Tumor necrosis factor-α induces apoptosis via inducible nitric oxide synthase in neonatal mouse cardiomyocytes
Bibliographic record
Abstract
OBJECTIVE: It has been demonstrated that tumor necrosis factor-alpha (TNF alpha) induces apoptosis in cardiac myocytes. However, its mechanism of action is still not well understood. In the present study, we hypothesized that TNF alpha induces myocardial apoptosis by induction of inducible nitric oxide synthase (iNOS). METHODS: Neonatal cardiac myocytes were isolated from iNOS (-/-) mutant and C57BL6 wild type mice. Cells were cultured for 3 days before treatment with an NO donor or TNF alpha. Following treatment with S-nitroso-N-acetyl-penicillamine (SNAP) or TNF-alpha, cells were tested for apoptosis by terminal deoxynucleotidyl transfer-mediated end labeling (TUNEL) staining and cell death detection ELISA. NO production was measured by nitrite concentration in the culture medium. Cardiomyocyte expression of iNOS and TNF type 1 receptor (TNFR1) mRNA was determined by reverse transcriptase-polymerase chain reaction (RT-PCR). RESULTS: SNAP (0.01-100 microM) induced apoptosis of cardiac myocytes in a concentration-dependent manner in the wild type mice (n = 5, P < 0.01). TNFR1 mRNA was expressed in neonatal cardiomyocytes from both wild type and iNOS (-/-) mutant mice. TNF alpha induced a concentration-dependent increase in iNOS mRNA expression and nitrite production as well as significant apoptosis of cardiomyocytes in the wild type mice (n = 4, P < 0.01). However, without iNOS expression, the apoptotic effects of TNF-alpha were significantly attenuated in cardiomyocytes from iNOS (-/-) mutant mice (n = 4, P < 0.05). CONCLUSION: TNF alpha induces apoptosis via iNOS expression and NO production in neonatal mouse cardiomyocytes.
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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 | 0.001 |
| 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".