Classification and Prognostication of B‐Cell and T‐Cell Multicentric Lymphoma in Dogs Using Serum <scp>MicroRNAs</scp>
Bibliographic record
Abstract
Canine multicentric lymphoma is a common malignancy in dogs. It often responds well to initial chemotherapy but frequently relapses and has a poor response to subsequent treatment. B-cell (BCL) and T-cell (TCL) lymphomas differ in both their prognoses and chemotherapeutic treatment protocols. Currently, immunophenotyping can be costly and can only be performed on specific high-quality samples. MicroRNAs (miRNAs) are small molecules present in blood and tissues and are dysregulated in both human and canine lymphoma. We investigated 59 miRNAs by RT-qPCR to establish a serum miRNA profile in dogs with B-cell and T-cell multicentric lymphoma. Multiple miRNA pruned decision tree models were used to classify BCL and TCL cases from each other and controls, and to predict prognosis in BCL cases receiving standard CHOP chemotherapy. Six individual miRNAs were differentially expressed in serum between BCL and controls, and three were differentially expressed between BCL and TCL. A three-miRNA model (miR-155-5p, miR-1 and miR-181b) could differentiate between BCL, TCL and control samples with an accuracy of 83.02%. A three-miRNA model (miR-125b-5p, miR-350 and let-7b-5p) in BCL samples separated the cases into four groups with hazard ratios ranging from 0.44 to 3.5 for overall survival. This study established a serum miRNA profile for both BCL and TCL and demonstrated the utility of multiple serum miRNA models to assist in the diagnosis of lymphoma and BCL prognostication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".