Background: The role of diabetes mellitus (DM) in hospitalized COVID-19 patients and of HbA1c in hospitalized COVID-19 patients with DM were not studied adequately in the past. Research design and methods: It was a retrospective cohort study. In this study, data from 305 hospitalized COVID-19 patients was analyzed. The study objective was to determine the association of DM with in-hospital mortality in COVID-19 patients. Another study objective was to determine the association of HbA1c with mortality in COVID-19 patients with DM. Results: In this retrospective study, DM was present in 41.3% (126/305) of the study population. The multivariate Cox regression analysis showed a significant association between DM and mortality (adjusted hazard ratio (aHR): 2.116, 95% CI: 1.088-4.116, p = 0.027). The median HbA1c in diabetic patients was 8.9% (7.5-11.0). HbA1c was found to be associated with mortality in diabetic patients in the multivariate cox-regression analysis (aHR:1.272, 95% CI: 1.028-1.574, p = 0.027). The multivariate Cox regression analysis also showed the association of HbA1c (10.5%<= HbA1c > 10.5%) as a dichotomous variable with in-hospital mortality (aHR: 2.53, 95% CI: 2.606-194.81, p = 0.005) in diabetic patients. Conclusions: DM was independently associated with mortality in hospitalized COVID-19 patients in the multivariate analysis. In COVID-19 patients with DM, HbA1c was associated with mortality as a continuous and dichotomous variable in the multivariate analysis.

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm x 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Chitosan nanoparticles (CNPs) are widely acknowledged for their versatility in various domains, particularly in areas relevant to pharmaceuticals and vaccines. This study aimed to produce CNPs as a foundational element for nanoparticle-based delivery systems intended for potential use in vaccine development. We outline a straightforward yet effective synthesis method for generating high-yield, uniform CNPs ranging in size from 100 to 250 nm. The synthesis involved ionic gelation using medium molecular weight chitosan (with a deacetylation level exceeding 75%) and sodium tripolyphosphate as a crosslinker. This was followed by thorough homogenization to ensure consistency, and purification via 0.45 µm polyethersulfone syringe filters. The CNPs underwent characterization for zeta size, zeta charge, and polydispersity index (PDI), while scanning transmission electron microscopy was employed for morphological analysis. Internalization of these chitosan nanoparticles into MDBK cells was achieved through the use of FITC-conjugated counterparts (FCNP). Furthermore, we investigated the impact of chitosan concentrations on CNP size, charge, and polydispersity. These synthesized CNPs exhibit a strong affinity for proteins, DNA, and other biomolecules, positioning them as potential candidates for the development of nanoparticle-based delivery systems tailored to vaccine development.

Epigenetic variations result from long-term adaptation to environmental factors. The Bos indicus (zebu) adapted to tropical conditions, whereas Bos taurus adapted to temperate conditions; hence native zebu cattle and its crossbred (B indicus × B taurus) show differences in responses to heat stress. The present study evaluated genome-wide DNA methylation profiles of these two breeds of cattle that may explain distinct heat stress responses. Physiological responses to heat stress and estimated values of Iberia heat tolerance coefficient and Benezra's coefficient of adaptability revealed better relative thermotolerance of Hariana compared to the Vrindavani cattle. Genome-wide DNA methylation patterns were different for Hariana and Vrindavani cattle. The comparison between breeds indicated the presence of 4599 significant differentially methylated CpGs with 756 hypermethylated and 3845 hypomethylated in Hariana compared to the Vrindavani cattle. Further, we found 79 genes that showed both differential methylation and differential expression that are involved in cellular stress response functions. Differential methylations in the microRNA coding sequences also revealed their functions in heat stress responses. Taken together, epigenetic differences represent the potential regulation of long-term adaptation of Hariana (B indicus) cattle to the tropical environment and relative thermotolerance.

The cross-species transmissibility of SARS-CoV-2 infection has necessitated development of specific reagents for detecting infection in various animal species. The spike glycoprotein of SARS-CoV-2, which is involved in viral entry, is a highly immunogenic protein. To develop assays targeting this protein, we generated eight monoclonal antibodies (mAbs) against the S1 and seven against the S1/S2 protein (ectodomain) of SARS CoV-2. Based on neutralization capability and reactivity profile observed in ELISA, the mAbs generated against the S1/S2 antigen exhibited a broader spectrum of epitope specificity than those produced against the S1 domain alone. The full-length ectodomain induced antibodies that could neutralize the two most important variants of the virus encountered during the pandemic, namely Delta and Omicron. The availability of these reagents could greatly enhance the development of precise diagnostics for detecting COVID-19 infections in various host species and contribute to the advancement of mAb-based therapeutics.

Background Various studies have observed an association between interleukin-6 (IL-6), serum ferritin, d-dimer, and in-hospital mortality in COVID-19 patients. However, multivariate regression analysis was not done in the majority of the studies, Also, the role of interleukin-6 (IL-6), serum ferritin, and d-dimer in hospitalized COVID-19 patients was not adequately studied and reported from our region. Method It was a retrospective cohort study in which the serum IL-6, serum ferritin, and d-dimer of 305 hospitalized COVID-19 patients were analyzed, and their association with mortality was determined. Results In COVID-19 patients, the levels of IL-6 (P = 0.007), serum ferritin (P = 0.011), and d-dimer (P = 0.004) were significantly elevated in patients with severe SARS-CoV-2 illness (SpO2 < 90 % at admission). IL-6 levels were significantly elevated (186 pg/ml vs. 215 pg/ml, P = 0.003) in non-survivors compared to survivors. However, d-dimer (mg/ml) (P = 0.129) and serum ferritin (mg/ml) (P = 0.051) levels were similar between the two groups. The ROC curve (receiver operating characteristic curve) analysis showed a significant but poor area under the curve (AUC) between elevated IL-6 (>208 pg/ml) and in-hospital mortality (P < 0.008, AUC = 0.61). Kaplan-Meir survival analysis showed poor survival in patients with elevated IL-6 (>208 pg/ml) (Pby log-rank: 0.010) and elevated d-dimer (>1780 mg/ml) (P by log-rank: 0.036). The multivariate cox-regression analysis did not show any association between IL-6, serum ferritin, d-dimer, and in-hospital mortality (P > 0.05). Also, no association was found between serum levels of IL-6, serum ferritin, d-dimer, and the use of a ventilator (P > 0.05) and the severity of SARS-CoV-2 illness (P > 0.05) on multivariate binary logistic regression analysis. Conclusion In this study, the serum levels of IL-6, serum ferritin, and d-dimer were not associated with in-hospital mortality in hospitalized COVID-19 patients on multivariate cox-regression analysis, and were the markers of severe SARS-CoV-2 illness.

Diagnostics employing multiple modalities have been essential for controlling and managing COVID-19, caused by SARS-CoV-2. However, scaling up Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR), the gold standard for SARS-CoV-2 detection, remains challenging in low and middle-income countries. Cost-effective and high-throughput alternatives like enzyme-linked immunosorbent assay (ELISA) could address this issue. We developed an in-house SARS-CoV-2 nucleocapsid capture ELISA, and validated on 271 nasopharyngeal swab samples from humans (n = 252), bovines (n = 10), and dogs (n = 9). This ELISA has a detection limit of 195 pg/100 µL of nucleocapsid protein and does not cross-react with related coronaviruses, ensuring high specificity to SARS-CoV-2. Diagnostic performance was evaluated using receiver operating characteristic curve analysis, showing a diagnostic sensitivity of 67.78 % and specificity of 100 %. Sensitivity improved to 74.32 % when excluding positive clinical samples with RT-qPCR Ct values > 25. Furthermore, inter-rater reliability analysis demonstrated substantial agreement (κ values = 0.73-0.80) with the VIRALDTECT II Multiplex RT-qPCR kit and perfect agreement with the CoVeasy™ COVID-19 rapid antigen self-test (κ values = 0.89-0.93). Our findings demonstrated that the in-house nucleocapsid capture ELISA is suitable for SARS-CoV-2 testing in humans and animals, meeting the necessary sensitivity and specificity thresholds for cost-effective, large-scale screening.

The studies regarding prevalence, outcomes, and predictors of prolonged corrected QT (QTc) among COVID-19 patients not on QTc-prolonging medication are not available in the literature. In this retrospective cohort study, the QTc of 295 hospital-admitted COVID-19 patients was analyzed and its association with in-hospital mortality was determined. The QTc was prolonged in 14.6

