Application of real-time polymerase chain reaction to quantitate induced expression of interleukin-1? mRNA in ischemic brain tolerance
Bibliographic record
Abstract
A short duration of ischemia (i.e., ischemic preconditioning) was shown to result in significant tolerance to subsequent ischemic injury. Since previous reports suggest that interleukin-1beta (IL-1beta) may be involved in both ischemic damage and neuroprotection, the present work examined the expression of IL-1beta mRNA in cortical brain tissue after an established preconditioning (PC) stimulus known to produce significant brain tolerance to focal stroke after 1-7 days. Significant induction of IL-1beta mRNA was observed in the ipsilateral cortex at 6 hr (87+/-9 copies of the mRNA per microgram of brain tissue compared to 16+/-5 copies in sham-operated samples, P < 0.001, n = 4) and 8 hr (46+/-4 copies, P < 0.01, n = 4) after PC by means of real-time Taqman polymerase chain reaction (PCR). The peak expression of IL-1beta mRNA after PC was significantly (P < 0.01) lower than that after permanent occlusion of the middle cerebral artery (MCAO), i.e., 87+/-9 and 546+/-92 copies of RNA per microgram tissue at peak levels for PC and focal stroke, respectively. Immunohistochemistry studies revealed a parallel induction of IL-1beta in the ipsilateral cortex after PC. The maximal expression of IL-1beta was observed during the first week post-PC, showing marked parallelism with the duration of ischemic tolerance. These data suggest that the significant but low levels of IL-1beta induction after PC may contribute to ischemic brain tolerance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".