BACKGROUND:Although previous studies have reported associations between lipids and the risk of osteoporotic pathological fractures,the specific causal relationships between lipid level and osteoporotic pathological fractures remain unclear.OBJECTIVE:To elucidate the causal relationship between lipids and osteoporotic pathological fractures using a two-sample bidirectional Mendelian randomization analysis.METHODS:The data for 178 lipid metabolites were obtained from the IEU-GWAS database(developed by the MRC Integrative Epidemiology Unit at the University of Bristol,UK,which provides extensive summary data from genome-wide association studies),while osteoporotic pathological fracture data(from 173 619 European participants)were acquired from the FinnGen database(constructed by the Finnish national gene research program,focusing on investigating relationships between genomics and health/disease in the Finnish population).Osteoporotic pathological fracture data were used as the outcome variable,with lipids serving as exposures,for the bidirectional Mendelian randomization study to evaluate the causal effects of different lipids on osteoporotic pathological fractures.The UK Biobank database was employed as a validation set by switching the outcome variable to verify the findings horizontally.RESULTS AND CONCLUSION:(1)The inverse variance weighted analysis indicated that each unit increase in sterol ester(27∶1/20∶2)levels was associated with a 25.55％increase in the risk of osteoporotic pathological fractures(odds ratio=1.256,95％confidence interval:1.001-1.575,P=0.049),suggesting a significant positive correlation between elevated sterol ester levels and increased fracture risk.Reverse Mendelian randomization analysis revealed a significant negative association between osteoporotic pathological fractures and three types of phosphatidylcholine.Horizontal validation yielded consistent results,confirming sterol ester as a risk factor for osteoporotic pathological fractures.(2)The results indicate that sterol ester is a risk factor for osteoporotic pathological fractures,while phosphatidylcholine serves as a protective factor.These findings strengthen the evidence supporting the effect of lipids on the risk of osteoporotic pathological fractures.Although the GWAS data used in this study were derived from European populations,given the broad commonality of human genetics,the results provide valuable reference significance for improving osteoporosis in Chinese populations through lipid regulation.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Diabetic cardiomyopathy （DCM） is one of the most common complications of diabetes mellitus and is a major threat to global health. As a key mechanism in the occurrence and progression of DCM， the inflammatory response persists throughout the entire course of the DCM. The Gaozhuo theory suggests that the basic pathogenesis of inflammatory injury in DCM is the Qi deficiency of spleen and kidney and Gaozhuo invasion， and divides the pathological process into three phases： Gaozhuo invasion， turbid heat damage to the channels， and turbid blood stasis and heat junction. Among them， the Qi deficiency of spleen and kidney and the endogenous formation of Gaozhuo represent the process of inflammatory factor formation induced by glucose metabolism disorders. Turbid heat damage to the channels refers to the process of myocardial inflammatory injury mediated by inflammatory factors， and turbid blood stasis and heat junction are the process of myocardial injury developing toward myocardial fibrosis and ventricular remodeling. As the disease continues to progress， it eventually develops into a depletion of the heart Yang， leading to the ultimate regression of heart failure. According to the theory of Gaozhuo， traditional Chinese medicine （TCM） should regulate inflammatory injury in DCM by strengthening the spleen and tonifying the kidney to address the root cause， and resolving dampness and lowering turbidity to treat the symptoms. If the turbidity has been stored for a long time and turns into heat， strengthening the spleen and tonifying the kidney， and clearing heat and resolving turbidity should be the therapy. If the turbidity， stasis， and heat are knotted in the heart and collaterals， strengthening the spleen and tonifying the kidney， and resolving stasis and lowering turbidity should be the therapy. TCM compounds and monomers can regulate the inflammatory response in DCM. TCM compounds can be divided into the categories for benefiting Qi to resolve turbidity， benefiting Qi and clearing heat to resolve turbidity， and benefiting Qi and activating blood to reduce turbidity. The compounds can inhibit upstream signals of inflammation and expression of inflammatory factors， improve the inflammatory damage to myocardium and blood vessels， myocardial fibrosis， and cardiac systole and diastole， and thus slow down the onset and progression of DCM.

