Dynamic antibody response in SARS-CoV-2 infected patients and COVID-19 vaccine recipients alongside vaccine effectiveness in comorbid and multimorbid groups.

Objectives: Underlying medical conditions are critical risk factors for COVID-19 susceptibility and its rapid clinical manifestation. Therefore, the preexisting burden of non-communicable diseases (NCDs) makes the preparedness for COVID-19 more challenging for low- and middle-income countries (LMICs). These countries have relied on vaccination campaigns as an effective measure to tackle COVID-19. In this study, we investigated the impact of comorbidities on humoral antibody responses against the specific receptor-binding domain (RBD) of SARS-CoV2. Methods: A total of 1005 patients were selected for the SARS-CoV-2 specific immunoglobulin G (IgG1, IgG2, IgG3, and IgG4 subclasses) and total antibody (TAb) tests (IgG and IgM), of which 912 serum samples were ultimately selected based on the specimen cutoff analyte value. Patients with multimorbidity (N = 60) were recruited for follow-up studies from the initial cohort, and their immune response (IgG and TAb) was measured at multiple time points after the second dose of vaccination. Siemens Dimension Vista SARS-CoV-2 IgG (CV2G) and SARS-CoV-2 TAb assay (CV2T) were used to carry out the serology test. Results: Out of a total of 912 participants, vaccinated individuals (N = 711) had detectable antibody responses up to 7-8 months. The synergistic effect of natural infection and vaccine response was also studied. Participants with breakthrough infections (N = 49) mounted a greater antibody response compared to individuals with normal vaccination response (N = 397) and those who were naturally infected before receiving the second dose of vaccine (N = 132). Investigation of the impact of comorbidities revealed that diabetes mellitus (DM) (N = 117) and kidney disease (N = 50) had a significant negative impact on the decline of the humoral antibody response against SARS-CoV-2. IgG and TAb declined more rapidly in diabetic and kidney disease patients compared to the other four comorbid groups. Follow-up studies demonstrated that antibody response rapidly declined within 4 months after receiving the second dose. Conclusion: The generalized immunization schedule for COVID-19 needs to be adjusted for high-risk comorbid groups, and a booster dose must be administered early within 4 months after receiving the second dose.

AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19.

Alzheimer's disease (AD) is the leading cause of dementia globally, with a growing morbidity burden that may exceed diagnosis and management capabilities. The situation worsens when AD patient fatalities are exposed to COVID-19. Because of differences in clinical features and patient condition, a patient's recovery from COVID-19 with or without AD varies greatly. Thus, this situation stimulates a spectrum of imbalanced data. The inclusion of different features in the class imbalance offers substantial problems for developing of a classification framework. This study proposes a framework to handle class imbalance and select the most suitable and representative datasets for the hybrid model. Under this framework, various state-of-the-art resample techniques were utilized to balance the datasets, and three sets of data were finally selected. We developed a novel hybrid deep learning model AD-CovNet using Long Short-Term Memory (LSTM) and Multi-layer Perceptron (MLP) algorithms that delineate three unique datasets of COVID-19 and AD-COVID-19 patient fatality predictions. This proposed model achieved 97% accuracy, 97% precision, 97% recall, and 97% F1-score for Dataset I; 97% accuracy, 97% precision, 96% recall, and 96% F1-score for Dataset II; and 86% accuracy, 88% precision, 88% recall, and 86% F1-score for Dataset III. In addition, AdaBoost, XGBoost, and Random Forest models were utilized to evaluate the risk factors associated with AD-COVID-19 patients, and the outcome outperformed diagnostic performance. The risk factors predicted by the models showed significant clinical importance and relevance to mortality. Furthermore, the proposed hybrid model's performance was evaluated using a statistical significance test and compared to previously published works. Overall, the uniqueness of the large dataset, the effectiveness of the deep learning architecture, and the accuracy and performance of the hybrid model showcase the first cohesive work that can formulate better predictions and help in clinical decision-making.

In Silico Identification and Characterization of a Hypothetical Protein From Rhodobacter capsulatus Revealing S-Adenosylmethionine-Dependent Methyltransferase Activity.

Rhodobacter capsulatus is a purple non-sulfur bacteria widely used as a model organism to study bacterial photosynthesis. It exhibits extensive metabolic activities and demonstrates other distinctive characteristics such as pleomorphism and nitrogen-fixing capability. It can act as a gene transfer agent (GTA). The commercial importance relies on producing polyester polyhydroxyalkanoate (PHA), extracellular nucleic acids, and commercially critical single-cell proteins. These diverse features make the organism an exciting and environmentally and industrially important one to study. This study was aimed to characterize, model, and annotate the function of a hypothetical protein (Accession no. CAA71016.1) of R capsulatus through computational analysis. The urf7 gene encodes the protein. The tertiary structure was predicted through MODELLER and energy minimization and refinement by YASARA Energy Minimization Server and GalaxyRefine tools. Analysis of sequence similarity, evolutionary relationship, and exploration of domain, family, and superfamily inferred that the protein has S-adenosylmethionine (SAM)-dependent methyltransferase activity. This was further verified by active site prediction by CASTp server and molecular docking analysis through Autodock Vina tool and PatchDock server of the predicted tertiary structure of the protein with its ligands (SAM and SAH). Normally, as a part of the gene product of photosynthetic gene cluster (PGC), the established roles of SAM-dependent methyltransferases are bacteriochlorophyll and carotenoid biosynthesis. But the STRING database unveiled its association with NADH-ubiquinone oxidoreductase (Complex I). The assembly and regulation of this Complex I is mediated by the gene products of the nuo operon. As a part of this operon, the urf7 gene encodes SAM-dependent methyltransferase. As a consequence of these findings, it is reasonable to propose that the hypothetical protein of interest in this study is a SAM-dependent methyltransferase associated with bacterial NADH-ubiquinone oxidoreductase assembly. Due to conservation of Complex I from prokaryotes to eukaryotes, R capsulatus can be a model organism of study to understand the common disorders which are linked to the dysfunctions of complex I.