AIM: To analyze the pathological features, immunophenotype, and imaging findings of conjunctival lymphangiectasia(CL), and to explore the etiological mechanisms and provide a theoretical basis for clinical diagnosis and treatment. METHODS:This single-center descriptive cross-sectional study enrolled postoperative specimens from patients with CL who underwent surgical treatment in the hospital between Feb. 2023 and Sept. 2025. Routine hematoxylin and eosin(HE)staining and immunohistochemical staining(D2-40, CD31, CD34, CK)were performed. Anterior segment optical coherence tomography(AS-OCT)was used to observe the lesion morphology. The pathological results were comprehensively analyzed combined with clinical data. RESULTS: A total of postoperative specimens from 32 eyes of 32 patients with CL were enrolled, including 23 females(72%)and 9 males(28%), with a mean age of 53.03±12.47 y. All patients presented with single or multiple transparent cystic elevations beneath the bulbar conjunctiva. The postoperative pathological manifestations were characterized by dilation of conjunctival lymphatic vessels lined with a single layer of flattened endothelial cells, accompanied by edema and inflammatory infiltration in the surrounding stroma. All cases were positive for D2-40, confirming a lymphatic origin; some cases also expressed CD31 and CD34. AS-OCT revealed the lesions as unilocular or multilocular cystic spaces with low reflectivity. After complete surgical resection, the mean follow-up period was 16.2 mo with no recurrence.CONCLUSION:CL is a benign ocular surface lesion characterized by lymphatic vessel dilation. The endothelium co-expresses lymphatic and some vascular markers, suggesting that CL may belong to the spectrum of vascular malformations. AS-OCT has adjunctive diagnostic value, and surgical resection has definitive therapeutic efficacy.

Autoimmune hepatitis （AIH） is a chronic inflammatory liver disease mediated by T lymphocytes， and it can progress to liver cirrhosis or even liver failure without timely intervention. As a natural flavonoid compound， naringenin （NAR） shows a potential value in the prevention and treatment of AIH through multiple mechanisms such as remodeling immune homeostasis， targeted inhibition of inflammatory pathways， antioxidation， regulating hepatocyte metabolism and apoptosis， improving mitochondrial function， and regulating intestinal flora. However， the clinical translation and application of NAR is limited by issues such as low bioavailability and insufficient efficiency of liver-targeted delivery. This article systematically reviews the mechanism of action of NAR in the prevention and treatment of AIH， explores the potential signaling pathways involved in this process， and analyzes existing challenges in its translation and application and future research directions， so as to provide a reference for further research on NAR and its application in the prevention and treatment of AIH.

It is believed that dampness is a key pathological factor contributing to digestive dysfunction in chronic renal failure. Damp pathogens tend to be entangled with or transformed into other pathogenic factors， obstructing the flow of qi， disturbing the functions of the zang-fu organs， and impairing the spleen and stomach's transportation and transformation functions， ultimately leading to disease onset. Based on this understanding， the treatment principle emphasizes dispelling dampness， regulating qi， and harmonizing the five zang organs. For the syndrome of spleen and kidney qi deficiency with dampness， the self-formulated Buyuan Fengzang Decoction （补元封藏煎） is used to tonify the kidney， secure essence， strengthen the spleen， and eliminate dampness. For the syndrome of turbid dampness obstructing the lung and stomach， the self-formulated Sulian Xiexin Decoction （苏连泻心汤） is applied to open with acrid， descend with bitter， dry dampness， and discharge turbidity. For the syndrome of dampness stagnating and transforming into heat， with concurrent spleen deficiency and liver qi stagnation， the self-formulated Chailian Wendan Decoction （柴连温胆汤） is employed to soothe the liver， strengthen the spleen， and disperse the accumulation. For the syndrome of damp obstruction with qi stagnation and constrained yang， the self-formulated Caozhi Erchen Decoction （草知二陈汤） is used to resolve dampness， relieve constraint， raise yang， and promote the defensive qi.

Targeted protein degradation(TPD)technology is an emerging approach that utilizes intracellu-lar proteasome or lysosome systems to eliminate disease-causing proteins,providing novel strategies for dis-ease treatment.Compared with conventional therapies,TPD has advantages such as targeting"undruppable"proteins,overcoming drug resistance and high selectivity.Among them,proteolysis-targeting chimeras(PRO-TACs)and molecular gels are representative methods,which have achieved significant progress in E3 ligase expansion,linker design,and delivery systems,and are gradually entering clinical research.PROTACs and mo-lecular glues can not only target key oncoproteins and overcome resistance,but also modulate immune respon-ses for therapeutic purposes,demonstrating broad clinical application prospects.This article reviews the re-search progress of the two types of technologies and their current application status in cancer treatment,and discusses their potential and challenges in efficacy monitoring and individualized medication from the perspec-tive of laboratory medicine.

ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

Acute lung injury （ALI） is a common clinical inflammatory respiratory emergency with high morbidity and mortality， for which there is no effective and safe therapeutic drug. Nuclear factor-κB （NF-κB）， as a classic inflammatory signaling pathway， can interact with upstream and downstream regulatory factors such as Toll-like receptor 4 （TLR4）， mitogen- activated protein kinase （MAPK）， nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3）， high mobility group box-1 protein 1 （HMGB1）， to jointly affect ALI. This review summarizes the latest research findings in recent years regarding the treatment of ALI through traditional Chinese medicine （TCM） interventions targeting NF-κB signaling pathways. It has been found that a variety of TCM monomers （danshensu methyl ester， salidroside total glycosides， berberine， Codonopsis pilosula polysaccharides， ursolic acid， chrysophanol， and polyphenols from longan seed kernels， etc.） and compound formulas （Resolving-dampness and defeating-toxins formula， Jinyin qingre oral liquid， Xuebijing injection， Combined treatment of lung and intestine， Huangqi baihe decoction， etc.） can modulate NF-κB signaling pathway， and can prevent and control ALI by inhibiting inflammation， improving oxidative stress， reducing apoptosis and modulating the intestinal flora in a multi-pathway manner.

Conjunctival lymphangiectasia is a low-incidence ocular surface disease that is currently rarely reported in the relevant literature. It may be related to cosmetic eyelid surgery, tumor, radiation or chemotherapy and other factors and often causes a foreign body sensation, lacrimation, eye pain, visual fatigue and other discomfort. These symptoms of constant eye irritation affect the patient's quality of life. At present, anterior segment optical coherence tomography can be used for clinical diagnosis, and the novel monoclonal antibody D2-40, as a marker of lymphatic endothelial cell dilatation, has high specificity in pathological diagnosis. Previous studies have not fully defined the pathogenesis of the disease, and treatment methods vary. Conventional treatment has resulted in varying degrees of damage to the conjunctiva in patients. In recent years, anti-vascular endothelial growth factor drugs have been reported to be effective in treating the disease with few complications. This article reviews the pathogenesis, diagnosis and treatment of this rare disease in order to gain a better understanding of conjunctival lymphangiectasia and provide more support for clinical diagnosis and treatment.

Objective This study aims to explore the efficacy of tetrahydrocurcumin(THC),the major active metabolite of curcumin,on high glucose(HG)-induced human platelet aggregation and activation as well as to clarify the underlying mechanisms in vitro.Methods Purified platelets prepared from healthy subjects were pre-incubated with various concentrations of THC(0.5 μmol/L,1 μmol/L or 10 μmol/L)or vehicle control(0.05％DMSO)for 40 min at 37℃,followed by the stimulation of normal glucose(NG,5 mmol/L)or HG(25 mmol/L)for additional 90 min.The maximal aggregation rate was determined by an aggregometer.Flow cytometry was used to measure platelet surface expression of CD62P(a typical marker of platelet activation)and generation of total intraplatelet reactive oxygen species(ROS).Meanwhile,the phosphorylation level of platelet p53 was detected by Western blot assay.Results Compared with NG group,HG intervention significantly increased platelet aggrega-tion(P<0.05)and CD62P expression(P<0.001),which were greatly inhibited by different concentrations of THC(P<0.05).Mechanistically,when compared with solvent control,THC significantly decreased the level of total ROS production(P<0.001)and p53 phosphorylation(P<0.05).In addition,HG-induced total intraplatelet ROS generation(P<0.001)and p53 phosphorylation(P<0.05)were greatly attenuated by adding a ROS scavenger N-acetyl-L-cysteine(NAC).The combination of NAC with THC(10 μmol/L)showed no additive inhibitory effects(P>0.05).Moreover,platelet aggregation and activation induced by HG were greatly decreased by NAC and a p53 specific inhibitor PFT-μ(P<0.05).The combination of THC(10 μmol/L)and NAC resulted no additive inhibitory effects on HG-increased platelet aggregation and activation(P>0.05).THC(10 μmol/L)exhibited additive inhibitory effects on platelet aggregation(P<0.05)but no additive inhibitory effects on platelet activation when combined with PFT-μ(P>0.05).Conclusions THC exerts a protective effect on HG-induced platelet aggregation and activation possibly through down-regulating ROS/p53 signaling pathway in human platelets in vitro.The current study may provide potential value for THC to improve thrombosis in diabetes mellitus and the related chronic metabolic diseases.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.