Flow Cytometric Immunophenotype of Canine Lymph Node Aspirates
Bibliographic record
Abstract
Increasing availability of reagents able to distinguish subtypes of lymphocytes and other leukocytes has enabled greater understanding of lymphocyte biology and pathology in the dog. Lymphocytes in circulation most commonly are subjected to immunophenotypic assessment by flow cytometry, but needle aspirates of lymph nodes can be similarly suitable for immunophenotypic examination. In this investigation, the feasibility of immunophenotyping samples obtained by needle aspiration of lymph nodes from 32 dogs with no physical abnormalities and 6 dogs with lymphoma was determined. In addition, samples from 6 dogs were stored overnight at 4 degrees C and reanalyzed 24 hours later. For each sample, stained smear preparations were examined microscopically for lymphocyte morphology, neoplasia, and the presence of inflammatory cells. Expression of antigens on a corresponding sample of aspirated cells was determined by flow cytometric detection of antibody binding on a minimum of 10,000 events. The distribution of data was determined with Anderson-Darling tests, and reference intervals incorporating the central 95% of values were established. Adequate samples were obtained from 30 of 32 clinically normal dogs. Immunophenotypic results after 24 hours of storage were consistent with those obtained immediately after sampling. Reference intervals for lymphocyte subsets from normal dog lymph nodes were similar to the proportions of CD3+, CD4+, CD8+, and CD21+ lymphocytes found in blood. Aspirates of enlarged lymph nodes from dogs with lymphoma were readily classified by this technique. Aspiration of lymph nodes from dogs for comprehensive analysis by flow cytometry is feasible and applicable to immunophenotyping of lymphoma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".