The objective of the present study was to localize endothelin receptors in the human prostate using quantitative autoradiography. Slide-mounted tissue sections 20 microns. in thickness were obtained from the transition zones of seven patients undergoing radical prostatectomies for low volume prostate cancer. Sarafotoxin (S6C) and BQ123 have been used to distinguish endothelin receptor subtypes (ETA and ETB). The prostatic tissue sections were incubated in four different stock solutions containing the following: 0.1 nM. 125I-endothelin-1 (125I-ET-1) (total ET-1 binding); 0.1 nM. 125I-ET-1 and 100 nM. S6C (total ETA binding); 0.1 nM. 125I-ET-1 and 1 microM. BQ123 (total ETB binding); and 0.1 nM. 125I-ET-1 and 1 microM. ET-1 (nonspecific ET-1 binding). Nonspecific binding accounted for only 12 and 15% of total 125I-ET-1 binding in the stroma and glandular epithelium. Autoradiograms were quantitatively analyzed using a computerized image analysis system. Specific radioactive densities (nCi/mg.) were determined for the stromal and glandular epithelial elements of the prostate. The specific radioactive densities of ETA and ETB binding sites in the stroma were 7.57 +/- 0.65 and 2.98 +/- 0.81. The specific radioactive densities of ETA and ETB binding sites in the glandular epithelium were 1.59 +/- 0.15 and 7.87 +/- 1.35. The present study demonstrates that the predominant endothelin receptors in the stroma and glandular epithelium are the ETA and ETB subtypes, respectively.

Aims This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling. Methods and results Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835). Conclusions ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867

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China Mental Health Survey (CMHS), which was carried out from July 2013 to March 2015, was the first national representative community survey of mental disorders and mental health services in China using computer-assisted personal interview (CAPI). Face-to-face interviews were finished in the homes of respondents who were selected from a nationally representative multi-stage disproportionate stratified sampling procedure. Sample selection was integrated with the National Chronic Disease and Risk Factor Surveillance Survey administered by the National Centre for Chronic and Non-communicable Disease Control and Prevention in 2013, which made it possible to obtain both physical and mental health information of Chinese community population. One-stage design of data collection was used in the CMHS to obtain the information of mental disorders, including mood disorders, anxiety disorders, and substance use disorders, while two-stage design was applied for schizophrenia and other psychotic disorders, and dementia. A total of 28,140 respondents finished the survey with 72.9% of the overall response rate. This paper describes the survey mode, fieldwork organization, procedures, and the sample design and weighting of the CMHS. Detailed information is presented on the establishment of a new payment scheme for interviewers, results of the quality control in both stages, and evaluations to the weighting.

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The China Mental Health Survey (CMHS) is the first nationally representative community survey on mental disorders and mental health services in China. One-step diagnoses for mood disorders, anxiety disorders and substance use disorders were obtained using the Composite International Diagnostic Interview-3.0 (CIDI-3.0), according to the criteria and definition of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). A two-step procedure was applied for schizophrenia and other psychotic disorders, using psychosis screening section in CIDI-3.0 as a screening instrument and the Structured Clinical Interview for DSM-IV Axis I disorders (SCID) as a diagnostic tool. Dementia was diagnosed by the 10/66 dementia diagnosis package in a two-step design. The main aims of the CMHS were: (1) to investigate the prevalence of mood disorders, anxiety disorders, substance use disorders, schizophrenia and other psychotic disorders, and dementia; (2) to obtain data of service use of individuals with mental disorders in China; and (3) to analyse the social and psychological risk factors or correlates of mental disorders and mental health services. This paper presents a brief review of the background of the CMHS, its aims and measures.

Background: Risk factors of all-cause mortality have not been reported in Chinese retired military veterans. The objective of the study was to examine the risk factors and proportional mortality in a Chinese retired military male cohort.