One-year epidemiological patterns of respiratory pathogens across age, gender, and seasons in Chengdu during the post-COVID era.

Respiratory tract infections caused by various pathogens remain a significant public health concern due to their high prevalence and potential for severe complications. This study systematically analyzed the epidemiological characteristics of six common respiratory pathogens-Chlamydia pneumoniae (CP), Mycoplasma pneumoniae (MP), Adenovirus (AdV), Influenza A virus (FluA), Influenza B virus (FluB), and Respiratory Syncytial Virus (RSV)-in patients from Sichuan Jinxin Xinan Women and Children's Hospital between April 2023 and March 2024. Throat swab samples were collected from a total of 22,717 individuals.

siRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database.

Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post-transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data.

[Prediction of Land Use and Habitat Quality in Harbin City Based on the PLUS- InVEST Model].

Analyzing the spatiotemporal evolution of urban land use and habitat quality can reveal the correlation between land use and habitat quality, aiding in rational urban land policies and high-quality ecological environment development. This study was based on land use transition matrices and an Intensity-Migration （IM） model to analyze the changes in land use in Harbin from 2000 to 2020. It combined the PLUS model to explore the driving factors of land use expansion for various land types and predicted land use scenarios for 2030 under natural development, ecological protection, farmland protection, and urban development scenarios.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model.

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan.

Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review).

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided.

MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19.

In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent.

GADF-VGG16 based fault diagnosis method for HVDC transmission lines.

Transmission lines are most prone to faults in the transmission system, so high-precision fault diagnosis is very important for quick troubleshooting. There are some problems in current intelligent fault diagnosis research methods, such as difficulty in extracting fault features accurately, low fault recognition accuracy and poor fault tolerance. In order to solve these problems, this paper proposes an intelligent fault diagnosis method for high voltage direct current transmission lines (HVDC) based on Gramian angular difference field (GADF) domain and improved convolutional neural network (VGG16).