Les cytokines préviennent les tumeurs<i>via</i>un mécanisme de sénescence cellulaire
Bibliographic record
Abstract
M/S n° 2, vol. 25, fevrier 2009 un shARN supprimant l’expression de p53. Les resultats, bien que n’excluant pas l’intervention de la senescence hepatocytaire dans la physiopathologie de la maladie, confirment la notion que la senescence des HSC limite la progression de la fibrose. Les auteurs ont ensuite etudie la phase de resolution de la fibrose qui intervient rapidement apres le retrait de l’agent inducteur. Vingt jours apres le retrait de l’agent toxique, les mutants p53-/ont un foie plus fibreux que celui des animaux controles avec une persistance de cellules senescentes. Neanmoins, la pente de regression de la fibrose, qui semble identique a celle des controles, ne permet pas d’affirmer formellement que la senescence des HSC est requise pour la resolution de la fibrose. Enfin, la comparaison du profil d’expression d’HSC en phase de proliferation avec celui de cellules senescentes a permis d’identifier parmi les genes induits au cours de la senescence, ceux qui codent pour des cytokines et des recepteurs qui potentialisent la fonction des cellules natural killer (NK). Or, apres depletion des cellules NK par des anticorps neutralisants au cours de la periode de retrait de l’agent toxique, les souris sauvages presentent plus de fibrose que les souris non traitees. A contrario, l’augmentation de l’activite des cellules NK reduit le nombre de cellules senescentes ainsi que la fibrose de facon significative.
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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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".