The autophagy inhibitor spautin-1, either alone or combined with doxorubicin, decreases cell survival and colony formation in canine appendicular osteosarcoma cells
Bibliographic record
Abstract
Dogs diagnosed with appendicular osteosarcoma typically succumb to metastatic disease within a year of diagnosis. The current standard of care for curative intent, amputation followed by adjuvant chemotherapy, increases survival time but chemoresistance is a major contributor to mortality. Unfortunately, the mechanisms driving the progression of metastatic disease and the development of chemoresistance are unknown. One theory is that autophagy may contribute to chemoresistance by providing neoplastic cells with a mechanism to survive chemotherapy treatment. Our objective was to evaluate the effect of combining an autophagy inhibitor with a standard chemotherapeutic drug on response to chemotherapy in canine appendicular osteosarcoma cells. We hypothesized that combining the autophagy inhibitor spautin-1 with doxorubicin treatment would enhance chemoresponsiveness. Using commercial (D17) and primary cell lines derived from 1° and 2° sites of osteosarcoma, we showed that this combination treatment enhances cell killing and inhibits colony formation. Our findings support the theory that autophagy contributes to chemoresistance in canine appendicular osteosarcoma and indicate that adding an autophagy inhibitor to the standard of care has the potential to improve outcome.
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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".