Purpose: The aim of this study was to investigate the relationship between obesity and cognitive impairment in the Chinese elderly. Patients and methods: Patients aged 60 years or above were enrolled from the Department of Geriatrics of The First Affiliated Hospital of Chongqing Medical University from March 2013 to May 2017. Mini-Mental State Examination scale was used to assess the cognitive function. Body mass index (BMI) and waist–hip ratio were used to classify obesity. Student’s t-test, Mann–Whitney U-test, or chi-squared test was used to compare the data between participants with normal cognition and participants with cognitive impairment as appropriate. Univariate logistic regression models and multivariate logistic regression models were performed to explore the relationship between BMI or abdominal obesity and cognitive impairment. Results: A total of 1,100 patients including 568 men and 532 women aged 60–98 years (median age 79 years) were enrolled. After adjusting for age, gender, smoking, drinking, education level, hypercholesterolemia, hypertension, and diabetes, overweight was significantly associated with a decreased risk of cognitive impairment (OR=0.458, 95% CI=0.298–0.703, P<0.001). After adjustment for age, education level, hypercholesterolemia, hypertension, and diabetes, abdominal obesity remained significantly associated with an increased risk of cognitive impairment (OR=1.532, 95% CI=1.037–2.263, P=0.032). Conclusion: Overweight is associated with a decreased risk of cognitive impairment in the Chinese elderly, while abdominal obesity is associated with an increased risk of cognitive impairment independent of conventional sociodemographic, lifestyle, and health-related comorbid factors.

Purpose: To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs).Methods: This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses.Results: SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model.Conclusion: A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Purpose: The increasing incidence of dementia and home-based care exposes family caregivers to a variety of challenges as they endure strong stressors underlying the caregiver role. Despite growing publications on positive aspects of caregiving in dementia, few studies have identified the extent, nature, and gaps in the existing literature based on a holistic view. The aim was to identify key issues and a holistic view of literature regarding positive aspects of caregiving in dementia.Methods: A scoping review was conducted underlying a five-stage framework by Arksey and O'Malley. Five databases, including PubMed, CINAHL, PsycINFO, Embase, and Web of Science, were searched, and references were listed accordingly. Data were extracted by two researchers, comprising article characteristics, forms of positive aspects of caregiving and measurements, theories, forms of dementia and family caregiver, and keywords. Descriptive statistics and narrative synthesis were performed to analyze data. Network analysis of keywords and authors was conducted using VOSviewer software. Word cloud analysis of titles was examined by NVivo.Results: The review included 230 articles, most of which have been published in the last decade (62.61%). Most articles (40.00%) were contributed by the United States. Cross-sectional studies (41.30%) ranked first, followed by qualitative studies (13.48%). Over a quarter of the literature (28.26%) focused on Alzheimer's care, and nearly 90% included all forms of family caregivers. The Positive Aspects of Caregiving Scale and stress coping theory were most frequently cited. Four clusters dominated by Casey D, Quinn C, Joling KJ, and Teahan A were identified in the network of co-authorship. Six themes were identified: current situations of caregiver experiences, antecedents, consequences, measurement development, effects of interventions, and the concept of positive aspects of caregiving. These were in line with network analysis of keywords and word cloud analysis of titles.Conclusions: Positive aspects of caregiving in dementia have been widely concerned, but most of them are based on the theory of the negative stress process and are limited to current situations and influencing factors. Building theories focus on the positive aspects of caregiving, subsequently developing a comprehensive measurement and effective interventions, should be further studied.

Purpose: ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine. Methods: This scope follows Arksey and O’Malley’s five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis. Results: Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism. Conclusion: The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.