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.

This study aimed to explore the mechanisms and molecular targets of total flavones of Abelmoschus manihot(TFA) plus empagliflozin(EM) in attenuating diabetic tubulopathy(DT) by targeting mitochondrial homeostasis and pyroptosis-apoptosis-necroptosis(PANoptosis). In the in vivo study, the authors established the DT rat models through a combination of uninephrectomy, administration of streptozotocin via intraperitoneal injections, and exposure to a high-fat diet. Following modeling successfully, the DT rat models received either TFA, EM, TFA+EM, or saline(as a vehicle) by gavage for eight weeks, respectively. In the in vitro study, the authors subjected the NRK52E cells with or without knock-down Z-DNA binding protein 1(ZBP1) to a high-glucose(HG) environment and various treatments including TFA, EM, and TFA+EM. In the in vivo and in vitro studies, The authors investigated the relative characteristics of renal tubular injury and renal tubular epithelial cells damage induced by reactive oxygen species(ROS), analyzed the relative characteristics of renal tubular PANoptosis and ZBP1-mediatted PANoptosis in renal tubular epithelial cells, and compared the relative characteristics of the protein expression levels of marked molecules of mitochondrial fission in the kidneys and mitochondrial homeostasis in renal tubular epithelial cells, respectively. Furthermore, in the network pharmacology study, the authors predicted and screened targets of TFA and EM using HERB and SwissTargetPrediction databases; The screened chemical constituents and targets of TFA and EM were constructed the relative network using Cytoscape 3.7.2 network graphics software; The relative targets of DT were integrated using OMIM and GeneCards databases; The intersecting targets of TFA, EM, and DT were enriched and analyzed signaling pathways by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG) software using DAVID database. In vivo study results showed that TFA+EM could improve renal tubular injury, the protein expression levels and characteristics of key signaling molecules in PANoptosis pathway in the kidneys, and the protein expression levels of marked molecules of mitochondrial fission in the kidneys. And that, the ameliorative effects in vivo of TFA+EM were both superior to TFA or EM. Network pharmacology study results showed that TFA+EM treated DT by regulating the PANoptosis signaling pathway. In vitro study results showed that TFA+EM could improve ROS-induced cell injury, ZBP1-mediatted PANoptosis, and mitochondrial homeostasis in renal tubular epithelial cells under a state of HG, including the protein expression levels of marked molecules of mitochondrial fission, mitochondrial ultrastructure, and membrane potential level. And that, the ameliorative effects in vitro of TFA+EM were both superior to TFA or EM. More importantly, using the NRK52E cells with knock-down ZBP1, the authors found that, indeed, ZBP1 was mediated PANoptosis in renal tubular epithelial cells as an upstream factor. In addition, TFA+EM could regulate the protein expression levels of marked signaling molecules of PANoptosis by targeting ZBP1. In summary, this study clarified that TFA+EM, different from TFA or EM, could attenuate DT with multiple targets by ameliorating mitochondrial homeostasis and inhibiting ZBP1-mediated PANoptosis. These findings provide the clear pharmacological evidence for the clinical treatment of DT with a novel strategy of TFA+EM, which is named "coordinated traditional Chinese and western medicine".
Animals ; Rats ; Mitochondria/metabolism* ; Benzhydryl Compounds/administration & dosage* ; Glucosides/administration & dosage* ; Abelmoschus/chemistry* ; Male ; Homeostasis/drug effects* ; Flavones/administration & dosage* ; Rats, Sprague-Dawley ; Diabetic Nephropathies/physiopathology* ; Drugs, Chinese Herbal/administration & dosage* ; DNA-Binding Proteins/genetics* ; Humans ; Apoptosis/drug effects*

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective To construct risk models for predicting the occurrence of high altitude de-acclimatization syndrome(HADAS)in the population returning from the plateau to the plain based on different machine learning algorithms and validate the predicting efficiency of these models.Methods Field or online surveys were conducted on the individuals who had ended their high-altitude living and returned to the plain areas from November 2020 to February 2024.Basic information,chronic mountain sickness(CMS),HADAS symptoms and other data were collected.With the inclusion and exclusion criteria,totally 1 095 individuals were subjected and assigned into the modeling group.Positive events were defined as HADAS score>5.Then the modelling group was divided into a training set(n=766)and an internal test set(n=329)in a 7∶3 ratio.Least absolute shrinkage and selection operator(LASSO)regression was used to select independent variables.Risk prediction models for high-altitude adaptation symptoms were built based on 8 machine learning methods,including multiple factor logistic regression(LR),decision tree(DT),random forest(RF),eXtreme gradient boosting(XGB),support vector machine(SVM),K-nearest neighbor(KNN),light gradient boosting(LGB)and na?ve bayes(NB).The models were compared and evaluated using receiver operating characteristic(ROC)curves,calibration curves and confusion matrices in the internal test set.The final model was presented using a nomogram or Shapley additive explanations(SHAP)algorithm.In August 2024,another 132 individuals who returned to the plains and met the same criteria were recruited and served as the external validation group.Results There were 549 individuals(50.14%)out of the 1 095 subjects having HADAS symptoms.LASSO regression identified CMS score,age and duration of high-altitude residence as significant predictors.Among the 8 machine learning algorithms,the LR model was identified as the best,with an area under the curve(AUC)value of 0.819(95%CI:0.789～0.850)and 0.841(95%CI:0.799～0.884),and an F1 score of 0.801 in the internal test set,respectively,and the AUC value and F1 score of the LR model were the largest among the 8 models in the internal test set.Spiegelhalter Z test of the calibration curve of the LR model indicated that its P=0.703 in the training set while P=0.281 in the internal test set.The AUC value of the LR model was 0.867(95%CI:0.765～0.969)in the external validation set.Conclusion The LR model constructed based on indicators including CMS score,age and duration of high-altitude residence has a good overall performance in the internal test set,and good discriminating effect in the external validation set.The constructed nomogram is convenient for application.

