Proteins serve as the fundamental building blocks of life processes. Their functions are highly determined by the spatial structure, making analyzing these spatial structures essential for fully comprehending their roles. The application of artificial intelligence (AI) models has significantly advanced the development of algorithms for predicting protein spatial structure. AlphaFold2 represents a groundbreaking advancement in this field, enabling rapid, accurate, and large-scale predictions of protein spatial structures. In the AlphaFold era, substantial progress has been made in fields such as protein language model development, protein-protein interaction prediction, and protein design, with notable models including ESM2, ScanNet, RFdiffusion, and RoseTTAFold-All Atom, among others. These novel AI-based algorithms have profoundly facilitated research into protein function, disease mechanisms, and drug design.

Cardiovascular diseases (CVDs) are the leading cause of death among both urban and rural residents in China, which are characterized by an extended disease course, diverse symptoms, and complex etiological factors, posing significant challenges to their diagnosis and management. In recent years, the rapid development of artificial intelligence (AI) technologies has provided unprecedented opportunities for early health management and disease prediction in CVDs. By leveraging deep learning and the intelligent analysis of massive health data, AI technologies can accurately identify the potential risks for CVDs and facilitate personalized health management, effectively reducing their incidence and delaying disease progression. The applications of AI technologies have facilitated the improvement of early health monitoring and CVD prevention, as well as improved the accuracy and effectiveness of diagnostic imaging and surgical interventions. In addition, AI technologies have also been applied in biological big data analysis and new drug development. The purpose of this review is to summarize the current applications of AI technologies in CVDs, exploring their prospects and potential challenges in improving diagnostic and therapeutic efficiency. It is anticipated that AI technologies will play an increasingly critical role in the prevention, diagnosis, and treatment of CVDs, thus promoting the advancement of smart healthcare and precision medicine.

Spatial transcriptomics plays a crucial role in identifying specific gene expression patterns, discovering novel cell type markers, and revealing cellular self-organization and cooperation. This article systematically classifies and reviews spatial transcriptomics data analysis methods developed in recent years based on artificial intelligence theories and techniques, each with distinct characteristics suited to different research scenarios. Through in-depth analysis of these methods, it offers a comprehensive perspective on understanding the cutting-edge analytical technologies in the field of spatial transcriptomics, promotes their application in biomedical research, and aids in exploring the spatial heterogeneity and ecological niches of cells within complex tissues.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that profiles genome-wide gene expression at the single-cell level,and can efficiently resolve cellular heterogeneity.It is widely applied in fields such as developmental biology and disease research. However,scRNA-seq data often exhibit characteristics such as high noise,high dimensionality, and high sparsity,which pose significant challenges to traditional data analysis methods.In recent years,deep learning models,represented by autoencoders and generative adversarial networks, have been extensively applied to scRNA-seq data analysis tasks,including expression imputation, batch effect correction,dimensionality reduction,cell clustering,and cell type annotation. These applications demonstrate the power of deep learning. Notably, Transformer-based deep learning models, leveraging self-attention mechanisms to capture implicit dependencies among genes and associations between gene expression and cells, offer a novel strategy and direction for scRNA-seq data analysis, and provide innovative solutions with promising applications for the integration of multimodal omics data.

The rapid accumulation of biological big data, primarily comprising genomics, transcriptomics, proteomics, and more, coupled with the swift advancement of artificial intelligence technologies, notably deep learning, has given rise to a variety of biological large models. Characterized by complex deep-learning architectures, massive parameter counts, high computational power requirements, and vast amounts of pre-training data, these large models' capabilities are largely dictated by the types and volumes of pre-training data, while different model architectures support various downstream tasks. Over the past two years, a variety of general-purpose and specialized large models have emerged in multiple application scenarios, including the analysis and mining of DNA, RNA, and protein sequences, single-cell expression atlases, structure prediction of biomacromolecules, de novo drug design, and interpretation of biological mechanisms. These models have demonstrated significant potential in the domains of biomedical research and translational applications. This paper aims to provide an overview of the characteristics of biological data and the technical methods used for training biological large models, considering the unique features and research application needs of different types of biological data. Furthermore, it reviews the application progress of existing models in biomedical research and disease diagnosis and treatment, offering new insights for enhancing model capabilities and expanding their application scope.

Long noncoding RNAs (lncRNAs) are a class of RNAs exceeding 200 nucleotides in length that lack protein-coding capabilities, and play crucial roles in the regulation of gene expression and epigenetic modifications at the post-transcriptional level. Research has demonstrated that lncRNAs are integral to various cell functions, including glucose metabolism, lipid metabolism, liver tissue fibrosis, inflammatory responses, tumorigenesis, and hepatocyte autophagy. The pathogenesis of non-alcoholic fatty liver disease (NAFLD) is gaining increasing attention. This article offers a comprehensive analysis of the intricate relationship between lncRNAs and the pathogenesis of NAFLD, identifies novel targets for lncRNA action, and lays a foundational framework along with innovative perspectives for future research and prevention of NAFLD.

