The Immunophenotype of Blood and Cerebrospinal Fluid Mononuclear Cells in Dogs
Bibliographic record
Abstract
Inflammatory neurologic diseases are common in dogs, but establishing a definitive diagnosis often is difficult. Nucleated cell number and type in cerebrospinal fluid (CSF) rarely are suggestive of an etiologic agent. We speculated that CSF leukocyte immunophenotyping would be a useful adjunct in the investigation of canine inflammatory neurologic diseases by yielding more specific etiologic information. The goals of this study were to establish the feasibility of flow cytometric evaluation of individual canine CSF samples and to identify the cell distribution in healthy dogs. The mononuclear cell populations of paired blood and CSF samples from 23 healthy dogs were characterized by labeling of cells with antibodies against CD4, CD8α, CD21, and CD14 molecules and by flow cytometric analysis of their expression. The mean proportion of CD4+ and CD21+ cells was significantly higher in blood than in the CSF (P < .002 and P < .001, respectively). In contrast, the mean proportion of CD14+ and CD8α+ cells was not significantly different between blood and CSF (P= .5 and p= .9, respectively). These findings demonstrate differences in the distribution and function of mononuclear cells in the circulating venous and subarachnoid compartments in the dog.
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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 | 0.001 |
| 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) | 0.000 | 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 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".