Melatonin and <scp>IL</scp>‐25 modulate apoptosis and angiogenesis mediators in metastatic (<scp>CF</scp>‐41) and non‐metastatic (<scp>CMT‐U229</scp>) canine mammary tumour cells
Bibliographic record
Abstract
BACKGROUND: Melatonin has oncostatic actions and IL-25 is active in inflammatory processes that induce apoptosis in tumor cells AIM: The aim of this study was to evaluate melatonin and IL-25 in metastatic (CF-41) and non-metastatic (CMT-U229) canine mammary tumor cells cultured as monolayers and tridimensional structures. MATERIALS AND METHODS: The cells were treated with melatonin, IL-25 and IL-17B silencing gene and performed cell viability, gene and protein expression of caspase-3 and VEGFA (Vascular endothelial growth factor A) and an apoptosis membrane protein array. RESULTS: Treatment with 1 mM of melatonin reduced cell viability of both tumor cell lines, all treatments alone and combined significantly increased caspase-3 cleaved and proteins involved in the apoptotic pathway and reduced pro-angiogenic VEGFA, confirming the effectiveness of these potential promising treatments. CONCLUSION: This is the first study evaluating the potential use of these strategies in CF-41 and CMT-U229 cell lines and together encourages subsequent in vitro and in vivo studies for further exploration of clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".