Cerebral Ischemic Preconditioning Aggravates Death of Oligodendrocytes
Bibliographic record
Abstract
Neurodegeneration can benefit from ischemic preconditioning, a natural adaptive reaction to sublethal noxious stimuli. Although there is growing interest in advancing preconditioning to preserve brain function, preconditioning is not yet considered readily achievable in clinical settings. One of the most challenging issues is that there is no fine line between preconditioning stimuli and lethal stimuli. Here, we show deleterious effect of preconditioning on oligodendrocyte precursor cells (OPCs). We identified Bcl-2/adenovirus E1B 19-kDa interacting protein 3 (BNIP3), a mitochondrial BH3-only protein specifically involved in OPCs loss after preconditioning. Repeated ischemia stabilized BNIP3 and increased the vulnerability of OPCs to subsequent ischemic events. BNIP3 became mitochondrial-bound and was concurrent with the dysfunction of monocarboxylate transporter 1 (MCT1). Inhibition of BNIP3 by RNAi or necrostatin-1 (Nec-1) and knocking out of BNIP3 almost completely prevented OPCs loss and preserved white matter integrity. Together, our results suggest that the unfavorable effect of BNIP3 on OPCs should be noted for safe development of ischemic tolerance. BNIP3 inhibition appears to be a complementary approach to improve the efficacy of preconditioning for ischemic stroke.
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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".