Infection metallomics for critical care in the post-COVID era.

Infection metallomics is a mass spectrometry (MS) platform we established based on the central concept that microbial metallophores are specific, sensitive, noninvasive, and promising biomarkers of invasive infectious diseases. Here we review the in vitro, in vivo, and clinical applications of metallophores from historical and functional perspectives, and identify under-studied and emerging application areas with high diagnostic potential for the post-COVID era. MS with isotope data filtering is fundamental to infection metallomics; it has been used to study the interplay between "frenemies" in hosts and to monitor the dynamic response of the microbiome to antibiotic and antimycotic therapies. During infection in critically ill patients, the hostile environment of the host's body activates secondary bacterial, mycobacterial, and fungal metabolism, leading to the production of metallophores that increase the pathogen's chance of survival in the host. MS can reveal the structures, stability, and threshold concentrations of these metal-containing microbial biomarkers of infection in humans and model organisms, and can discriminate invasive disease from benign colonization based on well-defined thresholds distinguishing proliferation from the colonization steady state.

Seroprevalence of SARS-CoV-2 specific Immunoglobin G antibodies in rural population of Western Maharashtra, India.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, responsible for the coronavirus disease 2019 (COVID-19) pandemic, has been a major public health concern requiring continuous efforts for understanding its epidemiology. Patients infected with SARS-CoV-2 have a wide range of clinical features ranging from asymptomatic infection to mild or severe illness with fatal outcomes or recovery. Population-based seroepidemiological studies are an effective method for measuring the rapid spread of SARS-CoV-2 and monitoring the pandemic's progress. Methods: We conducted repeated cross-sectional community-based sentinel surveillance between January and June 2021 in the rural parts of the Pune district of Maharashtra, India to assess the seroprevalence against SARS-CoV-2 in three age categories. We selected 30 clusters for each round using a proportional population sampling method and 30 individuals in each of the three age groups (1-17 years, 18-49 years, and ≥50 years). We took blood samples from consenting study participants to check for the presence of Immunoglobulin G (IgG) antibodies against SARS-CoV-2 in all five rounds. Results: We included 14 274 individuals across five rounds; 29% were from the 1-17, 39% from the 18-49, and 32% from the ≥50-year-old group. Overall seroprevalence combining all rounds was 45%. There was an increase in seropositivity in rounds four (51.15%) and five (58.32%) contributed mostly by adults. We found that about 72% of elderly individuals ≥50 years in round five were seropositive. The factors strongly associated with the seropositivity were being in contact with suspected or confirmed cases of COVID-19 (odds ratio (OR) = 7.15; 95% confidence interval (CI) = 4.2-12.14), receiving at least one dose of COVID-19 vaccine (OR = 3.13 (95% CI = 0.70-14.07), being aged ≥50 years (OR = 1.97; 95% CI = 1.81-2.15), and being in an occupation belonging to a high-risk category (OR = 1.92; 95% CI = 1.65-2.26). Among 135 hospitalizations reported due to COVID-19-like illness, 91 (67%) were in the elderly age group of ≥50 and 33 (24%) were in the 18-49-year-old age group. Conclusions: Seroprevalence of SARS-CoV-2 was high in the last two rounds (April to June 2021) which coincide with the second wave of the pandemic (Delta variant B.1.617.2) in India. Overall, one in three children and one in two adults had antibodies for SARS-CoV-2. The suspected or confirmed case of COVID-19 emerged as the significant factor strongly associated with the seropositivity followed by COVID-19 vaccination.

A Bibliographic Survey on Detection of COVID-19 Patients using various sensors in the field of IOT

Due to a pandemic situation arising from the past few decades and the covid -19 patients are increasing at the rapid rate. Looking in the near future an IOT model is build which can be useful for people in coming years and allows rapid testing and efficient testing methodologies using various sensors such as Temperature, Respiration, RFID etc which takes various parameters. The study focuses around 412 scientific documents such as Journals, articles, book chapters and Patents in various papers. These documents are extracted from the scopus databases after querying with the keywords related to covid patients and IOT. The articles were analysed for the time period of a few months. For this model, open source tools are used where analysis is done through Node XL, Gephi and VOSviewer. The survey prominently focuses on the type of publications, Publication by Country, Based on the geographical locations, Documents by Year, By Subject Area, By Affiliation and By Author. It was observed from the sides as English was a major language which was used in publishing of the documents. Study of Journal shows the sufficient contribution of Computer Science and Technology in detecting the Covid-19 patients.