Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

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.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

BACKGROUND: Enzalutamide, a second-generation androgen receptor (AR) pathway inhibitor, is widely used in the treatment of castration-resistant prostate cancer. However, after a period of enzalutamide treatment, patients inevitably develop drug resistance. In this study, we characterized leucine-rich repeated G-protein-coupled receptor 5 (LGR5) and explored its potential therapeutic value in prostate cancer. METHODS: A total of 142 pairs of tumor and adjacent formalin-fixed paraf-fin-embedded tissue samples from patients with prostate cancer were collected from the Pathology Department at Sun Yat-sen Memorial Hos-pital. LGR5 was screened by sequencing data of enzalutamide-resistant cell lines combined with sequencing data of lesions with different Gleason scores from the same patients. The biological function of LGR5 and its effect on enzalutamide resistance were investigated in vitro and in vivo . Glutathione-S-transferase (GST) pull-down, coimmunoprecipitation, Western blotting, and immunofluorescence assays were used to explore the specific binding mechanism of LGR5 and related pathway changes. RESULTS: LGR5 was significantly upregulated in prostate cancer and negatively correlated with poor patient prognosis. Overexpression of LGR5 promoted the malignant progression of prostate cancer and reduced sensitivity to enzalutamide in vitro and in vivo . LGR5 promoted the phosphorylation of glycogen synthase kinase-3β (GSK-3β) by binding heat shock protein 90,000 alpha B1 (HSP90AB1) and mediated the activation of the Wingless/integrated (WNT)/β-catenin signaling pathway. The increased β-catenin in the cytoplasm entered the nucleus and bound to the nuclear AR, promoting the transcription level of AR, which led to the enhanced tolerance of prostate cancer to enzalutamide. Reducing HSP90AB1 binding to LGR5 significantly enhanced sensitivity to enzalutamide. CONCLUSIONS LGR5 directly binds to HSP90AB1 and mediates GSK-3β phosphorylation, promoting AR expression by regulating the WNT/β-catenin signaling pathway, thereby conferring resistance to enzalutamide treatment in prostate cancer.
Male ; Humans ; Phenylthiohydantoin/pharmacology* ; Benzamides ; Receptors, G-Protein-Coupled/genetics* ; Nitriles ; Cell Line, Tumor ; HSP90 Heat-Shock Proteins/metabolism* ; Drug Resistance, Neoplasm/genetics* ; Prostatic Neoplasms/drug therapy* ; beta Catenin/metabolism* ; Receptors, Androgen/genetics* ; Animals ; Mice ; Wnt Signaling Pathway/physiology*

Dipsaci Radix is a commonly used Chinese herbal medicine in China, with triterpenoid saponins as the main active components. β-Amyrin synthase, a member of the oxidosqualene cyclase superfamily, plays a crucial role in the biosynthesis of oleanane-type triterpenoid saponins. Asperosaponin Ⅵ is an oleanane-type triterpenoid saponin. To explore the β-amyrin synthase genes involved in the biosynthesis of asperosaponin Ⅵ in Dipsacus asper, this study screened the candidate genes from the transcriptome data of D. asper. Two β-amyrin synthase genes, Da OSC1 and Da OSC2, were identified by phylogenetic analysis and correlation analysis. The coding sequences of Da OSC1 and Da OSC2 were 2 286 bp and 2 295 bp in length, encoding 761 and 764 amino acids,respectively. Multiple sequence alignments showed that Da OSC1 and Da OSC2 had three conserved motifs( DCTAE, QW, and MWCYCR) unique to the oxidosqualene cyclase family. Real-time quantitative PCR results showed that Da OSC1 and Da OSC2 had the highest expression levels in the roots. Compared with normal growth conditions, the low-temperature treatment significantly upregulated the expression of Da OSC1 and Da OSC2. Agrobacterium-mediated transient expression of Da OSC1 and Da OSC2 in Nicotiana benthamiana resulted in the production of β-amyrin, which suggested that Da OSC1 and Da OSC2 were able to catalyze the synthesis of β-amyrin. This study clarified the catalytic functions of two β-amyrin synthases in D. asper, analyzed their expression patterns in different tissue and at low temperatures. The findings provide a foundation for further studying the biosynthetic pathway and regulatory mechanism of asperosaponin Ⅵ in D. asper.
Intramolecular Transferases/chemistry* ; Phylogeny ; Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Dipsacaceae/classification* ; Saponins/metabolism* ; Oleanolic Acid/metabolism*

Objective To study the effect of Mycobacterium tuberculosis (Mtb) Pro-Pro-Glu-59 (PPE59) protein on the biological function of Mycobacterium smegmatis (Ms) and the regulation of host cell immune response. Methods PPE59 gene fragment was obtained by PCR amplification, cloned into pALACE, constructed into recombinant pALACE-PPE59 vector, and electro-transformed into Ms. Western blot was applied to analyse PPE59 expression and subcellular localization. The survival of Ms_Vec and Ms_PPE59 under low acid (pH=3 and pH=5) conditions and active surface pressure sodium dodecyl sulfate (SDS) conditions and their intracellular survival in macrophages were analyzed. ELISA was used to detect the cytokine (IL-1β, IL-6, IL-12, TNF-α and IL-10) expression levels of Ms_Vec and Ms_PPE59 infected macrophages. Results PPE59 protein localized to the cell wall of Ms can enhance the acid-resistance and anti-SDS effect of Ms, which is conducive to the survival of Ms in macrophages. PPE59 significantly decreased the secretion levels of pro-inflammatory cytokines (IL-1β, IL-6, IL-12 and TNF-α), and promoted the secretion levels of anti-inflammatory cytokine (IL-10). Conclusion PPE59 enhances the survival ability of Ms under low acid and SDS pressure and promotes its intracellular survival by regulating the cytokine secretion levels.
Mycobacterium smegmatis/metabolism* ; Macrophages/metabolism* ; Cytokines/metabolism* ; Mycobacterium tuberculosis/metabolism* ; Bacterial Proteins/metabolism* ; Animals ; Mice ; Antigens, Bacterial/metabolism*

