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.

BACKGROUND: Treatment with chimeric antigen receptor-T (CAR-T) cells has shown promising effectiveness in patients with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL), although the process of preparing for this therapy usually takes a long time. We have recently created CD19 Fast-CAR-T (F-CAR-T) cells, which can be produced within a single day. The objective of this study was to evaluate and contrast the effectiveness and safety of CD19 F-CAR-T cells with those of CD19 conventional CAR-T cells in the management of R/R B-ALL. METHODS: A multicenter, retrospective analysis of the clinical data of 44 patients with R/R B-ALL was conducted. Overall, 23 patients were administered with innovative CD19 F-CAR-T cells (F-CAR-T group), whereas 21 patients were given CD19 conventional CAR-T cells (C-CAR-T group). We compared the rates of complete remission (CR), minimal residual disease (MRD)-negative CR, leukemia-free survival (LFS), overall survival (OS), and the incidence of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) between the two groups. RESULTS: Compared with the C-CAR-T group, the F-CAR-T group had significantly higher CR and MRD-negative rates (95.7% and 91.3%, respectively; 71.4% and 66.7%, respectively; P = 0.036 and P = 0.044). No significant differences were observed in the 1-year or 2-year LFS or OS rates between the two groups: the 1-year and 2-year LFS for the F-CAR-T group vs.C-CAR-T group were 47.8% and 43.5% vs. 38.1% and 23.8% (P = 0.384 and P = 0.216), while the 1-year and 2-year OS rates were 65.2% and 56.5% vs. 52.4% and 47.6% (P = 0.395 and P = 0.540). Additionally, among CR patients who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CAR-T-cell therapy, there were no significant differences in the 1-year or 2-year LFS or OS rates: 57.1% and 50.0% vs. 47.8% and 34.8% (P = 0.506 and P = 0.356), 64.3% and 57.1% vs. 65.2% and 56.5% (P = 0.985 and P = 0.883), respectively. The incidence of CRS was greater in the F-CAR-T group (91.3%) than in the C-CAR-T group (66.7%) (P = 0.044). The incidence of ICANS was also greater in the F-CAR-T group (30.4%) than in the C-CAR-T group (9.5%) (P = 0.085), but no treatment-related deaths occurred in the two groups. CONCLUSION Compared with C-CAR-T-cell therapy, F-CAR-T-cell therapy has a superior remission rate but also leads to a tolerably increased incidence of CRS/ICANS. Further research is needed to explore the function of allo-HSCT as an intermediary therapy after CAR-T-cell therapy.

Diterpenoid ferruginol is a key intermediate in biosynthesis of active ingredients such as tanshinone and carnosic acid.However, the traditional process of obtaining ferruginol from plants is often cumbersome and inefficient. In recent years, the increasingly developing gene editing technology has been gradually applied to the heterologous production of natural products, but the production of ferruginol in microbe is still very low, which has become an obstacle to the efficient biosynthesis of downstream chemicals, such as tanshinone. In this study, miltiradiene was produced by integrating the shortened diterpene synthase fusion protein,and the key genes in the MVA pathway were overexpressed to improve the yield of miltiradiene. Under the shake flask fermentation condition, the yield of miltiradiene reached about(113. 12±17. 4)mg·L~(-1). Subsequently, this study integrated the ferruginol synthase Sm CYP76AH1 and Sm CPR1 to reconstruct the ferruginol pathway and thereby realized the heterologous synthesis of ferruginol in Saccharomyces cerevisiae. The study selected the best ferruginol synthase(Il CYP76AH46) from different plants and optimized the expression of pathway genes through redox partner engineering to increase the yield of ferruginol. By increasing the copy number of diterpene synthase, CYP450, and CPR, the yield of ferruginol reached(370. 39± 21. 65) mg·L~(-1) in the shake flask, which was increased by 21. 57-fold compared with that when the initial ferruginol strain JMLT05 was used. Finally, 1 083. 51 mg·L~(-1) ferruginol was obtained by fed-batch fermentation, which is the highest yield of ferruginol from biosynthesis so far. This study provides not only research ideas for other metabolic engineering but also a platform for the construction of cell factories for downstream products.
Saccharomyces cerevisiae/genetics* ; Diterpenes/metabolism* ; Metabolic Engineering ; Fermentation ; Abietanes

