Risk factors for pulmonary mucormycosis in subjects with diabetes mellitus-A case-control study.

BACKGROUND: Factors associated with pulmonary mucormycosis (PM) among subjects with diabetes mellitus (DM) remain unclear. Following the coronavirus disease (COVID-19)-associated mucormycosis outbreak in India, specific environmental exposures (especially cattle dung exposure) were proposed as possible aetiology. We hypothesized that environmental factors are associated with PM. We compared subjects with DM with (cases) and without PM (controls). METHODS: In this case-control study, for each PM case, we included five unmatched diabetic controls (hospital [n = 2], community [n = 3]) without PM. We collected information on demography, COVID-19 infection, glycated haemoglobin% (HbA1c), the type of house (pucca vs. kutcha) where the participants reside, and other environmental factors. The primary exposure tested was cattle dung exposure (CDE; using cattle dung cakes as fuel or cattle handling). We performed a multivariate logistic regression to explore factors associated with PM and report the association as an adjusted odds ratio (OR) with 95% confidence intervals (CI). RESULTS: We enrolled 39 PM cases and 199 controls (hospital [n = 80], community [n = 119]). CDE (OR 0.68, 95% CI [0.14-3.31]; p = 0.63) was not associated with increased PM in DM. We found male sex (OR 4.07, 95% CI [1.16-14.31]), higher HbA1c (OR 1.51, 95% CI [1.18-16.32]), COVID-19 (OR 28.25, 95% CI [7.02-113.6]) and residence at kutcha house (OR 4.84, 95% CI [1.33-17.52]) associated with PM. CONCLUSION: Cattle dung exposure was not associated with PM in subjects with DM. Instead, male sex, poor glycaemic control, COVID-19 and the type of housing were associated with pulmonary mucormycosis.

Flavan-based phytoconstituents inhibit Mpro, a SARS-COV-2 molecular target, in silico.

A well-validated in-silico approach can provide promising drug candidates for the treatment of the ongoing CoVID19 pandemic. In this study, we have screened 32 phytochemical constituents (PCCs) with Mpro binding site (PDB:6W63) based on which we identified three possible candidates that are likely to be effective against CoVID19-viz., licoleafol (binding energy: -8.1 kcal/mol), epicatechin gallate (-8.5 kcal/mol) and silibinin (-8.4 kcal/mol) that result in higher binding affinity than the known inhibitor, X77 (-7.7 kcal/mol). Molecular dynamics (MD) simulations of PCCs-Mpro complex confirmed molecular docking results with high structural and dynamical stability. The selected compounds were found to exhibit low mean squared displacements (licoleafol: 2.25 ± 0.43 Å, epicatechin gallate: 1.93 ± 0.35 Å, and silibinin: 1.39 ± 0.19 Å) and overall low fluctuations of the binding complexes (root mean squared fluctuations below 2 Å). Visualization of the MD trajectories and structural analyses revealed that they remain confined to the initial binding region, with mean fluctuations lower than 3 Å. To access the collective motion of the atoms, we performed principal component analysis demonstrating that the first 10 principal components are the major contributors (approximate contribution of 80%) and are responsible for the overall PCCs motion. Considering that the three selected PCCs share the same flavan backbone and exhibit antiviral activity against hepatitis C, we opine that licoleafol, epi-catechin gallate, and silibinin can be promising anti-CoVID19 drug candidates.Communicated by Ramaswamy H. Sarma.

Determining clinically relevant features in cytometry data using persistent homology.

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

Spectrum of Endocrine Dysfunction and Association With Disease Severity in Patients With COVID-19: Insights From a Cross-Sectional, Observational Study.

Introduction: Evidence on new-onset endocrine dysfunction and identifying whether the degree of this dysfunction is associated with the severity of disease in patients with COVID-19 is scarce. Patients and Methods: Consecutive patients enrolled at PGIMER Chandigarh were stratified on the basis of disease severity as group I (moderate-to-severe disease including oxygen saturation <94% on room air or those with comorbidities) (n= 35) and group II (mild disease, with oxygen saturation >94% and without comorbidities) (n=49). Hypothalamo-pituitary-adrenal, thyroid, gonadal axes, and lactotroph function were evaluated. Inflammatory and cell-injury markers were also analysed. Results: Patients in group I had higher prevalence of hypocortisolism (38.5 vs 6.8%, p=0.012), lower ACTH (16.3 vs 32.1pg/ml, p=0.234) and DHEAS (86.29 vs 117.8µg/dl, p= 0.086) as compared to group II. Low T3 syndrome was a universal finding, irrespective of disease severity. Sick euthyroid syndrome (apart from low T3 syndrome) (80.9 vs 73.1%, p= 0.046) and atypical thyroiditis (low T3, high T4, low or normal TSH) (14.3 vs 2.4%, p= 0.046) were more frequent in group I than group II. Male hypogonadism was also more prevalent in group I (75.6% vs 20.6%, p=0.006) than group II, with higher prevalence of both secondary (56.8 vs 15.3%, p=0.006) and primary (18.8 vs 5.3%, p=0.006) hypogonadism. Hyperprolactinemia was observed in 42.4% of patients without significant difference between both groups. Conclusion: COVID-19 can involve multiple endocrine organs and axes, with a greater prevalence and degree of endocrine dysfunction in those with more severe disease.

