Purpose The study aimed to analyze the relationship between MET gene variants and clinicopathologi-cal features in patients with non-small cell lung cancer(NSCLC).Methods Next-generation sequencing technology was used to detect MET gene variants in NSCLC specimens.The association between MET gene variant status and clini-copathological features was then analyzed.Results Among 1 633 cases of NSCLC,the overall MET mutation rate was 4.53％(74/1 633).Variants were mainly observed in male patients,never-smokers,those older than 60 years,ade-nocarcinoma histology,and patients with TNM stage Ⅲ+Ⅳ disease(P＜0.05).MET gene variant status showed no significant assocication with patient age,sex,smoking history,or pathological subtype(P＞0.05),but was statistical-ly correlated with clinical stage and presence of distant metastasis(P＜0.05).The two major variant types were MET exon 14 skipping and MET amplification,which together accounted for 71.62％of all variants.In addition,MET am-plification was positively correlated with EGFR(P=0.003,rs=0.340)and TP53 mutations(P=0.002,rs=0.362),but showed no correlation with KRAS or ALK gene mutations.In contrast,MET exon 14 skipping was nega-tively correlated with EGFR gene mutations(P＜0.001,rs=-0.409),and showed no significant correlation with KRAS,ALK,or TP53 mutations.Conclusion Different types of MET gene variants(amplification,exon 14 skip-ping,fusion,and others)are significantly associated with clinical advanced clinical stage and distant metastasis in NSCLC,but are independent of patient age,sex,smoking history,and pathological subtype.MET amplification fre-quently co-occur with EGFR and TP53 co-mutations.

Purpose The study aimed to analyze the relationship between MET gene variants and clinicopathologi-cal features in patients with non-small cell lung cancer(NSCLC).Methods Next-generation sequencing technology was used to detect MET gene variants in NSCLC specimens.The association between MET gene variant status and clini-copathological features was then analyzed.Results Among 1 633 cases of NSCLC,the overall MET mutation rate was 4.53％(74/1 633).Variants were mainly observed in male patients,never-smokers,those older than 60 years,ade-nocarcinoma histology,and patients with TNM stage Ⅲ+Ⅳ disease(P＜0.05).MET gene variant status showed no significant assocication with patient age,sex,smoking history,or pathological subtype(P＞0.05),but was statistical-ly correlated with clinical stage and presence of distant metastasis(P＜0.05).The two major variant types were MET exon 14 skipping and MET amplification,which together accounted for 71.62％of all variants.In addition,MET am-plification was positively correlated with EGFR(P=0.003,rs=0.340)and TP53 mutations(P=0.002,rs=0.362),but showed no correlation with KRAS or ALK gene mutations.In contrast,MET exon 14 skipping was nega-tively correlated with EGFR gene mutations(P＜0.001,rs=-0.409),and showed no significant correlation with KRAS,ALK,or TP53 mutations.Conclusion Different types of MET gene variants(amplification,exon 14 skip-ping,fusion,and others)are significantly associated with clinical advanced clinical stage and distant metastasis in NSCLC,but are independent of patient age,sex,smoking history,and pathological subtype.MET amplification fre-quently co-occur with EGFR and TP53 co-mutations.

Objective:To investigate the relationship between 18F-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography(18F-FDG PET/CT)and tumor markers and the prognosis of patients with differentiated thyroid cancer(DTC)treated with surgery combined with iodine-131,and to construct a nomogram prediction model.Methods:The clinical data of 134 patients with DTC who underwent surgery combined with iodine-131 treatment in our hospital from January 2021 to January 2023 were retrospectively analyzed.According to the prognosis after 1 year of treatment,they were divided into a good prognosis group(n=106)and a poor prognosis group(n=28).The general data,18F-FDG PET/CT related parameters[maximum standardized uptake value(SUVmax),metabolic volume(MTV),total lesion gly-colysis(TLG)]and serum tumor markers[thyroglobulin(Tg),thyroglobulin antibody(TgAb)]levels were compared between the two groups.Pearson correlation coefficient was used to analyze the correlation between the related parameters and the tumor marker levels.Logistic multivariate analysis was used to analyze the influencing factors of DTC prognosis.Re-ceiver operating characteristic curve(ROC)was used to analyze the predictive efficacy of related parameters combined with tumor markers on poor prognosis.Anomogram prediction model for poor prognosis was constructed and the predic-tive efficacy of the model was evaluated.Results:The proportion of stage Ⅲ-Ⅳ,the proportion of total resection and the levels of thyroid stimulating hormone(TSH),SUVmax,MTV,TLG,Tg and TgAb in the poor prognosis group were higher than those in the good prognosis group,and the differences were statistically significant(P<0.05).Pearson correlation co-efficient showed that SUVmax,MTV,TLG and Tg,TgAb levels were positively correlated with tumor markers(P<0.05).Lo-gistic analysis showed that after adjusting for confounding variables,SUVmax,MTV,TLG,Tg and TgAb were independent influencing factors for the poor prognosis of DTC(P<0.05).ROC analysis showed that the combination of SUVmax,MTV,TLG,Tg and TgAb was significantly better than each parameter alone in predicting poor prognosis(P<0.05).The nomo-gram prediction model was constructed.ROC evaluation showed that the model had good prediction performance.K-fold cross validation showed that the model had stable performance and good generalization ability.Conclusion:The 18F-FDG PET/CT related parameters SUVmax,MTV,TLG and tumor markers Tg and TgAb are all independent factors affecting the poor prognosis of DTC patients treated with surgery combined with iodine-131.The prognostic nomogram prediction model based on the above factors has good predictive efficacy and can be used to guide clinical decision-making.

