MicroRNAs in serum and tissue can differentiate splenic hemangiosarcoma from other splenic masses in dogs
Bibliographic record
Abstract
Splenic masses are common in dogs and vary dramatically in their clinical behavior. Clinically, and even with histology, it can be challenging to differentiate between benign and malignant splenic masses. Hemangiosarcoma (HSA), the most common malignancy of the spleen, is a very aggressive tumor with a poor prognosis. We hypothesize that microRNAs (miRNAs) in mass tissue and serum can differentiate between HSA and other splenic masses. Fifty-nine miRNAs were investigated by reverse-transcription followed by real-time quantitative polymerase chain reaction (RT-qPCR) in serum and/or tissue from dogs with HSAs (serum n = 24 and tissue n = 17; postsplenectomy serum n = 11), lymphomas (serum n = 8 and tissue n = 11), nonangiomatous nonlymphomatous sarcomas (serum n = 6 and tissue n = 10), histiocytic sarcomas (tissue n = 4), benign splenic masses (myelolipomas, nodular hyperplasia, and hematomas; total serum n = 21 and total tissue n = 35), and normal dogs (serum n = 14 and tissue n = 7). Numerous miRNAs were differentially expressed in serum and tissue of HSA cases compared to those with other splenic masses or normal spleens. In serum, our 5-miRNA model (miR-135a-5p, miR-10a, miR-450b, miR-152-3p, and miR-126-5p) accurately classified 100% (24/24) of dogs with HSA from normal dogs and those with a benign splenic mass (recall = 1 for HSA). The overall accuracy of the model was 86%. In HSA and benign splenic mass tissues, our 3-miRNA model (miR-126-5p, miR-502-3p, and miR-452-5p) accurately classified 96% of the cases. This study demonstrates the utility of miRNA models in serum and tissue for screening and diagnosis of HSA in dogs. Future studies include the evaluation of prospective and prediagnosis serum samples.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".