Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective To investigate the role of bufalin(BU)in inhibiting M2-type macrophage-mediated colorec-tal cancer metastasis.Methods Human acute leukemia mononuclear cells(THP-1)were differentiated into M0 macrophages using phorbol ester induction(PMA)for 48 hours.The M0 macrophages were then treated with IL-4 and IL-13 medium.Surface markers and morphological changes were observed through ELISA,morphology,and RT-qPCR experiments.RT-PCR and ELISA experiments were conducted to detect the surface markers TGF-β and IL-10 of M2 macrophages.The secretion level of IL-6 in the supernatant of M2 macrophages and colorectal cancer cells HCT116 was compared using ELISA.Additionally,the effect of conditioned medium on colorectal cancer cell HCT116 was assessed through Transwell,Wound healing,RT-qPCR,and Western blot experiments.Subsequent-ly,bufalin was added to the conditioned medium and the changes in AKT/PI3K protein,migration,and epithelial-mesenchymal transition ability in HCT116 were observed using Western blot,Transwell,Wound healing and RT-qPCR experiments.Results THP-1 were successfully differentiated into M2 macrophages.The activation of AKT/PI3K protein in HCT116 cells was induced by the secretion of IL-6 from M2 macrophages,which in turn promoted the migration and epithelial-mesenchymal transition ability of the HCT116 cells.The migration and epithelial-mes-enchymal transition mediated by M2 macrophages in HCT116 cells were effectively inhibited by Bufalin.Conclu-sion The release of IL-6 from M2 macrophages activates the AKT/PI3K signaling pathway in colorectal cancer cells,thereby promoting their migration and epithelial-mesenchymal transition capacity.Moreover,bufalin exhibits inhibitory effects on this effect.

Objective To investigate the protective effect of NDUFA13 protein against acute liver injury and liver fibrosis in mice and explore the possible mechanisms.Methods BALB/C mice(7 to 8 weeks old)were divided into normal group,CCl4 group,CCl4+AAV-NC group and CCl4+AAV-NDU13 group(n=18).Mouse models of liver fibrosis were established by intraperitoneal injection of CCl4 twice a week for 3,5 or 7 weeks,and the recombinant virus AAV8-TBG-NC or AAV8-TBG-NDUFA13 was injected via the tail vein 7-10 days prior to CCl4 injection.After the treatments,pathological changes in the liver of the mice were observed using HE and Masson staining.Hepatic expression levels of NDUFA13 and α-SMA were detected with Western blotting,and the coexpression of NDUFA13 and NLRP3,TNF-α and IL-1β,and α-SMA and collagen Ⅲ was analyzed with immunofluorescence assay.Results HE and Masson staining showed deranged liver architecture,necrotic hepatocytes and obvious inflammatory infiltration and collagen fiber deposition in mice with CCl4 injection(P<0.001).NDUFA13 expression markedly decreased in CCl4-treated mice(P<0.001),while a significant reduction in inflammatory aggregation and fibrosis was observed in mice with AAV-mediated NDUFA13 overexpression(P<0.001).In CCl4+AAV-NDU13 group,immunofluorescence assay revealed markedly weakened activation of NLRP3 inflammasomes(P<0.001),significantly decreased TNF-α and IL-1β secretion(P<0.001),and inhibited hepatic stellate cell activation(P<0.05)and collagen formation in the liver(P<0.001).Conclusion Mitochondrial NDUFA13 overexpression in hepatocytes protects against CCl4-induced liver fibrosis in mice by inhibiting activation of NLRP3 signaling.

Objective To investigate the protective effect of NDUFA13 protein against acute liver injury and liver fibrosis in mice and explore the possible mechanisms.Methods BALB/C mice(7 to 8 weeks old)were divided into normal group,CCl4 group,CCl4+AAV-NC group and CCl4+AAV-NDU13 group(n=18).Mouse models of liver fibrosis were established by intraperitoneal injection of CCl4 twice a week for 3,5 or 7 weeks,and the recombinant virus AAV8-TBG-NC or AAV8-TBG-NDUFA13 was injected via the tail vein 7-10 days prior to CCl4 injection.After the treatments,pathological changes in the liver of the mice were observed using HE and Masson staining.Hepatic expression levels of NDUFA13 and α-SMA were detected with Western blotting,and the coexpression of NDUFA13 and NLRP3,TNF-α and IL-1β,and α-SMA and collagen Ⅲ was analyzed with immunofluorescence assay.Results HE and Masson staining showed deranged liver architecture,necrotic hepatocytes and obvious inflammatory infiltration and collagen fiber deposition in mice with CCl4 injection(P<0.001).NDUFA13 expression markedly decreased in CCl4-treated mice(P<0.001),while a significant reduction in inflammatory aggregation and fibrosis was observed in mice with AAV-mediated NDUFA13 overexpression(P<0.001).In CCl4+AAV-NDU13 group,immunofluorescence assay revealed markedly weakened activation of NLRP3 inflammasomes(P<0.001),significantly decreased TNF-α and IL-1β secretion(P<0.001),and inhibited hepatic stellate cell activation(P<0.05)and collagen formation in the liver(P<0.001).Conclusion Mitochondrial NDUFA13 overexpression in hepatocytes protects against CCl4-induced liver fibrosis in mice by inhibiting activation of NLRP3 signaling.

