Revised Glycemic Index for Diagnosing and Monitoring of Diabetes Mellitus in South Indian Population

AIM: To find the optimal threshold of fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) for diagnosis of diabetes mellitus (DM) and to evaluate the association with diabetic retinopathy (DR) in the South Indian population. SETTINGS AND DESIGN: A retrospective population-based study. METHODS AND MATERIALS: A total of 909 newly detected type 2 DM patients were selected from our two previously conducted studies, which include an urban and a rural population of South India. All underwent estimation of fasting, postprandial plasma glucose (PPG), and other biochemical tests. A comprehensive and detailed ophthalmic examination was carried out. The fundi of patients were photographed using 45°, four-field stereoscopic photography. Based on receiver operating characteristic (ROC) curves, sensitivity and specificity were derived. RESULTS:  The optimal cut-off values determined by maximizing the sensitivity and specificity of FPG and HbA1c using the Youden index were ≥ 6.17 mmol/L and ≥ 6.3%, respectively. By distributing the cut-off points into deciles and comparing them to the WHO criteria, we found that our HbA1c level of 6.60% was more than the WHO threshold (6.5%), with higher sensitivity (81.6%) and lower specificity (48.3%). The FPG level of 6.80 mmol/L was lower to the WHO criteria (7 mmol/L) with increased sensitivity (77.0%) and lower specificity (45.7%). Prevalence of DR by HbA1c levels between 6.5% and 6.9% was 15.3%. The prevalence of DR was more in the FPG category between 6.4 and 6.9 mmol/L and ≥ 7.5 mmol/L. CONCLUSION: Our population-based data indicate that for the South Indian population HbA1c value of ≥63 % and FPG value of ≥6.17 mmol/L may be optimal for diagnosing DM with a high level of accuracy and will be useful for the identification of mild and moderate DR

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Cohort Profile: Burden of Obstructive Lung Disease (BOLD) study

The Burden of Obstructive Lung Disease (BOLD) study was established to assess the prevalence of chronic airflow obstruction, a key characteristic of chronic obstructive pulmonary disease, and its risk factors in adults (≥40 years) from general populations across the world. The baseline study was conducted between 2003 and 2016, in 41 sites across Africa, Asia, Europe, North America, the Caribbean and Oceania, and collected high-quality pre- and post-bronchodilator spirometry from 28 828 participants. The follow-up study was conducted between 2019 and 2021, in 18 sites across Africa, Asia, Europe and the Caribbean. At baseline, there were in these sites 12 502 participants with high-quality spirometry. A total of 6452 were followed up, with 5936 completing the study core questionnaire. Of these, 4044 also provided high-quality pre- and post-bronchodilator spirometry. On both occasions, the core questionnaire covered information on respiratory symptoms, doctor diagnoses, health care use, medication use and ealth status, as well as potential risk factors. Information on occupation, environmental exposures and diet was also collected

Computer simulations of biological systems: from protein dynamics to drug discovery

Computational biophysics methods such as molecular dynamics (MD) simulations are often used in combination with experimental techniques like neutron scattering, NMR, and FTIR to explore protein conformational landscapes. With the improvements in experimental techniques, there is also a need to continually optimize the MD forcefield parameters to make precise predictions that match experimental results. To complement many of these experiments, an accurate model of deuteration is frequently required, but has been elusive. In our work, we developed a novel method to capture isotope effects in classical MD simulations by re-parameterization of the bonded terms of the CHARMM forcefield using quantum mechanical (QM) calculations. Apart from this, MD simulations can also be applied to explore a range of protein motions over different timescales, which are otherwise experimentally challenging. This work captures three such studies on protein dynamics- 1) the role of a) global and b) local motions in facilitating ligand binding to various adhesin protein homologs, and 2) a comparative study on the effect of temperature and pressure changes on the dynamics of thermophilic and mesophilic pyrophosphatases. In the case of adhesin proteins, we identified that the local motion in the loops near their binding pockets is critical for ligand selectivity, whereas the global inter-domain orientation in the protein is important for binding to the platelets. In the case of pyrophosphatases, our studies revealed that the number of hydrogen bonds, in the respective catalytic pockets of the two homologs, vary with temperature which potentially causes the observed differences in their experimental enzymatic activities. Finally, another emerging application of computational biophysics is in the field of therapeutic research, i.e., to identify new drugs and therapies to cure lethal diseases by incorporating information about the target protein’s dynamics into structure-based drug discovery. Implementing a pipeline that includes ensemble docking and consensus scoring, we successfully targeted two proteins: 1) Histone deacetylase (HDAC) 4 and 2) adhesin protein Hsa, which are known to cause prostate cancer and infective endocarditis, respectively. For both the targets, we identified multiple novel small molecules that also inhibit these proteins in-vitro