Non-alcoholic fatty liver disease （NAFLD） is one of the most common complications of type 2 diabetes mellitus （T2DM）， and hepatic stellate cell （HSC） activation is the key link in the progression of NAFLD to liver fibrosis. According to the theory of "Gaozhuo"， spleen deficiency and Qi stagnation， along with Gaozhuo invasion， are the causes of NAFLD progression to liver fibrosis， which reveals the pathogenesis essence of HSC activation in traditional Chinese medicine （TCM）. Among them， spleen deficiency and Qi stagnation are the root causes of the endogenous formation of Gaozhuo. Spleen deficiency indicates the insulin sensitivity decrease and glucose metabolism disorders， and Qi stagnation means the dysregulation of hepatic glucose and lipid metabolism， which creates the preconditions for HSC activation. Gaozhuo invasion is the direct cause of HSC activation， including three stages： Internal retention of Gaozhuo， turbidity and stasis stagnation， and toxic stasis and consolidation. Internal retention of Gaozhuo refers to the abnormal metabolism and deposition of hepatic lipids， as well as the microcirculatory disorders. Turbidity and stasis stagnation is the process by which lipotoxicity stimulates the transformation of HSC into myofibroblast （MFB）， and toxic stasis and consolidation represent the secretion of a large amount of extracellular matrix （ECM） by MFB to promote the fibrosis. According to the theory of Gaozhuo and the activation process of HSC， syndromes for T2DM combined with NAFLD can be classified into spleen deficiency and Qi stagnation with internal retention of Gaozhuo， spleen Qi deficiency with turbidity and stasis stagnation， and spleen Qi deficiency with toxic stasis and consolidation. Clinically， the treatment principle is to strengthen the spleen and promote Qi， resolve turbidity， and eliminate blood stasis. Both TCM compounds and monomers can effectively inhibit the HSC activation. TCM compounds can be classified into categories for regulating spleen and harmonizing liver， resolving turbidity and removing stasis， and detoxifying and removing stasis. They mainly work by improving lipid metabolism， reducing lipid accumulation in the liver， alleviating inflammatory and oxidative stress responses， inhibiting the activation and proliferation of HSC， and reducing ECM deposition， thereby delaying the progression of liver fibrosis.

Objective:To investigate the main causes of failed endoscopic retrograde cholangiopancreatography (ERCP) in children and the remedial treatment strategies.Methods:This retrospective cohort study analyzed the clinical data of 21 children who experienced failed ERCP at Beijing Children′s Hospital, Capital Medical University between January 2021 and December 2024. Data was collected included demographic information, clinical diagnoses, and ERCP outcomes. The annually trend in the ERCP failure rate was analyzed. Patients were categorized by etiology into the following groups: chronic pancreatitis, post-surgical status, pancreatic trauma, pancreas divisum, non-neoplastic pancreatic lesions, and ulcerative colitis. The relationship between etiologies and ERCP failure was analyzed by Fisher exact test.Results:A total of 175 ERCP procedures were included, of which 21 procedures failed (12.0%). The failure rate decreased annually from 2021 to 2024: 18.2% (4/22), 13.2% (5/38), 11.4% (5/44), and 9.9% (7/71), respectively. The etiological distribution among the 21 failed cases was as follows: chronic pancreatitis 28.6% (6/21), pancreatic duct stenosis following pancreatic trauma 23.8% (5/21), post-surgical status 14.3% (3/21), pancreas divisum 9.5% (2/21), acute pancreatitis 9.5% (2/21), non-neoplastic pancreatic lesions 9.5% (2/21), and pancreatic duct stenosis associated with ulcerative colitis 4.8% (1/21). Remedial treatments included surgical intervention (2 cases), ultrasound-guided percutaneous drainage (3 cases), and repeat ERCP (2 cases). For post-surgical and pancreatic trauma patients, failure was primarily due to bile duct stenosis, pancreatic duct stenosis, or pancreaticobiliary duct disruption. Active surgical interventions such as cholangiojejunostomy and pancreaticojejunostomy, and ultrasound-guided drainage following ERCP failure led to significant clinical improvement.Conclusions:A stratified management approach guided by etiology is essential following failed ERCP in children. Early and active remedial treatment, particularly surgical interventions, is recommended for post-surgical and pancreatic trauma cases.