The relationship between hyperuricemia (HUA) and erectile dysfunction (ED) remains inadequately understood. Given that HUA is often associated with various metabolic disorders, this study aims to explore the multivariate linear impacts of metabolic parameters on erectile function in ED patients with HUA. A cross-sectional analysis was conducted involving 514 ED patients with HUA in the Department of Andrology, Jiangsu Province Hospital of Chinese Medicine (Nanjing, China), aged 18 to 60 years. General demographic information, medical history, and laboratory results were collected to assess metabolic disturbances. Sexual function was evaluated using the 5-item version of the International Index of Erectile Function (IIEF-5) questionnaire. Based on univariate analysis, variables associated with IIEF-5 scores were identified, and the correlations between them were evaluated. The effects of these variables on IIEF-5 scores were further explored by multiple linear regression models. Fasting plasma glucose ( β = -0.628, P < 0.001), uric acid ( β = -0.552, P < 0.001), triglycerides ( β = -0.088, P = 0.047), low-density lipoprotein cholesterol ( β = -0.164, P = 0.027), glycated hemoglobin (HbA1c; β = -0.562, P = 0.012), and smoking history ( β = -0.074, P = 0.037) exhibited significant negative impacts on erectile function. The coefficient of determination ( R ²) for the model was 0.239, and the adjusted R ² was 0.230, indicating overall statistical significance ( F -statistic = 26.52, P < 0.001). Metabolic parameters play a crucial role in the development of ED. Maintaining normal metabolic indices may aid in the prevention and improvement of erectile function in ED patients with HUA.
Humans ; Male ; Erectile Dysfunction/metabolism* ; Hyperuricemia/metabolism* ; Adult ; Middle Aged ; Cross-Sectional Studies ; Glycated Hemoglobin/metabolism* ; Blood Glucose/metabolism* ; Uric Acid/blood* ; Young Adult ; Triglycerides/blood* ; Adolescent ; Cholesterol, LDL/blood* ; Penile Erection/physiology* ; Surveys and Questionnaires

OBJECTIVE: To analyze the changes in coagulation during the treatment of acute promyelocytic leukemia (APL) and explore the influencing factors of coagulation in patients with APL. METHODS: Data of 166 APL patients admitted to our hospital from November 2018 to May 2023 were retrospectively analyzed, and the changes of various clinical indicators before and during treatment were compared. 166 APL patients were divided into abnormal coagulation group (n =115) and normal coagulation group (n =51) according to whether they experienced coagulation dysfunction. The basic information, clinical data and laboratory indicators of the two groups were compared. Multivariate logistic regression analysis was used to screen risk factors for coagulation dysfunction and established logistic regression model. Then we developed a neural network model and ranked the importance of the influencing factors, and used receiver operating characteristic (ROC) curves to evaluate the predictive performance of the two models. RESULTS: The comparative results of various clinical indicators in 166 APL patients before and during treatment showed that systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), platelet (PLT) and fibrinogen (FIB) were significantly increased during the treatment (P < 0.05), while glycosylated hemoglobin (HbA1c), high density lipoprotein cholesterol (HDL-C), blood urea nitrogen (BUN), serum creatinine (SCr), high-sensitivity C reactive protein (hs-CRP), IL-6, TNF-α, TGF-β, white blood cells (WBC), absolute neutrophil count (ANC), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D), fibrinogen degradation products (FDP) and lactate dehydrogenase (LDH) were significantly decreased during the treatment (P < 0.05). The proportion of patients with hemorrhage and high-risk APL in the abnormal coagulation group was significantly higher than that in the normal coagulation group (P < 0.05). The levels of IL-6, TNF-α, WBC, ANC, D-D, FDP and LDH in the abnormal coagulation group were significantly higher than those in the normal coagulation group (P < 0.05). The influencing factors selected by univariate analysis were incorporated into logistic regression analysis and neural network model to predict the risk of coagulation dysfunction in APL patients. ROC curves showed that the AUC of the two models were 096 and 0.908, the sensitivity were 0.824 and 0.892, the specificity were 0.940 and 0.904, the Youden index were 064 and 0.796, and the accuracy were 0.882 and 0.898, respectively. CONCLUSION High risk stratification, hemorrhage, elevated WBC, LDH, ANC and FDP levels are independent risk factors for coagulation dysfunction in APL patients. The logistic regression model and neural network model based on these risk factors demonstrate good predictive performance for coagulation dysfunction in APL patients.
Humans ; Leukemia, Promyelocytic, Acute/therapy* ; Blood Coagulation ; Retrospective Studies ; Male ; Female ; Risk Factors ; Logistic Models ; Middle Aged ; Adult ; ROC Curve