Objective To study the correlation between traditional Chinese medicine(TCM)constitution and pathogenic factors in patients with ankylosing spondylitis(AS).Methods One hundred patients of AS and their family members who had medical consultation in the Fifth Hospital of Xi'an(i.e.,Shaanxi Hospital of Integrated Traditional Chinese and Western Medicine)in August 2019 and September 2020 were selected as the study subjects.The guidelines of Classification and Determination of Traditional Chinese Medicine Constitution issued by the China Association of Chinese Medicine were adopted to determine the traditional Chinese medicine(TCM)constitution types of the study subjects.The sociodemographic information,living habits,clinical symptoms,and TCM constitution types of the AS patients and their family members were collected by means of questionnaires and clinical investigations,and then the pathogenic factors of the patients with AS were investigated.The binomial Logistic regression model was used to analyze the correlation between TCM constitution types and pathogenic factors in patients with AS.Results(1)Among the 100 AS patients,the majority of them had the biased constitutions,and the biased constitutions with the occurrence frequency in descending order were yang deficiency constitution,qi deficiency constitution,and damp-heat constitution,which accounted for 33.00%,14.00%,and 18.00%,respectively.(2)The prevalence rates of AS in the first-,second-,and third-degree relatives of AS patients were 56.25%,40.00%and 25.00%,respectively.For the positive rates of human leukocyte antigen B27(HLA-B27)in AS patients and their family members,HLA-B27 in AS patients was all positive,while the positive rates of HLA-B27 in the first-,second-,and third-degree relatives of AS patients were 44.31%,30.67%and 15.63%,respectively.(3)The results of regression analysis showed that the disease duration of AS patients was significantly correlated with qi deficiency constitution,the grading of sacroiliac arthritis was correlated with qi stagnation constitution,and age was correlated with blood stasis constitution(P＜0.05 or P＜0.01).The results indicated that disease duration and age were the important factors affecting the constitution types of AS patients,and disease duration was closely related to qi deficiency while age was closely related to blood stasis.Conclusion AS is a highly hereditary autoimmune disease,and its onset is associated with HLA-B27.Yang deficiency is the basic constitution type of AS,and damp-heat constitution is the main constitution type in the progression of AS(especially in the active stage of the disease).The prolongation of the disease will exacerbate the illness condition of AS and then the manifestations of qi deficiency will be more obvious.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Sepsis-induced coagulopathy(SIC),a critical and potentially lethal condition arising from sepsis,results in endothelial damage and significant coagulation dysregulation,making it a major factor contributing to mortality among sepsis patients.Early diagnosis and treatment of SIC are expected to improve the prognosis of sepsis patients.In 2019,the International Society on Thrombosis and Hemostasis(ISTH)issued the first guidelines for the diagnosis and treatment of SIC,but there are no corresponding protocols in China.Therefore,Chinese Society of Thrombosis,Hemostasis and Critical Care,Chinese Medicine Education Association,and Chinese People's Liberation Army Professional Committee of Critical Care Medicine jointly formulated the"Chinese Expert Consensus on the Diagnosis and Treatment of Sepsis-induced Coagulopathy(2024 edition)."This consensus includes 5 parts:pathogenesis,classification,laboratory approaches,diagnosis and treatment,with a total of 14 evidence-based recommendations to guide clinical practice.

Activation of nuclear factor erythroid 2-related factor 2(Nrf2)by Kelch-like ECH-associated protein 1(Keap1)alkylation plays a central role in anti-inflammatory therapy.However,activators of Nrf2 through alkylation of Keap1-Kelch domain have not been identified.Deoxynyboquinone(DNQ)is a natural small molecule discovered from marine actinomycetes.The current study was designed to investigate the anti-inflammatory effects and molecular mechanisms of DNQ via alkylation of Keap1.DNQ exhibited signif-icant anti-inflammatory properties both in vitro and in vivo.The pharmacophore responsible for the anti-inflammatory properties of DNQ was determined to be the α,β-unsaturated amides moieties by a chemical reaction between DNQ and N-acetylcysteine.DNQ exerted anti-inflammatory effects through activation of Nrf2/ARE pathway.Keap1 was demonstrated to be the direct target of DNQ and bound with DNQ through conjugate addition reaction involving alkylation.The specific alkylation site of DNQ on Keap1 for Nrf2 activation was elucidated with a synthesized probe in conjunction with liquid chromatography-tandem mass spectrometry.DNQ triggered the ubiquitination and subsequent degra-dation of Keap1 by alkylation of the cysteine residue 489(Cys489)on Keap1-Kelch domain,ultimately enabling the activation of Nrf2.Our findings revealed that DNQ exhibited potent anti-inflammatory capacity through α,β-unsaturated amides moieties active group which specifically activated Nrf2 signal pathway via alkylation/ubiquitination of Keap1-Kelch domain,suggesting the potential values of targeting Cys489 on Keap1-Kelch domain by DNQ-like small molecules in inflammatory therapies.

Objective: To describe the prevalence of alcohol consumption and the burden of hemorrhagic stroke and hypertensive heart disease attributed to alcohol consumption in adults aged ≥20 years in 31 provinces in China from 2005 to 2018. Methods: Data from several national representative surveys was used to estimate provincial alcohol exposure level of adults aged ≥20 years from 2005 to 2018 by using kriging interpolation and locally weighted regression methods. Global disease burden research method and data, and China's death cause surveillance data were used to calculate the population attributable fraction (PAF) of hemorrhagic stroke and hypertensive heart disease and the deaths due to alcohol consumption in men and women aged ≥20 years in 31 provinces in China. China census data of 2010 were used to calculate the attributable standardized mortality rate. Results: In 2005 and 2018, the prevalence of alcohol consumption was 58.7% (95%CI: 57.8%-59.5%) and 58.4% (95%CI: 57.6%-59.3%), respectively, in men and 17.0% (95%CI: 16.6%-17.4%) and 18.7% (95%CI:18.1%-19.3%), respectively, in women. The daily alcohol intake was 24.6 (95%CI: 23.8-25.3) g and 27.7 (95%CI: 26.8-28.7) g, respectively, in men and 6.3 (95%CI: 6.0-6.5) g and 5.3 (95%CI: 5.0-5.6) g, respectively, in women. Alcohol exposure level was higher in the provinces in central and eastern China than in western provinces. The lowest exposure level was found in northwestern provinces. From 2005 to 2018, the PAF of hemorrhagic stroke death due to alcohol consumption increased from 5.5% to 6.8%, the attributable deaths increased from 50 200 to 59 100, while the PAF of hypertensive heart disease death due to alcohol consumption increased from 7.0% to 7.7%, the attributable deaths increased from 15 200 to 29 300. The PAF of hypertensive heart disease and hemorrhagic stroke was higher in men than in women, and in central and eastern provinces than in western provinces. In 2018, the standardized mortality rates of hemorrhagic stroke and hypertensive heart disease attributed to alcohol consumption were 4.58/100 000 and 2.11/100 000, respectively. Conclusions: The prevalence of alcohol consumption in men and daily alcohol intake of drinkers were relatively high in China, especially in eastern provinces. Alcohol exposure level was lower in women than in men. Regional measures should be taken to reduce the alcohol intakes in men and current drinkers in order to reduce the health problems caused by alcohol consumption.
Adult ; Male ; Humans ; Female ; Hemorrhagic Stroke ; Hypertension/epidemiology* ; Alcohol Drinking/epidemiology* ; Heart Diseases/epidemiology* ; China/epidemiology*

OBJECTIVE To analyze the risks of prescription drugs sold online by drug retail enterprises， and to provide countermeasures and suggestions for risk prevention and control of prescription drugs sold online. METHODS The risk hierarchy structure model of prescription drugs sold online by drug retail enterprises was constructed by using analytic hierarchy process. Multiple rounds of risk research and judgment were carried out on 123 pairs of evaluation indicators by using Delphi expert survey method. The normalized weight calculation and consistency test of risk judgment matrix were carried out to perform fuzzy quantitative research. RESULTS The risk of prescription dispensing and review （6.48%）， the risk of drug first and prescription later （5.48%）， the risk of rational drug use guidance （4.99%）， the risk of buying drugs by abnormal channel （4.97%）， the risk of “first diagnosis， non-chronic disease and non-common disease” （4.43%）， and the quality and safety risk of returned drugs （4.34%） and the application risk of regulatory technology （4.06%） were high risks； the overall risk of drug retail enterprises （chain） selling prescription drugs online was 38.67%， and the overall risk of drug retail enterprises （individual） selling prescription drugs online was 61.33%， with a difference of 22.66% between them. CONCLUSIONS There were 7 high-risk indicators for prescription drugs sold online by drug retail enterprises. Among them， the risk of prescription dispensing and review， the risk of drug first and prescription later， and the risk of rational drug use guidance are the top three high-risk points. The risk of prescription drugs sold online by drug retail enterprises （individual） is higher than that of drug retail enterprises （chain）. It is recommended that regulatory authorities focus on and regulate the prescription drugs sold online by drug retail enterprises （individual）， and encourage drug retail enterprises （chain） to establish a systematic online sales process for prescription drugs； for high-risk points of prescription drugs sold online， it is recommended that regulatory authorities and drug retail enterprises focus on it and take effective risk prevention and control measures to ensure the safe use of prescription drugs by the general public.