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Background: COVID-19 modulates many serological biomarkers during the progress of disease severity. The study aimed to determine COVID-19 severity-associated perturbance in the serum profile. Methods: A retrospective study including COVID-19-positive individuals (n = 405) was accomplished. The serum profile of COVID-19 participants was mined from laboratory records. Severity-associated alteration in the serum profile was evaluated using Pearson correlation, regression, VCramer, Bayesian posterior VCramer, and bias factor using R-base-RStudio-version-3.3.0 with a significant cut-off of p < 0.05. Results: Significantly different mean ± standard deviation (SD) (highly versus moderately severe) of C-reactive protein (CRP), ferritin, neutrophil-lymphocyte ratio (NLR), D-dimer, platelets, prothrombin time (PT), partial prothrombin time (PTT), troponin 1, lactate dehydrogenase (LDH), aspartate-aminotransferase (AST), alanine aminotransferase (ALT), and AST/ALT ratio was observed (p < 0.001). Highly severe COVID-19 associated with CRP, ferritin, NLR, in D-dimer, PT, PTT, troponin 1, AST/ALT ratio, AST and ALT (adjusted odds ratio (AOR): 1.346, 1.05, 1.46, 1.33, 1.42, 1.23, 4.07, 3.9, 1.24, 1.45, p < 0.001). CRP with ferritin (r = 0.743), NLR (r = 0.77), white blood cells (WBC) (r = 0.8), troponin1 with LDH (r = 0.757), and D-dimer with platelets (r = -0.81) were highly correlated. X2pearson (p < 0.001), VCramer (0.71), Bayesian-VCramer (0.7), and bias-factor (-125) for troponin 1 indicate the strong association of troponin 1 level and with COVID-19 severity. X2pearson (p < 0.001), VCramer (1), Bayesian-VCramer (0.98), and bias-factor (-266.3) for NLR exhibited a very strong association of pathologic conditions with the high severity of the disease. Conclusion: These biomarkers of inflammation (CRP, Ferritin, NLR), coagulation disorders (D-dimer, PT, and PTT) cardiac abnormality (troponin 1), and liver injury (AST/ALT) could be crucial in low-medical resource settings as potential prognosticator/predictors of the COVID-19 severity and clinical outcomes. Moreover, the outcome of this study could be leveraged for the early prediction of disease severity during SARS-CoV or Middle East Respiratory Coronavirus (MERS-CoV) infection.
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BACKGROUND: Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers. METHODS: A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken. COVID-19 exposure was assessed by performing real-time PCR on nasopharyngeal (NP) swabs. Descriptive and inferential statistics were applied for both continuous and categorical variables using Rstudio-version-4.0.2. Pearson correlation and regression were executed with a cut-off of p < 0.05 for evaluating significance. Data representation by R-packages and ggplot2. RESULTS: A significant variation in the mean ± SD (highly-sever (HS)/moderately severe (MS)) of CRP (HS/MS: 102.4 ± 22.9/21.3 ± 6.9, p-value < 0.001), D-dimer (HS/MS: 661.1 ± 80.6/348.7 ± 42.9, p-value < 0.001), and ferritin (HS/MS: 875.8 ± 126.8/593.4 ± 67.3, p-value < 0.001) were observed. Thrombocytopenia, high PT, and PTT exhibited an association with the HS individuals (p < 0.001). CRP was correlated with neutrophil (r = 0.77), ferritin (r = 0.74), and WBC (r = 0.8). D-dimer correlated with platelets (r = -0.82), PT (r = 0.22), and PTT (r = 0.37). The adjusted odds ratios (Ad-OR) of CRP, ferritin, D-dimer, platelet, PT, and PTT for HS compared to MS were 1.30 (95% CI -1.137, 1.50; p < 0.001), 1.048 (95% CI -1.03, 1.066; p < 0.001), 1.3 (95% CI -1.24, 1.49, p > 0.05), -0.813 (95% CI -0.734, 0.899, p < 0.001), 1.347 (95% CI -1.15, 1.57, p < 0.001), and 1.234 (95% CI -1.16, 1.314, p < 0.001), respectively. CONCLUSION: SARS-CoV-2 caused alterations in vital laboratory parameters and raised ferritin, CRP, and D-dimer presented an association with disease severity at a significant level.
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OBJECTIVES: H. pylori-associated dyslipidemia has been reported to be a major risk factor for atherosclerosis and coronary heart diseases. We aimed to investigate the association of the H. pylori infection with dyslipidemia. METHODS: A retrospective case-control study was undertaken to evaluate H. pylori-associated dyslipidemia, where H. pylori-positive individuals were treated as the case group (n = 260) while H. pylori-negative individuals were considered as the control group (n = 250). The mean ± SD of the age of the patients included (n = 510) was 44.01 ± 13.58 years. Study subjects with a total cholesterol level of >5.17 mmol/L and/or a triglyceride level of >1.69 mmol/L and/or an LDL-C level of >2.59 mmol/L and/or an HDL-C level of <1 mmol/L in males and/or an HDL-C level of <1.3 mmol/L in females were defined as dyslipidemia. Descriptive (mean, standard deviation, median, and IQR) and inferential (t-test, chi-square test, and logistic regression) statistical analyses were undertaken using the R-base/R-studio (v-4.0.2)/tidyverse package. Univariate and bivariate logistic regressions were executed to calculate the crude and adjusted odds ratio along with the p-value. A p-value of <0.05 was the cut-off for statistical significance. We used ggplot2 for data visualization. RESULTS: The differences in overall mean ± SD (H. pylori positive vs. negative) of the cholesterol (5.22 ± 1.0 vs. 5.49 ± 0.85, p < 0.01), triglyceride (1.66 ± 0.75 vs. 1.29 ± 0.71, p < 0.001), LDL-C (3.43 ± 0.74 vs. 3.26 ± 0.81, p < 0.05), and HDL-C (1.15 ± 0.30 vs. 1.30 ± 0.25, p < 0.001) levels were statistically significant. The cholesterol and LDL-C levels in ages >60, age = 30-60, in females, and LDL-C levels in males were not significantly different for the H. pylori-positive and -negative groups. The proportion (H. pylori positive vs. negative) of hypercholesterolemia (190/59.9% vs. 127/40% p < 0.01), hypertriglyceridemia (136/68% vs. 64/32% p < 0.001), high LDL-cholesterolemia levels (234/53% vs. 201/46% p < 0.01), and low HDL-cholesterolemia levels (149/71% vs. 60/28.7% p < 0.01) were statistically significant. The odds of having hypercholesterolemia (AOR: 2.64, 95%CI: 1.824-3.848, p < 0.001), hypertriglyceridemia (AOR: 3.24, 95%CI: 2.227-4.757, p < 0.001), an increased LDL-C level (AOR: 2.174, 95%CI: 1.309-3.684, p < 0.01), and a decreased HDL-C level (AOR: 4.2, 95%CI: 2.937-6.321, p < 0.001) were 2.64, 3.24, 2.17, and 4.2 times higher in the H. pylori-infected individuals as compared with the H. pylori-uninfected group. CONCLUSION: Our results demonstrate that an enhanced risk of dyslipidemia is associated with the H. pylori infection, which can aggrandize the atherosclerosis process. The evaluation of temporal variation in the lipid profile in H. pylori-infected individuals is recommended for the effective management of H. pylori-infected patients.
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Hepatitis C virus (HCV) is a hepatotropic virus that affects millions of human lives worldwide. Direct-acting antiviral (DAA) regimens are the most effective HCV treatment option. However, amino acid substitution-dependent resistance to DAAs has been a major challenge. This study aimed to determine the increasing risk of DAA resistance due to substitutions in DAA target non-structural proteins (NS3/4A, NS5A, and NS5B). Using a Sequence Retrieval System (SRS) at the virus pathogen resource (ViPR/BV-BRC), n = 32763 target protein sequences were retrieved and analyzed for resistance-associated amino acid substitutions (RAASs) by the Sequence Feature Variant Type (SFVT) antiviral-resistance assessment modeling tool. Reference target protein sequences with 100% identity were retried from UniProt following NCBI BLAST. The types and locations of RAASs were identified and visualized by AlphaFold and PyMol. Linux-r-base/R-studio was used for the data presentation. Multi-drug-resistant variants of NS3/4A in genotype 1 (n = 9) and genotype 5 (n = 5) along with DAA-specific NS3/4A, NS5A, and NS5B variants were identified pan-genotypically. A total of 27 variants (RAASs) of all the targets were identified. Fourteen genotype 1-specific substitutions: V1196A, V1158I, D1194A/T/G, R1181K, T1080S, Q1106R, V1062A, S1148G, A1182V, Y2065N, M2000T, and L2003V were identified. The most frequent substitutions were V1062L and L2003M, followed by Q2002H. L2003V, Q2002H, M2000T, Y2065N, and NL2003M of NS5A and L2003M of NS5B conferred resistance to daclatasvir. S2702T NS5B was the sofosbuvir-resistant variant. D1194A NS3/4A was triple DAA (simeprevir, faldaprevir, and asunaprevir) resistant. The double-drug resistant variants R1181K (faldaprevir and asunaprevir), A1182V and Q1106K/R (faldaprevir and simeprevir), T1080S (faldaprevir and telaprevir), and single drug-resistant variants V1062L (telaprevir), D1194E/T (simeprevir), D1194G (asunaprevir), S1148A/G (simeprevir), and Q1106L (Boceprevir) of NS3/4A were determined. The molecular phenomenon of DAA resistance is paramount in the development of HCV drug candidates. RAASs in NS3, NS5A, and NS5B reduce the susceptibility to DAAs; therefore, continuous RAAS-dependent resistance profiling in HCV is recommended to minimize the probability of DAA therapeutic failure.
