ObjectiveAge-related macular degeneration （AMD） is one of the leading causes of low vision and blindness in people over 50 years old， and dry AMD （dAMD） is one type for which there is currently no clear treatment. On the basis of the diagnosis and clinical characteristics of dAMD in traditional Chinese and Western medicine， this paper evaluated the fitting degrees of existing animal models of dAMD with clinical characteristics according to the evaluation methods of animal models， and put forward suggestions and prospects. MethodsLiterature on animal models of dAMD was searched against database， and the characteristics of the models were assigned according to the diagnosis criteria of diseases and syndromes of traditional Chinese and Western medicine， and the fitting degrees of the models with clinical characteristics were analyzed and evaluated. ResultsAt present， the animal models of dAMD are mainly established targeting complement factors， chemokines， oxidative damage， lipid/glucose metabolism， and natural strains. Most of the models can simulate the major pathological changes of dAMD， showing the fitting degree of 25%-50% with clinical characteristics in Western medicine. However， the evaluation of traditional Chinese medicine （TCM） syndromes， especially the evaluation of secondary syndromes， is missing， and the models present low fitting degrees with the clinical characteristics in TCM. ConclusionExisting animal models of dAMD are mostly established under the guidance of Western diagnostic standards， which reproduce the main disease characteristics of Western medicine and lack observation of TCM syndromes. Future studies can pay attention to the intervention factors and evaluation systems of spleen deficiency Qi deficiency and liver-kidney Yin deficiency syndrome and build the animal model of dAMD with integration of disease and syndrome based on clinical characteristics of traditional Chinese and Western medicine.

ObjectiveAge-related macular degeneration （AMD） is one of the leading causes of low vision and blindness in people over 50 years old， and dry AMD （dAMD） is one type for which there is currently no clear treatment. On the basis of the diagnosis and clinical characteristics of dAMD in traditional Chinese and Western medicine， this paper evaluated the fitting degrees of existing animal models of dAMD with clinical characteristics according to the evaluation methods of animal models， and put forward suggestions and prospects. MethodsLiterature on animal models of dAMD was searched against database， and the characteristics of the models were assigned according to the diagnosis criteria of diseases and syndromes of traditional Chinese and Western medicine， and the fitting degrees of the models with clinical characteristics were analyzed and evaluated. ResultsAt present， the animal models of dAMD are mainly established targeting complement factors， chemokines， oxidative damage， lipid/glucose metabolism， and natural strains. Most of the models can simulate the major pathological changes of dAMD， showing the fitting degree of 25%-50% with clinical characteristics in Western medicine. However， the evaluation of traditional Chinese medicine （TCM） syndromes， especially the evaluation of secondary syndromes， is missing， and the models present low fitting degrees with the clinical characteristics in TCM. ConclusionExisting animal models of dAMD are mostly established under the guidance of Western diagnostic standards， which reproduce the main disease characteristics of Western medicine and lack observation of TCM syndromes. Future studies can pay attention to the intervention factors and evaluation systems of spleen deficiency Qi deficiency and liver-kidney Yin deficiency syndrome and build the animal model of dAMD with integration of disease and syndrome based on clinical characteristics of traditional Chinese and Western medicine.

