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.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

ObjectiveTo explore the association between the concentration of maternal serum biomarkers， including free beta-human chorionic gonadotropin （fβ-hCG）， alpha fetoprotein （AFP）， unconjugated estriol （uE3）， and inhibin A （INH A） during the second trimester pregnancy and the risk of fetal presenting with copy number variation （CNV）. MethodsA retrospective analysis was conducted on the diagnostic results of pregnant women who were identified as high risk in the second trimester quadruple serological screening. These pregnant women underwent the interventional prenatal diagnosis and performed amniotic fluid karyotype analysis and comparative genomic hybridization array （aCGH） or CNV sequencing （CNV-seq） simultaneously.The detection rates of different diagnostic methods were calculated. The concentration of serum biomarkers， fβ-hCG， AFP， uE3， and INH A were divided into three levels： abnormally low， normal， and abnormally high. The prevalence of these serum biomarkers concentration in pregnant women with aberrant aCGH/CNV-seq results compared to normal controls was statistically calculated. ResultsAmong the 1 516 cases with high-risk quadruple serological screening in the second trimester， 59 chromosomal abnormalities were revealed by karyotype analysis， while 87 abnormalities were detected by aCGH/CNV-seq， indicating a significant statistical difference in detection rate. In comparison to the result of karyotype analysis， aCGH/CNV-seq identified an additional 46 cases of fetal CNV abnormalities， whereas it failed to detect 18 cases of chromosomal structural abnormalities. Among the 1 457 cases with normal karyotype results， the most frequent abnormality was observed in the concentration of fβ-hCG， followed by abnormalities in INH A and uE3 concentrations， yet the least common abnormality was noted in AFP concentration. When the maternal serum uE3 concentration was less than 0.5 multiples of median （MoM）， there was a significant difference in the risk of fetal CNV compared to the CNV results corresponding to normal concentration of uE3. No significant differences were found in the levels of fβ-hCG， AFP， and INH A between the two groups. ConclusionLow serum uE3 concentration （<0.5 MoM） is associated with an increased risk of fetal presenting with aberrant CNV.

Digital intelligence technology is a comprehensive technical system with data as the key element and artificial intelligence as the core driving force. It provides innovative methodological support for addressing core bottlenecks in rare disease etiology research, including sample scarcity, high-dimensional datacomplexity, intricate pathogenic mechanisms, and challenges in causal validation. This paper systematically reviews the application pathways and current implementation of digital intelligence across four major stages of rare disease research, including etiological clue mining, causal inference, mechanism analysis, and clinical translation. Furthermore, it summarizes the technical strengths, existing limitations, and developmental challenges, aiming to inform optimization of the research paradigm and promote the sustainable advancement of rare disease research.

ObjectiveStroke remains the leading cause of acquired disability in adults worldwide, imposing a substantial socioeconomic burden on patients, families, and healthcare systems. Among the various neurological deficits caused by stroke, motor dysfunction——particularly contralateral hemiplegia——is the most common and debilitating consequence, and it predominantly results from damage to the cortical motor conduction pathways. When motor cortex neurons or their projecting fibers are damaged, the distal axons and their associated myelin sheaths undergo a series of progressive, programmed degeneration, fragmentation, and eventual phagocytic clearance. This pathological process is termed Wallerian degeneration (WD). However, the temporal progression of WD following ischemic stroke and the associated alterations in the local glial microenvironment remain incompletely understood. The present study was designed to systematically elucidate the time course of corticospinal tract (CST) axonal WD after ischemic stroke in mice, and to simultaneously characterize the dynamic pathological changes of astrocytes, microglia, oligodendrocyte lineage cells, and myelinating oligodendrocytes during the WD process. MethodsEight-week-old male C57BL/6J mice were randomly assigned to control and ischemic stroke groups. A focal cortical ischemic stroke model was established in the right sensorimotor cortex using the photothrombotic method. This method induces localized intravascular thrombosis in the target cortical region through precisely controlled interaction between the photosensitizer and laser light of specific wavelength. At day 1 post‑surgery, 2,3,5-triphenyltetrazolium chloride (TTC) staining was performed to evaluate infarct volume, hematoxylin and eosin (HE) staining for histopathological examination, and Nissl staining for assessment of neuronal injury. For axonal degeneration time-course analysis, rAAV-CAG-mCherry anterograde viral tracing was employed to specifically label CST neurons. Cervical spinal cord sections at the CST region were collected at day 3, 5, 7, 14, and 60 post-injury. Morphological changes of mCherry-positive CST fibers were observed under fluorescence microscopy, and the fluorescence signal area fraction within the defined region was quantitatively analyzed to evaluate the dynamic process of axonal degeneration. For glial response assessment, immunofluorescence staining against Iba1 (microglia), GFAP (astrocytes), Olig2 (oligodendrocyte lineage cells), and APC (myelinating oligodendrocytes) was performed at day 7, 14, and 60 post‑surgery. The numbers of positive cells were counted to quantitatively analyze the dynamic changes in each glial population at different time points after ischemia, thereby revealing the evolution of the glial microenvironment during WD. ResultsThe photothrombotic method stably and reliably established an ischemic brain injury model confined to the sensorimotor cortex. Viral tracing revealed that the proportion of mCherry‑positive CST fibers was significantly reduced at day 7 after ischemic injury to the neuronal soma. Immunofluorescence staining demonstrated that during WD, the numbers of microglia and astrocytes in the CST region were significantly increased, whereas the numbers of oligodendrocyte lineage cells and myelinating oligodendrocytes showed no significant changes. ConclusionWD of CST axons is observed at day 7 post-ischemic stroke in a delayed manner. This degenerative process selectively activates microglia and astrocytes, while leaving the number of oligodendrocyte lineage cells largely unaffected.

