Bibliographic record
Abstract
An argument is made that small-vessel stroke, which usually results in lacunar infarction, is a serious medical problem. Therefore, it is surprising that only a few animal models exist that mimic small-vessel stroke and that these models have not been used for a systematic investigation of the genesis of lacunar infarctions. We make a case that the modified pial vessel class II disruption model mimics certain important aspects of lacunar infarctions, namely cavitation caused specifically by ischemia of smaller vessels. We found evidence that upregulation of inflammatory properties within a few days of inducing lesions prevents repopulation of the lesion with reactive astrocytes. We propose that this is the key mechanism by which cavitation occurs weeks later. We also found that treatment with minocycline after induction of lesions but before cavitation prevented the formation of the fluid-filled cavity. Rather than being walled off, the lesion apparently became part of the brain parenchyma and consisted of reactive astrocytes. We conclude that this new model can be used to investigate the mechanism of lacune formation and its prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".