ObjectiveTo establish a new noninvasive， simple， and convenient clinical predictive model by identifying independent predictive factors for rebleeding after endoscopic therapy in cirrhotic patients with esophagogastric variceal bleeding （EGVB）， and to provide a basis for individualized risk assessment and development of clinical intervention strategies. MethodsCirrhotic patients with EGVB who were diagnosed and treated in The First Affiliated Hospital of Xi’an Medical University from September 2018 to October 2023 were enrolled as subjects， and according to whether the patient experienced rebleeding within 1 year after endoscopic therapy， they were divided into rebleeding group with 93 patients and non-rebleeding group with 84 patients. Clinical data were collected and analyzed. The independent samples t-test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. A Logistic model was established based on the results of the univariate and multivariate analyses， and the receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the accuracy of the model. R software was used to visualize the model by plotting a nomogram， and the Bootstrap method was used for internal validation of the model. ResultsThe multivariate analysis showed that red blood cell count （RBC）， cholinesterase （ChE）， alkaline phosphatase （ALP）， albumin （Alb）， thrombin time （TT）， portal vein trunk diameter， sequential therapy， and primary prevention were independent predictive factors for rebleeding. Based on the results of the multivariate analysis， a logistic model was established as logit（P）=-0.805-1.978×（RBC）+0.001×（ChE）-0.020×（ALP）-0.314×（Alb）+0.567×（TT）+0.428×（portal vein trunk diameter）-2.303×［sequential therapy （yes=1， no=0）］-2.368×［primary prevention （yes=1， no=0）］. The logistic model （AUC=0.928， 95% confidence interval ［CI］： 0.893—0.964， P<0.001） had a better performance in predicting rebleeding than MELD score （AUC=0.603， 95%CI： 0.520—0.687， P=0.003）， Child-Pugh class （AUC=0.650， 95%CI： 0.578—0.722， P=0.001）， and FIB-4 index （AUC=0.587， 95%CI： 0.503—0.671， P=0.045）. The model had an optimal cut-off value of 0.607， a sensitivity of 0.817， and a specificity of 0.817. Internal validation confirmed that the model had good predictive performance and accuracy. ConclusionSequential therapy， implementation of primary prevention， an increase in RBC， and an increase in Alb are protective factors against rebleeding， while prolonged TT and widened main portal vein diameter are risk factors. The logistic model based on these independent predictive factors can predict rebleeding and thus holds promise for clinical application.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project "Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.
Stomach Neoplasms/genetics* ; Humans ; Precision Medicine/methods* ; China ; Immunotherapy/methods*

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Although clinical evidence suggests that nonalcoholic fatty liver disease is an established major risk factor for heart failure, it remains unexplored whether sleep disorder-caused hepatic damage contributes to the development of cardiovascular disease (CVD). Here, our findings revealed that sleep fragmentation (SF) displayed notable hepatic detrimental phenotypes, including steatosis and oxidative damage, along with significant abnormalities in cardiac structure and function. All these pathological changes persisted even after sleep recovery for 2 consecutive weeks or more, displaying memory properties. Mechanistically, persistent higher expression of nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) in the liver was the key initiator of SF-accelerated damage phenotypes. SF epigenetically controlled the acetylation of histone H3 lysine 27 (H3K27ac) enrichment at the Nox4 promoter and markedly increased Nox4 expression in liver even after sleep recovery. Moreover, fine coordination of the circadian clock and hepatic damage was strictly controlled by BMAL1-dependent Sirtuin 1 (Sirt1) transcription after circadian misalignment. Accordingly, genetic manipulation of liver-specific Nox4 or Sirt1, along with pharmacological intervention targeting NOX4 (GLX351322) or SIRT1 (Resveratrol), could effectively erase the epigenetic modification of Nox4 by reducing the H3K27ac level and ameliorate the progression of liver pathology, thereby counteracting SF-evoked sustained CVD. Collectively, our findings may pave the way for strategies to mitigate myocardial injury from persistent hepatic detrimental memory in diabetic patients.

Arginine methylation is a critical post-translational modification that plays multifaceted biological functions. However, the manipulation of protein arginine methylation largely depends on genetic or pharmaceutic inhibition of the regulatory enzymes, protein arginine methyltransferases (PRMTs), or non-methylation substitution of corresponding arginine residue to lysine or alanine of protein of interest (POI), which inevitably affects other substrates, or disrupts the structure of POI. Thus, it urges an approach to specifically modulate the arginine methylation of a POI under physiological conditions. To this end, we report the discovery of a methylation tagging system (MeTAG), that enables targeted modification of protein arginine methylation. Through bridging the methyltransferase PRMT5 proximity to a POI, MeTAG facilitates the arginine methylation of POIs, including known arginine methylated proteins, androgen receptor (AR) and protein kinase B (AKT), as well as a neo-substrate E1A binding protein (p300), in a reversible and PRMT5-dependent manner. Moreover, MeTAG can regulate downstream signaling in a methylation dependent manner, leading to downregulation of PSMA mRNA level and activation of AKT. Therefore, MeTAG represents a feasible approach to modulate protein methylation and thereby perturbs protein function in biological and therapeutic contexts.