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.

Bone Transplantation/adverse effects* ; Transplantation, Autologous/adverse effects*

Amorphous solid dispersion (ASD) is one of the most effective formulation approaches to enhance the water solubility and oral bioavailability of poorly water-soluble drugs. However, maintenance of physical stability of amorphous drug is one of the main challenges in the development of ASD. Crystallization is a process of nucleation and crystal growth. The nucleation is the key factor that influences the physical stability of the ASD. However, a theoretical framework to describe the way to inhibit the nucleation of amorphous drug is not yet available. We reviewed the methods and theories of nucleation for amorphous drug. Meanwhile, we also summarized the research progress on the mechanism of additives influence on nucleation and environmental factors on nucleation. This review aims to enhance the better understanding mechanism of nucleation of amorphous drug and controlling over the crystal nucleation during the ASD formulation development.

OBJECTIVE To study the effect of different doses of aspirin on clinical efficacy in acute stage of Kawasaki’s disease, and to explore the optimal dose of aspirin. METHODS A total of 150 patients suffered from Kawasaki’s disease were randomly selected by hospital information system from March to May 2022 for retrospective analysis. According to different doses of aspirin, they were divided into three groups: high dose group(>50 mg·kg−1·d−1), medium dose group(30−50 mg·kg−1·d−1) and low dose group(<30 mg·kg−1·d−1). The antipyretic time, the incidence of non-response to intravenous human immunoglobulin, the improvement of laboratory indexes and prevalence of adverse drug reaction were compared among the three groups. RESULTS There was no significant difference in body temperature recovery among the three groups under different doses of aspirin. There was no significant difference in patients with non-response to intravenous human immunoglobulin among the three groups. Before treatment, there were no significant differences in white blood cell(WBC) count, blood platelet(PLT) count and C-reactive protein(CRP) concentration among the three groups. After treatment, the count of WBC, PLT and CRP in the three groups was significantly improved compared with that before treatment, and the difference was statistically significant(P<0.05). However, there was no significant difference in the above indexes among the three groups after treatment. There was a higher incidence of adverse reactions in children treated with medium or high dose aspirin. CONCLUSION Different doses of aspirin combined with intravenous human immunoglobulin have good therapeutic effect on Kawasaki’s disease, but considering the safety and economy of aspirin, low dose administration is recommended.

Near infrared spectroscopy(NIRS)technology combined with chemometrics algorithms has been widely used in quantitative and qualitative analysis of food and medicine.However,traditional chemometrics methods,especially linear classification methods,often yield unsatisfactory results when addressing multi-class classification problems.Convolutional neural network(CNN)is adept at extracting deep-level features from data and suitable for handling non-linear relationships.The modeling performance of CNN depends on the size and diversity of sample,while the collection and preprocessing of NIRS sample data is often time-consuming and labor-intensive.This study proposed a NIRS qualitative analysis method based on data augmentation strategies and CNN.The data augmentation strategy included two steps.Firstly,applying Bootstrap resampling and generative adversarial network(GAN)methods to augment three NIRS datasets(Medicine,coffee and grape).Secondly,combining the original samples(Y)with the Bootstrap augmented samples(B)and GAN augmented samples(G)to obtain three augmented datasets(Y-B,Y-G and Y-B-G).Based on this,a CNN model structure suitable for these datasets was designed,consisting of 2 one-dimensional convolutional layers,1 max-pooling layer,and 1 fully connected layer.The results showed that compared to the optimal models of partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and back propagation neural network(BP),the CNN model based on Y-B dataset achieved average accuracy improvements of 3.998%,9.364%,and 4.689%for medicine(Binary classification);the CNN model based on the Y-B-G dataset achieved average accuracy improvements of 6.001%,2.004%,and 7.523%for coffee(7-class classification);and the CNN model based on the Y-B dataset achieved average accuracy improvements of 33.408%,51.994%,and 34.378%for grapes(20-class classification).It was evident that the models established based on data augmentation strategies and CNN demonstrated better classification accuracy and generalization performance with different datasets and classification categories.

Objective:To investigate the safety and therapeutic effect of split liver transplantation (SLT) in clinical application.Methods:This is a retrospective case-series study. The clinical data of 203 consecutive SLT, 79 living donor liver transplantation (LDLT) and 1 298 whole liver transplantation (WLT) performed at the Third Affiliated Hospital of Sun Yat-sen University from July 2014 to July 2023 were retrospectively analyzed. Two hundred and three SLT liver grafts were obtained from 109 donors. One hundred and twenty-seven grafts were generated by in vitro splitting and 76 grafts were generated by in vivo splitting. There were 90 adult recipients and 113 pediatric recipients. According to time, SLT patients were divided into two groups: the early SLT group (40 cases, from July 2014 to December 2017) and the mature SLT technology group (163 cases, from January 2018 to July 2023). The survival of each group was analyzed and the main factors affecting the survival rate of SLT were analyzed. The Kaplan-Meier method and Log-rank test were used for survival analysis.Results:The cumulative survival rates at 1-, 3-, and 5-year were 74.58%, 71.47%, and 71.47% in the early SLT group, and 88.03%, 87.23%, and 87.23% in the mature SLT group, respectively. Survival rates in the mature SLT group were significantly higher than those in the early SLT group ( χ2=5.560, P=0.018). The cumulative survival rates at 1-, 3- and 5-year were 93.41%, 93.41%, 89.95% in the LDLT group and 87.38%, 81.98%, 77.04% in the WLT group, respectively. There was no significant difference among the mature SLT group, the LDLT group and the WLT group ( χ2=4.016, P=0.134). Abdominal hemorrhage, infection, primary liver graft nonfunction,and portal vein thrombosis were the main causes of early postoperative death. Conclusion:SLT can achieve results comparable to those of WLT and LDLT in mature technology liver transplant centers, but it needs to go through a certain time learning curve.

