Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

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.

The nursing experience of buttonhole cannulation for an autogenous arteriovenous fistula with an Eluvia drug-eluting stent in a patient undergoing maintenance hemodialysis was summarized.Key nursing points included:developing a scientific and rational buttonhole cannulation plan for the autogenous arteriovenous fistula stent based on its characteristics post-stent implantation;conducting preoperative ultrasonic evaluations of the arteriovenous fistula and stent;establishing and maintaining a buttonhole tunnel at the stent site;regularly monitoring and following up on the cannulation site and the condition of the autogenous arteriovenous fistula;strengthening the observation and prevention of potential complications.Through rigorous and standardized assessments,procedures,and nursing care,the patient maintained good autogenous arteriovenous fistula function without stent-related complications such as fracture,stenosis,or infection over a 14-month follow-up period.

The nursing experience of buttonhole cannulation for an autogenous arteriovenous fistula with an Eluvia drug-eluting stent in a patient undergoing maintenance hemodialysis was summarized.Key nursing points included:developing a scientific and rational buttonhole cannulation plan for the autogenous arteriovenous fistula stent based on its characteristics post-stent implantation;conducting preoperative ultrasonic evaluations of the arteriovenous fistula and stent;establishing and maintaining a buttonhole tunnel at the stent site;regularly monitoring and following up on the cannulation site and the condition of the autogenous arteriovenous fistula;strengthening the observation and prevention of potential complications.Through rigorous and standardized assessments,procedures,and nursing care,the patient maintained good autogenous arteriovenous fistula function without stent-related complications such as fracture,stenosis,or infection over a 14-month follow-up period.

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 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 evaluate inflammation early in the infarct zone and its dynamic changes after acute myocardial infarction (AMI) using 18F-FDG PET imaging, and analyze its relationship with left ventricular remodeling progression (LVRP). Methods:Sixteen Bama miniature pigs (4-6 months old, 8 females) were selected. AMI models were established by balloon occlusion of the left anterior descending artery. 18F-FDG PET imaging was performed before AMI and at days 1, 5, 8, and 14 post-AMI to evaluate the regional inflammation response. 18F-FDG SUV ratio (SUVR) and the percentage of uptake area of left ventricle (F-extent) in the infarct zone, and the SUVRs of the spleen and bone marrow, were measured. Echocardiography and 99Tc m-methoxyisobutylisonitrile(MIBI) SPECT myocardial perfusion imaging (MPI) were performed at the above time points and on day 28 post-AMI to assess left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular ejection fraction (LVEF), and myocardial perfusion defect extent. The degree of LVRP at day 28 post-AMI was defined as ΔLVESV(%)=(LVESV AMI 28 d-LVESV AMI 1 d)/LVESV AMI 1 d×100%. Data were analyzed using repeated measures analysis of variance, Kruskal-Wallis rank sum test and Pearson correlation analysis. Results:Twelve pigs were successfully modeled and completed the study. Inflammation in the infarct zone persisted until day 14 post-AMI. The SUVR of the infarct zone pre-AMI and at days 1, 5, 8, and 14 post-AMI were 1.03±0.08, 3.49±1.06, 2.93±0.90, 2.38±0.76, and 1.63±0.62, respectively ( F=49.31, P<0.001). The F-extent values in the infarct zone pre-AMI and at days 1, 5, 8, and 14 post-AMI were 0, (40.08±12.46)%, (40.00±12.76)%, (31.08±12.82)%, and 16.50%(7.25%, 22.00%), respectively ( H=37.61, P=0.001). There were no significant differences in the SUVRs of bone marrow and spleen before and after AMI ( F values: 0.69 and 0.77, both P>0.05). At day 1 post-AMI, both SUVR and F-extent in the infarct zone were significantly correlated with LVRP ( r values: 0.82 and 0.70, P values: 0.001 and 0.035). Conclusions:18F-FDG PET imaging can be used to evaluate inflammation in the infarct area and its dynamic changes after AMI. Inflammation in the infarct area is severe at day 1, and then gradually decreases. The extent and severity of inflammation visible on 18F-FDG PET imaging 1 d after AMI are closely related to LVRP.

Objective:To analyze the prevalence of parturiophobia and its association with preferred mode of delivery in pregnant women in Changning District, Shanghai.Methods:A cross- sectional study was conducted among 1 560 pregnant women in the third trimester who had their antenatal examination in Changning Maternity and Infant Health Hospital from September 2020 to March 2021. Fear of childbirth was measured with a validated Chinese version of Wijma Delivery Expectancy/Experience Questionnaire version A (W-DEQ-A). Based on the W-DEQ-A scores, the participants were divided into two groups: non-clinical parturiophobia group [<85 scores, including mild (≤37 scores), moderate (38-65 scores) and severe (66-84 scores) parturiophobia] and clinical parturiophobia group (≥85 scores). Rank-sum test, Chi-square test and t-test were used for univariate analysis. Multivariate binary logistic regression was used to analyze the factors associated with fear of childbirth and its relationship with preferred mode of delivery. Results:The detection rates of mild, moderate, severe and clinical parturiophobia were 18.8% (294/1 560), 44.9% (700/1 560), 31.1% (485/1 560) and 5.2% (81/1 560), respectively. Multivariate binary logistic regression showed that the participants who were supported by relatives and friends to have cesarean section ( OR=3.45, 95% CI: 1.29-9.22) or had antenatal anxiety ( OR=4.73, 95% CI: 2.49-8.97) were more likely to have clinical parturiophobia, while those with planned pregnancy ( OR=0.49, 95% CI: 0.29-0.82), high intensity physical activity ( OR=0.36, 95% CI: 0.18-0.72) or better/well understanding of the delivery process ( OR=0.42, 95% CI: 0.19-0.97) were less likely to develop clinical parturiophobia (all P<0.05). Compared with the non-clinical parturiophobia women, those with clinical parturiophobia were more likely to choose cesarean section ( OR=2.15, 95% CI: 1.22-3.78, P=0.008). Conclusions:The detection rates of severe and clinical parturiophobia are 31.1% and 5.2% in Changning District, Shanghai. The associated factors mainly include the attitudes of relatives and friends towards the mode of delivery, antenatal anxiety, planned pregnancy or not, physical activity level and the understanding of delivery process. Clinical parturiophobia might be an important factor for cesarean section on maternal request.