AIM:To construct a risk prediction model for postoperative dry eye in glaucoma patients using the XGBoost algorithm.METHODS:A retrospective analysis was performed on glaucoma patients who received surgical treatment at the hospital from July 2022 to June 2025. All patients were divided into a dry eye group and a non-dry eye group according to the occurrence of postoperative dry eye disease. Clinical data of the patients were collected, and univariate and multivariate logistic regression analyses were employed to screen out the risk factors for postoperative dry eye. The patients were randomly allocated into a training set and a validation set at a ratio of 7:3. An XGboost risk prediction model was built with the risk factors as feature variables, and the SHapley Additive exPlanations(SHAP)bar plot and beeswarm plot were used for visual interpretation of the model. The predictive efficacy of the model was evaluated via receiver operating characteristic(ROC)curve analysis. RESULTS:The study included 300 glaucoma patients(300 eyes). The non-dry eye group comprised 204 patients(204 eyes, 104 males and 100 females), and the dry eye group comprised 96 patients(96 eyes, 55 males and 41 females). The incidence rate of postoperative dry eye was 32.0%. Univariate analysis revealed statistically significant differences between the two groups in terms of age, comorbid diabetes, meibum viscosity grade, tear film breakup time, meibomian gland dysfunction, and operative time(all P<0.05). Multivariate logistic regression analysis showed that all the above factors were risk factors for postoperative dry eye(all P<0.01). The XGBoost model showed that these risk factors were ranked in descending order of predictive importance as: tear film breakup time, comorbid diabetes, operative time, age, meibomian gland dysfunction, and meibum viscosity grade. ROC curve analysis demonstrated that the area under the curve(AUC)of the XGboost model was 0.84(95%CI: 0.78-0.90)for the training set and 0.83(95%CI: 0.74-0.92)for the validation set, with both values showing statistical significance(both P<0.05).CONCLUSION:The XGboost algorithm-based risk prediction model for postoperative dry eye in glaucoma patients exhibits favorable predictive performance. It can be clinically applied to identify patients at high risk of developing postoperative dry eye, thereby facilitating targeted interventions for preventive purposes.

Over the past decade, high-throughput RNA sequencing (RNA-seq) has vastly expanded our understanding of transcriptome dynamics in human physiology and disease. As a powerful tool for investigating systematic changes in RNA biology, RNA-seq has facilitated the discovery of novel functional RNA species. Mature RNA transcripts, which transmit genetic information from DNA to proteins, undergo intricate transcriptional and post-transcriptional regulation. This process allows a single gene to produce multiple RNA transcripts, each performing specific functions depending on the physiological or pathological context. Specific RNA transcripts (SRTs) are uniquely expressed in particular tissues or tumors and are closely associated with tissue-specific functions or disease states, particularly cancer. This review explores the generation of SRTs through key mechanisms, such as alternative splicing (AS), transcriptional regulation, polyadenylation (polyA), and the influence of transposable elements (TEs). We also examine their critical roles in normal tissue development and diseases, with an emphasis on their relevance to cancer. Furthermore, the potential applications of SRTs in diagnosing and treating diseases, especially malignancies, are discussed. By serving as diagnostic markers and therapeutic targets, SRTs hold significant promise in the development of personalized medicine and precision therapies. This review aims to provide new insights into the importance of SRTs in advancing the understanding and treatment of human diseases.
Humans ; Neoplasms/genetics* ; Alternative Splicing/genetics* ; RNA/genetics* ; Animals ; Sequence Analysis, RNA/methods* ; Polyadenylation/genetics*

OBJECTIVE: The present study investigated how mild moxibustion treatment affects the intestinal microbiome and expression of NLRP3-related immune factors in a rat model of intestinal mucositis (IM) induced with 5-fluorouracil (5-Fu). METHODS: Forty male Sprague-Dawley rats were randomly divided into control, chemotherapy, moxibustion and probiotics groups. The IM rat model was established by intraperitoneal injection of 5-Fu. Mild moxibustion treatment and intragastric probiotic administration were provided once daily for 15 days. Tissue morphology, serum levels of inflammatory factors and the expression levels of tight junction proteins, caspase-1, gasdermin D and NLRP3 were evaluated in colon tissue, through hematoxylin and eosin staining, electron microscopy, enzyme-linked immunosorbent assay, Western blotting, quantitative real-time reverse transcription polymerase chain reaction and immunofluorescence. Gut microbiome profiling was conducted through 16S rRNA amplicon sequencing. RESULTS: Moxibustion and probiotic treatments significantly increased the expression levels of tight junction proteins, reduced cell apoptosis and the expression levels of caspase-1, gasdermin D and NLRP3; they also decreased the serum levels of tumor necrosis factor-α, interleukin (IL)-6, IL-1β and IL-18, while increasing serum levels of IL-10. Moxibustion and probiotic treatments also corrected the reduction in α-diversity and β-diversity in IM rats, greatly increased the proportion of the dominant bacterial genus Lactobacillus and reduced the abundance of the genera Roseburia and Escherichia in chemotherapy-treated rats to levels observed in healthy animals. We also found that these dominant genera were firmly correlated with the regulation of pyroptosis-associated proteins and inflammatory factors. Finally, moxibustion and probiotic treatments elicited similar effects in regulating intestinal host-microbial homeostasis and the expression of NLRP3 inflammasome-related factors. CONCLUSION Moxibustion exerts its therapeutic effect on IM by ameliorating mucosal damage and reducing inflammation. Moreover, moxibustion modulates the gut microbiota, likely via decreasing the expression levels of the NLRP3 inflammasome.