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2026

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Evaluation Of Mycoplasma Genitalium Positivity And Co-Infection With Chlamydia Trachomatis And/Or Neisseria Gonorrhoeae At Various Care Sites Across The United States, Rebecca Lillis, Stacey House, Et Al. Jan 2026

Evaluation Of Mycoplasma Genitalium Positivity And Co-Infection With Chlamydia Trachomatis And/Or Neisseria Gonorrhoeae At Various Care Sites Across The United States, Rebecca Lillis, Stacey House, Et Al.

2020-Current year OA Pubs

OBJECTIVES: We evaluated Mycoplasma genitalium (MG) positivity and co-infection with Chlamydia trachomatis (CT) and/or Neisseria gonorrhoeae (NG), based on results from a trial assessing clinical performance of the Cobas® Liat CT/NG/MG point-of-care test.

METHODS: A prospective, US, multicenter, noninterventional study assessed MG positivity in prospective urine samples (male) and clinician-collected vaginal swabs (female) from symptomatic/asymptomatic patients aged ≥14 years attending various clinical settings. Participants were designated positive or negative for MG, CT, and NG based on combined results from three US Food and Drug Administration-approved assays and one laboratory-developed test. MG co-infection with CT and/or NG was assessed.

RESULTS: Among …


Spatial Variation In Socio-Economic Vulnerability To Influenza-Like Infection For The Us Population, Shrabani S. Tripathy, Joseph V. Puthussery, Taveen S Kapoor, John R. Cirrito, Rajan K. Chakrabarty Jan 2026

Spatial Variation In Socio-Economic Vulnerability To Influenza-Like Infection For The Us Population, Shrabani S. Tripathy, Joseph V. Puthussery, Taveen S Kapoor, John R. Cirrito, Rajan K. Chakrabarty

2020-Current year OA Pubs

This study aims to quantify environmental health impacts and assess risk by understanding the disproportionate burden of infectious diseases, specifically Influenza-like Illness (ILI), across regions with varying socio-economic characteristics. We introduce a novel vulnerability-based approach to better understand the complex relationship between socio-economic factors and ILI burden. We develop a machine-learning-driven framework to assess and map state-level socio-economic vulnerability to ILI in the United States. A vulnerability index was created by integrating 39 diverse socio-economic and health indicators from the latest CENSUS. A Random Forest Regression model then weighed these indicators to quantify each state's vulnerability for the ILI values …