Immunophenotyping of Clinically Isolated Syndrome Patients Who Did or Did Not Convert to Multiple Sclerosis
Bibliographic record
Abstract
Abstract BackgroundClinically isolated syndrome (CIS) is the prodromal phase of multiple sclerosis (MS) disease course, with patients having experienced one neurological episode. Up to 90% of CIS patients develop subsequent MS, so it is important to differentiate those who will and will not convert to Relapsing Remitting Multiple Sclerosis (RRMS) in the future, which will allow for earlier treatment. We aimed to test whether peripheral blood immunophenotype could predict conversion from clinically isolated syndrome (CIS) to multiple sclerosis (MS).MethodsMulticolor flow cytometry was used to identify up to 50 peripheral blood mononuclear cell (PBMC) subpopulations in fresh blood samples. Further, cryopreserved PBMCs processed in another center were identically immunophenotyped in the same facility where the fresh blood specimens were analysed.Results In addition to major subpopulations (T and B lymphocytes, monocytes, dendritic cells (DC) and natural killer (NK) cells), we measured the frequencies of multiple CD4+ and CD8+ T cell, monocyte, and NK subsets. Among the studied subpopulations, DCs were significantly lower in CIS who converted to MS (CIS-C) compared to patients who did not (CIS-N). Also, CD56-CD16+ atypical NK subset was lower in CIS-C. We also studied cryopreserved CIS patient samples from a previous clinical trial involving the CIS populations. We observed significant differences between fresh and cryopreserved PBMC subpopulations. We were not able to detect the same differences in the cryopreserved samples.ConclusionsOur data using fresh blood samples reveals that while there were significant differences between CIS-N and CIS-C in the DCs and in one of the NK subsets, there was no overall difference between the immunophenotypes of CIS patients who did and did not convert to MS. These changes could be of physiological relevance to the disease pathogenesis. The fact that these differences were not observed in cryopreserved samples could mean that using fresh blood from patients may provide a more accurate tool to study the immune system composition.
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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) | 0.002 | 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".