Thymosin beta-4 (Tβ4), a ubiquitously expressed peptide in mammalian tissues and cells, exerts diverse biological effects in the cardiovascular system. Tβ4 regulates cardiac and vascular development during embryogenesis, maintains stemness characteristics and cellular activity of stem/progenitor cells, induces angiogenesis and vascular maturation, and stabilizes vascular integrity.Exogenous Tβ4 supplementation can regulate immune responses, reduce inflammation, inhibit tissue fibrosis, activate and mobilize myocardial/endothelial progenitor cells for cardiovascular regeneration and repair.These mechanisms collectively enhance functional recovery in ischemic hearts, demonstrating Tβ4's dual regulatory roles in both cardiac and vascular systems. Currently, Tβ4 has entered clinical trials for patients with ST-segment elevation myocardial infarction (STEMI), highlighting its therapeutic potential in ischemic diseases. This review summarizes the structural features and biological functions of Tβ4, focusing on its roles in cardiovascular development and injury repair. We further elucidate its cardioprotective mechanisms to provide a theoretical foundation for advancing Tβ4-based therapeutic strategies.

Alzheimer's disease (AD) is a complex neurodegenerative brain disorder that leads to severe cognitive impairments, particularly memory deficits, affecting millions of people worldwide. Currently, there is no effective therapy to halt or slow its progression. Poor dietary habits and lifestyle choices are major risk factors for AD, and metabolic dysregulation is commonly observed in individuals with the disease. Leptin, a crucial metabolic hormone, has been shown to possess cognitive-enhancing properties. Increasing evidence suggests that deficiencies and dysfunctions in leptin are associated with an increased risk of AD. Leptin not only improves the pathological features of AD but also significantly affects hippocampal synaptic function, exerting neuroprotective effects. Furthermore, leptin has shown promising prospects in the clinical treatment of AD, potentially serving as both a new therapeutic approach and a diagnostic tool.

Zinc, as one of the essential trace elements in the human body, is involved in regulating various biological functions, and primarily exerts a protective effect on vascular physiology and pathophysiology. Dysregulation of zinc homeostasis promotes the initiation and progression of vascular diseases through oxidative stress, inflammation, extracellular matrix degradation, vascular calcification, and other pathways. It is closely related to atherosclerosis, aortic aneurysm, aortic dissection, and peripheral artery disease. This review summarizes the potential mechanisms by which zinc deficiency contributes to arterial diseases, the role of zinc-related proteins, and the protective effects of zinc supplementation on vascular function, providing insights and clues for the treatment and study of arterial diseases.

The nucleus reuniens is the largest nucleus in the midline of the thalamus, with extensive afferent inputs and relatively limited efferent outputs. Functionally, this nucleus serves as a pivotal hub connecting the medial prefrontal cortex with the hippocampus, playing significant roles in spatial learning and memory, recognition memory, and fear memory. In addition to these basic physiological functions, recent research has demonstrated the involvement of the nucleus reuniens in various neurological disorders, such as schizophrenia, epilepsy, Alzheimer's disease, depression, and anxiety. This review summarizes the evidence on the role of the nucleus reuniens in the progression of these disorders, aiming to elucidate novel pathophysiological mechanisms underlying the onset and development of brain disorders, and to provide insights for both basic and clinical research.

Fibromyalgia syndrome (FMS) is primarily characterized by pain and depression, both of which significantly impair patients' physical and mental health. Currently,clinical therapies for FMS include both pharmacological and non-pharmacological methods.Exercise therapy is an effective non-pharmacological approach that helps alleviate pain and depressive symptoms in patients with FMS,thereby improving their quality of life.Based on relevant literature, this article analyzes the effects of aerobic exercise and resistance exercise on pain and depression in patients with FMS, further explores the pathogenesis of FMS and the physiological mechanisms by which exercise exerts its therapeutic benefits.The findings indicate that regular medium to high intensity aerobic exercise and resistance exercise can substantially improve pain and depression symptoms in patients with FMS through mechanisms such as inducing hypoalgesia, exerting anti-inflammatory effects,improving autonomic dysfunction,and promoting reward effects. Despite the evident effect of exercise on improving symptoms in patients with FMS, exercise protocols still need further optimization to enhance their scientific rigor and ensure safety.

Gastric cancer is one of the most common upper gastrointestinal malignancies in China, with a significant proportion of patients diagnosed at advanced stages, leading to a poor clinical prognosis. Forkhead box O (FOXO) is a class of transcription factors widely present in eukaryotes, playing key roles in various physiological and pathological processes, including development, metabolism, and the occurrence and progression of malignant tumors. Clinical studies have demonstrated that FOXO is abnormally expressed in gastric cancer and is closely associated with its proliferation, migration, treatment response, and drug resistance. This article summarizes the regulatory mechanisms of FOXO, its role in the occurrence and progression of gastric cancer, and its potential as a therapeutic target for the disease.