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Clarifying the prevalence of vitamin D deficiency in diabetic patients, and the relationship between vitamin D concentration and insulin resistance, fasting plasma glucose, and HbA1C in patients in Hue City, Vietnam.
A cross-sectional study on 110 diabetic patients examined at Hue Central Hospital and Hue University of Medicine and Pharmacy Hospital. These patients were collected venous blood sampling, and the 25(OH)D test, fasting plasma glucose test, fasting insulin test, HOMA-IR and QUICKI calculation.
Vitamin D deficiency and insufficiency prevalence were 51.8%. The average concentration of 25(OH)D (ng/mL) was 30.67 ± 8.55; this concentration in fasting glucose level ≤8 mmol/l group and >8 mmol/l group was 32.08 ± 9.26 and 28.55±6.91 (p = 0.033); it was 32.95 ± 8.58 and 28.97 ± 8.17 in HOMA-IR ≤3.5 and HOMA-IR >3.5 group, (p = 0.015); in QUICKI ≤0.32 group, it was 29.16 ± 8.12; in QUICKI >0.32 group, it was 32.85 ± 8.76 (p = 0.025). Patients with an ideal exercise level have higher average , such as exercise level and sex, are related to vitamin D status.
To create an appropriate chronic kidney disease (CKD) management program, we developed a predictive model to identify patients in a large administrative claims database with CKD stages 3 or 4 who were at high risk for progression to kidney failure.
The predictive model was developed and validated utilizing a subset of patients with CKD stages 3 or 4 derived from a large Aetna claims database. The study spanned 36 months, comprised of a 12-month (2015) baseline period and a 24-month (2016-2017) prediction period. All patients were ≥18 years of age and continuously enrolled for 36 months. Multivariate logistic regression was used to develop models. Guanylate Cyclase inhibitor Prediction model performance measures included area under the receiver operating characteristic curve (AUROC), calibration, and gain and lift charts.
Of the 74,114 patients identified as having CKD stages 3 or 4 during the baseline period, 2476 (3.3%) had incident kidney failure during the prediction period. The predictive model included the effect of numerous ifying, from a large national database, patients with CKD who were at high risk of progressing to kidney failure within 2 years. Early identification using this model could potentially lead to improved health outcomes and reduced healthcare expenditures in this at-risk population.
The global impact of COVID-19 on mental health increases from time to time. Several studies show that depression is highly prevalent among quarantined individuals. COVID-19 is a pandemic with a rapidly increasing incidence of infections and deaths. People are depressed and psychologically overwhelmed by the illness and possible loss of their friends and loved ones.
To assess the prevalence and associated factors of depression symptoms among quarantined individuals in Tigrai treatment center, Tigrai, Ethiopia, 2020.
A multicenter Institution-based cross-sectional study was employed among individuals in the Tigrai quarantine centers. A simple random sampling technique was used between April and October 2020 until an adequate sample size was reached. Depression was assessed by using the depression, anxiety, and stress scale (DASS). Epi data manager version 4.4 was used to enter data and data was analyzed using SPSS version 20. Logistic regression was carried out and an odds ratio with 95% confidence intervals during the COVID-19 pandemic was 18.1%. In multivariable logistic regression analysis, being female, duration of quarantine, unemployment, and having perceived stigma were significantly associated with depression. So, clinicians, mental health professionals, and policymakers should work together to address the problem.
This study aims to present a modified Laboratory Risk Indicator for Necrotizing Fasciitis (m-LRINEC) scoring system and to evaluate its ability in discriminating necrotizing fasciitis (NF) from other severe soft-tissue infections.
Patients with NF diagnosed by surgical findings in our institution between January 2014 and December 2020 were included as the case group, matched by controls with severe soft-tissue infections other than NF in a ratio of 21, based on demographics, calendar time and immunosuppressant status. Patients' demographics, comorbidities and laboratory test results were extracted from medical records. Logistic regression analyses were used to determine the association with NF after adjustment for confounders, whereby m-LRINEC was developed. Receiver operating characteristics (ROC) curves and the area under the curve (AUC) were used to evaluate its discriminating ability.
There were 177 patients included, 59 in the NF group and 118 in the non-NF group. We added comorbid diabetes and kidney disease to the original LRINEC scoring system, used high-sensitivity C-reactive protein (HCRP) to replace the CRP and redefined the cut-off values for the other four variables, to develop the m-LRINEC system. The cut-off value for m-LRINEC was 17 points, with corresponding sensitivity of 93.2% and specificity of 86.9%, and the AUC was 0.935 (95% CI 0.892 to 0.977;
<0.001).
The m-LRINEC scoring system shows a high sensitivity and specificity in discriminating NF from other severe soft-tissue infections. Patients with an m-LRINEC score of >17 points should have a high index of suspicion for the presence of NF. The validity of the m-LRINEC needs to be confirmed in studies with larger samples and better design.
17 points should have a high index of suspicion for the presence of NF. The validity of the m-LRINEC needs to be confirmed in studies with larger samples and better design.
Central nervous system infections (CNSI) are serious diseases that endanger human health. Identifying pathogens and their susceptibility to antibiotics, and promptly using antibiotics under this guidance is essential for treatment. The purpose of this study is toinvestigate the pathogen characteristics of CNSI patients, which can help clinicians choose appropriate empiric antibiotic .
We retrospectively collected data on CNSI patients with cerebrospinal fluid (CSF) culture positive from 2012 to 2020, including demographic characteristics, laboratory data, pathogenic bacteria, and antimicrobial susceptibility test results.
A total of 166 patients with 168 isolates out of 8188 patients were available for data analysis. Among the isolates, Gram-positive bacteria, Gram-negative bacteria and fungi accounted for 59.5%, 36.3%, and 4.2%, respectively. Among newborns, children under 12, and patients over 12, the most isolated strains were
(24/46, 52.2%),
(21/68, 30.9%) and
(10/54, 18.5%), respectively.
is more sensitive to linezolid and vancomycin.