Deciphering the Significance of Platelet-Derived Chloride Ion Channel Gene (BEST3) Through Platelet-Related Subtypes Mining for Non-Small Cell Lung Cancer.

This study investigates platelet-related subtypes in non-small cell lung cancer (NSCLC) and seeks to identify genes associated with prognosis, focusing on the clinical significance of the chloride ion channel gene BEST3. We utilised sequencing and clinical data from GEO, TCGA and the Xena platform, building a risk model based on genetic features. TCGA and GSE37745 served as training cohorts, while GSE50081, GSE13213, GSE30129 and GSE42127 were validation cohorts.

Similarity or complementarity? Understanding marital relationships in terms of sexual dimorphism in brain morphometry and gender roles.

"Birds of a feather flock together" and "opposites attract" are two contrasting statements regarding interpersonal relationships. Sex differences provide a theoretical integration of these two conflicting statements. Here, we explored the relationship between marital satisfaction and sex differences in social attributes and neuroanatomical characteristics in 48 married couples.

Ab-Amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model.

Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development.

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.

Building a Chinese ancient architecture multimodal dataset combining image, annotation and style-model.

In this rapidly evolving era of multimodal generation, diffusion models exhibit impressive generative capabilities, significantly enhancing the realm of creative image synthesis by intricately textual prompts. Yet, their effectiveness is limited in certain niche sectors, like depicting Chinese ancient architecture. This limitation is primarily due to the insufficient data that fails to encompass the unique architectural features and corresponding text information.

PhIP-Seq: methods, applications and challenges.

Phage-immunoprecipitation sequencing (PhIP-Seq) technology is an innovative, high-throughput antibody detection method. It enables comprehensive analysis of individual antibody profiles. This technology shows great potential, particularly in exploring disease mechanisms and immune responses.

Mapping autophagy-related membrane contact site proteins and complexes with AutoMCS Navigator.

In eukaryotic cells, membrane contact sites (MCSs) mediate interactions and communication between organelles by bringing their membranes into close proximity without fusion. These sites play crucial roles in intracellular transport, signal transduction, and the regulation of organelle functions. In a recent study, we compiled data on MCS proteins and complexes from publications to create the MCSdb database.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb.

Background: Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions.

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RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly.

Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder.

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD.

A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model.

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis.

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.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP.

DOTAD: A Database of Therapeutic Antibody Developability.

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection.

ncRS: A resource of non-coding RNAs in sepsis.

Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.

: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments.

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy.

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability.

MCSdb, a database of proteins residing in membrane contact sites.

Organelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called "membrane contact sites (MCSs)". And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking.

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.

Prevalence of body mass index categories among adults living alone in China: Observational study.

Background: Adults living alone represent a growing population group in China. Understanding the prevalence of body mass index (BMI) categories and their associations with demographic and lifestyle factors among this group is essential for informing targeted interventions and public health policies.

Methods: In this population-based cross-sectional study, we used individual-level data from the 2011-2021 China General Social Survey.