Flood assessment in the data scare region is the most challenging task for decision-makers and scientists to estimate the flood catastrophe and flood vulnerability in a flood-prone area. In addition, future preparedness and flood mitigation plan solely depend on the accuracy of the flood assessment after the catastrophic flood. The present case shows state of the art for flood assessment using UAV (drone) techniques. Nowadays, flood assessment is performed using hydrodynamic modeling, where DEM/DTM is the basic input for flood simulation. Dhanera city, Rel River catchment of Banaskantha district, which was affected by the flood in 2015 and 2017, is considered for preparing high-resolution DEM. Phantom 4 Pro RTK and Pix4D software are applied to process the 9222 images across the study reach. The point cloud and high-resolution DEM (3.6 × 3.6 cm) have been extracted from Dhanera city for flood assessment using advanced software techniques. Prepared high-resolution DEM will be utilized for pluvial and fluvial flood assessment through hydrodynamic modeling under unsteady flow conditions. UAV-based high-resolution DEM/DTM has been described, and a significant achievement for preparing high-resolution DEM/DTM for flood modeling to improve the decision-making system has been discussed.

A woman in her mid-20s was evaluated for a history of dyspnea on exertion for 2 years. A 6F pigtail catheter was placed in the left ventricle via the right femoral artery, but the patient developed acute onset of dyspnea along with retrosternal chest pain; the catheter was pulled back into the right atrium, and chest fluoroscopy was performed. What would you do next?

Peste des petits ruminants virus (PPRV) causes a severe disease in sheep and goats. PPRV infection is a major problem, causing significant economic losses to small ruminant farmers in regions of endemicity.

ABSTRACT Objectives The type of arrhythmias, and their prevalence in mild/moderate and severe COVID-19 patients admitted to the hospital are unknown from a prospective cohort study. Methods We did continuous electrocardiograms along with multiple ECGs in 305 consecutive hospitalized COVID-19 patients. Results The incidence of arrhythmias was 6.8% (21/305) in the target population. The incidence of arrhythmias was 9.2% (17/185) in patients with severe COVID-19 illness and 3.3% (4/120) in patients with mild/moderate COVID-19 illness with no significant difference (p = 0.063). All the arrhythmias were new-onset arrhythmias in this study. 95% (20/21) of these arrhythmias were atrial arrhythmia with 71.42% (15/21) being atrial fibrillation and one episode of sustained polymorphic ventricular tachycardia. No episode of high-grade atrioventricular block, sustained monomorphic ventricular arrhythmia, or torsades de pointes arrhythmias were observed in this study. The patients with arrhythmias were admitted to the intensive care unit (80.9% vs. 50.7%; p: 0.007), were on a ventilator (47.6% vs. 21.4%; p: 0.006), and had high in-hospital mortality (57.1% vs. 21.1%; p: 0.0001) than patients without arrhythmias. Conclusion Atrial arrhythmias were the most frequent arrhythmias in hospital-admitted COVID-19 patients with atrial fibrillation being the most common arrhythmia. Trial registration Clinical Trial Registry India (CTRI) (CTRI/2021/01/030788). (https://www.ctri.nic.in/)

Knowing the dielectric properties of the interfacial region in polymer nanocomposites is critical to predicting and controlling dielectric properties. They are, however, difficult to characterize due to their nanoscale dimensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements, but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. This paper demonstrates a combined EFM and machine learning (ML) approach to measuring interfacial permittivity in 50 nm silica particles in a PMMA matrix. We show that ML models trained to finite-element simulations of the electric field profile between the EFM tip and nanocomposite surface can accurately determine the interface permittivity of functionalized nanoparticles. It was found that for the particles with a polyaniline brush layer, the interfacial region was detectable (extrinsic interface). For bare silica particles, the intrinsic interface was detectable only in terms of having a slightly higher or lower permittivity. This approach fully accounts for the complex interplay of filler, matrix, and interface permittivity on the force gradients measured in EFM that are missed by previous semianalytic approaches, providing a pathway to quantify and design nanoscale interface dielectric properties in nanodielectric materials.

Environmental heat stress in dairy cattle leads to poor health, reduced milk production and decreased reproductive efficiency. Multiple genes interact and coordinate the response to overcome the impact of heat stress. The present study identified heat shock regulated genes in the peripheral blood mononuclear cells (PBMC). Genome-wide expression patterns for cellular stress response were compared between two genetically distinct groups of cattle viz., Hariana (B. indicus) and Vrindavani (B. indicus X B. taurus). In addition to major heat shock response genes, oxidative stress and immune response genes were also found to be affected by heat stress. Heat shock proteins such as HSPH1, HSPB8, FKB4, DNAJ4 and SERPINH1 were up-regulated at higher fold change in Vrindavani compared to Hariana cattle. The oxidative stress response genes (HMOX1, BNIP3, RHOB and VEGFA) and immune response genes (FSOB, GADD45B and JUN) were up-regulated in Vrindavani whereas the same were down-regulated in Hariana cattle. The enrichment analysis of dysregulated genes revealed the biological functions and signaling pathways that were affected by heat stress. Overall, these results show distinct cellular responses to heat stress in two different genetic groups of cattle. This also highlight the long-term adaptation of B. indicus (Hariana) to tropical climate as compared to the crossbred (Vrindavani) with mixed genetic makeup (B. indicus X B. taurus).