Objective:To investigate the value of intratumoral and peritumoral radiomics models based on 18F-FDG PET-CT in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma and interpret peritumoral radiomics features. Methods:This study was a cross-sectional study. Patients with lung adenocarcinoma who underwent 18F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set (309 cases) and a test set (206 cases) in a 6∶4 ratio randomly. Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images, respectively, and the optimal feature sets were selected. Radiomics models were established using the XGBoost algorithm, and radiomics scores (intratumoral CT label, peritumoral CT label, intratumoral PET label, peritumoral PET label) were calculated. Logistic regression analysis was used to construct a clinical model and a combined model (incorporating PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features). The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Unsupervised clustering, Spearman correlation analysis, and visualization methods were used for the interpretability of peritumoral radiomics features. Results:In both the training and test sets, the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma (training set: Z=3.84, P<0.001; test set: Z=1.99, P=0.046). In the test set, the AUC value of PET intratumoral labels (0.684) was slightly higher than that of PET peritumoral labels (0.672) for predicting EGFR mutation status, but the difference was not statistically significant ( P>0.05). The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better than the clinical model (training set: Z=6.52, P<0.001; test set: Z=2.31, P=0.021). Interpretability analysis revealed that CT peritumoral radiomics features were correlated with CT shape features, and there were significant differences in CT peritumoral features between different EGFR mutation statuses. Conclusions:The value of CT peritumoral labels is superior to that of CT intratumoral labels in predicting EGFR mutation status in lung adenocarcinoma. The predictive performance of the model can be improved by combining PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features.

Objective: To investigate the characteristics of HIV/AIDS cases among 15-24 year old population reported in Hefei from 2004 to 2022, so as to provide insights into AIDS control among adolescents. Methods: The epidemiological data regarding HIV/AIDS cases between 15 and 24 years old reported in Hefei from 2004 to 2022 were captured from the AIDS comprehensive prevention and control information system of Chinese disease prevention and control information system, and data regarding temporal distribution, population distribution, and routes of infections and detection were descriptively analyzed by descriptive epidemiological methods. Results: From 2004 to 2022, 865 cases of HIV/AIDS were reported in Hefei among 15-24 years old youth, accounting for 21.80% of the total reported cases. Among the HIV/AIDS patients, males accounted for 92.60%(801 cases), the unmarried ones accounted for 93.41% (808 cases)，those with college degree or above accounted for 60.12% (520 cases)，and 25.78%(223 cases) of them were students. The proportion of student cases increased annually( χ 2 trends =47.67, P <0.01). Homosexual transmission accounted for 81.39%, both showed an increasing trend( χ 2 trends =51.23, P <0.01)．Totally 55.49% of cases were found through testing and consultation, and the proportion of cases increased by year( χ 2 trends =112.18, P <0.01). In 2004-2022，the number of newly reported cases among people aged 15-24 showed a rising trend at an average rate of 24.46% by year( Z=4.92, P <0.01), which was higher than the average rate of 21.54% for the entire population( Z=12.75, P <0.01). Conclusion The epidemic of HIV/AIDS among population aged 15-24 years is serious in Hefei. Comprehensive measures for HIV education and prevention intervention are desperately needed to be reinforced among targeted students.

Objective To summarize clinicopathologic features of papillary thyroid carcinoma (PTC) coexistent with chronic lymphocytic thyroiditis (CLT) and investigate risk factors for lymph node metastasis.Methods The medical records of 4 264 consecutive papillary thyroid carcinoma patients who received surgical treatment from Oct 2013 to Oct 2015 in Peking Union Medical College Hospital were reviewed.The diagnoses was confirmed by histopathological tests.Univariate analysis was performed to identify specific clinicopathologic features of PTC with CLT.Univariate and multivariate analysis were performed to determine whether each clinicopathologic feature was an independent risk factor for lymph node metastasis.Results In all 4 265 cases,there were 3 059 papillary thyroid microcarcinoma (PTMC) (71.7％),1 010 PTC patients (23.7％) with CLT.909 female patients (90％),624 cases with multifocal lesions (61.8％),422 cases with extra-thyroid extension (41.8％),429 cases with lymph node metastasis (42.5％),and 133 cases with metastatic lymph nodes(LNs) ≥6 (13.2％).The median age was 43 years old and median tumor size was 0.8 cm.Patients with CLT were more females (90.0％ vs.70.2％;P ＜ 0.001),younger median age (43 vs.44 years;P =0.001),and lower incidence of lymph node metastasis (42.5％ vs.50.9％;P ＜0.001).CLT was not associated with tumor size,multifocal lesions,extra-thyroid extension and metastatic LNs≥6 (0.8 cm vs.0.7 cm,61.8％ vs.62.9％,41.8％ vs.42.1％ and 13.2％ vs.14.8％,respectively,all P ＞ 0.05).In multivariate analysis,CLT was an independent protective factor for lymph node metastasis (OR =0.713,95％ CI 0.609-0.835,P ＜0.001).In PTC patients with lymph node metastasis,CLT was not associated with lymph node metastasis number (3 vs.3,P =0.300).Conclusions Chronic lymphocytic thyroiditis was an independent protective factor for papillary thyroid carcinoma patients with lymph node metastasis.But in patients with lymph node metastasis,the metastatic number didn't decrease.