Endocrine Dysfunction in Covid-19

Introduction: Evidence pertaining to new-onset endocrine dysfunction in patients with COVID-19 is currently limited and extrapolated from prior SARS epidemics. Further, identifying whether the quantum of this dysfunction is associated with the severity of disease in patients with COVID-19 is unknown. We aimed to to comprehensively explore the prevalence, nature and degree of endocrine dysfunction stratified based on disease severity at a dedicated COVID care centre.

Dipeptidyl peptidase-4 inhibitor use and mortality in COVID-19 patients with diabetes mellitus: an updated systematic review and meta-analysis.

BACKGROUND: Few observational studies have shown a beneficial effect of dipeptidyl peptidase-4 inhibitors (DPP4i) in patients with coronavirus disease 2019 (COVID-19), although results are not consistent. The present systematic review and meta-analysis was undertaken to provide a precise summary of the effect of DPP4i use (preadmission or in-hospital) and mortality in COVID-19 patients with diabetes mellitus (DM). METHODS: PubMed and Google Scholar databases were systematically searched using appropriate keywords to 4 January 2021, to identify observational studies reporting mortality in COVID-19 patients with DM using DPP4i versus those not using DPP4i. Preadmission and in-hospital use of DPP4i were considered. Study quality was assessed using the Newcastle-Ottawa Scale. Unadjusted and adjusted pooled odds ratio (OR) with 95% confidence intervals (CIs) were calculated. Subgroup analysis was performed for studies reporting preadmission and in-hospital use of DPP4i. RESULTS: We identified nine observational studies of high quality pooling data retrieved from 7008 COVID-19 patients with DM. The pooled analysis of unadjusted and adjusted data did not show any significant association between DPP4i use and mortality in COVID-19 patients with DM. However, on subgroup analysis, we found that in-hospital (and not preadmission) DPP4i use was associated with reduced mortality (unadjusted OR 0.37, 95% CI 0.23, 0.58, p < 0.0001, I 2 = 0% and adjusted OR 0.27, 95% CI 0.13, 0.55, p = 0.0003, I 2 = 12%). CONCLUSIONS: In-hospital use of DPP4i is associated with a significant reduction in COVID-19 mortality. Hence, it would be prudent to initiate or continue DPP4i in COVID-19 patients with DM if not contraindicated.

Impaired anti-SARS-CoV-2 antibody response in non-severe COVID-19 patients with diabetes mellitus: A preliminary report.

BACKGROUND AND AIMS: Patients with diabetes mellitus (DM) often demonstrate impaired antibody response to influenza/hepatitis B vaccines. Hence, we compared anti-SARS-CoV-2 antibody response in non-severe COVID-19 patients with and without type 2 diabetes mellitus (T2DM). METHODS: Records of non-severe COVID-19 patients admitted at our institution between April 10, 2020 and May 20, 2020 were retrieved. Qualitative detection of total (IgG + IgM) anti-SARS-CoV-2 antibody was performed using electrochemiluminescence immunoassay in plasma samples collected at least 14 days post-polymerase chain reaction (PCR) confirmation of diagnosis. RESULTS: Thirty-one non-severe COVID-19 patients were included. Nine patients (29%) had T2DM with mean HbA1c at admission of 8.3 ± 1.0%. Anti-SARS-CoV-2 antibody was estimated at a median of 16 (14-17) days post-PCR confirmation of COVID-19 diagnosis. Only three patients (10%) were seronegative, and all had T2DM. Patients with T2DM were more likely to have non-detectable anti-SARS-CoV-2 antibodies than those without DM (p = 0.019). CONCLUSIONS: COVID-19 patients with T2DM may not undergo seroconversion even after two weeks of diagnosis. Impaired seroconversion could theoretically increase the risk of reinfections in patients with DM. However, the finding requires validation in large-scale studies involving serial estimations of anti-SARS-CoV-2 antibodies in patients with and without DM.