Background The incidence, predictors, and association of cardiac troponin with mortality in hospitalized COVID-19 were not adequately studied in the past and were also not reported from an Indian hospital. Methods In this retrospective cohort study, the cardiac troponin of 240 hospitalized COVID-19 patients was measured. The incidence, predictors, and association of elevated cardiac troponin with in-hospital mortality were determined among hospitalized COVID-19 patients. Results The cardiac troponin was elevated in 12.9% (31/240) of the patients. The troponin was elevated in the patients in the older age group (64 years vs. 55 years, p = .002), severe COVID-19 illness (SpO2 < 90%) (93.5% vs. 60.8%, p < .001), low arterial oxygen saturation (SpO2) (80% vs. 88%, p = .001), and low PaO2/FiO2 ratio ( p < .0001). The patients with elevated cardiac troponin had elevated total leukocyte counts (TLC) ( p = .001), liver enzyme ( p = .025), serum creatinine ( p = .011), N-terminal-Pro Brain natriuretic peptide ( p < .0001), and d-dimer ( p < .0001). The majority of the patients with elevated cardiac troponin were admitted to the intensive care unit (90.3% vs. 51.2%; p < .0001), were on a ventilator (61.3% vs. 21.5%; p < .0001), and had higher mortality (64.5% vs. 19.6%; p < .0001). The Kaplan–Meir survival analysis showed that the patients with elevated troponin had worse survival ( p log-rank<.0001). Age, NT-ProBNP, d-dimer, and ventilator were the predictors of elevated troponin in multivariate logistic regression analysis. The Cox-regression analysis showed a significant association between elevated cardiac troponin and in-hospital mortality (adjusted hazard ratio 2.13; 95% confidence interval [CI] 1.145–3.97; p = .017). Two-thirds (65%) of patients with elevated cardiac troponin died during their hospital stay. Conclusions COVID-19 patients with elevated cardiac troponin had severe COVID illness, were more commonly admitted to an intensive care unit, were on a ventilator, and had high in-hospital mortality.

Prediction and validation of Compound factors for prioritization of watersheds are an essential application using machine learning (ML) techniques in water resource engineering. The current paper proposes a methodology to derive 14 morphometric and 3 topo-hydrological parameters using remote sensing (RS) and geographical information systems (GIS). Compound factor (CF) values are calculated using weighted sum analysis (WSA), ReliefF, and the Pearson correlation coefficient, and the important parameters are identified. Two machine learning models, multilayer perceptron (MLP) and support vector machine (SVM), are utilized to predict CF values. Predication accuracy of ML models is evaluated with three parameters, mean absolute error (MAE), Pearson correlation coefficient (PCC), and root mean square error (RMSE). It is observed that the maximum value of PCC equal to 1 is achieved through ReliefF and SVM, whereas minimum MAE and RMSE are observed with ReliefF and SVM when Tenfold cross-validation is applied. Since ReliefF shows better results, CF values are calculated and applied to create the watershed. The proposed methodology is helpful for accurately predicting CF values and advantageous to allocating the proper watershed, which will be useful for decision-making and implementation of conservation techniques for soil and water.

Atrial fibrillation (AF), the most prevalent cardiac arrhythmia encountered in clinical practice, is linked with substantial morbidity and mortality due to accompanying risk of stroke and thromboembolism. Patients with AF are at a five-fold higher risk of suffering from a stroke. Anticoagulation therapy, with either vitamin K antagonists or novel oral anticoagulants (NOACs), is a standard approach to reduce the risk. Consultant physicians (CPs) in India are the primary point of contact for the majority of patients before they approach a specialist. The CPs may face challenges in screening and diagnosing AF patients. The apprehensions associated with managing AF patients with anticoagulants, further add to the challenges of a CP. This review aimed to identify the key decision points for the CPs to diagnose AF and initiate anticoagulation in patients with non-valvular AF (NVAF) and bring to the table a simplified recommendation supported by expert opinion and guidelines for stroke prevention in NVAF patients.