Year-long wastewater monitoring for SARS-CoV-2 signals in combined and separate sanitary sewers.

COVID-19 wastewater-based epidemiology has been performed in catchments of various sizes and sewer types with many short-term studies available and multi-seasonal studies emerging. The objective of this study was to compare weekly observations of SARS-CoV-2 genes in municipal wastewater across multiple seasons for different systems as a factor of sewer type (combined, separate sanitary) and system size. Sampling occurred following the first wave of SARS-CoV-2 cases in the study region (June 2020) and continued through the third wave (May 2021), the period during which clinical testing was widely available and different variants dominated clinical cases. The strongest correlations were observed between wastewater N1 concentrations and the cumulative clinical cases reported in the 2 weeks prior to wastewater sampling, followed by the week prior, new cases, and the week after wastewater sampling. Sewer type and size did not necessarily explain the strength of the correlations, indicating that other non-sewer factors may be impacting the observations. In-system sampling results for the largest system sampled are presented for 1 month. Removing wet weather days from the data sets improved even the flow-normalized correlations for the systems, potentially indicating that interpreting results during wet weather events may be more complicated than simply accounting for dilution. PRACTITIONER POINTS: SARS-CoV-2 in wastewater correlated best with total clinical cases reported in 2 weeks before wastewater sampling at the utility level. Study performed when clinical testing was widespread during the year after the first COVID-19 wave in the region. Sewer type and size did not necessarily explain correlation strength between clinical cases and wastewater-based epidemiology results. Removing wet weather days improved correlations for 3/4 utilities studied, including both separate sanitary and combined sewers.

Meta-analysis of the microbial biomarkers in the gut-lung crosstalk in COVID-19, community-acquired pneumonia and Clostridium difficile infections.

Respiratory infections are the leading causes of mortality and the current pandemic COVID-19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unravelling the correlations and interplay of the human microbiota in the gut-lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID-19 infected gut, COVID-19 infected lungs, Clostridium difficile infected gut and community-acquired pneumonia infected lungs. The metagenome data sets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID-19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community-acquired pneumonia whereas abundance of Faecalibacterium and Clostridium is associated with the C. difficile infections in gut. A machine learning random forest classifier applied to the data sets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut-lung axis during dysbiosis of two anatomically different organs.

Social Interaction & Service Robot for Hospital Wards

As the era of industrial revolution 5.0 has begun, most of the robots are developed to have cyber inter-physical functionalities which are deemed to replace human activities. However, robots are rarely being utilized in the health care sector. In a medical institution, countless activities and events are happening simultaneously. Most of these are very precise, lifesaving and are on a time constraint. Heavy machinery and equipment are required to execute such events which is time-consuming and inconvenient. The robot specified helps with regular processes occurring on a day-to-day basis in the institution such as taking vitals and sanitization as well as transporting products on the go intelligently and safely. This robot is good at mapping rooms using the internal GPS, the robot can effectively communicate and output simple messages with the patients via, a display screen. Human intervention plays a vital role in preventing the health care workers from coming in contact with the covid-19. © 2022 IEEE.

COVID-19 Prevention Melioration: Face Mask and Social Distancing Detection

The pandemic Coronovirus disease 2019 (COVID-19) gave rise to a huge impact on everyone's lives. Millions of people have been infected worldwide. Now the vaccination has been found, WHO has still insisted on wearing mask and maintaining social distance is necessary as it is the most feasible way to prevent ourselves from the virus. This notion motivated me to contribute an efficient computer vision based detection system to public safety.The detection system detects if there is any violations through the camera and or on any video or image input. In this system, we have used deep learning algorithm. This system I have proposed here can be applied in places where people gather in huge amount. This automated detection technique makes it easier to inspect the public gathering in malls, streets, any firms, apartments, etc. © 2021 IEEE.

Application of green synthesised copper iodide particles on cotton fabric-protective face mask material against COVID-19 pandemic.

