Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

Radiotherapy is a crucial treatment modality for head and neck tumors. However, while effectively killing tumor cells, it significantly disrupts the homeostasis of the oral microecology, which is closely associated with various complications such as radiation-induced oral mucositis. Literature review indicates that as radiotherapy doses accumulate and treatment durations extend, the richness and diversity of the oral microbiota show a declining trend, with the genus Streptococcus decreasing most markedly. In contrast, radiotherapy selectively promotes the proliferation of bacterial phyla such as Proteobacteria and Bacteroidetes, which are rich in opportunistic pathogens. Mechanistically, radiotherapy activates the nuclear factor-kappa B pathway, triggering chronic inflammation and oxidative stress, damaging the epithelial barrier, suppressing local immunity, and causing damage to organs such as the salivary glands. It can also induce systemic diseases via the oral-gut axis, forming a multi-level, interconnected pathogenic network. In terms of interventions, treatment strategies including probiotics and prebiotics have shown promising efficacy against side effects such as radiation-induced oral mucositis. Saliva-based oral microbiota transplantation is an emerging strategy that is expected to become widely utilized for restoring oral microecological balance. Existing interventions provide preliminary pathways for clinical practice, but this field still faces several key scientific questions. The association between oral microecology and systemic diseases remains largely correlative, lacking causal evidence. Furthermore, critical parameters for oral microbiota transplantation, such as donor screening criteria, transplantation protocols, and long-term safety, are not yet well-defined. Therefore, future research should focus on conducting large-scale clinical trials to establish standardized protocols and safety evaluation systems for oral microecological interventions, and explore combined treatment therapies such as probiotics, prebiotics, and microbiota transplantation to advance the development of personalized precision modulation. These will enable more effective management of radiotherapy-induced oral microecological dysbiosis and improve treatment outcomes and quality of life for patients with head and neck tumors.

[摘 要] 目的：系统评价CD19 CAR-T细胞免疫疗法对比标准治疗在复发/难治性弥漫性大B细胞淋巴瘤（R/R DLBCL）患者中疗效与安全性，通过探索性分析探索不同CAR-T细胞产品对疗效的潜在影响，为临床治疗决策提供循证参考。方法：计算机检索中国知网、万方数据库、维普期刊数据库、中国生物医学文献数据库、PubMed、Embase和Cochrane Library数据库，搜集CD19 CAR-T细胞疗法对比标准治疗用于R/R DLBCL的随机对照试验（RCT），检索时限均为建库至2025年10月25日。由2位研究者独立进行文献筛选、数据提取和质量评价，采用R4.2.2软件进行Meta分析。结果：共纳入2项Ⅲ期RCT研究（ZUMA-7、TRANSFORM），各结局指标均根据异质性检验结果选择固定效应模型合并数据。疗效方面，CD19 CAR-T细胞疗法可显著改善患者无事件生存期（HR=0.455，95%CI：0.363~0.570，P < 0.001）和降低死亡风险（HR = 0.738，95%CI：0.575~0.947，P = 0.017）；同时可显著提高完全缓解率（RR = 1.879，95%CI：1.574~2.242，P < 0.001）。按产品类型的探索性分析显示，liso-cel和axi-cel均优于标准治疗（liso-cel：HR = 0.380，95%CI：0.260~0.540，P < 0.001；axi-cel：HR = 0.510，95%CI：0.380~0.680，P < 0.001），但该分析为不同试验间的对比，证据等级有限。安全性结局显示：CAR-T细胞疗法的免疫效应细胞相关神经毒性综合征（ICANS）发生率显著升高（RR = 22.387，95%CI：4.353~115.132，P < 0.001）；≥3级细胞因子释放综合征（CRS）发生率数值升高（RR = 8.181，95%CI：0.935~71.574，P = 0.058），差异无统计学意义。纳入研究的偏倚风险整体为低；敏感性分析证实结果稳健。结论：基于2项RCT的Meta分析结果，CD19 CAR-T细胞（liso-cel/axi-cel）可作为R/R DLBCL二线治疗的选择之一，其疗效优于标准治疗，且特征性不良反应（CRS/ICANS）经规范管理后可控，可根据患者基线状态个体化选择CAR-T细胞产品。本研究证据基础薄弱，上述结论尚需更多高质量、大样本RCT验证。