Objective To investigate the nutritional status of children with Crohn's Disease (CD) at diagnosis and its association with clinical characteristics. Methods A retrospective analysis was performed for the clinical data and nutritional status of 118 children with CD who were admitted to Beijing Children's Hospital,Capital Medical University,from January 2016 to January 2024. A multivariate logistic regression analysis was used to investigate the risk factors for malnutrition. Results A total of 118 children with CD were included,among whom there were 68 boys (57.6％) and 50 girls (42.4％),with a mean age of (11±4) years. Clinical symptoms mainly included recurrent abdominal pain (73.7％,87/118),diarrhea (37.3％,44/118),and hematochezia (32.2％,38/118),and 63.6％ (75/118) of the children had weight loss at diagnosis. The incidence rate of malnutrition was 63.6％ (75/118),and the children with moderate or severe malnutrition accounted for 67％ (50/75). There were 50 children (42.4％) with emaciation,8 (6.8％) with growth retardation,and 9 (7.6％) with overweight or obesity. Measurement of nutritional indices showed a reduction in serum albumin in 83 children (70.3％),anemia in 74 children (62.7％),and a reduction in 25 hydroxyvitamin D in 15 children (60％,15/25). The children with malnutrition had significantly higher disease activity,proportion of children with intestinal stenosis,and erythrocyte sedimentation rate and a significant reduction in serum albumin (P<0.05). The multivariate logistic regression analysis showed that intestinal stenosis was an independent risk factor for malnutrition in children with CD (OR=4.416,P<0.05). Conclusions There is a high incidence rate of malnutrition in children with CD at diagnosis,which is associated with disease activity and disease behavior. The nutritional status of children with CD should be closely monitored.

Objective To detect the expression changes of interleukin-10(IL-10)and transforming growth factor-β1(TGF-β1)during the development of deep vein thrombosis in mice,and to explore the application value of them in thrombus age estimation.Methods The mice in the experimental group were subjected to ligation of inferior vena cava.The mice were sacrificed by excessive anesthesia at 1 d,3 d,5 d,7 d,10 d,14 d and 21 d after ligation,respectively.The inferior vena cava segment with thrombosis was extracted below the ligation point.The mice in the control group were not ligated,and the inferior vena cava segment at the same position as the experimental group was extracted.The ex-pression changes of IL-10 and TGF-β1 were detected by immunohistochemistry(IHC),Western blot-ting and real-time qPCR.Results IHC results revealed that IL-10 was mainly expressed in monocytes in thrombosis and TGF-β1 was mainly expressed in monocytes and fibroblast-like cells in thrombosis.Western blotting and real-time qPCR showed that the relative expression levels of IL-10 and TGF-β1 in each experimental group were higher than those in the control group.The mRNA and protein levels of IL-10 reached the peak at 7 d and 10 d after ligation,respectively.The mRNA expression level at 7 d after ligation was 4.72±0.15 times that of the control group,and the protein expression level at 10 d after ligation was 7.15±0.28 times that of the control group.The mRNA and protein levels of TGF-β1 reached the peak at 10 d and 14 d after ligation,respectively.The mRNA expression level at 10 d after ligation was 2.58±0.14 times that of the control group,and the protein expression level at 14 d after ligation was 4.34±0.19 times that of the control group.Conclusion The expressions of IL-10 and TGF-β1 during the evolution of deep vein thrombosis present time-dependent sequential changes,and the expression levels of IL-10 and TGF-β1 can provide a reference basis for thrombus age estimation.

Objective:To investigate the safety and therapeutic effect of split liver transplantation (SLT) in clinical application.Methods:This is a retrospective case-series study. The clinical data of 203 consecutive SLT, 79 living donor liver transplantation (LDLT) and 1 298 whole liver transplantation (WLT) performed at the Third Affiliated Hospital of Sun Yat-sen University from July 2014 to July 2023 were retrospectively analyzed. Two hundred and three SLT liver grafts were obtained from 109 donors. One hundred and twenty-seven grafts were generated by in vitro splitting and 76 grafts were generated by in vivo splitting. There were 90 adult recipients and 113 pediatric recipients. According to time, SLT patients were divided into two groups: the early SLT group (40 cases, from July 2014 to December 2017) and the mature SLT technology group (163 cases, from January 2018 to July 2023). The survival of each group was analyzed and the main factors affecting the survival rate of SLT were analyzed. The Kaplan-Meier method and Log-rank test were used for survival analysis.Results:The cumulative survival rates at 1-, 3-, and 5-year were 74.58%, 71.47%, and 71.47% in the early SLT group, and 88.03%, 87.23%, and 87.23% in the mature SLT group, respectively. Survival rates in the mature SLT group were significantly higher than those in the early SLT group ( χ2=5.560, P=0.018). The cumulative survival rates at 1-, 3- and 5-year were 93.41%, 93.41%, 89.95% in the LDLT group and 87.38%, 81.98%, 77.04% in the WLT group, respectively. There was no significant difference among the mature SLT group, the LDLT group and the WLT group ( χ2=4.016, P=0.134). Abdominal hemorrhage, infection, primary liver graft nonfunction,and portal vein thrombosis were the main causes of early postoperative death. Conclusion:SLT can achieve results comparable to those of WLT and LDLT in mature technology liver transplant centers, but it needs to go through a certain time learning curve.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.