Biomarkers.

Background: The unique lesion of chronic traumatic encephalopathy (CTE) is the perivascular deposition of hyperphosphorylated tau at the depth of the cortical sulci. The distribution and molecular composition of p-tau is distinct from Alzheimer's disease (AD), but differential diagnostic challenges remain. Understanding disease differences in regional density of p-tau will inform differential diagnosis and interpretation of in vivo biomarkers.

Global Groundwater Carbon Mass Flux and the Myth of Atmospheric Weathering.

Our recent steady-state mass-balance modeling suggests that most global carbonic-acid weathering of silicate rocks occurs in the vadose zone of aquifer systems not on the surface by atmospheric CO. That is, the weathering solute flux is nearly equal to the total global continental riverine carbon flux, signifying little atmospheric weathering by carbonic acid. This finding challenges previous carbon models that utilize silicate weathering as a control of atmospheric CO levels.

Neurodegeneration in the cortical sulcus is a feature of chronic traumatic encephalopathy and associated with repetitive head impacts.

Neurodegeneration is a seminal feature of many neurological disorders. Chronic traumatic encephalopathy (CTE) is caused by repetitive head impacts (RHI) and is characterized by sulcal tau pathology. However, quantitative assessments of regional neurodegeneration in CTE have not been described.

Duration of Ice Hockey Play and Chronic Traumatic Encephalopathy.

Importance: Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impacts (RHIs). Prior research suggests a dose-response association between American football play duration and CTE risk and severity, but this association has not been studied for ice hockey.

Objective: To investigate associations of duration of ice hockey play with CTE diagnosis and severity, functional status, and dementia.

CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data.

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data.