How to effectively using the electronic health records (EHR) in contracted family doctors service was studied according to the new model of HER-based community medical service with community health service station as a center in combination with large hospitals,and suggestions were put forward for the solution of problems in contracted family doctor service,namely establishing and perfecting the community health service system,enlarging the scale of HER,strengthening the construction of general doctors team,and training the technically competent community health service staff.

Objective:To investigate the clinical features and prognosis of patients with pancreatic metastasis from clear cell renal cell carcinoma(CCRCC).Methods:From Jan 2000 to May 2020, the clinical data of patients pathologically diagnosed as CCRCC with pancreatic metastasis and admitted in Cancer Institute and Hospital of Tianjin Medical University were analyzed retrospectively. The gender, age, metastasis time, relapse time, metastatic sites, numbers of metastatic lesions and whether metastatic pancreatic lesions should be surgerically removed were recorded and the influencing factors were analyzed.Results:Among the 20 patients, there were 12 males and 8 females. The median age of diagnosis was 50 years. There were 12 patients(60%) of left renal carcinoma and 8 patients(40%)of the other side. 12 cases(60%) had single pancreatic metastatic lesion and the other 8 cases(40%) had multiple metastatic lesions. Seven patients(35%) had other organs metastasis besides pancreatic metastasis. Two patients(10%) had simultaneous pancreatic metastasis and renal cancer, and the other eighteen patients(90%) had pancreatic metachronous metastasis after being diagnosed as renal cancer. The median time from the diagnosis of CCRCC to pancreatic metastasis was 102 months. Thirteen patients(65%)had recurrences within 10 years and the other seven patients(35%)had recurrences after 10 years. Pancreatectomy was performed in nine patients(45%) and targeted therapy was conducted in thirteen patients. The mean follow-up was 122.9 months (1-256 months). Three patients (15%) died and 17 patients (85%) survived. The median overall survival was 75.9 months, and the 5 year-survival rate was 66.7%. Simultaneous metastasis and extra-pancreatic metastasis were prognostic factors in patients with CCRCC with pancreatic metastasis.Conclusions:Pancreatic metastases from renal clear cell carcinoma were rare, but the prognosis was good, especially in patients with only pancreatic metastases several years after renal carcinoma was diagnosed.

Enteral tube feeding is usually a relatively straightforward method of nutritional support,which is very common in critical patients.The fourmost common methods of enteral tube feeding are nasogastric tubes,nasojejunal tubes,gastrostomy and nesteostomy at present.Identifying the at-risk patient with careful monitoring is crucial,and should be facilitated by a multiprofessional team.This article mainly reviewed the recent progress of tube feeding in severe cases.

Objective:To explore the situation of insomnia, fatigue, depression, anxiety and cognitive impairment in adolescents with different characteristics, and the chain mediating effect of fatigue, depression and anxiety on insomnia and cognitive impairment.Methods:The general demographic data of 6 014 adolescents were collected.Insomnia severity index (ISI), the multidimensional fatigue inventory (MFI-20), patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and perceived deficits questionnaire-depression(PDQ-D) were used to compare the insomnia, fatigue, depression, anxiety and cognitive impairment of adolescents with different characteristics.Spearman correlation analysis was conducted by SPSS 20.0 software.The significance of mediating effect was tested by deviation corrected nonparametric percentile Bootstrap method using SPSS macro program PROCESS.Results:The detection rates of insomnia, fatigue, depression, anxiety and cognitive impairment were different in adolescents with different genders, grades and academic achievements.Cognitive impairment was significantly positively correlated with insomnia ( r=0.654, P<0.01), fatigue ( r=0.476, P<0.01), depression ( r=0.677, P<0.01), anxiety ( r=0.655, P<0.01), which was suitable for mediating effect test.Fatigue, depression and anxiety in adolescents had a chain mediating effect between insomnia and cognitive impairment.Insomnia indirectly affects cognition by affecting fatigue and depressive symptoms.The intermediary effect value was 1.253, the effect amount was 55.60%, and the total effect was 2.254.Insomnia indirectly affects cognition by affecting fatigue and anxiety symptoms.The intermediary effect value was 1.161, the effect amount was 51.50%, and the total effect was 2.254. Conclusion:Insomnia can not only directly affect their cognitive impairment, but also indirectly affect their cognitive impairment through the chain mediation of fatigue, depression and anxiety of adolescents.