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Purpose: Omicron (B.1.1.529) is one of the highly mutated variants of concern of SARS-CoV-2. Lineages of Omicron bear a remarkable degree of mutations leading to enhanced pathogenicity and upward transmission trajectory. Mutating Omicron lineages may trigger a fresh COVID-19 wave at any time in any region. We aimed at the whole-genome sequencing of SARS-CoV-2 to determine variants/subvariants and significant mutations which can foster virus evolution, monitoring of disease spread, and outbreak management. Methods: We used Illumina-NovaSeq 6000 for SARS-CoV-2 genome sequencing, MEGA 10.2 and nextstrain tools for phylogeny; CD-HIT program (version 4.8.1) and MUSCLE program for clustering and alignment. At the same time, UCSF Chimera was employed for protein visualization. Results: Predominant Omicron pango lineages in Al-Baha were BA.5.2/B22 (n=4, 57%), and other lineages were BA.2.12/21L (n=1, 14.28%), BV.1/22B (n=1, 14.28%) and BA.5.2.18/22B (n=1, 14.28%). 22B nextstrain clade was predominant, while only one lineage showed 21L. BA.5.2/22B, BA.5.2/22B harbored a maximum of n=24 mutations in the spike region. Twelve crucial RBD mutations: D405N, R408S, K417N, N440K, L452R, S477N, T478K, E484A, F486V, Q498R, N501Y, and Y505H were identified except the lineage BA.5.2/22B in which F486V mutation was not observed. Critical deletions S106 in membrane protein NSP6, E31in nucleocapsid, and L24 in spike region were observed in all the lineages. Furthermore, we identified common mutations of Omicron variants of SARS-CoV-2 in therapeutic hot spot spike region: T19I, D405N, R408S, K417N, N440K, L452R, S477N, T478K, E484A, F486V, Q498R, N501Y, Y505H, D614G, A653V, H655Y, N679K, P681H, N764K, D796Y, Q954H, N969K, D1146D, L452R, F486V, N679K and D796Y. The effect of RBD-targeted mutations on neutralizing (NAbs) binding was considerable. Conclusion: The outcome of this first report on SARS-CoV-2 variants identification and mutation in the Al-Baha region could be used to lay down the policies to manage and impede the regional outbreak of COVID-19 effectively.
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H. pylori (ubiquitous) and anemia together represent one of the growing health concerns globally. Gastroduodenal sequelae of H. pylori infection are distinguished; however, for the H. pylori infection and its implication in the development of anemia, iron has a significant health impact. We aimed to evaluate H. pylori infection-associated anemia by employing a logistic regression analysis model. A retrospective (case-control) study design-based assessment of the H. pylori associated-anemia. The study area was geo-referenced by QGIS/QuickMapServies. Descriptive and inferential statistical analyses were accomplished using the R-base-R-studio (v-4.0.2)-tidyverse. A p-value < 0.05 was the statistical significance cut-off value. A ggplot2 package was used for data representation and visualization. Mean ± SD age, Hb, MCV, ferritin, and RBC for overall study participants were measured to be 44.0 ± 13.58, 13.84 ± 2.49, 83.02 ± 8.31, 59.42 ± 68.37, and 5.14 ± 0.75, respectively. Decreased levels of Hb (infected vs. uninfected: 13.26 ± 2.92 vs. 14.42 ± 1.75, p < 0.001) ferritin (infected vs. uninfected: 48.11 ± 63.75 vs. 71.17 ± 71.14, p < 0.001), and MCV (infected vs. uninfected: 81.29 ± 9.13 vs. and 84.82 ± 6.93, p < 0.05) were measured to be associated with H. pylori infection when compared with H. pylori uninfected control group. Moreover, the magnitude (prevalence) of anemia (infected vs. uninfected: 78% vs. 21%, p < 0.001), iron deficiency anemia (IDA) (infected vs. uninfected: 63.3% vs. 36.6%, p < 0.001), and microcytic anemia (infected vs. uninfected: 71.6% vs. 46.1%, p < 0.001) were significantly different among the H. pylori-infected participants. The higher likelihood of developing anemia (AOR; 4.98, 95% CI; 3.089-8.308, p < 0.001), IDA (AOR; 3.061, 95% CI; 2.135-4.416, p < 0.001), and microcytic anemia (AOR; 3.289, 95% CI; 2.213-4.949, p < 0.001) by 398%, 206.1%, and 229%, respectively, was associated with H. pylori-infected. We recommend the regular monitoring of hematological parameters and eradication of H. pylori infection to minimize the extra-gastric health consequences of H. pylori infection.
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To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.
Subject(s)
Phosphatidylinositol 3-Kinases , Prostatic Neoplasms , Humans , Male , Phosphatidylinositol 3-Kinases/genetics , Gene Expression Profiling/methods , Prostatic Neoplasms/genetics , Protein Interaction Maps/genetics , Computer Simulation , Sodium-Potassium-Exchanging ATPase/geneticsABSTRACT
Dengue is a viral infection caused by RNA infection of the family Flaviviridae and spread by the Aedes mosquitoes. Dengue NS5 methyltransferase is a known drug target for the disease. Therefore, it is of interest to design potential inhibitors for the target using molecular docking analysis. Our analysis shows the binding of compounds STOCK1N-98943, STOCK1N-98872, STOCK1N-98956, STOCK1N-98865, and STOCK1N-98950 with the protein drug target with optimal binding features for further in vitro and in vivo evaluations.