Microorganisms cause variety of diseases that constitutes a severe threat to mankind. Due to the upsurge of many infectious diseases, there is a high requirement and demand for the development of safety products finished with antimicrobial properties. The study involves the antimicrobial activity of natural cotton coated with copper iodide capped with Hibiscus rosa-sinensis L. flower extract (CuI-FE) which is rich in anthocyanin, cyanidin-3-sophoroside by ultrasonication method. The coated and uncoated cotton fabric was characterised through XRD, SEM, AFM, tensile strength and UV-Visible spectroscopic techniques. XRD confirmed the formation of CuI particles, SEM showed that CuI-FE was prismatic in shape. The average size of CuI-FE particles was found to be 552.45 nm. Anti-bacterial studies showed copper iodide particles to be a potent antimicrobial agent. AFM images confirmed the rupture of bacterial cell walls in the presence of prismatic CuI-FE. In-vitro cytotoxicity investigation of CuI-FE was performed against cancer and spleen cell lines to evaluate the cell viability. Cytotoxicity analysis revealed the IC50 value of 233.93 µg/mL in the presence of CuI-FE. Molecular docking study was also carried out to understand the interaction of CuI-FE with COVID-19 main protease. This paper has given an insight on the usage of CuI-FE coated on the cotton fabric that has proved to have strong inhibition against the nano ranged bacterial, cancerous cell line and a strong interaction with the COVID-19 protease. Such eco-friendly material will provide a safe environment even after the disposable of medical waste from the infectious diseases like influenza and current pandemic like COVID-19.

From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors.

The recent pandemic Coronavirus disease-19 outbreak had traumatized global countries since its origin in late December 2019. Though the virus originated in China, it has spread rapidly across the world due its firmly established community transmission. To successfully tackle the spread and further infection, there needs a clear multidimensional understanding of the molecular mechanisms. Henceforth, 942 viral genome sequences were analysed to predict the core genomes crucial in virus life cycle. Additionally, 35 small interfering RNA transcripts were predicted that can target specifically the viral core proteins and reduce pathogenesis. The crystal structure of Covid-19 main protease-6LU7 was chosen as an attractive target due to the factors that there were fewer mutations and whose structure had significant identity to the annotated protein sequence of the core genome. Drug repurposing of both recruiting and non recruiting drugs was carried out through molecular docking procedures to recognize bitolterol as a good inhibitor of Covid-19 protease. The study was extended further to screen antiviral phytocompounds through quantitative structure activity relationship and molecular docking to identify davidigenin, from licorice as the best novel lead with good interactions and binding energy. The docking of the best compounds in all three categories was validated with molecular dynamics simulations which implied stable binding of the drug and lead molecule. Though the studies need clinical evaluations, the results are suggestive of curbing the pandemic.

A detailed review on the transmissibility of covid

The 2019 Coronavirus disease (COVID-19), caused by extreme ART 2 (SARS-CoV-2), has easily spread all over the world. At the end of June 2020, there were more than 5 million confirmed cases and over 500,000 deaths. The features of this disease show that SARSCoV-2 can be spread by droplets extended during close contact and fomites. Potential airborne distribution was also envisaged in healthcare facilities because of such aerosol-generating practices 1,2. After identification of the virus in stools, the position of the fecal-oral route in indoor environments must still be determined. Nevertheless, it remains important that fomites, direct contact and the alleged fecal-oral path are of relative significance. In clinics, there has also been major contamination. The CDC Weekly 2020 reports that 1716 of 44,672 COVID-19 cases in China reported their presence by 11 February, 2020. In order to protect health staff, it is also important to consider the risk of infection in a hospital setting.

Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19.

The coronavirus disease, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a global health crisis that is being endured with an increased alarm of transmission each day. Though the pandemic has activated innumerable research attention to decipher an antidote, fundamental understanding of the molecular mechanisms is necessary to halt the disease progression. The study focused on comparison of the COVID-19 infected lung tissue gene expression datasets -GSE155241 and GSE150316 with the GEO2R-limma package. The significant up- and downregulated genes were annotated. Further evaluation of the enriched pathways, transcription factors, kinases, noncoding RNAs and drug perturbations revealed the significant molecular mechanisms of the host response. The results revealed a surge in mitochondrial respiration, cytokines, neurodegenerative mechanisms and deprived oxygen, iron, copper, and glucose transport. Hijack of ubiquitination by SARS-CoV-2, hox gene differentiation, histone modification, and miRNA biogenesis were the notable molecular mechanisms inferred. Long non-coding RNAs such as C058791.1, TTTY15 and TPTEP1 were predicted to be efficient in regulating the disease mechanisms. Drugs-F-1566-0341, Digoxin, Proscillaridin and Linifanib that reverse the gene expression signatures were predicted from drug perturbations analysis. The binding efficiency and interaction of proscillaridin and digoxin as obtained from the molecular docking studies confirmed their therapeutic potential. Two overlapping upregulated genes MDH1, SGCE and one downregulated gene PFKFB3 were appraised as potential biomarkers candidates. The upregulation of PGM5, ISLR and ANK2 as measured from their expressions in normal lungs affirmed their possible prognostic biomarker competence. The study explored significant insights for better diagnosis, and therapeutic options for COVID-19. Communicated by Ramaswamy H. Sarma.