Bibliographic record
Abstract
The blood-brain (BBB) is a crucial system that regulates selective brain circulation with the periphery, as an example, allowing necessary nutrients to enter and expel excessive amino acids or toxins from the brain. To model how the BBB can be compromised in diseases like vascular dementia (VaD) or Alzheimer's disease (AD), researchers developed novel methods to model vessel dilatation. A compromised BBB in these disease states can be detrimental and result in the dysregulation of the BBB leading to untoward and pathological consequences impacting brain function. We were able to modify an existing technique that enabled us to inject directly into the Cisterna magna (CM) to induce dilatation of blood vessels using elastase, and disrupt the tight junctions (TJ) of the BBB. With this method, we were able to see various metrics of success over previous techniques, including consistent blood vessel dilatation, reduced mortality or improved recovery, and improving the fill/opacifying agent, a silicone rubber compound, delivery for labeling blood vessels for dilatation analysis. This modified minimally invasive method has had promising results, with a 19%-32% increase in sustained dilatation of large blood vessels in mice from 2 weeks to 3 months post-injection. This improvement contrasts with previous studies, which showed increased dilatation only at the 2 week mark. Additional data suggests sustained expansion even after 9.5 months. This increase was confirmed by comparing the diameter of blood vessels of the elastase and the vehicle-injected group. Overall, this technique is valuable for studying pathological disorders that affect the central nervous system (CNS) using animal models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.502 | 0.315 |
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".