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Patients with diabetes have an increased risk of fractures. In this study, subtrochanteric and femoral shaft fractures were increased in patients with type 1 diabetes compared with the general population. In the light of this, more evidence points towards an association between diabetes and atypical femoral fractures.INTRODUCTION: Patients with diabetes have an increased risk of femoral fractures, but little is known about the risk of atypical femoral fractures (AFFs). The aim of this study was to identify the risk of subtrochanteric and femoral shaft (ST/FS) fractures and estimate the risk of AFFs in patients with type 1 (T1D) and type 2 diabetes (T2D).METHODS: From the nationwide Danish National Patient Register, we identified patients with T1D (n = 19,896), T2D (n = 312,188), and sex- and aged-matched controls without diabetes (n = 996,252) from the general population and all ST/FS fractures (n = 7509). Data were analyzed using a Cox proportional-hazards model and the incidence rate and rate ratio of ST/FS fractures were estimated.RESULTS: The incidence rate of ST/FS fractures in T1D was 52.14 events per 100,000 person years and 73.21 per 100,000 person years in T2D. T1D was associated with an increased risk of ST/FS (HR 2.07 (95% CI 1.68-2.56)), whereas T2D was not (HR 0.99 (95% CI 0.94-1.10)). Previous ST/FS fractures were associated with an increased risk of subsequent ST/FS fractures (HR 6.95 (95% CI 6.00-8.05)) and the use of bisphosphonates with an increased risk of ST/FS fractures (HR 1.72 (95% CI 1.54-1.91)).CONCLUSION: Patients with T1D have a higher risk of ST/FS fractures compared with sex- and age-matched controls. Since a proportion of ST/FS fractures are classified as AFFs, this could point towards the fact that AFFs also are increased in patients with T1D, but not T2D.

For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.

Objective Venous (VTE) and arterial (AT) thrombosis in systemic lupus erythematosus (SLE) are poorly explained and difficult to predict. Leptin and tumour necrosis factor-like weak inducer of apoptosis (TWEAK) have been linked to subclinical atherosclerosis and galectin-3-binding protein (G3BP) to type I interferon activation and a pro-thrombotic environment. Thus, we explore serum G3BP, interferon gamma-induced protein 10 (IP-10), soluble CD163 (sCD163), TWEAK and leptin as predictors of VTE and AT, damage accrual, and all-cause mortality during follow-up in a Swedish SLE cohort. Methods Baseline data were available from 162 SLE patients. VTE (deep vein thrombosis and/or pulmonary embolism), AT (myocardial infarction and/or stroke), damage accrual, and survival data were the main study outcomes and available at follow-up (median of five years). Baseline serum G3BP, IP-10, sCD163, TWEAK and leptin were measured and analysed by univariable and multivariable methods for association to the study outcomes. Results During the follow-up, 10 (6%) VTE and 13 (8%) AT events occurred. The SLICC/ACR Damage Index increased in 78 (48%) patients, and 19 (12%) patients died. In the univariable regression analysis G3BP levels were significantly associated with an increased risk of VTE (hazard ratio (HR) 1.11, 95% confidence interval (CI): 1.01-1.22, p=0.03). This persisted in the adjusted multivariable analyses (HR 1.18, 95% CI: 1.05-1.33, p=0.007). The other biomarkers were not associated with AT/VTE, damage accrual, or all-cause mortality. Conclusion Our study identifies serum G3BP as a novel predictor of VTE in SLE. Further studies are needed to understand the role of G3BP in VTE and translate this into clinical practice.

© 2019 Wiley Periodicals, Inc. In 1972 Body Mass Index, BMI was put forth by physiologist Ancel Keys in his analysis of Seven Countries Study heart disease epidemiological data as the best available measure of obesity. This work culminated more than 20 years of effort by Keys to discredit the accepted measure of obesity, weight relative to height, along with a major public health campaign in the United States to fight heart disease through weight control. Here, I retrace his campaign to replace weight as a measure of obesity and analyze its methodology and relationship to the broader research field of heart disease epidemiology. I also explore why the epidemiological community accepted BMI despite Keys's failure to demonstrate that either it or adiposity (body fat content), were superior as predictors of heart disease—one of the Seven Countries Study's central aims.