Hidradenitis suppurativa and its association with obesity, smoking, and diabetes mellitus: A systematic review and meta-analysis.

Our meta-analysis aimed to quantify the association between Hidradenitis suppurativa (HS) and several risk factors including obesity, smoking, and type 2 diabetes mellitus (T2DM). We searched PubMed, Scopus, Embase, Web of Science, and cumulative index to nursing and allied health literature for articles reporting either the odds ratio (OR) or the numbers of HS cases associated with obesity, smoking, or T2DM, and including HS negative controls. Risk of bias was assessed against the risk of bias in non-randomized studies of interventions tool. Data synthesis was done using the random effects model with heterogeneity being evaluated with I2 statistic. Twenty-three studies with a total of 29 562 087 patients (average age of 36.6 years) were included. Ten studies relied on country-level data, while six studies collected their data from HS clinics. The analysis showed a significant association between HS and female sex (OR 2.34, 95% CI 1.89-2.90, I2 = 98.6%), DM (OR 2.78, 95% CI 2.23-3.47, I2 = 98.9%), obesity (OR 2.48, 95% CI 1.64-3.74, I2 = 99.9%), and smoking (OR 3.10 95% CI 2.60-3.69, I2 = 97.1%). Our meta-analysis highlights HS links to sex, DM, obesity, and smoking, with emphasis on holistic management approach. Further research is needed on molecular mechanisms and additional risk factors for improved patient care.

Salivary α-amylase activity is associated with cardiometabolic and inflammatory biomarkers in overweight/obese, non-diabetic Qatari women.

Introduction: Obesity, prevalent in approximately 80% of Qatar's adult population, increases the risk of complications like type 2 diabetes and cardiovascular diseases. Predictive biomarkers are crucial for preventive strategies. Salivary α-amylase activity (sAAa) inversely correlates with obesity and insulin resistance in adults and children. However, the connection between sAAa and cardiometabolic risk factors or chronic low-grade inflammation markers remains unclear. This study explores the association between serum sAAa and adiposity markers related to cardiovascular diseases, as well as markers indicative of chronic low-grade inflammation. Methods: Serum samples and clinical data of 1500 adult, non-diabetic, Overweight/Obese participants were obtained from Qatar Biobank (QBB). We quantified sAAa and C reactive protein (CRP) levels with an autoanalyzer. Cytokines, adipokines, and adiponectin of a subset of 228 samples were quantified using a bead-based multiplex assay. The associations between the sAAa and the adiposity indices and low-grade inflammatory protein CRP and multiple cytokines were assessed using Pearson's correlation and adjusted linear regression. Results: The mean age of the participants was 36 ± 10 years for both sexes of which 76.6% are women. Our analysis revealed a significant linear association between sAAa and adiposity-associated biomarkers, including body mass index ß -0.032 [95% CI -0.049 to -0.05], waist circumference ß -0.05 [95% CI -0.09 to -0.02], hip circumference ß -0.052 [95% CI -0.087 to -0.017], and HDL ß 0.002 [95% CI 0.001 to 0.004], albeit only in women. Additionally, sAAa demonstrated a significant positive association with adiponectin ß 0.007 [95% CI 0.001 to 0.01]while concurrently displaying significant negative associations with CRP ß -0.02 [95% CI -0.044 to -0.0001], TNF-α ß -0.105 [95% CI -0.207 to -0.004], IL-6 ß [95% CI -0.39 -0.75 to -0.04], and ghrelin ß -5.95 [95% CI -11.71 to -0.20], specifically within the female population. Conclusion: Our findings delineate significant associations between sAAa and markers indicative of cardiovascular disease risk and inflammation among overweight/obese adult Qatari females. Subsequent investigations are warranted to elucidate the nuances of these gender-specific associations comprehensively.

Metabolic-associated fatty liver disease: a selective review of pathogenesis, diagnostic approaches, and therapeutic strategies.

Background: Metabolic associated fatty liver disease (MAFLD) is a novel terminology introduced in 2020 to provide a more accurate description of fatty liver disease associated with metabolic dysfunction. It replaces the outdated term nonalcoholic fatty liver disease (NAFLD) and aims to improve diagnostic criteria and tailored treatment strategies for the disease. NAFLD, the most prevalent liver disease in western industrialized nations, has been steadily increasing in prevalence and is associated with serious complications such as cirrhosis and hepatocellular carcinoma. It is also linked to insulin resistance syndrome and cardiovascular diseases. However, current studies on NAFLD have limitations in meeting necessary histological endpoints. Objective: This literature review aims to consolidate recent knowledge and discoveries concerning MAFLD, integrating the diverse aspects of the disease. Specifically, it focuses on analyzing the diagnostic criteria for MAFLD, differentiating it from NAFLD and alcoholic fatty liver disease (AFLD), and exploring the epidemiology, clinical manifestations, pathogenesis, and management approaches associated with MAFLD. The review also explores the associations between MAFLD and other conditions. It discusses the heightened mortality risk associated with MAFLD and its link to chronic kidney disease (CKD), showing that MAFLD exhibits enhanced diagnostic accuracy for identifying patients with CKD compared to NAFLD. The association between MAFLD and incident/prevalent CKD is supported by cohort studies and meta-analyses. Conclusion: This literature review highlights the importance of MAFLD as a distinct terminology for fatty liver disease associated with metabolic dysfunction. The review provides insights into the diagnostic criteria, associations with CKD, and management approaches for MAFLD. Further research is needed to develop more accurate diagnostic tools for advanced fibrosis in MAFLD and to explore the underlying mechanisms linking MAFLD with other conditions. This review serves as a valuable resource for researchers and healthcare professionals seeking a comprehensive understanding of MAFLD.

