Effects of Anesthetic Agents on Blood Brain Barrier Integrity: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The blood brain barrier (BBB) is a highly selective permeable barrier that separates the blood and the central nervous system. Anesthesia is an integral part of surgery, and there is little known about the impact of anesthetics on the BBB. Therefore, it is imperative to explore reversible or modifiable variables such as anesthetic agents that influence BBB integrity. We aimed to synthesize the literature pertaining to the various effects of anesthetics on the BBB. METHODS: MEDLINE, Embase, and Cochrane were searched from inception up to September 2022. RESULTS: A total of 14 articles met inclusion into the review. The articles included nine randomized control studies (64.3%) and five quasi-experimental studies (35.7%). Twelve studies used volatile anesthetics, one study used fentanyl intravenously, and one study used pentobarbital or ketamine intraperitoneally. BBB structural deficits following the administration of an anesthetic agent included ultrastructural deficits, decreases in tight junctions, and decreases in BBB components. BBB functional deficits included permeability increases following exposure to volatile anesthetics. However, two studies found decreased permeability after fentanyl, pentobarbital, or ketamine exposure. Moreover, the impact of anesthetics on the BBB seems to be related to the duration of exposure. Notably, study findings also suggest that changes following anesthetic exposure demonstrate some reversibility over the short-term. CONCLUSION: Overall, our systematic review highlights interesting findings pertaining to the impact of anesthetic agents on BBB integrity in previously healthy models. These findings and mechanisms should inspire future work to aid practitioners and healthcare teams potentially better care for patients.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".