NF-kappaB inhibitors significantly attenuate the transcription of high affinity type-2 cationic amino acid transporter in LPS-stimulated rat kidney.
Bibliographic record
Abstract
BACKGROUND: Sepsis-induced renal failure is closely related to inducible nitric oxide synthase (iNOS) upregulation and nitric oxide (NO) overproduction. Trans-membrane L-arginine transportation mediated by type-2 cationic amino acid transporter (CAT-2) isozymes, including CAT-2, CAT-2A, and CAT-2B, is one of the crucial mechanisms that regulate NO biosynthesis by iNOS. We previously had shown that endotoxemia significantly upregulated renal CAT-2 and CAT-2B but not CAT-2A expression. This study was, thus, conducted to further explore the role of nuclear factor-kappaB (NF-kappaB) in regulating the expression of CAT-2 isozymes in lipopolysaccharide (LPS)-treated rat kidney. METHODS: Adult male Sprague-Dawley rats were randomly given intra-peritoneal injections of normal saline (N/S), LPS, LPS plus NF-kappaB inhibitor pre-treatment (PDTC, dexamethasone, or salicylate), or an NF-kappaB inhibitor alone. The rats were sacrificed at 6 hours after LPS injection and enzyme expression and renal injury were examined. RESULTS: Renal iNOS, CAT-2, and CAT-2B were significantly upregulated in LPS-stimulated rat kidney. NF-kappa B inhibitors significantly attenuated this upregulation induced by LPS and resultantly attenuated renal NO biosynthesis and renal injury induced by LPS. In contrast, renal CAT-2A expression was not affected by either LPS or NF-kappaB inhibitors. CONCLUSIONS: LPS co-induces iNOS, CAT-2 and CAT-2B expression in LPS-stimulated rat kidney. Furthermore, inhibition of NF-kappaB significantly attenuates NO biosynthesis through inhibition of iNOS, CAT-2, and CAT-2B, and, in turn, significantly reduces endotoxemia-induced renal injury.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".