The prevalence of adaptive immunity to COVID-19 and reinfection after recovery - a comprehensive systematic review and meta-analysis.

This study aims to estimate the prevalence and longevity of detectable SARS-CoV-2 antibodies and T and B memory cells after recovery. In addition, the prevalence of COVID-19 reinfection and the preventive efficacy of previous infection with SARS-CoV-2 were investigated. A synthesis of existing research was conducted. The Cochrane Library, the China Academic Journals Full Text Database, PubMed, and Scopus, and preprint servers were searched for studies conducted between 1 January 2020 to 1 April 2021. Included studies were assessed for methodological quality and pooled estimates of relevant outcomes were obtained in a meta-analysis using a bias adjusted synthesis method. Proportions were synthesized with the Freeman-Tukey double arcsine transformation and binary outcomes using the odds ratio (OR). Heterogeneity was assessed using the I2 and Cochran's Q statistics and publication bias was assessed using Doi plots. Fifty-four studies from 18 countries, with around 12,000,000 individuals, followed up to 8 months after recovery, were included. At 6-8 months after recovery, the prevalence of SARS-CoV-2 specific immunological memory remained high; IgG - 90.4% (95%CI 72.2-99.9, I2 = 89.0%), CD4+ - 91.7% (95%CI 78.2-97.1y), and memory B cells 80.6% (95%CI 65.0-90.2) and the pooled prevalence of reinfection was 0.2% (95%CI 0.0-0.7, I2 = 98.8). Individuals previously infected with SARS-CoV-2 had an 81% reduction in odds of a reinfection (OR 0.19, 95% CI 0.1-0.3, I2 = 90.5%). Around 90% of recovered individuals had evidence of immunological memory to SARS-CoV-2, at 6-8 months after recovery and had a low risk of reinfection.RegistrationPROSPERO: CRD42020201234.

Assessing the Response of Diverse Sesame Genotypes to Waterlogging Durations at Different Plant Growth Stages.

Sesame is sensitive to waterlogging, and its growth is devastatingly impacted under excess moisture conditions. Thus, waterlogging tolerance is crucial to alleviate yield constraints, particularly under expected climate change. In this study, 119 diverse sesame genotypes were screened for their tolerance to 12, 24, 48, and 72 h of waterlogging relative to non-waterlogged conditions. All plants died under 72 h of waterlogging, while 13.45%, 31.93%, and 45.38% of genotypes survived at 48, 24, and 12 h, respectively. Based on the seedling parameters and waterlogging tolerance coefficients, genotypes BD-7008 and BD-6985 exhibited the highest tolerance to waterlogging, while BD-6996 and JP-01811 were the most sensitive ones. The responses of these four genotypes to waterlogged conditions were assessed at different plant growth stages-30, 40, and 50 days after sowing (DAS)-versus normal conditions. Waterlogging, particularly when it occurred within 30 DAS, destructively affected the physiological and morphological characteristics, which was reflected in the growth and yield attributes. Genotype BD-7008, followed by BD-6985, exhibited the highest chlorophyll and proline contents as well as enzymatic antioxidant activities, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). These biochemical and physiological adjustments ameliorated the adverse effects of waterlogging, resulting in higher yields for both genotypes. Conversely, JP-01811 presented the lowest chlorophyll and proline contents as well as enzymatic antioxidant activities, resulting in the poorest growth and seed yield.

Classifying MRI motion severity using a stacked ensemble approach.

Motion artifacts are a common occurrence in Magnetic Resonance Imaging exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiologists, affecting their diagnostic quality. There is a paucity of clinical tools to identify and quantitatively assess the severity of motion artifacts in MRI. An image with subtle motion may still have diagnostic value, while severe motion may be uninterpretable by radiologists and requires the exam to be repeated. Therefore, a tool for the automatic identification of motion artifacts would aid in maintaining diagnostic quality, while potentially driving workflow efficiencies. Here we aim to quantify the severity of motion artifacts from MRI images using deep learning. Impact of subject movement parameters like displacement and rotation on image quality is also studied. A state-of-the-art, stacked ensemble model was developed to classify motion artifacts into five levels (no motion, slight, mild, moderate and severe) in brain scans. The stacked ensemble model is able to robustly predict rigid-body motion severity across different acquisition parameters, including T1-weighted and T2-weighted slices acquired in different anatomical planes. The ensemble model with XGBoost metalearner achieves 91.6% accuracy, 94.8% area under the curve, 90% Cohen's Kappa, and is observed to be more accurate and robust than the individual base learners.

A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops.

Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants-canola, corn, soybean, and wheat-are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination () of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an of 18.02%, corn showed an of 68.41%, soybean showed an of 46.38%, and wheat showed an of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.