Mcmillanmarsh3744

New antihyperglycemic medications have been proven to have cardiovascular (CV) and renal benefits in type 2 diabetes mellitus (T2DM); however, an evidence-based decision tree in specific clinical scenarios is lacking.

Systematic review and meta-analysis of randomized controlled trials (RCTs), with trial sequential analysis (TSA). Randomized controlled trial inclusion criteria were patients with T2DM from 1 of these subgroups elderly, obese, previous atherosclerotic CV disease (ASCVD), previous coronary heart disease (CHD), previous heart failure (HF), or previous chronic kidney disease (CKD). Randomized controlled trials describing those subgroups with at least 48 weeks of follow-up were included. Outcomes 3-point major adverse cardiovascular events (MACE), CV death, hospitalization due to HF, and renal outcomes. We performed direct meta-analysis with the number of events in the intervention and control groups in each subset, and the relative risk of the events was calculated.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1 RA) were the only antihyperglycemic agents related to a reduction in CV events in different populations. For obese and elderly populations, GLP-1 RA were associated with benefits in 3-point MACE; for patients with ASCVD, both SGLT2i and GLP-1 RA had benefits in 3-point MACE, while for patients with CHD, only SGLT2i were beneficial.

SGLT2i and GLP-1 RA reduced CV events in selected populations SGLT2i led to a reduction in events in patients with previous CHD, ASCVD, and HF. GLP-1 RA led to a reduction in CV events in patients with ASCVD, elderly patients, and patients with obesity. Trial sequential analysis shows that these findings are conclusive. This review opens a pathway towards evidence-based, personalized treatment of T2DM.

PROSPERO CRD42019132807.

PROSPERO CRD42019132807.

Vascular endothelial growth factor (VEGF)-induced angiogenesis is a critical compensatory response to microvascular rarefaction in the diabetic retina that contributes to proliferative diabetic retinopathy (PDR). In this study, we sought to determine the role of specific micro ribonucleic acids (RNAs) (miRs) associated with VEGF in patients with PDR pathology.

RNA sequencing was employed to detect differentially circulating miR associated with VEGF in patients with diabetes mellitus (DM), nonproliferative diabetic retinopathy (NPDR) and PDR. Quantitative real-time polymerase chain reaction was performed to measure the concentration of miR-15b in the serum of patients with DM (n = 115), NPDR (n = 47), or PDR (n = 76). The effects of miR-15b on DR and regulation of VEGF and endothelial cell function were also characterized.

We demonstrated that circulating miR-15b was directly associated with VEGF compared with other miRs in patients with PDR. We found a significant inverse relationship between low levels of miR-15b and high levels of VEGF in patients with PDR when compared with the DM or NPDR groups. We found that miR-15b regulates the expression of VEGF by targeting the 3'-untranslated regions to inhibit its transcription. Similarly, overexpression of miR-15b suppressed vascular abnormalities in vivo in diabetic GK rats, inhibiting endothelial tube formation and VEGF expression.

Circulating miR-15b is associated with PDR and may be targeted to regulate VEGF expression and angiogenesis.

Circulating miR-15b is associated with PDR and may be targeted to regulate VEGF expression and angiogenesis.

Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available.

We applied an existing deep sequence model that had been pre-trained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that three-dimensional structure is also potentially learned during the unsupervised pre-training.

Implementations of all used models can be found at https//github.com/stamakro/GCN-for-Structure-and-Function.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.Thrombotic thrombocytopenic purpura (TTP) is an acute, life-threatening thrombotic microangiopathy (TMA) caused by acquired or congenital severe deficiency of ADAMTS13. Pregnancy is a recognized risk factor for precipitating acute (first or recurrent) episodes of TTP. Differential diagnosis with other TMAs is particularly difficult when the first TTP event occurs during pregnancy; a high index of suspicion and prompt recognition of TTP are essential for achieving a good maternal and fetal outcome. An accurate distinction between congenital and acquired cases of pregnancy-related TTP is mandatory for safe subsequent pregnancy planning. In this article, we summarize the current knowledge on pregnancy-associated TTP and describe how we manage TTP during pregnancy in our clinical practice.

International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses.

To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19.

This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Glutaraldehyde clinical trial Data analysis was performed in April 2020.

The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status.