CSF IMMUNOPHENOTYPING IN PATIENTS WITH NEUROINFLAMMATORY DISEASE
Bibliographic record
Abstract
Introduction Multiple sclerosis (MS) has a presumed autoimmune aetiology involving complex interactions between at risk genotypes and environmental factors. Immune reactivity mediated by CD4+ and CD8+ T-cells in particular is thought to drive disease pathogenesis. As part of a wider research project, we used polychromatic flow cytometry to conduct a high definition analysis of infiltrating T-cell populations in the cerebrospinal fluid (CSF) of patients with neuroinflammatory disease. Methods Patients attending for routine diagnostic lumbar puncture as part of their neuroinflammatory disease work-up at the University Hospital of Wales were consented to participate in this study. Sample collection started in August 2013 and is currently ongoing. In each case, the entire cell population from 10 mL of CSF was stained with a purpose-built panel comprising 13 directly conjugated monoclonal antibodies. Phenotypic analysis was performed using a custom-modified FACS Aria II flow cytometer with FlowJo software. Results Across all patients (n=16), CD4 T-cells (mean=3,548/10 mL) predominated over CD8 T-cells (mean=545/10 mL) in CSF. The majority of CD8+ T cells had an effector memory phenotype with approximately 60% exhibiting a TEMRA phenotype (CD45RA+CCR7−). Discussion These data extend previous immunophenotypic studies and define highly characteristic subsets of memory T-cells in the CSF of patients with neuroinflammatory disease.
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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) | 0.001 | 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".