Background:The musk glands of adult male Chinese forest musk deer(Moschus berezovskii Flerov,1929)(FMD),which are considered as special skin glands,secrete a mixture of sebum,lipids,and proteins into the musk pod.Together,these components form musk,which plays an important role in attracting females during the breeding season.However,the relationship between the musk glands and skin of Chinese FMD remains undiscovered.Here,the musk gland and skin of Chinese FMD were examined using histological analysis and RNA sequencing(RNA-seq),and the expression of key regulatory genes was evaluated to determine whether the musk gland is derived from the skin.Methods:A comparative analysis of musk gland anatomy between juvenile and adult Chinese FMD was conducted.Then,based on the anatomical structure of the musk gland,skin tissues from the abdomen and back as well as musk gland tissues were obtained from three juvenile FMD.These tissues were used for RNA-seq,hematoxylin-eosin(HE)staining,immunohistochemistry(IHC),western blot(WB),and quantitative real-time polymerase chain reaction(qRT-PCR)experiments.Results:Anatomical analysis showed that only adult male FMD had a complete glandular organ and musk pod,while juvenile FMD did not have any well-developed musk pods.Transcriptomic data revealed that 88.24%of genes were co-expressed in the skin and musk gland tissues.Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway analysis found that the genes co-expressed in the abdomen skin,back skin,and musk gland were enriched in biological development,endocrine system,lipid metabolism,and other pathways.Gene Ontology(GO)enrichment analysis indicated that the genes expressed in these tissues were enriched in biological processes such as multicellular development and cell division.Moreover,the Metascape predictive analysis tool demonstrated that genes expressed in musk glands were skin tissue-specific.qRT-PCR and WB revealed that sex-determining region Y-box protein 9(Sox9),Caveolin-1(Cav-1),and androgen receptor(AR)were expressed in all three tissues,although the expression levels differed among the tissues.According to the IHC results,Sox9 and AR were expressed in the nuclei of sebaceous gland,hair follicle,and musk gland cells,whereas Cav-1 was expressed in the cell membrane.Conclusions:The musk gland of Chinese FMD may be a derivative of skin tissue,and Sox9,Cav-1,and AR may play significant roles in musk gland development.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.

ObjectiveTo discuss the relationship between objective sleep quality and cognitive function in remission depression patients with insomnia. MethodsA total of 47 patients with depression in remission in Beijing Huilongguan Hospital were enrolled. Insomnia Severity Index （ISI） and the sleep efficiency calculated by the first four items of Pittsburgh Sleep Quality Index （PSQI） were defined as insomnia. Among the selected patients， 22 subjects were classified into insomnia group， and 25 subjects in non-insomnia group. The objective sleep status of all subjects was evaluated by polysomnography （PSG）， and the cognitive function was evaluated with the Repeatable Battery for Assessment of Neuropsychological Status （RBANS）. A regression model was established to explore the relationship between objective sleep quality and cognitive function in remission depression patients with insomnia. ResultsThere was statistically significant difference in immediate memory factor scores of RBANS between depression patients in remission with and without insomnia （t=-2.102， P<0.05）. The sleep efficiency （Z=-2.113， P<0.05）， the proportion of N3 sleep stage （Z=-3.175， P<0.01） and the duration of N3 sleep stage （Z=-3.196， P<0.01） in insomnia group were all lower than those in non-insomnia group， and the nocturnal awakening （Z=-2.058， P<0.01）， the proportion of N1 sleep stage （Z=-2.399， P<0.05） and the duration of N1 sleep stage （Z=-2.463， P<0.05） were higher than those of the insomnia group. Within insomnia group， the increased proportion of REM sleep was correlated with the improvement of attention function （β=0.953， 95% CI： 0.123~2.038， P<0.05）， and the increase of nocturnal awakening was correlated with the decline of delayed memory （β=-0.173， 95% CI： -0.325~-0.024， P<0.05）. ConclusionObjective sleep is associated with cognitive impairment in remission depression patients with insomnia， the proportion of REM sleep has a positive impact on attention function， and frequent nocturnal awakening at night has a negative impact on delayed memory.