In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

With the deepening understanding of the pathogenesis of osteoarthritis (OA), therapeutic strategies targeting oxidative stress in chondrocytes have gradually become a research hotspot. This article summarizes the important role of oxidative stress in the development of OA, pointing out that it is closely related to chondrocyte senescence, inflammatory cascade reaction, and cartilage matrix degradation. Given the central role of oxidative stress in the pathological process of OA, inhibiting oxidative stress and the generation of reactive oxygen species (ROS) is considered the key to alleviating chondrocyte damage and senescence, and preventing the progression of OA. Although some progress has been made in current OA research, there are still many challenges, such as the in-depth understanding of the etiology of OA and the limited selection of therapeutic drugs. Future research will focus on a more comprehensive understanding of the mechanism of oxidative stress in OA, exploring new biomarkers and therapeutic targets, and developing new drugs or treatment methods targeting oxidative stress pathways. These efforts are expected to bring more effective treatment options to OA patients, thereby improving their quality of life and prognosis.
Humans ; Oxidative Stress ; Osteoarthritis/pathology* ; Chondrocytes/pathology* ; Animals ; Reactive Oxygen Species/metabolism*

Greater trochanteric pain syndrome(GTPS)is a disease caused by structural lesions of the muscles,fascia,ligaments,and bursae near the greater trochanter of the femur.GTPS causes lateral hip joint pain,severely affecting patients' quality of life.Ultrasound has many advantages,such as real-time diagnosis,portable operation,non-radiation,and high resolution,demonstrating a high application value in the diagnosis and interventional therapy of GTPS.This article reviews the current status of ultrasound in the diagnosis and interventional therapy of GTPS and prospects its application.
Humans ; Ultrasonography ; Femur/diagnostic imaging* ; Hip Joint/diagnostic imaging* ; Arthralgia/therapy*

The traditional commodity specifications of Chinese medicinal materials are mainly divided into different grades based on macroscopic characteristics. As the basis for high quality and good price, there is still a lack of systematic evaluation on whether they are consistent with the current standards and whether they can reflect the internal quality of medicinal material. Panax notoginseng is a commonly used, large consumption of Chinese medicinal material. At present, it is divided into 8 grades in the market based on "Tou" (the number of crude drug /500 g), but it is not related to the standard of total saponins of Panax notoginseng (the sum of three saponins) in Chinese Pharmacopoeia. In this study, ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF-MS/MS) coupled with mass spectrometry molecular network were used for the rapid identification of saponins of Panax notoginseng with different "Tou" and a total of 64 saponins were identified. Seventeen saponins related to "Tou" were screened by orthogonal partial least squares discriminant analysis (OPLS-DA). The content of five saponins R₁, Rb₁, Rg₁, Rd, and Re in Panax notoginseng with different "Tou" was determined by high performance liquid chromatography (HPLC). The results of correlation analysis showed that Rd and R₁ with the largest VIP values among the differential saponins, which significantly negatively correlated with "Tou" (P < 0.05). Based on the determination results of 36 batches of samples, using Rd/Total Panax notoginseng saponins (TPNS) ratio ( > 0.08) as the index, Panax notoginseng can be divided into two grades: 20-60 "Tou" (superior) and 80-200 "Tou" (qualified). Based on the concept of "macroscopic characteristics and chemical profiling", this study integrates the non-targeted analysis and quantitative determination methods to provide a new strategy for quality evaluation of Panax notoginseng.

Viral myocarditis(VMC)is the leading cause of dilated cardiomyopathy,which can lead to heart failure and sudden cardiac death.With the development of high-throughput sequencing technology,non-coding RNA(ncRNA)plays an important role in the occurrence and development of VMC.ncRNA promotes the occurrence and development of VMC by regulating viral replication,immune cell function,myocardial cell death,myocardial interstitial fibrosis,and other pathological processes.This article reviews the research progress of ncRNA in VMC and provides new ideas for the pathogenesis,diagnosis,and treatment of VMC.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Chirality is one of the fundamental properties of nature, and most of the important molecules in living organisms contain chiral structures. The efficacy and safety of drugs are often closely related to the chiral structure of compounds, however, there are relatively more studies on synthetic characterization, pharmacology, and toxicology of chiral small molecule chemical drugs, but relatively less studies on chiral compounds contained in natural drugs such as traditional Chinese medicines. Chiral separation, as the basis of chiral research, has a pivotal position in the study of chiral compounds. In this paper, we systematically describe the separation methods of chiral compounds from the classification of chiral splitting methods based on chromatographic and non-chromatographic methods, as well as chromatographic packing materials, chiral additives and chiral derivatization, and review the chiral compounds in natural drugs such as traditional Chinese medicines reported in the past ten years, in order to provide references for the splitting and evaluating the activity of chiral compounds, and the improvement of quality standards of traditional Chinese medicines.

Objective: To establish a dynamic syndromic surveillance system in the border areas of Yunnan Province based on information technology, evaluate its effectiveness and timeliness in the response to common communicable disease epidemics and improve the communicable disease prevention and control in border areas. Methods: Three border counties were selected for full coverage as study areas, and dynamic surveillance for 14 symptoms and 6 syndromes were conducted in medical institutions, the daily collection of information about students' school absence in primary schools and febrile illness in inbound people at border ports were conducted in these counties from January 2016 to February 2018 to establish an early warning system based on mobile phone and computer platform for a field experimental study. Results: With syndromes of rash, influenza-like illness and the numbers of primary school absence, the most common communicable disease events, such as hand foot and mouth disease, influenza and chickenpox, can be identified 1-5 days in advance by using EARS-3C and Kulldorff time-space scanning models with high sensitivity and specificity. The system is easy to use with strong security and feasibility. All the information and the warning alerts are released in the form of interactive charts and visual maps, which can facilitate the timely response. Conclusions: This system is highly effective and easy to operate in the detection of possible outbreaks of common communicable diseases in border areas in real time, so the timely and effective intervention can be conducted to reduce the risk of local and cross-border communicable disease outbreaks. It has practical application value.
Humans ; Influenza, Human ; Sentinel Surveillance ; Syndrome ; China ; Cell Phone

Objective: To explore a simple and feasible method for the isolation and purification of hepatocytes, hepatic stellate cells (HSC), and lymphocytes from mice. Methods: The cell suspension was obtained from male C57bl/6 mice by hepatic perfusion through the portal vein digestion method and then isolated and purified by discontinuous Percoll gradient centrifugation. Trypan blue exclusion was used to determine cell viability. Glycogen staining, cytokeratin 18, and transmission electron microscopy were used to identify hepatic cells. Immunofluorescence was used to detect α-smooth muscle actin combined with desmin in HSCs. Flow cytometry was used to analyze lymphocyte subsets in the liver. Results: After isolation and purification, about 2.7×10(7) hepatocytes, 5.7×10(5) HSCS, and 4.6×106 hepatic mononuclear cells were obtained from the liver of mice with a body weight of about 22g. The cell survival rate in each group was > 95%. Hepatocytes were apparent in glycogen deposited purple-red granules and cytokeratin 18. Electron microscopy showed that there were abundant organelles in hepatocytes and tight junctions between cells. HSC had expressed α-smooth muscle actin and desmin. Flow cytometry showed hepatic mononuclear cells, including lymphocyte subsets such as CD4, CD8, NKs, and NKTs. Conclusion: The hepatic perfusion through the portal vein digestion method can isolate multiple primary cells from the liver of mice at once and has the features of simplicity and efficiency.
Male ; Mice ; Animals ; Keratin-18 ; Actins ; Desmin ; Liver ; Hepatocytes ; Hepatic Stellate Cells