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

[摘 要] 目的：探讨年轻乳腺癌独特的DNA甲基化特征，筛选与预后相关的甲基化位点，构建并验证预后预测模型，并通过多组学整合及关键蛋白验证评估其临床价值。方法：收集癌症基因组图谱（TCGA）数据库中的乳腺癌样本DNA甲基化及临床数据，筛选年轻乳腺癌特有的甲基化位点。采用随机森林、LASSO回归及单因素和多因素Cox比例风险回归分析构建预后模型。使用受试者工作特征（ROC）曲线、校准曲线和一致性指数（C-index）评估模型性能，并比较高、低风险组间的多组学特征差异，在独立队列中通过免疫组化法检测模型关键基因LAMA5的蛋白表达，分析其与预后的关系。结果：年轻乳腺癌相较于老年乳腺癌，在启动子区域（尤其是CpG岛）呈现显著低甲基化特征。通过多步骤差异分析获得3 489个年轻乳腺癌特有的甲基化位点，经Cox比例风险回归筛选出12个与总生存期（OS）独立相关的位点，其中启动子区、基因体区和基因间区位点各4个。基于筛选出的12个甲基化位点构建的预测模型，在TCGA训练集中显示出良好的区分度，高风险组总生存期显著更差（P = 0.002 5），5、8和10年OS预测曲线下面积（AUC）达0.88～0.94。多组学分析显示，高风险组丝裂原活化蛋白激酶（MAPK）信号通路激活，TP53基因突变频率更高，雌激素受体α蛋白表达较低。将TP53突变状态及雌激素受体1（ESR1）mRNA表达水平等关键多组学指标纳入模型，构建综合列线图，其预测效能进一步提升。组织芯片验证显示，模型关键基因LAMA5蛋白表达阴性患者的OS更差（P = 0.046），且富集于三阴性乳腺癌，与模型高风险组的临床病理特征一致。结论：整合启动子和非启动子区域信息的基于12个甲基化位点的模型可用于年轻乳腺癌患者的预后分层。联合年龄、TP53突变及ESR1 mRNA表达水平构建的综合列线图显示出较单一甲基化模型更高的预测效能。

Objective To understand the epidemiological and genotypic characteristics of norovirus (NoV) in Jingzhou area，and to design primers and probes covering the variant genomes in the NoV gene library. Methods A total of 556 fecal samples were collected from suspected NoV patients from the First People's Hospital of Jingzhou from January 2022 to May 2023. The positive rate of NoV nucleic acid in fecal samples was detected by commercial kits. The differences in positive rates among different seasons and five age groups were statistically analyzed. Primers covering the NoV variant genome were designed to genotype some positive specimens. Results The detection rate of NoV nucleic acid in the tested samples was 30.04% (167/556). The detection rate in spring and winter was higher than that in summer and autumn (χ²=20.411，P<0.01). There were statistical differences in the positive rates among the five age groups of <1 year, 1-5 years, 6-10 years, 11-19 years, and >19 years (χ²=17.192，P<0.01), and the positive rate in young children (1~5 years old) was the highest (39.29%, 88/224). In addition, all the positive samples were NoV GII. Conclusion The epidemic situation of NoV is serious in winter and spring in Jingzhou area, with a high infection rate in young children (1-5 years old), and NoV GII is the main prevalent genotype. The primers designed in this study can be used for genotyping of NoV GI and GII.

Objective: To explore the risk of endometrial polyp (EP) based on lipid metabolism and vaginal micro- ecology combined with uterine volume line drawing model. Methods: 143 EP patients treated by hysteroscopic sur- gery were selected as the experimental group , and 113 healthy women were selected as the control group at the same time. The data were randomly divided into training set and validation set according to the ratio of 7 : 3. The clinical data of the two groups were collected and recorded , and t/χ2 test , LASSO regression and multifactorial lo- gistic regression analysis were used to screen the independent risk factors , construct the prediction model , and draw the column line graph. The performance of the model was evaluated by applying subject operating characteristic (ROC) curves , calibration curves , Hosmer-Lemeshow test and clinical decision-making (DCA) curves. Results: Multifactorial logistic regression analysis showed that total cholesterol ( TC) , low-density lipoprotein cholesterol (LDL-C) , vaginal microecological balance , and uterine volume were independent risk factors for the development of EP. ROC curve analysis showed that the AUC values of the training and validation sets of the column line graph model were 0. 935 and 0. 887 , respectively , and its sensitivity and specificity were 90. 21% , 83. 46% and 86. 29% , 80. 66% respectively , The Hosmer-Lemeshow test showed that the model fits well ( training set : χ2 = 2. 261 , P = 0. 840 ; validation set : χ2 = 4. 837 , P = 0. 441) and the calibration curves of the training and validation sets were close to the ideal curves , which indicated that the model had good prediction accuracy; the analysis of DCA curves of the training and validation sets both showed that the column-line graph model had a good clinical benefit rate in predicting EP. Conclusion TC , LDL-C , vaginal microecological balance and uterine volume are independent risk factors for EP , and the column-line diagram model constructed by the model has high clinical ben- efit , calibration and accuracy in predicting the risk of EP.

