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 evaluate the effect of modified and traditional subcostal and anterior quadratus lumborum block on postoperative analgesia in patients with laparoscopic nephrectomy.Methods 46 patients underwent laparoscopic nephrectomy.They were randomly divided into two groups:traditional subcostal quadratus lumborum block(group T)and modified subcostal quadratus lumborum block(group M),with 23 cases in each group.Before anesthesia induction,the T group was subjected to the traditional subcostal and anterior quadratus lumborum block under ultrasound guidance,and the M group was subjected to the modified subcostal quadratus lumborum block under ultrasound guidance.All were given 0.5％ropivacaine 20 ml.Patients controlled intravenous analgesia(PCIA)was performed in both groups.The dermatomal levels of 5min,10 min and 15min after block was recorded.VAS pain scores at rest and cough were recorded at 6,24 and 48 h after the block.The dosage of Sufentanil,the number of analgesic pump compressions and the flurbiprofen rescue were recorded 24 h after surgery.Results The number of block plane segments in group M at 5,10 and 15 minutes after block were 6(4,8),8(6,9)and 8(6,9),respectively,which were significantly more than the T group[4(2,6),6(2,9),6(2,9)](P＜0.05).The VAS pain scores of patients in group M were(1.6±0.5)points at rest and(3.7±0.6)points at cough 6 hours after block,24 hours after the block were(2.3±0.4)points and(3.4±0.4)points,respectively,48 hours after the block were(2.5±0.8)min and(3.2±0.7)min,respectively.The VAS pain scores of and at rest and cough 6 hours after block in T group were(2.5±0.7)points and(5.6±0.8)points,respectively,24 hours after the block were(3.1±0.5)points and(4.5±0.7)points,respectively,48 hours after the block were(3.3±0.6)min and(4.2±0.6)min,respectively.Group M was lower than group T(P＜0.05).In group M,the dosage of sufentanil,the times of analgesic pump and the rate of analgesic relief 24 hours after operation were(23.1±4.3)μg,5(4,7)times and 4.3％,respectively,which were significantly lower than those in T group[(34.7±6.8)μg,11(9,12)times,21.7％](P＜0.05).Conclusion Ultrasound-guided modified subcostal and anterior quadratus lumborum block has better analgesic effect than traditional technique in patients undergoing laparoscopic nephrectomy,which can reduce the amount of postoperative analgesic drugs and effectively relieve postoperative pain.

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.

The prevalence of Lyme disease in endogenous populations in Urumqi,Xinjiang was investigated.In total,795 serum samples were collected from residents of three townships in the surrounding area of Urumqi City from 2022 to 2023,which included 383 from Lucaogou Town,145 from Shuixigou Town,and,267 from Tori Township.Serum levels of IgG and IgM antibodies were screened with an enzyme linked immunosorbent assay(ELISA)and confirmed by western blot(WB)analysis.Clinical data of WB-positive indi-viduals were collected and comprehensive analysis was con-ducted for case diagnosis.The chi square test was used for statistical analysis of the results and the P＜0.05 was consid-ered statistically significant.In total,110(13.84％)of 795 samples were positive.The positivity rates was higher in females than males[16.26％(73/449)vs.10.69％(37/346),x2=5.076,P=0.024],while there was no significant difference among age groups(x2=2.569,P=0.766).The positivity rates for serum antibodies in Shuixigou Town,Lucaogou Town,and Tuoli Township were 17.98％(48/267),14.48％(21/145),and 10.70％(41/383),respectively,with a significantly higher rate in Tuoli Township than Lucaogou Town(x2=7.041,P=0.008).Of 110 individuals who were initially positive for IgG and IgM antibodies with the ELISA,82(10.31％)were con-firmed positive by WB analysis.In total,20(2.52％)patients were diagnosed with Lyme disease based on clinical manifesta-tions.Lyme disease is epidemic among the population in Urumqi,as the infection rate is higher than the national average.Hence,continued surveillance is recommended for prevention of Lyme disease.

The comparison between traditional Chinese medicine Jinzhen oral liquid (JZOL) and Western medicine in treating children with acute bronchitis (AB) showed encouraging outcomes. This trial evaluated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB. 480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 days. The primary outcome was time-to-cough resolution. The median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 day. This randomized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discomfort symptoms. Combined with clinical trials, the mechanism of JZOL against AB was uncovered by network target analysis, it was found that the pathways in TRP channels like IL-1β/IL1R/TRPV1/TRPA1, NGF/TrkA/TRPV1/TRPA1, and PGE2/EP/PKA/TRPV1/TRPA1 might play important roles. Animal experiments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.

Humans ; Consensus ; Hypersensitivity ; Desensitization, Immunologic/adverse effects* ; Immunotherapy/adverse effects* ; Injections, Subcutaneous ; Allergens

Objective: To examine the association between smoking status and related mortality among elderly people aged 60 and above in urban and rural areas of Beijing City. Methods: Based on Beijing City Elderly Comprehensive Health Cohort Study from 2009 to 2014, a total of 4 499 eligible older adults included in the baseline survey were followed up and investigated to collect information on survival and death. The Cox proportional hazards regression model was used to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs), and the dose-response relationship was estimated between the smoking index, the years of quitting and mortality. Results: The median (IQR) age of 4 499 subjects was 70.00 (10.00) years old, including 1 814 (40.32%) males. The proportion of non-smokers, former smokers and current smokers was 69.50% (3 127/4 499), 13.20% (594/4 499) and 17.30% (778/4 499), respectively. After adjusting for confounding factors such as demographic and sociological characteristics, lifestyle, etc., the results of multivariate Cox regression analysis showed that, compared to non-smokers, former smokers had a 30.6% increased risk of all-cause mortality [HR (95%CI): 1.306 (1.043-1.636)] and the HR (95%CI) of all-cause, malignant tumor and lung cancer mortality among current smokers has increased by 50.0% [HR (95%CI): 1.500 (1.199-1.877)], 80.3% [HR (95%CI): 1.803 (1.226-2.652)] and 212.6% [HR (95%CI): 3.126 (1.626-6.012)], respectively. The smoking index was positively associated with the increased risk of all-cause, malignant tumor and lung cancer mortality, while the years of smoking cessation were negatively associated with that risk (P<0.05). Conclusion: Smoking is associated with tobacco-related mortality among elderly people in Beijing City.
Aged ; Male ; Humans ; Child ; Female ; Beijing ; Cohort Studies ; Lung Neoplasms ; Smoking ; Tobacco Smoking