COVID-19 risk increases with comorbidities, and the effect is magnified due to the contribution of individual and combined comorbidities to the overall clinical outcomes. We aimed to explore the influence of demographic factors, clinical manifestations, and underlying comorbidities on mortality, severity, and hospital stay in COVID-19 patients. Therefore, retrospective chart reviews were performed to identify all laboratory-confirmed cases of SARS-CoV-2 infection in Apollo Hospitals, Hyderabad, between March 2020 and August 2020.A total of 369 confirmed SARS-CoV-2 cases were identified: 272 (73.7%) patients were male, and 97 (26.2%) were female. Of the confirmed cases, 218 (59.1%) had comorbidities, and 151 (40.9%) were devoid of comorbidities. This study showed that old age and underlying comorbidities significantly increase mortality, hospital stay, and severity due to COVID-19 infection. The presence of all four comorbidities, diabetes mellitus DM + Hypertension HTN + coronary artery disease CAD + chronic kidney disease CKD , conferred the most severity (81%). The highest mortality (OR: 44.03, 95% CI: 8.64-224.27) was observed during the hospital stay ( 12.73 ± 11.38 ; 95% CI: 5.08-20.38) in the above group. Multivariate analysis revealed that nonsurvivors are highest (81%) in ( DM + HTN + CAD + CKD ) category with an odds ratio (95% CI) of 44.03 (8.64-224.27). Age, gender, and comorbidities adjusted odds ratio decreased to 20.25 (3.77-108.77). Median survival of 7 days was observed in the ( DM + HTN + CAD + CKD ) category. In summary, the presence of underlying comorbidities has contributed to a higher mortality rate, greater risk of severe disease, and extended hospitalization periods, hence, resulting in overall poorer clinical outcomes in hospitalized COVID-19 patients.
Objective. We intend to identify differences in the clinicodemographic and laboratory findings of COVID-19 patients to predict disease severity and outcome on admission. Methods. This single-centred retrospective study retrieved laboratory and clinical data from 350 COVID-19 patients on admission, represented as frequency tables. A multivariate regression model was used to assess the statistically significant association between the explanatory variables and COVID-19 infection outcomes, where adjusted odds ratio (AOR), p value, and 95% CI were used for testing significance. Results. Among the 350 COVID-19 patients studied, there was a significant increase in the WBC count, neutrophils, aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (dNLR), neutrophil-to-lymphocyte and platelet ratio (NLPR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), D-dimer, interleukin-6 (IL-6), ferritin, lactate dehydrogenase (LDH), prothrombin time (PT), glucose, urea, urea nitrogen, creatinine, alanine phosphatase (ALP), and aspartate aminotransferase (AST) and a significant decrease in lymphocytes, eosinophils, total protein, albumin, prealbumin serum, and albumin/globulin (A/G) ratio in the severe group when compared with the mild and moderate groups. However, after adjusting their age, gender, and comorbidities, WBC count (adjusted odds ratio AOR = 6.888 , 95% CI = 1.590 -29.839, p = 0.010 ), neutrophils ( AOR = 5.912 , 95% CI = 2.131 -16.402, p = 0.001 ), and urea ( AOR = 4.843 , 95% CI = 1.988 -11.755, p = 0.001 ) were strongly associated with disease severity. Interpretation and Conclusion. On admission, WBC count, neutrophils, and urea, with their cut of values, can identify at-risk COVID-19 patients who could develop severe COVID-19.
Till date, cardiovascular diseases remain a leading cause of morbidity and mortality across the globe. Several commonly used treatment methods are unable to offer safety from future complications and longevity to the patients. Therefore, better and more effective treatment measures are needed. A potential cutting-edge technology comprises stem cell-derived exosomes. These nanobodies secreted by cells are intended to transfer molecular cargo to other cells for the establishment of intercellular communication and homeostasis. They carry DNA, RNA, lipids, and proteins; many of these molecules are of diagnostic and therapeutic potential. Several stem cell exosomal derivatives have been found to mimic the cardioprotective attributes of their parent stem cells, thus holding the potential to act analogous to stem cell therapies. Their translational value remains high as they have minimal immunogenicity, toxicity, and teratogenicity. The current review highlights the potential of various stem cell exosomes in cardiac repair, emphasizing the recent advancements made in the development of cell-free therapeutics, particularly as biomarkers and as carriers of therapeutic molecules. With the use of genetic engineering and biomimetics, the field of exosome research for heart treatment is expected to solve various theranostic requirements in the field paving its way to the clinics.
BackgroundRecent years have witnessed a growing interest in employing urine as a clinical source of renal pathology biomarkers. Urinary extracellular vesicles(UEVs) hold cellular RNAs, including small RNA and micro RNA. Quantitative polymerase chain reaction (qPCR) is one of the most sensitive methods for evaluating gene expression, which depends on comparative analysis with reference/house-keeping genes. However, reliable interpretation of UEVs gene expression data is biased due to the lack of reported ideal house-keeping reference-genes in UEVs.MethodsUEVs were isolated from 40 healthy human controls using Polyethylene glycol (P.E.G. Mn6000) based precipitation. UEVs were characterized by biophysical and biochemical assays. At the molecular biology level, the expression and stability of five commonly used housekeeping genes B2M, RPL13A, PPIA, HMBS, and GAPDH, were considered for comparing and finding out ideal reference gene. Data were analyzed using four practical algorithmic approaches, including Norm Finder, GeNorm, Best Keeper, and the Delta Ct for reference gene evaluation integrated with RefFinder. The final ranking of stable genes is derived from the weighted geometric means of all the above algorithms.Results12% PEG isolated UEVs were round and cup-shaped, ranging from 30 - 100nm, as per electron microscopy, nanoparticle-tracking-analysis, and dynamic-light-scattering profile. The functional purity of UEVs was determined with their acetylcholine esterase and Dipeptidyl peptidase-IV activity. RefFinder established the stability index of housekeeping genes. B2M and RPL13A genes were identified as stable genes with a mean stability score of 1.5(Genorm) and below 1 (Norm finder), indicating a reduced gene expression variation. ConclusionsThe comprehensive ranking analysis identified B2M and RPL13A as optimal reference genes for UEVs based gene expression studies.
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