Chloramphenicol alleviates 5-fluorouracil-induced cellular senescence through activation of autophagy
Bibliographic record
Abstract
5-Fluorouracil (5-FU) is a first-line treatment for colorectal cancer, but side effects such as severe diarrhea are common in clinical use and have been linked to its induction of normal cell senescence. Chloramphenicol (CAP) is an antibiotic commonly used to treat typhoid or anaerobic infections, but its senescence-related aspects have not been thoroughly investigated. Here, we used 5-FU to induce senescence in human umbilical vein endothelial cells (HUVECs) and investigated the relationship between CAP and cellular senescence at the cellular level. In a model of cellular senescence induced by 5-FU treatment, we discovered that CAP treatment reversed the rise in the percentage of senescence-associated galactosidase (SA-β-gal)-positive cells and decreased the expression of senescence-associated proteins (p16), senescence-associated genes (p21), and senescence-associated secretory phenotypes (SASPs: IL-6, TNF-α). In addition, CAP subsequently restored the autophagic process inhibited by 5-FU and upregulated the levels of autophagy-related proteins. Mechanistically, we found that CAP restored autophagic flux by inhibiting the mTOR pathway, which in turn alleviated FU-induced cellular senescence. Our findings suggest that CAP may help prevent cellular senescence and restore autophagy, opening up new possibilities and approaches for the clinical management of colorectal cancer.
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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".