COVID-19 may Enduringly Impact Cognitive Performance and Brain Haemodynamics in Undergraduate Students.

To date, 770 million people worldwide have contracted COVID-19, with many reporting long-term "brain fog". Concerningly, young adults are both overrepresented in COVID-19 infection rates and may be especially vulnerable to prolonged cognitive impairments following infection. This calls for focused research on this population to better understand the mechanisms underlying cognitive impairment post-COVID-19.

Gut microbiota profiling in injection drug users with and without HIV-1 infection in Puerto Rico.

Introduction: The full extent of interactions between human immunodeficiency virus (HIV) infection, injection drug use, and the human microbiome is unclear. In this study, we examined the microbiomes of HIV-positive and HIV-negative individuals, both drug-injecting and non-injecting, to identify bacterial community changes in response to HIV and drug use. We utilized a well-established cohort of people who inject drugs in Puerto Rico, a region with historically high levels of injection drug use and an HIV incidence rate disproportionately associated with drug use.

Polychlorinated biphenyl (PCB) concentrations and breakpoint trends across the waters of the Great Lakes by isotope-dilution high-resolution mass spectroscopy.

Water samples were collected during each of the 2012-2019 Cooperative Science and Monitoring Initiative (CSMI) cruises aboard the U.S. EPA R/V Lake Guardian as part of the Great Lakes Fish Monitoring and Surveillance Program (GLFMSP) lower food web contaminant assessment.

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

Background: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring.