified the adsorption mechanism of BB molecules on the electrode surface π-π conjugation and electrostatic interactions. Optimized experimental settings comprised the following: 16 μL of CS-H500 dispersion, 300 seconds of accumulation period, and a pH 7.

Interpretable multi-variable transformer network for regional-level short-term bicycle crash risk prediction.

Effective short-term prediction of bicycle crashes at the urban regional level is critical for proactive infrastructure safety interventions and data-driven traffic management. However, three key challenges persist: (1) inadequate modeling of complex spatiotemporal dependencies in multi-source heterogeneous data; (2) poor handling of extreme class imbalance and lack of interpretability in deep learning-based short-term predictions; and (3) limited exploration of bicycle infrastructure's role in regional crash risk assessment. In response to these challenges, we propose an Interpretable Multi-variable Transformer Network (IMTN) that employs four specialized Transformer encoder blocks to extract spatial and temporal dependencies from heterogeneous inputs.

CXCL12-targeting siRNA nanoparticles alleviate immunosuppression and inhibit tumor progression in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is associated with a highly immunosuppressive tumor microenvironment (TME), driven in part by cancer-associated fibroblasts (CAFs) that promote immune evasion through the secretion of CXCL12. CXCL12 interacts with the CXCR4 receptor on immune cells, disrupting CD8 T cell migration and anti-tumor function. To address this, we developed an innovative siRNA-based therapeutic approach targeting CXCL12 in CAFs.

Lipid-protein synergistic micronized salt composite: High-pressure homogenization for enhanced sodium dispersion and thermal stability.

This study presents an innovative sodium-reducing composite powder salt (CPS) developed by integrating whey protein isolate (WPI), milk fat, and micronized NaCl through high-pressure homogenization (HPH) and freeze-drying techniques. HPH at 300 Bar significantly optimized emulsion stability, resulting in a particle size reduction (25.24 % smaller compared to 100 Bar), enhanced surface hydrophobicity (contact angle: 76.

Regulatory T Cell Infiltration-Driven Single-Cell Transcriptomic Analysis Identifies SAP18 as a Prognostic Marker for Esophageal Squamous Cell Carcinoma.

Background: Advanced esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity and distinct patterns of immune cell infiltration. Regulatory T cells (Tregs), in particular, play a critical role in shaping an immunosuppressive tumor microenvironment (TME), which is associated with poor clinical outcomes.

Methods: We developed a prognostic model by integrating GEO-derived bulk RNA sequencing data and single-cell transcriptome.

A study on optimum insulation thickness of building walls in hot summer and warm winter zone in China.

As the proportion of building energy consumption gradually increases, adding insulation materials to the existing building envelope has become an important method to improve building thermal performance. Currently, most research on the optimum thickness of insulation layers is calculated from the perspective of heat transfer. However, in humid and hot areas, the influence of humidity and solar radiation should not be ignored.

Advancements in Nanoscale Chemical Analysis using Tip-Enhanced Raman Spectroscopy.

Tip-enhanced Raman spectroscopy (TERS) has established itself as a powerful tool in nanoscale chemical analysis, providing unprecedented spatial resolution with high molecular sensitivity and chemical specificity. TERS employs localized surface plasmon resonance at the apex of a sharp scanning probe microscopy tip to overcome the diffraction limit inherent in conventional Raman spectroscopy, achieving spatial resolutions down to the nanometer scale. In this article, we highlight major advancements in TERS over the past five years from our laboratory at ETH Zurich in the following key areas: heterogeneous catalysis, photovoltaic materials, biological membranes, and on-surface molecular assembly.

Monitoring the On-Surface Boronic Acid Condensation Process at the Nanoscale Using Tip-Enhanced Raman Spectroscopy.

The on-surface condensation of boronic acids is a key step in fabricating functional interfaces with tailored properties; yet, a clear understanding of the molecular structural transformations involved remains a significant challenge. Here, we directly monitor the condensation reaction in a self-assembled monolayer of 4-mercaptophenylboronic acid (MPBA) on Au(111) using tip-enhanced Raman spectroscopy (TERS). The structural evolution in the MPBA adlayer is tracked via the emergence of new peaks, blue shifts, and intensity changes in characteristic Raman bands.

Global, regional, and national esophageal cancer deaths and DALYs attributable to diet low in vegetables and fruits, 1990-2019: analysis for the global burden of disease study.

Background: This study aimed to comprehensively assess the global, regional, and national burden of esophageal cancer (EC) attributable to inadequate vegetable and fruit intake from 1990 to 2019 and explore the potential impact of existing dietary intervention programs on EC prevention.

Methods: Using the Global Burden of Disease Study 2019 (GBD 2019) database, we conducted descriptive analyses stratified by age, sex, Socio-demographic Index (SDI), and regional levels. Temporal trends were assessed using linear regression models, and cluster analysis was employed to explore burden patterns across different GBD regions.

Plasmonic Ag/PMMA/Eu nanocomposite for sensitive dual mode detection of malachite green.

Accurate and efficient determination of malachite green (MG) in aquaculture is crucial for ensuring environment and food safety. Herein, we present a dual-response fluorescence probe based on an Ag/PMMA/Eu nanocomposite for the sensitive detection of MG with low concentration and single droplet. The luminescence properties of the Ag/PMMA/Eu nanocomposite and the fluorescence resonance energy transfer (FRET) effect between Eu and MG are significantly improved due to the localized surface plasmon resonance (LSPR) effect.

Organoids in skin wound healing.

Stem cells (SCs) can self-replicate and differentiate into multiple lineages. Organoids, 3D cultures derived from SCs, can replicate the spatial structure and physiological characteristics of organs . Skin organoids can effectively simulate the physiological structure and function of skin tissue, reliably restoring the natural skin ecology in various environments.

FAM83A-AS1 predicts severe development of non-small cell lung cancer and adverse postoperative prognosis of thoracotomy.

Background: Thoracotomy is a common treatment for non-small cell lung cancer (NSCLC). However, the significant trauma from this procedure can limit patients' postoperative prognosis. Therefore, it's crucial to find an easily detected indicator that can predict the prognosis of NSCLC patients undergoing thoracotomy.

Regulation of intestinal tissue‑resident memory T cells: a potential target for inflammatory bowel disease.

Tissue-resident memory T (TRM) cells are populations which settle down in non-lymphoid tissues instead of returning to secondary lymph organs after the antigen presentation. These cells can provide rapid on-site immune protection as well as long-term tissue damage. It is reported that TRM cells from small intestine and colon exhibited distinctive patterns of cytokine and granzyme expression along with substantial transcriptional and functional heterogeneity.

CGPDTA: An Explainable Transfer Learning-Based Predictor With Molecule Substructure Graph for Drug-Target Binding Affinity.

Identifying interactions between drugs and targets is crucial for drug discovery and development. Nevertheless, the determination of drug-target binding affinities (DTAs) through traditional experimental methods is a time-consuming process. Conventional approaches to predicting drug-target interactions (DTIs) frequently prove inadequate due to an insufficient representation of drugs and targets, resulting in ineffective feature capture and questionable interpretability of results.

[Causal relationship between ferroptosis-related gene HSPA5 and hepatocellular carcinoma: a study based on mendelian randomization and mediation analysis].

Objectives: To explore a causal relationship between ferroptosis-related gene heat shock protein A5 (HSPA5) and hepatocellular carcinoma (HCC).

Methods: A two-sample Mendelian randomization (MR) design was employed to evaluate the causal relationships among HSPA5, regulatory T cells (Tregs), and HCC. Single nucleotide polymorphisms (SNPs) associated with HSPA5, Tregs and HCC were selected as instrumental variables through publicly available genome-wide association studies (GWAS) databases.

GDMol: Generative Double-Masking Self-Supervised Learning for Molecular Property Prediction.

Background: Effective molecular feature representation is crucial for drug property prediction. Recent years have seen increased attention on graph neural networks (GNNs) that are pre-trained using self-supervised learning techniques, aiming to overcome the scarcity of labeled data in molecular property prediction. Traditional GNNs in self-supervised molecular property prediction typically perform a single masking operation on the nodes and edges of the input molecular graph, masking only local information and insufficient for thorough self-supervised training.

Causal relationship between antihypertensive drugs and Hashimoto's thyroiditis: a drug-target Mendelian randomization study.

Introduction And Objectives: Recent studies have indicated a potential association of hypertension with Hashimoto's thyroiditis (HT) and other autoimmune diseases, yet the impact of antihypertensive drugs on HT risk is not well understood.

Methods: We employed a drug-target Mendelian randomization approach to investigate the prolonged impact of 9 classes of antihypertensive medications on HT susceptibility in European and Asian populations. Genetic variants close to or within genes associated with the drug targets and systolic blood pressure (SBP) were utilized to mimic the effects of antihypertensive medications.

Probing Deuteration-Induced Phase Separation in Supported Lipid Monolayers using Hyperspectral TERS Imaging.

In this study, we investigate the impact of deuteration on the formation of phase-separated domains in supported lipid monolayers using hyperspectral Tip-Enhanced Raman Spectroscopy (TERS) imaging. The intricate organization of biological membranes plays a crucial role in cellular functions. Various factors that influence domain formation have been identified in previous studies such as lipid tail length and cholesterol concentration.

GMS-YOLO: An Algorithm for Multi-Scale Object Detection in Complex Environments in Confined Compartments.

Many compartments are prone to pose safety hazards such as loose fasteners or object intrusion due to their confined space, making manual inspection challenging. To address the challenges of complex inspection environments, diverse target categories, and variable scales in confined compartments, this paper proposes a novel GMS-YOLO network, based on the improved YOLOv8 framework. In addition to the lightweight design, this network accurately detects targets by leveraging more precise high-level and low-level feature representations obtained from GhostHGNetv2, which enhances feature-extraction capabilities.

Progress in the application of artificial intelligence in molecular generation models based on protein structure.

The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyzing a series of novel molecular generation models predicated on protein structures. Initially, we categorize the molecular generation models based on protein structures and highlight the architectural frameworks utilized in these models.

Application progress of deep generative models in de novo drug design.

The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared.

Evaluating the performance of traffic conflict measures in real-time crash risk prediction using pre-crash vehicle trajectories.

The primary objective of this study was to evaluate the performance of traffic conflict measures for real-time crash risk prediction. Drone recordings were collected from a freeway section in Nanjing, China, over a year. Twenty rear-end crashes and their associated trajectories were obtained.