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.

Background::Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.Methods::This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.Results::When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899–0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796–0.995) and 0.870 (95% CI, 0.769–0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.Conclusions::DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.

Vaccines are among the most effec-tive measures for preventing infectious diseases and play a crucial role in controlling the spread of these diseases.Adjuvants,serving as auxiliary com-ponents in vaccines,are indispensable in the vac-cine development process.Ideal adjuvants not only enhance the immune response,enabling the body to achieve optimal protective immunity but also play important roles in reducing the dosage of im-munogens and lowering vaccine production costs.To meet the demands of novel vaccines,many new types of adjuvants have been developed.However,there is still a lack of adjuvants that are safe,effec-tive,easy to prepare,highly pure,and suitable for a variety of vaccines in clinical settings.This article categorizes adjuvants and summarizes their mecha-nisms of action and characteristics,focusing on tra-ditional aluminum salt adjuvants and more modern lipid-based and nucleic acid-based adjuvants.The summary is based on a computer search of data-bases including PubMed,Embase,The Cochrane Li-brary,CNKI(China National Knowledge Infrastruc-ture),VIP Database,and Wanfang Database,using English search keywords such as Adjuvants,Vac-cine,Vaccine Adjuvant,aluminum salts,MF59,AS03,Toll-like receptor agonist,etc.,and corre-sponding Chinese search terms.The aim is to pro-vide references for the development and applica-tion of adjuvants.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-positive bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-positive bacteria from blood cultures in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:A total of 3 163 strains of Gram-positive pathogens were collected from 51 member units，and the top five bacteria were Staphylococcus aureus（ n=1 147，36.3%），coagulase-negative Staphylococci（ n=928，29.3%）， Enterococcus faecalis（ n=369，11.7%）， Enterococcus faecium（ n=296，9.4%）and alpha-hemolyticus Streptococci（ n=192，6.1%）. The detection rates of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）were 26.4%（303/1 147）and 66.7%（619/928），respectively. No glycopeptide and daptomycin-resistant Staphylococci were detected. The sensitivity rates of Staphylococcus aureus to cefpirome，rifampin，compound sulfamethoxazole，linezolid，minocycline and tigecycline were all >95.0%. Enterococcus faecium was more prevalent than Enterococcus faecalis. The resistance rates of Enterococcus faecium to vancomycin and teicoplanin were both 0.5%（2/369），and no vancomycin-resistant Enterococcus faecium was detected. The detection rate of MRSA in southern China was significantly lower than that in other regions（ χ2=14.578， P=0.002），while the detection rate of MRCNS in northern China was significantly higher than that in other regions（ χ2=15.195， P=0.002）. The detection rates of MRSA and MRCNS in provincial hospitals were higher than those in municipal hospitals（ χ2=13.519 and 12.136， P<0.001）. The detection rates of MRSA and MRCNS in economically more advanced regions（per capita GDP≥92 059 Yuan in 2022）were higher than those in economically less advanced regions（per capita GDP<92 059 Yuan）（ χ2=9.969 and 7.606， P=0.002和0.006）. Conclusions:Among the Gram-positive pathogens causing bloodstream infections in China， Staphylococci is the most common while the MRSA incidence decreases continuously with time；the detection rate of Enterococcus faecium exceeds that of Enterococcus faecalis. The overall prevalence of vancomycin-resistant Enterococci is still at a low level. The composition ratio of Gram-positive pathogens and resistant profiles varies slightly across regions of China，with the prevalence of MRSA and MRCNS being more pronounced in provincial hospitals and areas with a per capita GDP≥92 059 yuan.

AIM To investigate the effects of Moluodan Dami Pills on chronic atrophic gastritis(CAG)rats and their mechanism.METHODS The rat models were randomly divided into the model group,the low-dose group and high-dose Moluodan Dami Pills groups(2.43 g/kg and 4.86 g/kg),and vitamin A group(0.32 g/kg),following the 16 weeks successful induction of CAG by five-factor modeling method,in contrast to another 10 normal rats of the control group.After 8 weeks corresponding administration,the rats of each group had their general physiological status and pH value of gastric juice assessed;their pathological changes of gastric mucosa observed by naked eyes combined with HE staining;their changes of gastrin-secreting cells(G cells)and somatostatin-secreting cells(D cells)in gastric mucosa observed by immunohistochemistry;and their serum levels of pepsinogen Ⅰ/pepsinogen Ⅱ(PG Ⅰ/PG Ⅱ)ratio,TNF-α and IL-6 detected by ELISA.RESULTS Compared with the model group,the groups intervened with Moluodan Damei Pills and vitamin A displayed lower pH values of gastric juice(P＜0.05),improved pathological changes of gastric mucosa,increased G and D cells counts(P＜0.05,P＜0.01),increased ratio of serum PGⅠ/PGⅡ,and decreased levels of IL-6 and TNF-α(P＜0.05,P＜0.01).CONCLUSION Moluodan Dami Pills can effectively improve the symptoms of CAG rats through its influence on the number and secretion abilityof G and D cells,the levels of serum PG Ⅰ/PG Ⅱ ratio and inflammatory factors.

