Introduction: Only 3 types of coronavirus cause aggressive respiratory disease in humans (MERS-Cov, SARS-Cov-1, and SARS-Cov-2). It has been reported higher infection rates and severe manifestations (ICU admission, need for mechanical ventilation, and death) in patients with comorbidities such as diabetes mellitus (DM). For this reason, this study aimed to determine the prevalence of diabetes comorbidity and its associated unfavorable health outcomes in patients with acute respiratory syndromes for coronavirus disease according to virus types. Methods: Systematic review of literature in Pubmed/Medline, Scopus, Web of Science, Cochrane, and Scielo until April of 2020. We included cohort and cross-sectional studies with no restriction by language or geographical zone. The selection and extraction were undertaken by 2 reviewers, independently. The study quality was evaluated with Loney’s instrument and data were synthesized by random effects model meta-analysis. The heterogeneity was quantified using an I² statistic. Funnel plot, Egger, and Begg tests were used to evaluate publication biases, and subgroups and sensitivity analyses were performed. Finally, we used the GRADE approach to assess the evidence certainty (PROSPERO: CRD42020178049). Results: We conducted the pooled analysis of 28 studies (n = 5960). The prevalence analysis according to virus type were 451.9 diabetes cases per 1000 infected patients (95% CI: 356.74-548.78; I² = 89.71%) in MERS-Cov; 90.38 per 1000 (95% CI: 67.17-118.38) in SARS-Cov-1; and 100.42 per 1000 (95% CI: 77.85, 125.26 I² = 67.94%) in SARS-Cov-2. The mortality rate were 36%, 6%, 10% and for MERS-Cov, SARS-Cov-1, and SARS-Cov-2, respectively. Due to the high risk of bias (75% of studies had very low quality), high heterogeneity ( I² higher than 60%), and publication bias (for MERS-Cov studies), we down rate the certainty to very low. Conclusion: The prevalence of DM in patients with acute respiratory syndrome due to coronaviruses is high, predominantly with MERS-Cov infection. The unfavorable health outcomes are frequent in this subset of patients. Well-powered and population-based studies are needed, including detailed DM clinical profile (such as glycemic control, DM complications, and treatment regimens), comorbidities, and SARS-Cov-2 evolution to reevaluate the worldwide prevalence of this comorbidity and to typify clinical phenotypes with differential risk within the subpopulation of DM patients.

Background: Telemedicine is increasingly being used to provide virtual medical care. However, the transition to virtual consultations presents challenges for non-verbal communication. This scoping review aimed to identify and summarize studies that present data on barriers to non-verbal communication during teleconsultation. Methods: We searched MEDLINE/Pubmed, Ovid, APA, EBSCO, Web of Science and Scielo, without language or region restrictions. Our study included case series, cross-sectional, retrospective, and prospective cohorts that addressed barriers in any aspect of the non-verbal communication during teleconsultation. The quality of the evidence was assessed by the New Castle-Ottawa and Murad tools, and a thematic analysis was used for the qualitative synthesis of results. Results: We included 18 studies that reported qualitative findings related to the dimensions of non-verbal communication in telemedicine, which include ‘head and face’, ‘voice and speech’, ‘body language’, and ‘technical aspects’. The most reported barriers were facial gestures, looks, and body posture. Conclusions: Our study identified several dimensions of non-verbal communication that may pose barriers during teleconsultation. These findings may help guide the development of strategies to address these barriers and improve the quality of telemedicine services.

Individualized risk prediction models for colorectal cancer (CRC) play a pivotal role in shaping risk-based screening approaches, garnering attention for use in informed decision making by patients and clinicians. While the incorporation of new predictors and the development of advanced yet complex prediction models can enhance model performance, their practical implementation in clinical settings remains challenging. This systematic review assessed individualized CRC risk prediction models for their validity and potential clinical utility. Utilizing the Cochrane Collaboration methods and PROBAST tool, we conducted comprehensive searches across key databases and risk of bias assessment, respectively. Out of 41 studies included evaluating 44 risk prediction models, 12 conventional and 3 composite models underwent external validation. All risk models exhibited varying discriminatory accuracy, with the area under the curve (AUCs) ranging from 0.57 to 0.90. However, most studies showed an unclear or high risk of bias, with concerns about applicability. Of the five models with promising clinical utility, only two underwent external validation and one employed a decision curve analysis. These models demonstrated a discriminating and well-calibrated performance. While high-performing CRC risk prediction models exist, a need for transparent reporting of performance metrics and their clinical utility persists. Further research on this area is needed to facilitate the integration of these models into clinical practice, particularly in CRC screening.

Introduction The development of mental disorders is multifactorial across the lifespan. The introduction of the exposome concept has enhanced the understanding of life-course environmental factors by encompassing the totality of environmental exposures. While most studies on chronic diseases have applied a single-exposure approach, the exposome approach remains underutilised in mental disorder research. There is a need to better recognise the environmental factors considered in exposome analysis of mental disorders, the methodologies used and the gaps reported. This scoping review aims to map the evidence on the relationship between the exposome and mental disorders across the lifespan, identifying and describing the methodologies used and highlighting the gaps reported. Methods and analysis This scoping review will follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Population-Concept-Context approach. It will include observational and interventional studies involving populations of all ages in the community or healthcare settings. The search strategy will contain indexed terms in MEDLINE and will be adapted for CINAHL (EBSCO), Scopus, Embase and PsycINFO without restrictions on language or date of publication. For the selection of articles, two independent researchers will screen articles by title and abstract, followed by a full-text assessment. Afterwards, the extracted data will be summarised using a narrative and descriptive analysis. Ethics and dissemination Ethics approval is not required for this scoping review. Dissemination activities will include peer-reviewed publications and academic presentations.