At present,all parts of our country and the units are on their own to explore the ways and methods of public health physician training,and to develop a unified national public health standardized training system is particularly important.This study introduces the composition and practice of British public health specialist training and its access system,draws on the successful experience of this system and puts forward some suggestions such as paying attention to the cultivation of clinical basic knowledge,rationally using industry associations and societies and so on from the public health doctor training system,which provides the basis for promoting the establishment of a national public health physician training system.

Objective To investigate the chemical constituents in the whole plant of Hedyotis corymbosa.Methods The compounds were isolated by column chromatography,pre-TLC,and recrystallization.Their structures were elucidated by spectroscopic methods.Results Thirteen compounds were isolated and identified as(+)-lyoniresinol-3?-O-?-D-glucopyranoside(Ⅰ),quercetin(Ⅱ),esculetin(Ⅲ),scopoletin(Ⅳ),hedyotiscone A(Ⅴ),p-hydroxybenzoic acid(Ⅵ),protocatechuic acid(Ⅶ),vanillic acid(Ⅷ),syringic acid(Ⅸ),(+)-vomifoliol(Ⅹ),(-)-dihydrovomifoliol(Ⅺ),S-(+)-dehydrovomifoliol(ⅩⅡ),and alizarin 1-methyl ether(ⅩⅢ),respectively.Conclusion Compounds Ⅰ—ⅩⅢ are isolated from this plant for the first time.

Objective :Humna WAF1 gene was cloned,and WAF1-pcDNA3 recombinant plasmid was con-structed. Methods :Human WAF1 gene was amplified by RT-PCR from Hela cell,then cloned into pcDNA3vector. Results :Human WAF1-pcDNA3 recombinant plasmid was constructed,and DNA direct sequencingindicated that was correct. Conclusion:Our study laid the foundation for studying WAF1 gene expressionand it＇s biological effects.

Objective:To investigate the factors and mechanisms in forming uric acid stones in patients with type 2 diabetes (T2DM).Methods:106 patients with diabetes were divided into observation group and control group according to the combination of urinary calculi,53 cases in each group,The differences of clinical data and biochemical indexes between the two groups were compared,The relationship between type 2 diabetes mellitus and urinary stones was analyzed by multi factor regression analysis.Results:There were no significant difference in observation group and control group in age,sex,SBP,DBP,TC,FBG,2hPBG and HbA1C (P＞0.05),and there were of statistical difference significance in BMI,urinary pH,HOMA-IR,SUA,TUA in the two gruops (P ＜0.05) and the Logistic regression analysis showed blood uric acid,the urinary pH,HOMA-IR,SUA were independent risk factors in urolithiasis in T2DM (P ＜ 0.05).Conclusion:High uric acid hematic disease,high uric acid excretion,insulin resistance,overweight or obesity,high blood triglycerides in patients with type 2 diabetes is risk factors for urinary stone formation,in which blood uric acid,urinary pH,HOMA-IR is the independent risk factor for type 2 diabetic patients with urinary calculi.

Objective:To investigate the collection efficiency of peripheral blood hematopoietic stem cells and explore its influencing factors.Method:The parameters of the collection process, blood routine indexes and the number of MNC and CD 34+ cells of the product were detected by Fresenius blood cell separator, Mindray blood cell analyzer and BD flow cytometer. A retrospective analysis was performed on 72 patients who underwent autologous peripheral blood hematopoietic stem cell transplantation in Southwest Hospital of Army Medical University from January 2013 to January 2021, including the correlation among gender, age, blood routine indexes, collection circulation volume and MNC and CD 34+ cell count in these cases, and influence of various factors on collection efficiency of peripheral blood stem cells. The correlation among gender, age, blood routine indexes, collection circulation volume and MNC and CD 34+ cell count in 72 cases of autologous transplantation patients, and influence of various factors on collection efficiency of peripheral blood stem cells were analyzed retrospectively. Results:There were no significant differences in collecting efficiency of peripheral blood stem cells among patients with different age, sex and disease type ( P>0.05). The collected MNC count of all patients was positively correlated with the collection cycle count ( r = 0.33, P<0.001) and WBC count after mobilization ( r = 0.41, P<0.001). The number of CD 34+ cells collected was positively correlated with MNC count after mobilization ( r = 0.38, P<0.001) and the amount of white membrane collected ( r = 0.48, P<0.001). Logistic multivariate regression analysis showed that MNC count after mobilization: P<0.001, 95% CI 0.07(0.05 - 0.09), collection cycle amount [ P<0.001, 95% CI 0.00(0.00 - 0.00)] and postharvest total amount [ P<0.001, 95% CI 0.07(0.05 - 0.10)] were the influencing factors of the collected MNC number. Meanwhile, these factorswere also the influencing factors of the collected CD 34+ number (MNC count after mobilization: P<0.001, 95% CI 0.09(0.04 - 0.14); collection cycle amount: P = 0.003, 95% CI 0.00(0.00 - 0.00); postharvest total amount: P = 0.005, 95% CI 0.08(0.03 - 0.14)). Conclusions:The collection efficiency of peripheral blood stem cells varies greatly among individuals. The more MNC counts after mobilization, the more peripheral blood stem cells could be collected. In order to obtain high collection efficiency, it is necessary to adjust the parameters of peripheral blood MNC before collection, and pay attention to the collection circulation quantity, postharvest total amount and white membrane volume.

Objective:To explore the effect of minimally invasive hematoma puncture and drainage in the treatment of elderly patients with cerebral hemorrhage by using 3D slicer and Sina software to conduct 3D reconstruction and preoperative localization of intracerebral hematoma.Methods:A total of 74 elderly patients with a first-onset intracerebral hematoma aged ≥75 years, having surgical indications and stable vital signs were grouped into 3D slicer plus Sina software localization group(as group A, n=40)or CT localization group(as group B, n=34). Based on the localization, hematoma puncture and drainage were performed after local anesthesia.Preoperative preparation time, hematoma location, puncture success rate, postoperative hematoma clearance rate, postoperative re-bleeding rate and GCS score were statistically analyzed.Glasgow coma scale(GCS)scores were used in predicting the mortality.Results:The preoperative preparation time was significantly shorter in group A than in group B[(5.5±3.4)min vs.(8.5±2.7)min, t=3.337, P=0.001]. The success rate of hematoma puncture and drainage(90.0% and 70.6%, χ2=4.51, P=0.034)and postoperative hematoma clearance rate[(87.5±3.4)% and(80.3±2.7)%, t=10.10, P=0.000]were higher in group A than in group B. There were no significant differences in operative time, the accuracy of hematoma localization, re-bleeding rate and GCS score between the two groups( P>0.05). Conclusions:3D slicer plus Sina software can precisely locate the intracerebral hematoma, and minimally invasive hematoma puncture and drainage of intracerebral hematoma under local anesthesia were safe and effective in the treatment of elderly patients with intracerebral hemorrhage.

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.

Gut microbiota plays an important role in development of diabetes with frailty. Therefore, it is of great significance to study the structural and functional characteristics of gut microbiota in Chinese with frailty. Totally 30 middle-aged and the aged participants in communities with diabetes were enrolled in this study, and their feces were collected. At the same time, we developed a metagenome analysis to explore the different of the structural and functional characteristics between diabetes with frailty and diabetes without frailty. The results showed the alpha diversity of intestinal microbiota in diabetes with frailty was lower.
Aged ; Diabetes Mellitus ; Epstein-Barr Virus Infections ; Frailty ; Gastrointestinal Microbiome ; Herpesvirus 4, Human ; Humans ; Middle Aged

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers