A comparative clinical study of efficacy of microimmuno assay with WIDAL-test in enteric fever in children

The diagnosis of typhoid fever in young children is also a dilemma because of its manifestations and typical presentation may not be seen in all cases. Antibodies to Salmonella typhi antigen are developed in the human body, which can be detected as a diagnostic test for the enteric fever. Objective: This study was undertaken to compare the efficacy of WIDAL-test with micro-immunoassay (dot enzyme immunosorbent assay). Method: 40 cases of clinically suspected enteric fever cases were included in this study. Result: In the present study, nearly 92% were positive for micro immunoassay (dot-enzyme immunosorbent assay) by Enterocheck-WB kit, 80% were positive for WIDAL and only 15% were culture positive. Immunoassay positive, but WIDAL negative cases were 20%, whereas WIDAL positive and immunoassay negative cases were only 7.5%. The positive predictivity of micro-immunoassay in diagnosing enteric fever is better than WIDAL both in 1st and 2nd week of illness. Micro-immunoassay done in the study was rapid in diagnosing the case. Conclusion: It is concluded from the present study that the micro-immunoassay (Enterocheck-WB) is better than WIDAL-test in the diagnosis of enteric fever in children

Analysis of codon usage bias of classical swine fever virus

Background and Aim: Classical swine fever (CSF), caused by CSF virus (CSFV), is a highly contagious disease in pigs causing 100% mortality in susceptible adult pigs and piglets. High mortality rate in pigs causes huge economic loss to pig farmers. CSFV has a positive-sense RNA genome of 12.3 kb in length flanked by untranslated regions at 5' and 3' end. The genome codes for a large polyprotein of 3900 amino acids coding for 11 viral proteins. The 1300 codons in the polyprotein are coded by different combinations of three nucleotides which help the infectious agent to evolve itself and adapt to the host environment. This study performed and employed various methods/techniques to estimate the changes occurring in the process of CSFV evolution by analyzing the codon usage pattern. Materials and Methods: The evolution of viruses is widely studied by analyzing their nucleotides and coding regions/ codons using various methods. A total of 115 complete coding regions of CSFVs including one complete genome from our laboratory (MH734359) were included in this study and analysis was carried out using various methods in estimating codon usage bias and evolution. This study elaborates on the factors that influence the codon usage pattern. Results: The effective number of codons (ENC) and relative synonymous codon usage showed the presence of codon usage bias. The mononucleotide (A) has a higher frequency compared to the other mononucleotides (G, C, and T). The dinucleotides CG and CC are underrepresented and overrepresented. The codons CGT was underrepresented and AGG was overrepresented. The codon adaptation index value of 0.71 was obtained indicating that there is a similarity in the codon usage bias. The principal component analysis, ENC-plot, Neutrality plot, and Parity Rule 2 plot produced in this article indicate that the CSFV is influenced by the codon usage bias. The mutational pressure and natural selection are the important factors that influence the codon usage bias. Conclusion: The study provides useful information on the codon usage analysis of CSFV and may be utilized to understand the host adaptation to virus environment and its evolution. Further, such findings help in new gene discovery, design of primers/probes, design of transgenes, determination of the origin of species, prediction of gene expression level, and gene function of CSFV. To the best of our knowledge, this is the first study on codon usage bias involving such a large number of complete CSFVs including one sequence of CSFV from India

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Not AvailableSince the identification of the SARS CoV 2 genus Beta Coronavirus in January 2020 the virus quickly spread in less than 3 months to all continents with a susceptible human population of about a 7.9 billion, and still in active circulation. In the process, it has accumulated mutations leading to genetic diversity. Regular emergence of variants of concern/significance in different ecology shows genetic heterogeneity in the base population of SARS CoV 2 that is continuously expanding with the passage of the virus in the vast susceptible human population. Natural selection of mutant occurs frequently in a positive sense single stranded (ss) RNA virus upon replication in the host. The Pressure of sub optimal levels of virus neutralizing antibodies and also innate immunity influence the process of genetic or antigenic selection. The fittest of the mutants, that could be more than one, propagate and emerge as variants. The existence of different lineages, clades, and strains as well as genetic heterogeneity of plaque purified virus population justifies SARS CoV 2 as Quasispecies that refers to swarms of mutant sequences generated during replication of the viral genome and all mutant sequences may not lead to virion. Viruses having a quasispecies nature may end up with progressive antigenic changes leading to antigenic plurality that is driven by ecology and this phenomenon challenges vaccination based control programsNot Availabl

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Not AvailableThe emergence of methicillin-resistant Staphylococcus aureus (MRSA) has increased and become a serious concern worldwide, including India. Additionally, MRSA isolates are showing resistance to other chemotherapeutic agents. Isolated and valuable reports on the prevalence of MRSA are available in India. There is no systematic review on the prevalence of MRSA in one place; hence, this study was planned. The overall prevalence of MRSA in humans in India was evaluated state-wise, zone-wise, and year-wise. A systematic search from PubMed, Indian journals, Google Scholar, and J-Gate Plus was carried out and retrieved 98 eligible articles published from 2015 to 2020 in India. The statistical analysis of data was conducted using R software. The overall prevalence of MRSA was 37% (95% CI: 32-41) from 2015 to 2019. The pooled prevalence of MRSA zone-wise was 41% (95% CI: 33-50), 43% (95% CI: 20-68), 33% (95% CI: 24-43), 34% (95% CI: 26-42), 36% (95% CI: 25-47), and 40% (95% CI: 23-58) for north, east, west, south, central, and northeast region-zones, respectively. The state-wise stratified results showed a predominance of MRSA in Jammu and Kashmir with 55% (95% CI: 42-67) prevalence, and the lowest was 21% (95% CI: 11-34) in Maharashtra. The study indicated that the prevalence data would help in formulating and strict implementation of control measures in hospital areas to prevent the outbreak of MRSA infection and management of antibiotic usage.Not Availabl

A New Methodology to Comprehend the Effect of El Niño and La Niña Oscillation in Early Warning of Anthrax Epidemic Among Livestock

Anthrax is a highly fatal zoonotic disease that affects all species of livestock. The study aims to develop an early warning of epidemiological anthrax using machine learning (ML) models and to study the effect of El Niño and La Niña oscillation, as well as the climate–disease relationship concerning the spatial occurrence and outbreaks in Karnataka. The disease incidence data are divided based on El Niño and La Niña events from 2004–2019 and subjected to climate-disease modeling to understand the disease pattern over the years. Machine learning models were implemented using R statistical software version 3.1.3 with Livestock density, soil profile, and meteorological and remote sensing variables as risk factors associated with anthrax incidence. Model evaluation is performed using statistical indices, viz., Cohen’s kappa, receiver operating characteristic (ROC) curve, true skill statistics (TSS), etc. Models with good predictive power were combined to develop an average prediction model. The predicted results were mapped onto the Risk maps, and the Basic reproduction numbers (R0) for the districts that are significantly clustered were calculated. Early warning or risk prediction developed with a layer of R0 superimposed on a risk map helps in the preparedness for the disease occurrence, and precautionary measures before the spread of the disease