Objective:To investigate the relationship between 18F-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography(18F-FDG PET/CT)and tumor markers and the prognosis of patients with differentiated thyroid cancer(DTC)treated with surgery combined with iodine-131,and to construct a nomogram prediction model.Methods:The clinical data of 134 patients with DTC who underwent surgery combined with iodine-131 treatment in our hospital from January 2021 to January 2023 were retrospectively analyzed.According to the prognosis after 1 year of treatment,they were divided into a good prognosis group(n=106)and a poor prognosis group(n=28).The general data,18F-FDG PET/CT related parameters[maximum standardized uptake value(SUVmax),metabolic volume(MTV),total lesion gly-colysis(TLG)]and serum tumor markers[thyroglobulin(Tg),thyroglobulin antibody(TgAb)]levels were compared between the two groups.Pearson correlation coefficient was used to analyze the correlation between the related parameters and the tumor marker levels.Logistic multivariate analysis was used to analyze the influencing factors of DTC prognosis.Re-ceiver operating characteristic curve(ROC)was used to analyze the predictive efficacy of related parameters combined with tumor markers on poor prognosis.Anomogram prediction model for poor prognosis was constructed and the predic-tive efficacy of the model was evaluated.Results:The proportion of stage Ⅲ-Ⅳ,the proportion of total resection and the levels of thyroid stimulating hormone(TSH),SUVmax,MTV,TLG,Tg and TgAb in the poor prognosis group were higher than those in the good prognosis group,and the differences were statistically significant(P<0.05).Pearson correlation co-efficient showed that SUVmax,MTV,TLG and Tg,TgAb levels were positively correlated with tumor markers(P<0.05).Lo-gistic analysis showed that after adjusting for confounding variables,SUVmax,MTV,TLG,Tg and TgAb were independent influencing factors for the poor prognosis of DTC(P<0.05).ROC analysis showed that the combination of SUVmax,MTV,TLG,Tg and TgAb was significantly better than each parameter alone in predicting poor prognosis(P<0.05).The nomo-gram prediction model was constructed.ROC evaluation showed that the model had good prediction performance.K-fold cross validation showed that the model had stable performance and good generalization ability.Conclusion:The 18F-FDG PET/CT related parameters SUVmax,MTV,TLG and tumor markers Tg and TgAb are all independent factors affecting the poor prognosis of DTC patients treated with surgery combined with iodine-131.The prognostic nomogram prediction model based on the above factors has good predictive efficacy and can be used to guide clinical decision-making.

Objective To investigate the molecular mechanism of hepatic fibrosis(HF)regulation by liver sinusoidal endothelial cells(LSECs)via endothelial mesenchymal transition(EnMT),and to predict the natural active components using bioinformatics,machine learning,and cellular experiments.Methods HF and EnMT gene matrices were obtained and the intersecting genes were extracted and enriched using Limma difference analysis and weighted gene co-expression network analysis(WGCNA).The diagnostic genes were screened using a combination of random forest method,support vector machine-recursive feature elimination and network topology analysis,and immune infiltration analysis and prediction of natural active ingredients were performed.The expression of diagnostic genes and the pharmacological effects of the predicted ingredients were finally verified by cellular experiments.Results Differential analysis yielded 3034 EnMT-associated and 4133 HF-associated differential genes.WGCNA analysis yielded 4589 EnMT-associated Hub genes and 763 HF-associated Hub genes.Thirty-eight intersecting genes were extracted,which were mainly enriched in the pathways of basement membrane and extracellular matrix receptor interaction.Four diagnostic genes,CFP,COL4A2,ITGA1,and GRPEL1,were screened by multidimensional analysis.Immune infiltration analysis showed that the diagnostic genes were closely associated with mast cell resting state,memory B cells,and memory CD4+T cells.Reverse transcription-polymerase chain reaction analysis showed significantly increased mRNA expression levels of the four diagnostic genes in the Jagged1-induced model group(P＜0.05).The predicted components,sterol,kaempferol,and quercetin,all had good binding activities with the diagnostic genes.Enzyme-linked immunosorbent assay result confirmed that all three active components significantly reduced the expression of collagen type Ⅳ α2 chain protein in Jagged1-induced LSECs,with quercetin having the most significant effect(P＜0.01).Conclusions This study elucidated the molecular mechanism of hepatic sinusoidal endothelial cells involved in the pathological process of HF through mesenchymal transition.We also propose a diagnostic marker system including CFP,COL4A2,ITGA1,and GRPEL1 as core genes.The result also suggest that natural active ingredients,such as quercetin,may exert anti-HF pharmacological effects by targeting these diagnostic genes.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective To explore the mediating role of intolerance of uncertainty(IU)and moderating role of the negative emotion differentiation in the influence of perceived stress on anxiety among college students from a cognitive perspective.Methods A total of 271 participants were surveyed using the perceived stress scale,intolerance of uncertainty scale,depression anxiety and stress scale(Chinese version),and the test on negative emotional differentiation.SPSS 22.0 was used to perform descriptive statistics and correlation analyses and to test the moderated mediation model.Results Perceived stress affected anxiety and IU played a mediating role-perceived stress could affect anxiety through influencing IU.At the same time,the influence of IU on anxiety could be adjusted through the negative emotion differentiation.The higher the degree of negative emotion differentiation,the lower the degree of anxiety increase(β=0.17,t=5.70,P＜0.01).Conclusion It may be effective to develop training programs to reduce anxiety by regulating perceived stress,increasing acceptance of uncertainty,and improving the negative emotion differentiation,which can help individuals reduce anxiety by perceiving and adjusting anxiety-related emotional or cognitive factors in a timely manner.