Comparison of Taxonomic Resolutions of Various Typing Methods for and Species Isolated from Landfill Leachate.

Anaerobic bacteria and methanogenic archaea at municipal landfill dumping sites play a vital role in the landfill ecology, waste degradation, global warming and climate change. Although landfill works as a cheap way of solid waste management, unmanaged landfill plays a significant role in spreading pollutants and pathogens in natural ecosystems. The genera and are important groups of anaerobic microbes from a public and environmental health perspective.

Clinical diagnosis and management of multiple cerebral ring-enhancing lesions-study of 50 patients at a tertiary healthcare center.

Aim Of The Study: Multiple ring-enhancing lesions are commonly experienced group of brain pathologies which we come across in day-to-day practice. Clinical symptoms in these lesions are quite non-specific, and hence, it is difficult to reach a final diagnosis. However, these lesions have a varied group of differential diagnosis and it is sometimes difficult to have an accurate diagnosis on conventional MRI.

Culture-based and culture-independent approach for the study of the methanogens and obligate anaerobes from different landfill sites.

The landfill is a cheap way of solid waste management in developing countries. The majority of landfills are non-sanitary and work as open garbage dumping sites and pose threats to public and environmental health. Therefore, an in-depth understanding of the chemistry and microbiology of landfills is imperative to develop the right policies for landfill management.

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach.