Trypanosomiasis or Chagas disease is a disease quite prevalent in Central America and other South American countries. With the high rate of infection spread over 7 million people in America, along with high number of mortality rate, this disease requires a good preventive vaccine to get eradicated. And for the same, there has been research on various kind of proteins to find the right vaccine candidate. In this paper, we are evaluating the protein DnaJ Chaperone to find out if it is a potential vaccine candidate or not. For evaluation, we procure DnaJ Chaperone protein sequence from NCBI and run through the Vax-Elan pipeline to get the result values. These values are further compared with the prediction tools cut-off values (Prediction tool is a table of tools which has different parameters for deciding the vaccine candidacy). And based on the cut-off value of different tools, we get to find the potential vaccine candidate.

Immunoproliferative small intestine disease (IPSID) is a collective name for a range of diseases caused by various microorganisms but the major and persistent organism is Campylobacter Jejuni. IPSID can lead to minor symptoms like diarrhea, nausea, imbalance of electrolytes in the body etc. to major consequences that may lead to death in case of prolonged untreated condition. IPSID leads to infiltration of lymphocytes as a consequence of an immune response to invasion by microbes, which eventually leads to the evolvement of IgA producing bodies and to the selection of a body that produces α heavy chains. Hence, it is also called “α- Heavy chain disease”. Until now there has been no successful development of a vaccine for this disease. N-acetylmuramoyl-L-alanine amidase is one of the proteins in Campylobacter Jejuni ssp. Jejuni which is also a Potential vaccine candidate (PVC) against IPSID as identified by Vaxigen. Here, we are utilizing deep learning softwares i.e, Vaxi-DL and VaxELAN for analyzing the given protein in terms of adhesion, secretory nature, trans-membrane helices, cleavage sites, MHC-I binding, CTL epitope prediction, essential genes, molecular weight, non-bacterial pathogen, non-homology with human genome, virulence factors, allergenicity, cellular localization and probability of being a PVC.

Leptospirosis vaccine candidates still need to be identified, which was a challenge. One such candidate, however, is OmpL37, a potential surface-exposed antigen with the highest elastin-binding capacity yet observed, indicating that it likely contributes significantly to host colonization. Cell viability is frequently evaluated using high throughput metabolic viability assays like MTT and MTS. Utilizing genomic, proteomic data, and computational methods, particularly deep learning systems, can be crucial in identifying vaccine targets. A new computational method for identifying and evaluating novel vaccination targets, which paves the way for the creation of a multi-epitope subunit vaccine candidate was using Vax-Elan. In order to uncover prospective vaccine candidates, the system screens genomic and proteomic datasets of multiple diseases, including leptospirosis species, using reverse vaccination and immuno-informatics. It uses supervised machine learning-based methods for vaccine discovery and an immuno-informatics approach. To computationally analyze and assess the pathogenic proteomes that were developed followed by narrowing down proteins that exhibit particular traits in order to rank them as prospective vaccination candidates. In this study, we developed new leptospirosis vaccine targets. Where the protein sequence of ligA [Id-ach98094.1] was extracted from the NCBI protein database. And the predicted amino acid sequence for LigA is a set of 90-amino-acid tandem repeats encoded by a 3,675-bp open reading frame. By using the Vax-Elan server to evaluate important properties, to check the suitability of this protein as a potential vaccine candidate. As per VaxElan, the protein is predicted to be 1 for Target p, if it is a signal peptide ligA is predicted to be a signal peptide and hence scored 1 as being a signal peptide is a criterion for a potential vaccine candidate. In the case of mTP and cTP, VaxElan gives a score of 0 and for 1TP VaxElan gives 0.05 which is followed by various research studies.

Background: The COVID-19 pandemic had a negative impact on the mental health of the global population. The purpose of this study is to investigate anxiety levels of adult population in relation to the COVID-19 pandemic.Methods: The study utilized a web-based cross-sectional survey design. A total of 236 participants were enrolled via snowball sampling method. Standardized tool coronavirus anxiety scale was used to collect the data regarding COVID-19 related anxiety. The data was collected during August-November 2021.Results: Statistical analysis was done using SPSS version 20. The mean age of the participants was 26.64+8.38 years, with the majority being female (65.3%). Approximately half (48.3%) of the participants were healthcare professionals (HCWs). The results revealed that only 5.5% of the participants were anxious about COVID-19, with healthcare workers being the most anxious.Conclusions: During the COVID-19 crisis, appropriate supportive interventions should be implemented with the goal of providing targeted mental health services to those who are more likely to suffer from mental disorders. The psychosocial intervention and support strategy should cover specifically frontline workers who are tasked with the role of combating virus.

Background: The health of future children depends on the nurturing practice in the initial years of life. Knowledge about the care of newborns among mothers plays a major role in reducing neonatal morbidities and mortalities. Therefore, the objective of the study was to assess the knowledge among postnatal mothers about newborn care. Methods: A descriptive study was done among 60 purposively selected post-natal mothers admitted at AIIMS, Jodhpur. Data was collected through self-structured questionnaires. The reliability of the self-structured knowledge questionnaire was determined by the KR-20 method and found reliable (0.81). Data collected was analyzed for frequency, mean, and standard deviation. Both descriptive and inferential statistics were used to compute the data. The Chi-square (Fisher’s Exact Test) was used to determine the relationship between selected socio-demographic variables and knowledge scores of post-natal mothers. Results: Findings showed that most (73%) of the respondents were from the age group 20-27 years. The mean knowledge score was 26.783±3.9234. Most of the respondents (60%) had excellent knowledge, whereas 28.33% had good knowledge levels. None of the participants was in the range of poor knowledge. Knowledge of participants about newborn care was found to have a significant association with the level of education, area of living and occupation. Conclusions: The result of this study provided information that postnatal mothers have adequate knowledge of newborn care. Some socio-demographic factors like occupation, literacy and area of residence were found to be associated with the knowledge of the mothers.