Modulation of Paracellular Permeability in SARS-CoV-2 Blood-to-Brain Transcytosis
Bibliographic record
Abstract
SARS-CoV-2 primarily infects the lungs via the ACE2 receptor but also other organs including the kidneys, the gastrointestinal tract, the heart, and the skin. SARS-CoV-2 also infects the brain, but the hematogenous route of viral entry to the brain is still not fully characterized. Understanding how SARS-CoV-2 traverses the blood-brain barrier (BBB) as well as how it affects the molecular functions of the BBB are unclear. In this study, we investigated the roles of the receptors ACE2 and DPP4 in the SARS-CoV-2 infection of the discrete cellular components of a transwell BBB model comprising HUVECs, astrocytes, and pericytes. Our results demonstrate that direct infection on the BBB model does not modulate paracellular permeability. Also, our results show that SARS-CoV-2 utilizes clathrin and caveolin-mediated endocytosis to traverse the BBB, resulting in the direct infection of the brain side of the BBB model with a minimal endothelial infection. In conclusion, the BBB is susceptible to SARS-CoV-2 infection in multiple ways, including the direct infection of endothelium, astrocytes, and pericytes involving ACE2 and/or DPP4 and the blood-to-brain transcytosis, which is an event that does not require the presence of host receptors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".