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*

AIM To evaluate the chemical constituent consistency in formula granules and traditional decoctions of Gouteng Jiangya Formula.METHODS HPLC characteristic chromatograms were established,the analysis was performed on a 30 ℃ thermostatic YMC-Triart C18 column(4.6 mm× 250 mm,5 μm),with the mobile phase comprising of acetonitrile-0.2％phosphoric acid flowing at 1.0 mL/min in a gradient elution manner,and the detection wavelength was set at 240 nm.Puerarin was used as an internal standard to calculate the relative correction factors of 3'-methoxy puerarin,puerarin apioside,magnolflorine,paeoniflora,daidzin,baicalin,palmatine,berberine,wogonoside and benzoylpaeoniflorin,after which the content detemination was made by quantitative analysis of multi-components by single-marker(QAMS).RESULTS The characteristic chromatograms of 9 batches of formula granules and 15 bacthes of traditional decoctions demonstrated the similarities of more than 0.90 at the detection wavelengths of 192,210,240,260,280,300,320,360 nm,along with similar total peak areas.Eleven constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 97.27％-101.64％with the RSDs of 0.36％-1.11％,the result obtained by QAMS and external standard method demonstrated no significant differences(P＞0.05).The contents of various constituents in the formula granules approximated those in the traditional decoctions.CONCLUSION The consistent kinds and contents of various constituents are obversable in formula granules and traditional decoctions of Gouteng Jiangya Formula,which can provide a reference for the reasonable clinical application of this formula.

Severe open tibiofibular fractures account for approximately 28.1% of all open fractures. Among them, Gustilo-Anderson type IIIB/C fractures present significant clinical challenges due to associated bone and soft tissue defects, high infection rates, and risk of amputation. Inadequate preoperative assessment may lead to suboptimal emergency surgical planning or intraoperative complications. Historically, external fixation was often preferred, but this approach has been associated with limitations such as restricted joint mobility, delayed bone union, joint stiffness, and disuse osteoporosis, resulting in poor functional recovery. With advancements of debridement techniques, standardization of antibiotic use, and popularization of early soft tissue coverage, early internal fixation has gained broader acceptance. Nevertheless, controversies persist regarding the choice of fixation method, timing of definitive fixation, use of reamed versus unreamed intramedullary nailing, and necessity of fibular fixation. To standardize the diagnosis and early management of severe open tibiofibular fractures, reduce complication rates, and improve functional recovery, the Society of Microsurgery of the Chinese Medical Association organized a panel of domestic experts to develop the Evidence-based guideline for the diagnosis and early fixation of severe open tibiofibular fractures ( version 2025), using evidence-based methodology. The guidelines provided 12 recommendations covering diagnostic and early fixation strategies of severe open tibiofibular fractures, aiming to provide clinicians with scientifically grounded and standardized guidance.

Objective:To establish an ion chromatography-integrated amperometric detection method for iodide in water.Methods:After the water sample was filtered through a filter membrane, the AS 11-HC anion chromatography column of ion chromatography method was used to separate iodide ions under the conditions of 70 mmol/L sodium hydroxide solution as the eluent, injection volume of 100 μl, column temperature of 30 ℃, and flow rate of 1.0 ml/min. The results were determined by silver working electrode integral amperometric detection method. Under the optimized experimental conditions, methodological evaluations such as method calibration curves, detection limits, quantification limits, precision, and accuracy were conducted.Results:Iodide followed a square correction curve within the concentration range of 0 - 100 μg/L, with a correlation coefficient ( r) > 0.999 9. The detection limit of the method was 0.30 μg/L, and the quantification limit was 1.00 μg/L. The determination results of the national standard substances GBW09113f and GBW09114f for iodine composition analysis in water were within the reference range [(8.4 ± 1.2), (55 ± 6) μg/L]. The recovery rates of low, medium, and high concentration spiked samples with low background values ranged from 91.7% to 97.2%, and the relative standard deviation ranged from 0.40% to 1.60%. Conclusion:This method has the characteristics of simple water sample pretreatment, high sensitivity, and good accuracy, which can meet the determination of trace iodides in bulk water samples for iodine deficiency disorders monitoring.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

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.

Over 40%of critically ill patients will develop coagulopathy.Once critically ill patients are complicated with coagulopathy,the incidence of bleeding and mortality can increase by more than 4 times.Early identification of coagulopathy and accurate evaluation of coagulation function are essential for correcting coagulopathy as soon as possible.Therefore,Chinese Society of Thrombosis,Hemostasis and Critical Care,Chinese Medicine Education Association,together with Chinese People's Liberation Army Professional Committee of Critical Care Medicine updated the"Chinese expert consensus on standardized assessment of severe coagulopathy(2025 Edition)"on the basis of the"Consensus of Chinese experts on standardized evaluation of coagulation dysfunction in severe patients"formulated in 2022.This consensus includes four parts:classification and typing,etiology and mechanism,assessment methods,and diagnostic criteria of severe coagulopathy,with a total of 14 recommendations,aiming to provide corresponding guidance for clinical practice.

Over 40%of critically ill patients will develop coagulopathy.Once critically ill patients are complicated with coagulopathy,the incidence of bleeding and mortality can increase by more than 4 times.Early identification of coagulopathy and accurate evaluation of coagulation function are essential for correcting coagulopathy as soon as possible.Therefore,Chinese Society of Thrombosis,Hemostasis and Critical Care,Chinese Medicine Education Association,together with Chinese People's Liberation Army Professional Committee of Critical Care Medicine updated the"Chinese expert consensus on standardized assessment of severe coagulopathy(2025 Edition)"on the basis of the"Consensus of Chinese experts on standardized evaluation of coagulation dysfunction in severe patients"formulated in 2022.This consensus includes four parts:classification and typing,etiology and mechanism,assessment methods,and diagnostic criteria of severe coagulopathy,with a total of 14 recommendations,aiming to provide corresponding guidance for clinical practice.