AIM To study the secondary metabolites from the endophytic fungi Candida sp.of Berberis atrocarpa Schneid.METHODS The ethyl acetate fraction and petroleum ether fraction from the secondary metabolites of Candida sp.fermentation extract were separated and purified by silica gel,Sephadex LH-20 and preparative liquid chromatography,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Eighteen compounds were isolated and identified as 1-phenyl-1,2-ethanediol(1),4-hydroxyphenethyl alcohol(2),4-hydroxybenzoic acid(3),4-hydroxyphenylacetic acid(4),3-hydroxyphenylacetic acid(5),3-methylsulfinyl propionic acid(6),phenylacetic acid(7),(S)-N-nitroso-1-amino-p-hydroxy phenylethanol(8),2-phenylacetamide(9),p-hydroxybenzaldehyde(10),ethyl 2-(4-hydroxyphenyl)acetate(11),dibutyl phthalate(12),5,5'-dimethoxybiphenyl-2,2'-diol(13),3-indolealdehyde(14),N-acetyl-L-phenylalanine(15),9-hydroxy-10E,12Z-octadecadienoic acid(16),9-hydroxy-10E,12E-octadecadienoic acid(17),(6E)-5-methylene-6-tetradecenoic acid(18).CONCLUSION Compounds 1,3-8 and 10-18 are isolated from Candida sp for the first time.

Objective:To analyze application value of magnetic resonance fat quantification(MRFQ)technique in assessing the therapeutic effect of modified Xiaochaihu decoction combined with silymarin capsules for patients with non-alcoholic fatty liver disease(NAfld).Methods:A total of 130 cases of NAfld patients admitted to Shuguang Hospital of Shanghai University of Traditional Chinese Medicine from October 2019 to September 2022 were selected,and they were randomly divided into a control group and an observation group,with 65 cases in each group according to the principle of 1:1 ratio.The control group was treated with silymarin capsules,and the observation group was treated with modified Xiao Chaihu decoction combined with silymarin capsules.Both two groups of patients were examined by FQD technique before and after treatment,and liver fat fraction(F)and proton-density fat fraction(PDFF)before and after treatment were compared between the two groups.The correlation between F value,FDFF value and fatty liver was further analyzed.Result:The total effective rate of the observation group was 96.92%(63/65),while that of the control group was 83.08%(54/65),with a statistically significant difference(x2=6.923,P<0.05).After treatment,the results of FQD technique examination indicated that 35 cases(53.85%)were normal,and 22 cases(33.85%)were mild fatty liver,and 8 cases(12.31%)were moderate fatty liver in 65 cases of observation group,and 24 cases(36.92%)were normal,and 24 cases(36.92%)were mild fatty liver,and 14 cases(21.54%)were moderate fatty liver,and 3 cases(4.62%)were severe fatty liver in 65 cases of control group.The difference of normal rate between the two groups was statistically significant(Z=8.755,P<0.05).After treatment,the body fat(BF),body fat rate(BFR),visceral fat area(VFA),body mass index(BMI)and waist hip ratio(WHR)in the observation group were lower than those in the control group,and the differences were statistically significant(t=6.092,3.991,4.733,5.437,2.413,P<0.05),respectively.After treatment,the F value and PDFF value in the observation group were lower than those in the control group,and the differences were statistically significant(t=9.577,6.589,P<0.05),respectively.With the increasing of the grades of fatty liver,the liver F-value and PDFF value of patients gradually increased.Spearson method was adopted to conduct correlation analysis,and the liver F-value and PDFF value appeared positive correlation with the grades of liver fat(r=0.618,0.648,P<0.05),respectively.Conclusion:FQD technique can find that the liver fat contents of NAfld patients significantly reduce after they receive the modified Xiaochaihu decoction combines with silymarin capsules.The FQD technique can corresponding evaluate the degree of disease condition,which can provide objective reference in evaluating the effect of clinical treatment.

