Macrophage Infiltration and Cellular Proliferation in the Non-Ischemic Kidney and Heart following Prolonged Unilateral Renal Ischemia
Bibliographic record
Abstract
BACKGROUND/AIMS: Although ischemic renal failure remains a major cause of morbidity and mortality, whether ischemic changes within a kidney might also have adverse effects on other organs has not been examined. Furthermore, given the protective effects of angiotensin II receptor (AT1) antagonism in renal ischemia, we considered whether a similar strategy might also modulate the response to acute renal insult. METHODS: Unilateral renal artery ligation was performed in Sprague-Dawley rats, treated with or without the AT1 antagonist losartan (30 mg/kg/day). After 24 h of renal ischemia, changes in the contralateral kidney and heart were examined. RESULTS: Contralateral non-ischemic kidneys displayed increased expression of platelet-derived growth factor-B (PDGF-B) in association with increased tubular cell proliferation. Gene expression for the macrophage chemokine osteopontin (OPN) was similarly increased along with substantial macrophage infiltration. In the heart, expression of OPN and macrophage numbers were increased. All of these changes, in both the heart and kidney were attenuated by losartan. CONCLUSION: Rather than affecting a single organ, the present study demonstrates that after prolonged renal ischemia, the contralateral kidney and heart undergo changes in growth factor and chemokine expression, resulting in pathological proliferation and inflammation that can be modulated by blockade of the AT1 receptor.
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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 | 0.000 |
| 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".