Metabolic reprogramming is a key characteristic of hepatocellular carcinoma， and abnormal fatty acid metabolism is closely associated with tumor immune escape. This article systematically reviews the molecular mechanisms of fatty acid metabolic reprogramming in hepatocellular carcinoma， focusing on key enzymes and their transcriptional regulatory networks within de novo lipogenesis and fatty acid oxidation pathways. This article also analyzes the metabolic heterogeneity between β-catenin-mutant hepatocellular carcinoma and AXIN1-mutant hepatocellular carcinoma and their therapeutic implications， summarizes the multidimensional mechanisms by which the lipid microenvironment regulates immune cell function， and reviews the targeted therapeutic strategies for different molecular subtypes of hepatocellular carcinoma.

ObjectiveTo explore the protective effect of Shengxiantang on cardiac function and myocardial microvascular injury in rats with chronic heart failure （CHF）. MethodsThe CHF rat model was prepared by aortic arch constriction （TAC）. Of the 72 SD rats， 8 were randomly selected as the sham operation group， where the chest was opened without ligating the aortic arch. The 40 successfully modeled rats were randomly divided into the model group， the Shengxiantang low-， medium-， and high-dose groups （5.1， 10.2， 20.4 g·kg^-1）， and the trimetazidine group （6.3 mg·kg^-1）， with 8 rats in each group. Drug administration began 4 weeks after modeling. The administration groups received the corresponding drugs by gavage， while the sham operation and model groups were given the same amount of distilled water for 8 consecutive weeks. Echocardiography was used to assess cardiac function. Enzyme-linked immunosorbent assay （ELISA） was used to detect the levels of nitric oxide （NO）， endothelin （ET-1）， vascular endothelial growth factor （VEGF）， and von Willebrand factor （vWF）. Ultrastructural changes of microvessels were observed by transmission electron microscopy. Immunohistochemistry was used to detect the expression levels of ATP synthase subunit （ATP5D） and F-actin in myocardial tissue. Western blot was used to detect the expression levels of occludin， claudin， vascular endothelial cadherin （VE-Cadherin）， and zonula occludens-1 （ZO-1）. Microvessel density was measured by immunofluorescence staining. ResultsCompared with the sham operation group， the ejection fraction （EF） and left ventricular shortening fraction （FS） in the model group were significantly decreased （P<0.01）， while the left ventricular diastolic diameter （LVIDd）， left ventricular systolic diameter （LVIDs）， left ventricular end-diastolic posterior wall thickness （LVPWd）， left ventricular end-systolic posterior wall thickness （LVPWs）， left ventricular end-diastolic volume （LVVOLd）， and left ventricular end-systolic volume （LVVOLs） were significantly increased （P<0.01）. The levels of NO and VEGF were significantly decreased （P<0.01）， while the levels of ET-1 and vWF were significantly increased （P<0.01）. Under electron microscopy， the microvascular basement membrane was incomplete and the tight junctions were blurred. The expression levels of ATP5D， F-actin， occludin， claudin， ZO-1， and VE-Cadherin were significantly decreased （P<0.05， P<0.01）， and the relative density of microvessels was significantly reduced （P<0.05， P<0.01）. After intervention with Shengxiantang， the EF and FS of CHF rats significantly increased （P<0.01）， while the LVIDd， LVIDs， LVPWd， LVPWs， LVVOLd， and LVVOLs significantly decreased （P<0.01）. The levels of NO and VEGF significantly increased （P<0.01）， while the levels of ET-1 and vWF significantly decreased （P<0.01）. Under electron microscopy， the microvascular basement membrane was relatively complete and the tight junctions were more continuous. The expression levels of ATP5D， F-actin， occludin， claudin， ZO-1， and VE-Cadherin significantly increased （P<0.05， P<0.01）， and the relative density of microvessels significantly increased （P<0.01）. ConclusionShengxiantang can effectively improve the cardiac function of CHF rats， reduce microvascular endothelial injury， strengthen the connection between endothelial cells， and increase microvessel density， thereby protecting myocardial microvascular injury.