Objective To explore the performance of routine blood test parameters,bone marrow parameters and the risk factors of leukemia conversion in higher-risk patients with myelodysplastic syndrome(MDS)treated with Bushen Jiedu Huayu Method in the real world.Methods The clinical data of 162 patients with higher-risk MDS who were admitted to the Department of Hematology,Xiyuan Hospital,China Academy of Chinese Medical Sciences from September 2017 to September 2022 were collected,and their clinical data,blood routine parameters,and bone marrow parameters were analyzed.Results A total of 162 higher-risk MDS patients were included,and the overall effective rate of the combination of traditional Chinese and Western medicine treatment,mainly using Bushen Jiedu Huayu Method being 48.8%.Patients with higher-risk MDS who were younger than 70 years old were more likely to obtain curative effect when treated with Bushen Jiedu Huayu Method combined with chemotherapy(P<0.05).After treatment with Bushen Jiedu Huayu Method,PLT levels in higher-risk MDS patients were significantly higher than those before treatment(P<0.05),and PLT levels in the ineffective group increased more significantly(P<0.05).After treatment,the HGB level in the effective group significantly increased(P<0.05).After treatment,the proportion of bone marrow granulocytes,megakaryocytes and lymphocytes in higher-risk MDS patients were significantly higher than those before treatment(P<0.05).Conclusion Bushen Jiedu Huayu Method,mainly using arsenic containing TCM compound,can treat higher-risk MDS.It can increase the HGB content and PLT level of patients,increase the proportion of bone marrow granulocytes,megakaryocytes and lymphocytes,and also play a certain role in reducing the proportion of bone marrow primitive cells,namely demethylation.

Objective:To investigate the incidence and risk factors of deep vein thrombosis(DVT)in patients with rheumatoid arthritis(RA).Methods:The clinical data of RA patients who were hospi-talized in the Department of Rheumatology and Immunology of Aerospace Center Hospital from May 2015 to September 2021 was retrospectively analyzed,including demographic characteristics,concomitant diseases,laboratory examinations(blood routine,biochemistry,coagulation,inflammatory markers,rheumatoid factor,antiphospholipid antibodies and lupus anticoagulant,etc.)and treatment regimens.The patients were compared according to the presence or absence of DVT,and the t test,Mann-Whitney U test or Chi-square test were applied to screen for relevant factors for DVT,followed by Logistic regres-sion analysis to determine risk factors for DVT in patients with RA.Results:The incidence of DVT in the RA patients was 9.6％(31/322);the median age of RA in DVT group was significantly older than that in non-DVT group[64(54,71)years vs.50(25,75)years,P＜0.001];the level of disease activity score using 28 joints(DAS28)-erythrocyte sedimentation rate(ESR)in DVT group was higher than that in non-DVT group[5.2(4.5,6.7)vs.4.5(4.5,5.0),P＜0.001];the incidence of hypertension,chronic kidney disease,fracture or surgery history within 3 months,and varicose veins of the lower ex-tremities in DVT group was higher than that in non-DVT group(P＜0.001).The levels of hemoglobin and albumin in DVT group were significantly lower than that in non-DVT group(P=0.009,P=0.004),while the D-dimer level and rheumatoid factor positive rate in DVT group were significantly higher than that in non-DVT group(P＜0.001).The use rate of glucocorticoid in DVT group was higher than that in non-DVT group(P=0.009).Logistic regression analysis showed that the age(OR=1.093,P＜0.001),chronic kidney disease(OR=7.955,P=0.005),fracture or surgery history with-in 3 months(OR=34.658,P=0.002),DAS28-ESR(OR=1.475,P=0.009),and the use of glu-cocorticoid(OR=5.916,P=0.003)were independent risk factors for DVT in RA patients.Conclu-sion:The incidence of DVT in hospitalized RA patients was significantly increased,in addition to tradi-tional factors,such as age and chronic kidney disease,increased DAS28-ESR level and the use of glu-cocorticoid were also independent risk factors for DVT.

Octapeptin has strong antibacterial activity against Gram-negative bacteria such as Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii, while it also has activity against some Gram-positive bacteria. This study used natural octapeptin A3 and B3 as lead compounds for structural modification. Twenty-one peptide derivatives (including A3 and B3) containing eight amino acid residues were prepared by solid-phase synthesis, and evaluated for antibacterial activity and renal cytotoxicity. Among them, three compounds 6, 7 and 17 exhibited broad-spectrum antibacterial activity and significantly enhanced the activity for Gram-positive bacteria while maintaining the activity of Gram-negative bacteria. Several compounds improved the activity for Pseudomonas aeruginosa. Compound 7 was active against all test strains and had relatively low renal cytotoxicity. The results provide a basis for the further development of novel polypeptide antibiotics.