Adaptive probabilistic approach for selecting tumour knee prosthesis

Tumour knee prostheses reconstruct bone gaps, left after resecting the tumour affected tissues, in limb salvage surgeries of bone cancer patients. They typically comprise of 6-12 different components chosen from a family of components that are manufactured in discrete variations (intended to cater to a wide range of patient conditions including gender, tumour position, leg (left/right), and resection length). These variations generate numerous combinations and selecting a correct set of components from a family of 100 or more total components has made the process difficult for a given patient. This article describes an adaptive probabilistic approach developed for selection of tumour knee prosthesis components, driven by geometric details. These details were extracted from the 3D virtual anatomical model, reconstructed from set of CT scan images of patients. The selection was performed in two steps. First, the grossly undersized and oversized components were eliminated. Then the geometric details of components were mapped, with the measured anatomical parameters of the patient, to form a fuzzy-logic based decision tree. This was based on pre-defined rules compiled from surgeons' experience. A set of measures (geometric difference, bone curvature, knee centre shift, and reconstruction length) were used to evaluate the selected prosthesis components. Evaluation was based on their suitability with respect to the patient's anatomy, and classified with a qualitative tag: 'most suitable', 'probably suitable', or 'not suitable'. A case study of distal femur replacement is presented to explain the proposed methodology. This approach eliminates the risk of over and under sizing of the prosthesis components and reduces the average inventory to be maintained for each patient

Semi-automated quantitative analysis of the middle limiting membrane in tubercular serpiginous-like choroiditis using swept-source optical coherence tomography

10.1038/s41598-021-02894-9Scientific Reports1112349

A CROSS-SECTIONAL STUDY ON CORRELATION BETWEEN SERUM URIC ACID LEVEL AND CAROTID ATHEROSCLEROSIS IN PATIENTS OF TYPE 2 DIABETES

Objective: The objective of the study was to assess correlation between uric acid (UA) level and carotid intima media thickness (CIMT) in type 2 diabetes mellitus (DM) patients. Methods: The study was conducted in the Department of Medicine, SMS Medical College and Attached Hospital, among adults with type 2 DM. It was cross-sectional study conducted from April 1, 2019, to December 31, 2020. Sample size is calculated 60 patients of Type 2 DM. As per previous study show correlation coefficient between serum UA (SUA) level and CIMT (r=0.779)52 (For 90% power and 1% α error). CIMT and carotid artery plaques were measured through Doppler ultrasound. The thickness was measured at 1 cm proximal to the dilatation of the carotid bulb. The mean of maximum IMT of both the common carotid artery is taken as the average CIMT. Results: Most patients (60%) were diabetics since 5 years and nearly 30% had diabetes since 5–15 years while only 10% had diabetes since more than 15 years. Mean duration of diabetes was 6.91±5.88 years. Two thirds of diabetic individuals (67%) had HDL cholesterol level above 40 mg/dL. About 42% of diabetic individuals had triglyceride level &lt;150 mg/dL and 5% of diabetic individuals had LDL cholesterol level &lt;100 mg/dL. Mean HDL cholesterol, Triglycerides, and LDL cholesterol were 47.4±15.49 mg/dL, 153.78±81.56 mg/dL, and 92.33±57.28 mg/dL, respectively. Mean bilirubin and serum creatinine in study population were 0.65±0.48 mg/dL and 0.96±0.18 mg/dL, respectively. Mean CRP was 3.95±2.40 mg/L. Mean SUA level in study population was 5.78±2.18 mg/dL. Mean of average CIMT was found to be 8.0±1.16 mm. Glycated hemoglobin (HbA1c) and UA had negative weak linear correlation which was statistically significant. HbA1c and average CIMT had no or week negative correlation which was not statistically significant and SUA and average CIMT showed positive moderate linear correlation which was statistically significant. Conclusion: Carotid atherosclerosis as measured by IMT is associated with SUA levels in patients with type 2 DM. In type 2 DM patients, HbA1C is negatively correlated with UA while HbA1C has no correlation with CIMT