Carnitine Deficiency: A Causative Clue or a Sequel in Carboplatin Myelosuppression
Bibliographic record
Abstract
We have previously demonstrated that carnitine deficiency aggravated paracetamol-induced hepatopathy and carboplatin-induced nephropathy. As a continuum, we have addressed in the current study as to whether carboplatin-induced myelosuppression would be exacerbated by carnitine deficiency. Challenging male Wistar rats with a single dose of carboplatin (35 mg/kg, IP) induced bone marrow suppression manifested as anemia, leucopenia, thrombocytopenia as well as increased frequencies of the micronucleated bone marrow cells; MPCE and MNCE with notable reduction in the P/N ratio. The platinum drug also elevated serum TNF-a and reduced serum free and total carnitine levels. Besides, ATP levels in red and T cells were lowered. Likewise, the mitochondrial membrane potential in T lymphocytes was reduced following the use of the potentiometric dye; JC-1, and this was well correlated with cellular ATP production. Carnitine deficiency exacerbated carboplatin myelotoxicity as it exaggerated all biochemical, hematological and cytogenetic parameters. To address as to whether carnitine deficiency was a causative clue or merely a sequel of carboplatin myelotoxicity, L-carnitine was supplemented ahead of carboplatin challenege. Herein, L-carnitine mitigated all the biochemical, hematological and cytogenetic effects possibly via modulating the release of TNF-a, cellular ATP production and restoring the mitochondrial membrane potential. Irrespective of the mechanisms involved, the current results may afford the potential role for carnitine supplementation as add-on nutraceutical in carboplatin-based chemotherapy.
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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 | 0.001 | 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".