Abstracts from the Virtual Symposium “Signal Transduction at the Blood–Brain Barriers”
Bibliographic record
Abstract
Fluids and Barriers of the CNS 2021, 18(1): A1 COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).COVID-19 pathogenesis causes vascular-mediated neurological disorders via still elusive mechanisms.SARS-CoV-2 infects host cells by binding to angiotensin-converting enzyme 2 (ACE2) that recognizes the viral spike (S) protein.Brain pericytes express ACE2 at the neurovascular interface, outlining their possible implication in microvasculature injury in COVID-19.Yet, pericyte responses to SARS-CoV-2 is not clear.Here we report that ACE2 expression in human brain vascular pericytes is highly dynamic and is increased upon S protein stimulation.Pericytes exposed to S protein underwent phenotypic changes translated by increased expression of contractile and myofibrogenic proteins and an altered intracellular calcium dynamic.Furthermore, S protein induced lipid peroxidation, oxidative and nitrosative stress in pericytes as well as triggered an immune reaction, which was potentiated by hypoxia, a condition associated with vascular comorbidities.S protein exposure combined with hypoxia enhanced the production of pro-inflammatory cytokines.Finally, we found that S protein could reach the mouse brain via the intranasal route and that reactive ACE2-expressing pericytes are recruited to the damaged tissue undergoing fibrotic scarring in a mouse model of cerebral multifocal micro-occlusions.Our data demonstrate that the released S protein is sufficient to mediate pericyte immunoreactivity, which may contribute to microvasculature injury in absence of a productive viral infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.137 | 0.062 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".