Copper is an essential trace element in the human body, extensively involved in key physiological and biochemical processes such as antioxidant defense, energy metabolism, neural signaling, and immune regulation. In recent years, increasing research has focused on the potential role of copper dyshomeostasis in the development of chronic diseases. Studies indicate that abnormal copper levels, particularly elevated free copper, may increase the risk of cardiovascular disease, neurodegenerative disorders, diabetes, and cancer by inducing oxidative stress, impairing mitochondrial function, and disrupting immune regulation. Concurrently, copper homeostasis abnormalities have been demonstrated to be closely associated with increased all-cause mortality and accelerated aging. This systematic review comprehensively examined physiological functions, metabolic pathways, and environmental exposure characteristics of copper. It emphasized the epidemiological and mechanistic links between copper metabolism disorders and multiple chronic diseases, while exploring the potential applications of copper ion transporters and chelating agents in disease intervention. This work provides scientific evidence for the prevention, control, and precision treatment of copper-related chronic diseases.

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.

OBJECTIVE To design and synthesize rifamycin S derivatives and load them into milk exosomes to evaluate their in vitro antimicrobial activity.METHODS Rifamycin S derivatives were synthe-sized and characterized by mass spectrometry and NMR.Using the dilution assay method,the inhibitory activity of each rifamycin S derivatives molecule against Staphylococcus aureus and Pseudomonas aerugi-nosa was determined,and the IC50 was calculated.Derivatives molecules with excellent antimicrobial activity were selected and loaded into milk exosomes using the ultrasonication method,resulting in the preparation of milk exosome-loaded rifamycin S derivatives.The antimicrobial activity against Staphylo-coccus aureus was determined using the dilution assay method.The inhibitory effect of the exosome-loaded rifamycin S derivatives on Staphylococcus aureus residing within macrophages was detected using the plate colony counting method.RESULTS Three rifamycin S derivatives were successfully designed and synthesized,which demonstrated superior antimicrobial activity against Staphylococcus aureus(the parent compound's antimicrobial activity is merely from 1/20 to 1/80 of that of the three rifamycin S derivatives)and Pseudomonas aeruginosa(the parent compound's antimicrobial activity is only 1/14 and 1/9 of that of compound 1 and compound 3)compared to the parent compound.The loading of milk exosomes with the rifamycin S derivatives compound 3 was successfully achieved,with a loading efficiency of 10.9％.The antimicrobial activity of the compound after exosome loading was significantly enhanced against Staphylococcus aureus in vitro and against Staphylococcus aureus residing within macrophages(P＜0.01).CONCLUSION The designed and synthesized derivatives of rifamycin S possess stronger anti-microbial activity,and their antibacterial efficacy against both extracellular and intracellular bacteria can be further enhanced after loading into exosomes.

Objective To evaluate the safety and efficacy of valve-in-valve transcatheter mitral valve replacement(ViV-TMVR)in patients with bioprosthetic valve degeneration who are at high surgical risk.Methods This study is a multi-center,retrospective cohort analysis of 20 consecutive patients who underwent transseptal ViV-TMVR using the Edwards SAPIEN 3 transcatheter heart valve(THV).The primary endpoints include technical success and procedural success,both defined according to the Mitral Valve Academic Research Consortium(MVARC)criteria,as well as mortality and functional change assessed based on New York Heart Association(NYHA)classification at 30-days and six months post-procedure.Clinical follow-up assessments are conducted at 30-days and six months.Results From February 2021 to October 2022,a total of 20 patients with symptoms of bioprosthetic valve degeneration were enrolled across nine sites in China.The patients had a mean age of(73.5±5.5)years,with 85.0％being females and 70.0％classified as NYHA class Ⅲ/Ⅳ.The study achieved a 100.0％technical success rate and a 90.0％procedural success rate finally.All patients remained alive during the 30-day follow-up period.However,six months post-intervention,two patients(10.0％)were re-hospitalized due to heart failure,and sadly,one of them(5.0％)died.None of the patients reported any adverse events related to ViV-TMVR during the follow-up period.Notably,there was a significant improvement in NYHA class compared to baseline(P=0.0004)at six-month follow-ups.Conclusions The transseptal ViV-TMVR technique proved to be highly successful and was associated with significant improvement in NYHA class function.These findings strongly suggest that it serves as a safe and efficient treatment alternative for high-risk patients suffering from bioprosthetic valve degeneration.

Objective:To investigate the predictive value of serum cholinesterase(CHE)and soluble growth stimula-tion gene 2 protein(sST2)for short-term death in patients with myocardial infarction and heart failure.Methods:A total of 100 patients with myocardial infarction and heart failure admitted in Nanjing Benq Hospital between March 2021 and March 2023 were screened.After 6-month follow-up,patients were grouped according to pres-ence of death.Multivariate Cox regression was used to analyze the factors associated with death during 6-month follow-up in patients with myocardial infarction and heart failure.The predictive value of CHE,sST2 and their combined detection for short-term death in patients with myocardial infarction and heart failure was analyzed by receiver operating characteristic(ROC)curve.Kaplan-Meier survival curve was used to compare short-term sur-vival rate between myocardial infarction and heart failure patients with different CHE and sST2 levels.Results:During 6-month follow-up,46 cases died.Compared to those in survival group,patients in death group had sig-nificant higher heart rate(HR)[(82.20±8.09)beats/min vs.(71.54±6.97)beats/min],mean arterial pressure(MAP)[(126.58±5.38)mmHg vs.(104.79±4.94)mmHg]and sST2[(76.48±4.82)ng/ml vs.(40.62±4.96)ng/ml],and significant lower CHE[(3.47±0.26)IU/L vs.(5.07±0.80)IU/L](P＜0.001 all).Multivariate Cox regression showed that HR(HR 1.046,95％CI 1.002～1.092,P=0.040),MAP(HR 1.988,95％CI 1.298～2.455,P＜0.001),and sST2(HR 1.068,95％CI 1.014～1.125,P=0.013)were independent risk factors for short-term death in patients with myocardial infarction and heart failure,while CHE was its independent protec-tive factor(HR=0.252,95％CI 0.145～0.561,P=0.023).ROC curve analysis indicated that the area under the curve(AUC)of CHE,sST2 and their combination for diagnosing short-term death in patients with myocardial in-farction and heart failure was 0.609(95％0.504～0.707),0.630(95％0.525～0.726)and 0.939(95％0.871～0.977)respectively,and the diagnostic efficacy of combined detection was significantly higher than CHE and sST2 alone(Z=5.814,5.524,P＜0.001 all).Kaplan-Meier survival curve showed that the survival rate of patients with low CHE level was significantly lower than that of patients with high CHE level,and the survival rate of pa-tients with high sST2 level was significantly lower than that of patients with low sST2 level(Log-rank x2=2.415,2.354,P＜0.001 all).Conclusion:CHE and sST2 were independent influencing factors for death during 6-month follow-up in patients with myocardial infarction and heart failure;their combined detection had good predictive value for short-term death in these patients.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective:Near-infrared(NIR)spectroscopy technology faces the problem of insufficient model generalization due to individual differences in non-invasive blood glucose testing,in order to solve this problem,to improve data utilization,and to build predictive models with stronger generalization ability,this study introduces a transfer learning method to study the NIR spectroscopy non-invasive glucose testing.Methods:Migration learning is a machine learning technique that aims to improve task performance in the target domain by transferring knowledge from the source domain to the target domain.In this study,we used community population data as the source domain and student population data as the target domain to improve the performance of the noninvasive glucose detection model on the target domain.In order to verify the effectiveness of migration learning,this study compares the performance of the model before and after migration learning.Results:the model's performance on the noninvasive glucose detection task is significantly improved by the migration learning strategy,and the MAPE and MAE of the migrated model decreases by 52.5460％and 6.0805％,respectively,and the RMSE and MSE decreases by 10.7215％and 12.1135％.Conclusions:This study demonstrates the promising application of transfer learning in the field of non-invasive blood glucose detection,which is expected to realize portable and continuous blood glucose monitoring in the future by migrating the features that are difficult to access in the source domain but are related to blood glucose values to the target domain,which will greatly improve the quality of life of diabetic patients.Advances in noninvasive glucose testing technology will not only reduce patients' pain,but also provide a more convenient means of glucose monitoring,which will provide strong support for diabetes management.

Objective:To investigate the effect of traditional Chinese medicine chronic disease management model based on empowerment theory in patients with chronic heart failure(CHF).Methods:A total of 115 CHF patients admitted in Guangdong Provincial Hospital of Chinese Medicine between January 2020 and December 2021 were se-lected.Patients received traditional Chinese medicine chronic disease management model based on empowerment theory according to voluntary principle,and were followed up for 12 months.Exercise capacity,scores of Tradition-al Chinese Medicine Symptom Grading and Quantification Scale,Hospital Anxiety and Depression Scale(HADS)and Minnesota Living with Heart Failure Questionnaire(MLHFQ)were compared between before and after inter-vention.Results:Compared to before intervention,scores of Traditional Chinese Medicine Symptom Grading and Quantification Scale[(6.40±6.11)points vs.(8.88±6.72)points],HADS[(5.95±4.68)points vs.(7.69±5.95)points],MLHFQ[(13.10±10.54)points vs.(25.53±11.16)points]and 3m round-trip movement time[(7.54±1.70)s vs.(8.86±3.65)s]were significantly lower,and right hand grip strength[(27.23±10.49)kg vs.(26.10±9.94)kg]and 6-minute walking distance[(464.79±80.78)m vs.(415.55±79.33)m]were sig-nificantly higher after 12-month intervention(P＜0.05 or＜0.01).Conclusion:The traditional Chinese medicine chronic disease management model based on empowerment theory may improve clinical symptoms of traditional Chi-nese medicine,mental state,exercise capacity and quality of life in patients with chronic heart failure.

Objective To investigate the consistency and diagnostic efficacy of 1.5T and 3.0T MRI in the kidney clear cell likeli-hood score(ccLS)v2.0.Methods A retrospective collection was conducted on the data of 176 pathologically confirmed small renal mass(SRM).Two radiologists independently scored the MRI images using the ccLS v2.0.The Kappa test was used to assess inter-observer consistency,and receiver operating characteristic(ROC)curves were plotted to analyze the diagnostic efficacy.The area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NP V)were calculated.Results The inter-observer consistency for ccLS v2.0 score was good for 1.5T MRI and excellent for 3.0T MRI(Kappa values were 0.754 and 0.836,respectively).On 1.5T MRI examination,a ccLS≥4 points was identified as the optimal threshold,with AUC of 0.935 and 0.923 for the two radiologists,sensitivities of 92.45％and 88.68％,and specificities of 88.00％and 92.00％,respec-tively.For 3.0T MRI examination,using the same threshold,the AUC were 0.933 and 0.901,with sensitivities of 91.43％and 90.00％,and specificities of 78.57％and 78.57％for both radiologists.Conclusion The ccLS v2.0 demonstrates good consistency across high and low field intensity MRI,and a threshold of ccLS≥4 pionts provides high diagnostic efficacy for clear cell renal cell carcinoma(ccRCC).

Aim To investigate the effect of Rtv(a P-gp inhibitor and inducer)on the pharmacokinetics of Y101(P-gp substrate)via P-gp.Methods In short-term studies,rats received a single dose of Rtv,where-as in long-term studies they received continuous dosing for seven days.Following this treatment,Y101 was o-rally administered to analyze its blood concentration in rats.Subsequently,the mechanism by which Rtv af-fected Y101 pharmacokinetics was investigated through the everted gut sac model(in vitro),cellular uptake studies,and so on.Results Short-term administra-tion of Rtv significantly increased Y101's AUC,liver-to-plasma partition coefficient,the everted gut sac model(in vitro),and cellular accumulation.Although long-term Rtv treatment had no effect on Y101 pharma-cokinetics or hepatic distribution,it markedly reduced Y101 cellular accumulation in Caco-2 cells,concomi-tant with an upregulation of P-gp expression.Conclu-sions Short-term Rtv administration acts as a compet-itive P-gp inhibitor,enhancing Y101 intestinal absorp-tion and hepatic distribution.In contrast,the plasma pharmacokinetics and hepatic distribution of Y101 are not altered after long-term administration of Rtv,po-tentially attributable to Rtv's dual modulatory effects on P-gp involving both induction and inhibition.Hence,the potential Rtv and Y101 interaction should be close-ly monitored in the clinic.