Objective To compare the mid- and long-term clinical results of mitral valve plasty (MVP) and mitral valve replacement (MVR) in the treatment of functional mitral regurgitation (FMR). Methods Patients with FMR who underwent surgical treatment in the Department of Cardiovascular Surgery of the General Hospital of Northern Theater Command from 2012 to 2021 were collected. The patients who underwent MVP were divided into a MVP group, and those who underwent MVR into a MVR group. The clinical data and mid-term follow-up efficacy of two groups were compared. Results Finally 236 patients were included. There were 100 patients in the MVP group, including 53 males and 47 females, with an average age of (61.80±8.03) years. There were 136 patients in the MVR group, including 72 males and 64 females, with an average age of (61.29±8.97) years. There was no statistical difference in baseline data between the two groups (P＞0.05). There was no statistical difference between the two groups in the extracorporeal circulation time, aortic occlusion time, postoperative hospital and ICU stay, intraoperative blood loss, or hospitalization death (P＞0.05), but the time of mechanical ventilation in the MVP group was significantly shorter than that in the MVR group (P=0.022). The total follow-up rate was 100.0%, the longest follow-up was 10 years, and the average follow-up time was (3.60±2.55) years. There were statistical differences in the left atrial diameter, left ventricular end-diastolic diameter, left ventricular end-systolic diameter and cardiac function between the two groups compared with those before surgery (P<0.05). The postoperative left ventricular ejection fraction in the MVP group was statistically higher than that before surgery (P=0.002), but there was no statistical difference in the MVR group before and after surgery (P=0.658). The left atrial diameter in the MVP group was reduced compared with the MVR group (P=0.026). The recurrence rate of mitral regurgitation in the MVP group was higher than that in the MVR group, and the difference was statistically significant (10.0% vs. 1.5%, P=0.003). There were 14 deaths in the MVP group and 19 in the MVR group. The cumulative survival rate (P=0.605) and cardiovascular events-free survival rate (P=0.875) were not statistically significant between the two groups by Kaplan-Meier survival analysis. Conclusion The safety, and mid- and long-term clinical efficacy of MVP in the treatment of FMR patients are better than MVR, and the left atrial and left ventricular diameters are statistically reduced, and cardiac function is statistically improved. However, the surgeon needs to be well aware of the indications for the MVP procedure to reduce the rate of mitral regurgitation recurrence.

Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

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 analyze the relationship between body mass index(BMI) during pre pregnancy, maternal lipid levels during early pregnancy and childhood overweight and obesity, as well as the mediating role of maternal lipid levels during early pregnancy in pre pregnancy BMI and childhood overweight and obesity, providing scientific evidence for developing obesity prevention strategies in preschool children. Methods: Using data from Peking University Birth Cohort in Tongzhou (PKUBC-T) collected between June 2018 and September 2022, the study included 1 292 mother-child pairs. Participants were stratified into two groups based on children s BMI Z scores at age 3: an overweight/obesity risk group (BMI Z >1, n =173) and a non overweight/obesity risk group (BMI Z ≤1, n =1 119).Multivariate Logistic regression was conducted to analyze the associations between pre pregnancy BMI, maternal lipid levels[total cholesterol(TC),triglyceride（TG）,high density lipoprotein cholesterol(HDL-C),low density lipoprotein cholesterol(LDL-C),TC/HDL-C,TG/HDL-C,LDL-C/HDL-C] during early pregnancy and childhood overweight and obesity. The mediating effect of maternal lipid levels during early pregnancy on pre pregnancy BMI and childhood overweight and obesity was further explored. Results: There were statistically significant differences in pregnancy BMI levels, early pregnancy blood LDL-C ,TC/HDL-C,LDL-C/HDL-C levels between the overweight and obesity risk group and the non overweight and obesity risk group ( χ 2/Z =19.01, 2.48, 2.48, 2.71, all P <0.05). Multivariate Logistic regression analysis showed that pre pregnancy BMI, LDL-C, TC/HDL-C and LDL-C/HDL-C in early pregnancy were significantly associated with childhood overweight and obesity ( OR =1.09, 1.42, 1.49, 1.60, all P <0.05). LDL-C, TC/HDL-C and LDL-C/HDL-C in early pregnancy played a significant mediating role on pre pregnancy BMI and childhood obesity and the mediating effects accounted for 7.3%, 10.2%, 23.5% of the total effects, respectively (all P <0.05). Conclusions Maternal hyperlipidemia during early pregnancy partially mediated the association between pre pregnancy obesity and childhood obesity. Both pre pregnancy obesity and maternal hyperlipidemia during early pregnancy are risk factors for obesity in preschool children.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

Myelodysplastic syndrome (MDS), a myeloid tumor derived from the malignant clones of hematopoietic stem cells, has an annually increasing incidence. The contemporary research direction has shifted to analyzing the synergistic effect of immune surveillance collapse and abnormal bone marrow microenvironment in the pathological process of MDS. Against this backdrop, the immune checkpoint molecule TIM3 has emerged as a key target because of its persistently high expression on the surface of important immune cells such as T and NK cells. The abnormal activation of the TIM3 pathway is the mechanism by which solid tumors and hematological malignancies achieve immune escape and is a key hub in the formation of immune exhaustion phenotypes. This work integrates the original discoveries of our team with the latest international progress, systematically demonstrating the bidirectional regulatory network of TIM3 between the malignant clone proliferation of MDS and the immunosuppressive microenvironment. Integrating the evidence from emerging clinical trials allows us to consider the clinical significance of TIM3-targeted blocking for MDS, providing a transformative path to overcome the resistance of traditional treatments and marking a new chapter in the active immune reconstitution of MDS treatment.

ObjectiveTo investigate the alleviating effect of calcium cyanamide （CaCN₂） soil fumigation on replanting problems of Rehmannia glutinosa. MethodsNewly soil （NP） was used as the control group， while three treatment groups were established： replanted soil （RP）， newly soil treated with CaCN₂ （120 g·m²， tillage depth 25 cm） （NPCC）， and replanted soil treated with CaCN₂ （RPCC）. R. glutinosa was cultivated in all groups. At harvest， the tuber agronomic traits （number of enlarged roots， maximum root diameter， fresh weight， dry weight） were measured. The content of catalpol and rehmannioside D was quantified by ultra-high-performance liquid chromatography （UPLC） to evaluate medicinal quality. Rhizosphere soil available nutrients and enzyme activities were analyzed by assay kits. The community structure and composition of fungi and bacteria in rhizosphere soil were assessed via internal transcribed spacer 2 （ITS2） sequencing and 16S rDNA sequencing， respectively. ResultsCompared with NP， the RP group showed obviously reduced in tuber agronomic traits and quality indicators （P0.05）. However， the RPCC group showed significant improvement in agronomic traits and a notable increase in rehmannioside D content compared to RP （P0.05）. The contents of available phosphorus and potassium in RPCC and NP groups were obviously lower than those in RP （P0.05）. The polyphenol oxidase soil （S-PPO） activity in RP was obviously lower than in NP （P0.05）， while sucrose soil （S-SC）， acid phosphatase soil （S-ACP）， and S-PPO activities in RPCC were obviously higher than in RP （P0.05）. Microbial richness and diversity in RP were obviously higher than in NP （P0.05）， whereas no significant differences were observed between the RPCC and NP. The relative abundances of fungal genera Nectria， Myrothecium， Tomentella， and bacterial genus Skermanella were obviousl lower in RPCC and NP than in RP （P0.05）. Correlation analysis that S-ACP activity was positively correlated with the content of rehmannioside D （P0.05）. Fungal genera Engyodontium and Alternaria， and bacterial genera Pir4 lineage， Pirellula， Methyloversatilis， Brevundimonas， Ralstonia， and Acidibacter were obviously positively correlated with tuber dry weight （P0.05）. Conversely， fungal genera Pseudaleuria， Nectria， Haematonectria， Ceratobasidium， and bacterial genera Streptomyces， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated with dry weight （P0.05）. The fungal genus Alternaria and bacterial genera Brevundimonas， Ralstonia， Acidibacter， and Dongia showed positive correlations with medicinal quality of R.glutinosa tuber， while fungal genera Pseudaleuria， Nectria， Stachybotrys， Fusarium， Gibberella， Ceratobasidium， and bacterial genera Sphingomonas， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated （P0.05）. ConclusionCaCN₂ soil fumigation can significantly improve enzyme activities in replanted Rehmannia rhizosphere soil， enhance the utilization of available nutrients， reshape microbial community structure of replanted R.glutinosa at the family and genus level， and notably improve tuber agronomic traits and medicinal quality. This study provides a novel approach to alleviating replanting problems and offers insights for the integrated development of standardized cultivation techniques， including soil disinfection， nutrient-targeted regulation， and microbial inoculant application.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

In order to explore the possible role and molecular mechanism of the combined action of leech and bear bile in liver and gallbladder diseases, this study first used network pharmacology methods to screen the components and targets of leech and bear bile, as well as the related target genes of liver and gallbladder diseases. The selected key genes were subjected to interaction network and GO/KEGG enrichment analysis. Then, using sodium oleate induced HepG2 cell lipid deposition model and DL-ethionine induced mouse fatty liver model, the activity of leech and bear bile alone and in combination in reducing liver fat was evaluated in vitro and in vivo, and the expression of key pathway related proteins suggested by network pharmacology was detected by Western blot. The results of network pharmacology analysis showed that the active ingredients of leech and bear bile have 295 intersecting targets with liver and gallbladder related diseases, involving more than 200 signaling pathways, including the PI3K/Akt signaling pathway and phospholipase D signaling pathway closely related to glucose and lipid metabolism. The results of in vitro validation experiments showed that both leech and bear bile, alone and in combination, can significantly inhibit the lipid deposition induced by sodium oleate in human liver cells, reduce the triacylglycerol level in cell culture supernatant, and inhibit the lipid content in liver cells. The observation results of Nile red staining confocal microscopy showed that the combination of leech and bear bile had better activity in reducing lipid deposition in liver cells compared to using them alone. In a mouse fatty liver model, the combination of leech and bear bile can better reduce elevated organ indices, blood lipids, and liver lipid levels, as well as lower the levels of serum liver injury biomarkers. The animals used in this experiment and related disposal meet the requirements of animal welfare. Before the experiment, it was reviewed and approved by IACUC, Institute of Materia Medica, Chinese Academy of Medical Sciences. The Western blot experiment results showed that the combination of leech and bear bile can significantly upregulate the expression levels of p-PI3K and p-Akt proteins, and increase the p-PI3K/PI3K and p-Akt/Akt ratios, which is consistent with the predicted results of network pharmacology. The combination of leech and bear bile has great potential for treating fatty liver disease, and activating the PI3K/Akt pathway may be one of the important mechanisms for reducing lipid deposition in liver cells.