AIM: The extent similarity algorithm was introduced, integrated clustering analysis to evaluate the quality of different sources of Flos lonicerae japonicae. METHODS: The fingerprint spectum of Flos lonicerae japonicae was established to calculate correlation coefficient, cosine of the included angle and extent similarity of thirty-five samples, and then clustering analysis was adopted. RESULTS: In combination of the extent similarity and the clustering analysis to evaluate the quality of Flos lonicerae japonicae, and the results showed that it accorded with reality. CONCLUSION: This method is accurate and reliable, and it is an apparent, credible and efficient method for quality evaluation of Chinese medicines.

Objective:To investigate the value of a preoperatively MRI-based deep learning (DL) radiomics machine learning model to distinguish low-grade and high-grade soft tissue sarcomas (STS).Methods:From November 2007 to May 2019, 151 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 131 patients in the Affiliated Hospital of Shandong First Medical University and the Third Hospital of Hebei Medical University were enrolled as external validation sets. According to the French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) system, 161 patients with FNCLCC grades Ⅰ and Ⅱ were defined as low-grade and 121 patients with grade Ⅲ were defined as high-grade. The hand-crafted radiomic (HCR) and DL radiomic features of the lesions were extracted respectively. Based on HCR features, DL features, and HCR-DL combined features, respectively, three machine-learning models were established by decision tree, logistic regression, and support vector machine (SVM) classifiers. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each machine learning model and choose the best one. The univariate and multivariate logistic regression were used to establish a clinical-imaging factors model based on demographics and MRI findings. The nomogram was established by combining the optimal radiomics model and the clinical-imaging model. The AUC was used to evaluate the performance of each model and the DeLong test was used for comparison of AUC between every two models. The Kaplan-Meier survival curve and log-rank test were used to evaluate the performance of the optimal machine learning model in the risk stratification of progression free survival (PFS) in STS patients.Results:The SVM radiomics model based on HCR-DL combined features had the optimal predicting power with AUC values of 0.931(95%CI 0.889-0.973) in the training set and 0.951 (95%CI 0.904-0.997) in the validation set. The AUC values of the clinical-imaging model were 0.795 (95%CI 0.724-0.867) and 0.615 (95%CI 0.510-0.720), and of the nomogram was 0.875 (95%CI 0.818-0.932) and 0.786 (95%CI 0.701-0.872) in the training and validation sets, respectively. In validation set, the performance of SVM radiomics model was better than those of the nomogram and clinical-imaging models ( Z=3.16, 6.07; P=0.002,<0.001). Using the optimal radiomics model, there was statistically significant in PFS between the high and low risk groups of STS patients (training sets: χ2=43.50, P<0.001; validation sets: χ2=70.50, P<0.001). Conclusion:Preoperative MRI-based DL radiomics machine learning model has accurate prediction performance in differentiating the histopathological grading of STS. The SVM radiomics model based on HCR-DL combined features has the optimal predicting power and was expected to undergo risk stratification of prognosis in STS patients.

Objective To explore the epidemiological characteristics of pulmonary infection in elderly patients with chronic obstructive pulmonary disease (COPD), and to construct a risk prediction model. Methods Among of 125 elderly patients with COPD from May 2020 to June 2022 were selected as the research subjects. The epidemiological characteristics of infected patients were counted, and the risk factors of pulmonary infection in patients were analyzed and a prediction model was constructed. Results A total of the 125 elderly patients with COPD, there were 46 cases of pulmonary infection, with the infection rate of 36.80%. The detection rate of Gram-negative bacteria was higher than that of Gram-positive bacteria or fungi (64.44% vs 33.33% or 2.22%, P<0.05). Smoking history, presence of diabetes mellitus, mechanical ventilation, irrational use of antibiotics, and hypoalbuminemia were risk factors for pulmonary infection in elderly patients with COPD (P<0.05). Prediction model of pulmonary infection in elderly patients with COPD obtained by multivariate logistic regression analysis was shown as PI=-1.981+0.657×smoking history+0.806×presence of diabetes mellitus+0.521×mechanical ventilation+0.639×irrational use of antibiotics+0.715×presence of hypoalbuminemia. Hosmer-Lemeshow test showed that Hosmer-Lemeshow χ²=0.812 and P=0.295. ROC curve analysis revealed that the AUC value of the prediction model on predicting the pulmonary infection in elderly patients with COPD was 0.802. Conclusion The pathogenic bacteria of elderly patients with COPD complicated with pulmonary infection are mainly Gram-negative bacteria. The prediction model constructed according to the risk factors of pulmonary infection in patients has predictive value on pulmonary infection in patients.

ObjectiveTo explore the mental health status and its influencing factors among middle school students in Xide County， Liangshan Yi Autonomous Prefecture， and to provide references for mental health interventions for local middle school students. MethodsUsing a cross-sectional study design， one junior middle school and one senior middle school in Xide County， Liangshan Prefecture， Sichuan Province， were randomly selected on September 16， 2021， and two classes per grade in each school involving 288 students were recruited. Subjects were assessed using Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）， Generalized Anxiety Disorder Scale-7 item （GAD-7）， PTSD Checklist for DSM-5 （PCL-5）， Multidimensional Scale of Perceived Social Support （MSPSS） and UCLA Loneliness Scale （ULS-3）. Then the scores of above scales were compared among middle school students with different demographic characteristics， and binary Logistic regression analysis was conducted to screen the influencing factors of post-traumatic stress disorder (PTSD） symptoms. ResultsAmong the respondents， 17.71% （95% CI： 0.133~0.221）， 8.68% （95% CI： 0.054~0.120）， 2.78% （95% CI： 0.009~0.047） and 45.83% （95% CI： 0.400~0.516） were reported to have symptoms of depression， anxiety， PTSD and loneliness， respectively. Students in senior middle school scored lower on PCL-5 and ULS-3 than those in junior middle school ［（6.46±8.91） vs. （8.87±9.42）， t=2.202， P<0.05； （4.67±1.65） vs. （5.60±1.88）， t=4.431， P<0.01］. Regression analysis denoted that depressive symptoms （OR=7.630， P<0.05） and anxiety symptoms （OR=3.789， P<0.01） were risk factors for PTSD symptoms among middle school students. ConclusionThe middle school students in Xide County， Liangshan Yi Autonomous Prefecture suffer a high prevalence rate of depressive symptoms and loneliness， and those in junior middle school are more likely to feel a sense of strong loneliness， furthermore， depressive symptoms and anxiety symptoms are risk factors for PTSD symptoms.