Role of leukocyte accumulation and oxygen radicals in ischemia-reperfusion-induced injury in skeletal muscle
Bibliographic record
Abstract
The role of leukocytes and nonleukocyte-derived reactive oxygen metabolites (ROMs) in reperfusion-induced skeletal muscle injury was determined. Male rats received 2 h no-flow hindlimb ischemia-reperfusion (I/R, n = 6) or were rendered neutropenic via antineutrophil serum (ANS) before I/R (I/R + ANS, n = 5). Oxygen radicals in the absence of neutrophils were tested by administration of dimethylthiourea (DMTU) (I/R + ANS + DMTU, n = 5). Perfused capillaries (CD(per)) and rolling (L(r)), adherent (L(a)), and extravasated leukocytes (L(e)) in the extensor digitorum longus muscle were measured every 15 min during 90 min of reperfusion using intravital microscopy. The vital dyes bisbenzimide (BB) and ethidium bromide (EB) provided direct measures of tissue injury (EB/BB). CD(per) decreased immediately on reperfusion in the I/R and I/R + ANS groups. CD(per) in the I/R + ANS + DMTU group remained at baseline throughout reperfusion. L(a) increased in the I/R group; however, EB/BB was the same between I/R and I/R + ANS groups. Injury in the I/R + ANS + DMTU group did not differ from other groups > or =60 min, after which EB/BB became significantly lower. L(e) did not differ between groups and was highly correlated to tissue injury. The results suggest that L(e) lead to parenchymal injury, and ROMs lead to perfusion deficits during the early reperfusion period after ischemia.
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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) | 0.001 | 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 | 0.000 |
| 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".