Objective : To investigate the effect of laminarin(LAM) on nonproliferative diabetes retinopathy by high throughput sequencing(RNA-seq). Methods : The diabetes model was established by intraperitoneal injection of streptozotocin(STZ), and the effect of LAM on diabetic mice was observed.C57BL/6 mice were randomly divided into three groups: Control group, Model group, and LAM group, with 8 mice in each group. After 8 weeks of modeling, the LAM group received a 4-week intraperitoneal injection of LAM treatment. Changes in blood glucose and body weight of the three groups of mice were recorded, HE staining was performed to examine retinal lesions, and RNA-seq was used to identify differentially expressed genes(DEGs) in diabetic retinopathy(DR) under the action of STZ and LAM. Results : STZ successfully established the model of DR, and LAM reduced the blood sugar in diabetic mice to a certain extent and improved the pathological morphology of retinal structural looseness in diabetic mice. After RNA-seq analysis of DEGs, it was found that there were a total of 214 DEGs in the retina of the Model group mice compared to the Control group. Enrichment analysis revealed that DR could exacerbate the lesions through the PI3K Akt signaling pathway. There were a total of 42 DEGs in the retina of the Model group and LAM group mice, and enrichment showed that LAM improved the lesions through the neutrophil extracellular trap pathway. Early growth response factor 1(Egr1), FBJ osteosarcoma oncogene(Fos), nuclear receptor subfamily 4A member 1(Nr4a1), and salt-induced kinase 1(Sik1) were regulated by STZ, and LAM significantly regulated their expression, which might be closely related to LAM′s treatment of diabetic retinopathy. Conclusion DEGs can exacerbate the severity of diabetic retinopathyviathe PI3K-Akt signaling pathway. LAM can mitigate diabetic retinopathyviathe neutrophil extracellular trap pathway. Egr1, Fos, Nr4a1, and Sik1 are key genes involved in LAM treatment of STZ-induced DR.

Objective: To explore the characteristic of obesity in adolescents with major depressive disorder and its relationship with inflammatory cytokines. Methods : One hundred and forty adolescents with major depressive disorder were enrolled. According to the classification standard of body mass index(BMI) for adolescents in China, the patients were classified into underweight group, normal group, overweight group and obese group. The center for epidemiologic studies depression scale(CES-D) was used to evaluate symptoms of depression in patients, and ultrasensitive multiplex electrochemiluminescence detection technology was used to measure the levels of plasma inflammatory cytokines including interleukin(IL)-6,IL-17A,IL-1β,IL-6,IL-8 and tumour necrosis factor(TNF)-α. One-way ANOVA or Kruskal-WallisHtest and chi-square test were used for comparison between groups. Binary Logistic regression was used to analyze the influencing factors of obesity in adolescent patients with major depressive disorder. Results : Among the 140 adolescent patients with major depressive disorder, wasting were 9.3%(13/140), overweight were 17.9%(25/140) and obesity were 6.4%(9/140) respectively. There were statistically significant differences in gender(χ2=8.301,P<0.05) and inflammatory cytokines IL-6(H=16.217,P<0.01), IL-8(H=10.926,P<0.05) and TNF-α(H=7.879,P<0.05) among the four groups. Analysis of covariance showed that the difference in levels of the inflammatory cytokine IL-6(F=4.486,P<0.01) remained statistically significant after controlling for age, gender and antidepressant use. The results of multiple comparisons showed that compared with the wasting group, the plasma IL-6(Z=-3.843,PBonferroni calibrate<0.01) were higher in the obese group; compared with the normal group, the obesity rate of males was higher than that of females(χ2=8.812,PBonferroni calibrate<0.01), and the level of IL-6 in the obese group(Z=-3.023,PBonferroni calibrate<0.05) was higher. Binary Logistic regression analysis showed that plasma IL-6(OR=2.500,P<0.01) and gender(OR=11.292,P<0.01) were independent influencing factors for obesity in patients with adolescent depressive disorders. Conclusion There are gender differences in obesity rates in adolescents with depressive disorders, and obesity is associated with elevated levels of inflammatory cytokines.

Objective : To explore the predictive value of depression severity plasma thyroid-stimulating hormone(TSH) and brain-derived neurotrophic factor(BDNF) levels for agitated symptoms in patients with adolescent depressive disorder(MDD). Methods : Ninety-one patients with adolescent depressive disorder were enrolled, and the degree of agitation was assessed according to the modified outward aggressive behavior scale(MOAS); 24-item hamilton depression scale(HAMD24) was used to determine the severity of depression; chemiluminescence immunoassay(CLIA) was used to determine the plasma thyroid-stimulating hormone(TSH) level; and electrochemiluminescence immunoassay(ECL) was used to determine the plasma BDNF. SPSS 26.0 was used for statistical analysis of the data, Spearman correlation analysis was used to analyze the relationship between HAMD24and plasma TSH and BDNF levels and the degree of agitation, multiple linear regression analysis was used to analyze the factors influencing the degree of agitation in adolescents with MDD, and binary Logistic regression analysis and subjects′ work characteristic curves(ROC) were used to establish predictive models. Results: The degree of agitation in adolescent MDD patients was positively correlated with HAMD24total score(P<0.001); both HAMD24total score and plasma BDNF level were identified as risk factors for agitation severity(bothP<0.05); both HAMD24total score and plasma BDNF levels were risk factors for the degree of agitation(allP<0.05); HAMD24total score, plasma TSH, BDNF levels were all risk factors for concomitant agitation symptoms in adolescent MDD patients; ROC curve analysis showed that the three combined prediction models(AUC=0.889,P<0.001) had a higher predictive value than the single prediction model(P<0.01). Conclusion Concomitant agitation symptoms in adolescents with MDD are strongly associated with HAMD24total score and plasma TSH and BDNF levels, and the three combined models have good predictive power.