OBJECTIVE: Psoriasis, a common chronic inflammatory skin condition with genetic underpinnings, is traditionally managed with cupping therapy. Although used historically, the precise mechanical effects and therapeutic mechanisms of cupping in psoriasis remain largely unexamined. This study aimed to evaluate cupping therapy's efficacy for psoriasis and investigate its role in modulating inflammatory responses and cellular metabolism. METHODS: Psoriasis was induced in mice using topical imiquimod (IMQ). The effects of cupping on psoriatic lesions were assessed using the Psoriasis Area and Severity Index score, histology, immunohistochemistry, and immunofluorescence staining. polymerase chain reaction sequencing (RNA-seq) and Western blotting were conducted to examine changes in mRNA expression and the AMP-activated protein kinase (AMPK) signaling pathway. RESULTS: Cupping therapy significantly reduced inflammation, epidermal thickness, and inflammatory cell infiltration in mice with IMQ-induced psoriasis. Immunohistochemistry and immunofluorescence showed lower expression of inflammatory markers and a shift in T-cell populations. RNA-seq and Western blotting indicated that cupping upregulated Piezo1 and activated the AMPK pathway, improving energy metabolism in psoriatic skin. CONCLUSION Cupping therapy reduces epidermal hyperproliferation and inflammation in psoriasis, rebalancing the local immune microenvironment. Mechanistically, cupping promotes calcium influx via Piezo1, activates AMPK signaling, and supports metabolic homeostasis, suggesting therapeutic potential for psoriasis. Please cite this article as: Xi RF, Liu X, Wang Y, Lu HZ, Yuan SJ, Guo DJ, Zhu JY, Li FL, Duan YJ. Mechanosensory activation of Piezo1 via cupping therapy: Harnessing neural networks to modulate AMPK pathway for metabolic restoration in a mouse model of psoriasis. J Integr Med. 2025; 23(6):721-732.
Animals ; Psoriasis/chemically induced* ; Mice ; AMP-Activated Protein Kinases/metabolism* ; Disease Models, Animal ; Cupping Therapy/methods* ; Signal Transduction ; Imiquimod ; Ion Channels/genetics* ; Male ; Mechanotransduction, Cellular

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

High mobility group box protein 1(HMGB1)is one of the most widely expressed protein member in the HMGs family,which is well known for its involvement in the body inflammatory response.Previous researches have found that it plays a significant role in cell migration,immune identification and neuroprotection.Spinal cord injury is a disease that causes severe damage to the nervous system,and neural circuits are disrupted after a spinal cord injury,which leads to many conditions including ischemia and hypoxia,inflammatory responses,demyelinating lesions,and glial scar formation that are detrimental to nerve regeneration and repair,making it one of the most difficult diseases to treat in the modern spinal surgery field.HMGB1 is upregulated after spinal cord injury,thereby regulating neuroinflam-matory responses,and participating in the neuronal apoptosis,promoting neuronal regeneration,and inducing neural stem cell differentiation and migration,which plays an important role in the process of neural function recovery.This paper summarizes the structure and function of HMGB1,as well as its role in spinal cord injury,in order to provide direction for founding therapeutic target for neurological function recovery after spinal cord injury.

Based on the Political Economic Social Technological(PEST)-Strengths Weakness Opportunity Threats(SWOT)model,by sorting out the macro-environmental factors such as politics,economy,society and technology,and combining the internal strengths and weaknesses of the hospital as well as the external opportunities and threats,it puts forward the strategies to optimize the comprehensive budget management.It is found that hospitals face problems in total budget management such as disconnection between budgeting and strategy,and low level of informatization,but they also have advantages such as resource optimization and support for decision-making.The external environment provides opportunities such as health insurance reform and financing channel expansion,while there are threats such as industry competition and data security.It is suggested to improve the budget management level and promote the high-quality development of hospitals by expanding funding sources,strengthening cost control,and promoting the integration of industry and finance.