A Blood-Brain Barrier Disruption Model Eliminating the Hemodynamic Effect of Ketamine
Bibliographic record
Abstract
OBJECTIVE: We propose a simple modification to an established blood-brain barrier disruption (BBBD) animal model that allows us to use ketamine/xylazine as the anaesthetic agent, therefore decreasing the complexity and the cost of the model, while maintaining similar efficiency. METHODS: Sixty-two Long Evans rats were anaesthetized by intraperitoneal injection of ketamine/xylazine. Osmotic BBBD was performed by administering 25% mannitol into the internal carotid artery in a retrograde fashion from the external carotid. The infusion rate of mannitol, as well as the duration was adjusted in a stepwise fashion to identify optimal parameters for BBBD and minimize complications. As a supplementary step to previously reported models, a vascular clip was applied to the common carotid artery prior to the infusion of mannitol, therefore isolating our model system from the depressant hemodynamic effects of ketamine/xylazine. Evans blue dye was used to control for BBBD intensity. RESULTS: Using this model at an initial infusion rate of 0.15 ml/sec, a significant incidence of brain hemorrhage (75%) and a death rate of 62.5% were observed. Decreasing the infusion rate in a stepwise fashion, 0.08 ml/sec was found to produce optimal BBBD, as demonstrated by Evans blue staining. At this rate, 6/7 animals depicted grade II staining, whereas one animal depicted grade IV. CONCLUSION: The application of a clip to the common carotid artery prior to mannitol infusion allowed us to isolate the cerebral circulation from the depressant hemodynamic effects of ketamine/xylazine. This supplementary step produced consistent and efficient BBBD in our animal model.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".