Effect of proteasome inhibitors on canine lymphoma cell response to <scp>CHOP</scp> chemotherapy in vitro
Bibliographic record
Abstract
Abstract The standard treatment for canine lymphoma is the CHOP chemotherapy regimen. Proteasome inhibitors have been employed with CHOP for the treatment of human haematological malignancies but remain to be fully explored in canine lymphoma. We identified an association between poor response to CHOP chemotherapy and high mRNA expression levels of proteasomal subunits in a cohort of 15 canine lymphoma patients, and sought to determine the effect of proteasome inhibitors on the viability of a canine B‐cell lymphoma cell line (CLBL‐1). The aim of this study was to investigate whether proteasome inhibitors sensitize these cells to the CHOP agents doxorubicin, vincristine and cyclophosphamide (as 4‐hydroxycyclophosphamide/4‐HC). CLBL‐1 cells were sensitive to proteasome inhibition by bortezomib and ixazomib. The IC50 of bortezomib was 15.1 nM and of ixazomib was 59.14 nM. Proteasome inhibitors plus doxorubicin had a synergistic effect on CLBL‐1 viability; proteosome inhibitors plus vincristine showed different effects depending on the combination ratio, and there was an antagonistic effect with 4‐HC. These results may have clinical utility, as proteasome inhibition could potentially be used with a synergizing CHOP compound to improve responsiveness to chemotherapy for canine lymphoma patients.
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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) | 0.002 | 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".