Administration of minocycline ameliorates damage in a renal ischemia/reperfusion injury model
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the effects of minocycline on the renal dysfunction and injury caused by bilateral ischemia/reperfusion (I/R) of murine kidneys in vivo. METHODS: Male C57BL/6 mice were administered minocycline (45 mg/kg i.v.) or saline (0.9%, v/v, NaCl) 36 hours prior to I/R. Mice were subjected to bilateral renal ischemia (35 min) followed by reperfusion (6 hours). Serum creatinine (sCr) and blood urea nitrogen (BUN) levels were measured. Additionally, renal superoxide dismutase (SOD) levels, malondialdehyde (MDA) levels and myeloperoxidase (MPO) activity were determined. The expression of intercellular adhesion molecule-1 (ICAM-1), caspase-3, caspase-8 and caspase-9 was determined using real time RT-PCR and Western blot analysis. RESULTS: Minocycline administration significantly reduced the increases in sCr and BUN caused by I/R, indicating attenuation of renal dysfunction and injury, and reduced histological evidence of renal damage caused by I/R. Minocycline administration also markedly reduced the evidence of oxidative stress (MPO activity, SOD and MDA levels), inflammation (ICAM-1 expression and MPO activity) and apoptosis (caspase-3, caspase-8 and caspase-9 expression) in mouse kidneys subjected to I/R. CONCLUSION: These findings provide good evidence that minocycline can reduce the renal dysfunction and injury caused by I/R of the kidney. Its mechanism may involve suppression of apoptosis, inflammatory response and oxidative stress.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".