Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective: To explore the bidirectional causal relationship between 731 immune cell phenotypes and recurrent aphthous ulcers (RAU) using Mendelian randomization (MR). Methods: A two-sample bidirectional MR study was conducted using publicly available genome-wide association study (GWAS) summary statistics for 731 immune cell phenotypes and the RAU GWAS summary data from the FinnGen consortium. The inverse-variance weighted (IVW) method was used as the primary analysis tool, with supplementary analyses including the weighted median (WM) method, MR-Egger regression, weighted mode, and simple mode. Sensitivity analyses were conducted using Cochran’s Q test, the mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method for detecting pleiotropy and outliers, and leave-one-out cross-validation. Furthermore, differential analysis was performed using a clinical cohort dataset from the Gene Expression Omnibus (GEO) to further validate the MR results. Results: In the forward MR analysis, 731 immune cell phenotypes were considered as exposures and RAU as the outcome. Among them, 52 immune cell phenotypes showed a significant causal effect on RAU (P<0.05). After false discovery rate (FDR) correction, two immune phenotypes remained significantly associated with RAU risk: with increased monocyte-derived myeloid suppressor cells (M-MDSC) (OR = 1.06; 95% CI: 1.03-1.09) and CD33 on granulocytic myeloid-derived suppressor cells (G-MDSC) (OR = 1.06; 95% CI: 1.03-1.09), the risk of RAU also increased. In reverse MR, RAU was found to have a significant causal effect on two immune cell phenotypes (P<0.05), but no significant effects were found after FDR correction. Sensitivity analysis showed no significant heterogeneity between SNPs (P>0.05). Differential analysis of the GEO dataset revealed that the characteristic genes of myeloid-derived suppressor cells (MDSC) (CTBS, IPMK, and UBA3) were significantly upregulated in RAU (P<0.05). Conclusion The MR results of 731 immune cell phenotypes suggest that M-MDSC and CD33 molecules on G-MDSC may be risk factors for RAU development. The clinical GEO dataset further validated that MDSC may play a role in RAU, while RAU did not show a significant causal association with the 731 immune cell phenotypes.

OBJECTIVE To evaluate the quality of evidence in the systematic evaluation/meta-analysis of traditional Chinese medicine （TCM） for diabetes retinopathy （DR） based on the GRADE system. METHODS Chinese and English databases were searched to obtain the relevant studies of systematic evaluation/meta-analysis of traditional Chinese medicine in the treatment of DR. The search time was from the establishment of each database to January 13th， 2024. According to the inclusion and exclusion criteria， literature screening was conducted. After extracting relevant information from the included literature， the GRADE system was used to evaluate the quality level of the evidence body in the included studies， and the evidence of the outcome indicators was integrated and summarized. RESULTS A total of 51 studies were ultimately included， encompassing 135 outcome indexes. Among these， 19 indicators （14.1%） were of high quality， 87 （64.4%） were of medium quality， 26 （19.3%） were of low quality， and 3 （2.2%） were of very low quality. Overall， the evidence quality of the outcome indicators in the included studies was medium to low quality. The integrated results of evidence on the efficacy of outcome indexes showed that compared with conventional Western medicine， calcium dobesilate or placebo， TCM had significant advantages in improving overall efficacy， reducing bleeding spot area， reducing macular foveal thickness， and increasing visual improvement rate. In addition，the combination of TCM and conventional Western medicine or calcium dobesilate was significantly more effective than using conventional Western medicine or calcium dobesilate alone. CONCLUSIONS The overall quality of the evidence in the systematic evaluation/meta-analysis study on the treatment of DR with TCM is medium to low quality. Based on existing research findings， TCM demonstrates good clinical efficacy in the treatment of DR.

The traditional Chinese medicine （TCM） myocardial infarction （MI） unit is a standardized， regulated， and continuous integrated care unit guided by TCM theory and built upon existing chest pain centers or emergency care units. This unit emphasizes multidisciplinary collaboration and forms a restructured clinical entity without altering current departmental settings， offering comprehensive diagnostic and therapeutic services with full participation of TCM in the treatment of MI. Its core medical model is patient-centered and disease-focused， providing horizontally integrated TCM-based care across multiple specialties and vertically constructing a full-cycle treatment unit for MI， delivering prevention， treatment， and rehabilitation during the acute， stable， and recovery phases. Additionally， the unit establishes a TCM-featured education and prevention mechanism for MI to guide patients in proactive health management， reduce the incidence of myocardial infarction， and improve quality of life.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

OBJECTIVE: To investigate the effect of miR-483-5p on hepatocellular carcinoma (HCC) cells proliferation and stemness, as well as the attenuating effect of Pien Tze Huang (PZH). METHODS: Differentially expressed miRNA between HepG2 cells and hepatic cancer stem-like cells (HCSCs) were identified by a miRNA microarray assay. miR-483-5p mimics were transfected into HepG2 cells to explore the effects of miR-483-5p on cell proliferation and stemness. HepG2 cells and HCSCs were treated with PZH (0, 0.25, 0.50 and 0.75 mg/mL) to explore the effects of PZH on the proliferation and stemness, both in non-induced state and the state induced by miR-483-5p mimics. RESULTS: miR-483-5p was significantly up-regulated in HCSCs and its overexpression increased cell proliferation and stemness in HepG2 cells (P<0.05). PZH not only significantly inhibited proliferation in HepG2 cells, but also significantly suppressed the cell proliferation and self-renewal of HCSCs (P<0.05). The effects of miR-483-5p mimics on proliferation and stemness of HepG2 cells were partially abolished by PZH. CONCLUSIONS miR-483-5p promotes proliferation and enhances stemness of HepG2 cells, which were attenuated by PZH, demonstrating that miR-483-5p is a potential molecular target for the treatment of HCC and provide experimental evidence to support clinical use of PZH for patients with HCC.
Humans ; MicroRNAs/metabolism* ; Cell Proliferation/drug effects* ; Liver Neoplasms/drug therapy* ; Carcinoma, Hepatocellular/drug therapy* ; Hep G2 Cells ; Neoplastic Stem Cells/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Gene Expression Regulation, Neoplastic/drug effects*