The promulgation of the Technical Specifications for Clinical Use of Blood (2025 Edition) signifies that China's clinical blood transfusion management has transitioned from mere technical operations to a new stage centered on patient blood management (PBM). Through an in-depth comparison of the new and old specifications, this paper analyzes the core transformations regarding conceptual reconstruction, legal alignment, technological upgrades, and closed-loop management. The new specifications establish PBM principles, reinforce legal safeguards for informed consent and emergency treatment, and construct a comprehensive, refined quality control system by specifying compatibility testing standards and introducing a post-transfusion evaluation system. Medical institutions should seize this opportunity to update management protocols and information systems, deepen multidisciplinary collaboration, and drive the profound transformation of clinical blood use from focusing solely on safety assurance to placing equal emphasis on science and value.

AIM:To investigate the effect of ranibizumab on blood flow density in different regions of the macula in patients with macular edema(ME)secondary to ischemic and non-ischemic branch retinal vein occlusion(BRVO).METHODS:This retrospective study enrolled patients with BRVO-ME who were treated at the hospital from September 2019 to March 2021. Patients were divided into ischemic and non-ischemic groups based on fundus findings. All patients received intravitreal injections of ranibizumab once monthly for three consecutive months. Best corrected visual acuity(BCVA), central macular thickness(CMT), and macular blood flow density were measured before treatment and at 1 d, 1 wk, 1 and 3 mo after treatment.RESULTS: A total of 46 patients(46 eyes)with BRVO-ME were included, comprising 21 eyes in the ischemic group(7 males, 14 females; mean age 55.81±10.36 y)and 25 eyes in the non-ischemic group(11 males, 14 females; mean age 54.84±9.81 y). At 3 mo after treatment, BCVA(LogMAR)in the non-ischemic group was superior to that in the ischemic group(0.19±0.19 vs 0.38±0.27, P=0.009). Analysis of CMT changes showed that the reduction amplitude in the ischemic group was significantly greater than that in the non-ischemic group at both 1 and 3 mo after treatment(all P<0.05). Blood flow densities in the whole, parafoveal, and perifoveal regions of the superficial capillary plexus(SCP), as well as in the whole and perifoveal regions of the deep capillary plexus(DCP), were significantly lower in ischemic patients than in non-ischemic patients, while blood flow density in the foveal region of DCP was significantly higher in the ischemic group(all P<0.05).CONCLUSION: Ranibizumab is effective for both types of patients. Non-ischemic patients have a better long-term visual prognosis, and the advantage may be related to better blood flow perfusion patterns in specific areas 3 mo after treatment. Monitoring changes in blood flow density in these areas can help provide personalized treatment for patients.

The American Heart Association (AHA) and the American College of Cardiology (ACC), in collaboration with multiple professional organizations, jointly released the "Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults" in August 2025. Based on the latest evidence-based medical findings from February 2015 to January 2025, the guideline proposes an individualized treatment strategy grounded in total cardiovascular disease risk stratification, incorporates the novel PREVENT risk assessment model, lowers the medication initiation threshold and control targets for high-risk populations, and provides specific management recommendations for special populations. This article provides an interpretation of these updates and conducts a comparative analysis with the current status of hypertension prevention and treatment in China as well as Chinese guidelines, aiming to offer reference for hypertension control practices in China.

The American Heart Association (AHA) officially released the "2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association" on January 27, 2025. This report systematically compiles the latest statistics on major cardiovascular diseases worldwide, while simultaneously integrating relevant outcome indicators, including quality of care, procedures, and economic costs, and updating the global prevalence patterns and evolving trends of diverse risk factors impacting cardiovascular health, providing essential guidance for the prevention, diagnosis, and treatment of cardiovascular diseases. Synthesizing insights from this pivotal report and other relevant studies, this article highlights key findings concerning the global prevalence and mortality of heart diseases, associated risk factors, and emerging diagnostic and therapeutic technologies.