The role of Th17 lymphocytes in drug-resistant epilepsy
Bibliographic record
Abstract
Abstract Epilepsy is a chronic neuronal disorder affecting ~65 millions people throughout the world. 1/3 of patients suffer from drug-resistant epilepsy (DRE). Objective to assess the relationship between different Th17 markers and the clinical course of epilepsy to uncover new potential immune biomarkers and therapeutic targets of interest for DRE. Approach Subjects between 19–55 years old with a focal diagnosed epilepsy were recruited at the CHUM epilepsy clinic. The profile of peripheral blood T lymphocytes from DRE patients (n=48) is compared to well-controlled epilepsy (WCE; n=30) and healthy controls (n=35). Peripheral blood mononuclear cells (PBMCs) were isolated by gradient density centrifugation before ex vivo analysis of surface markers (flow cytometry). Total CD4 T lymphocytes were magnetically isolated from PBMCs and stimulated overnight before analysis of cytokine expression or processed for RNA extraction before analysis of transcription factors expression by qRT-PCR. Results Our data suggest that antiepileptic drugs are associated with a relative ‘immunosuppression’, more pronounced in WCE. Epilepsy is associated with an altered distribution of immune cell populations with a higher CD4:CD8 ratio. A higher proportion of CD4 T lymphocytes from DRE subjects express CCR6 and CD161 compared to WCE. The proportion of CD4 T cells expressing pro-inflammatory cytokines from Th17/1 lineage (IL-17A, IL-22, IFN-γ, GM-CSF, TNFα) is higher in DRE than WCE, while anti-inflammatory cytokines (IL-4, IL-10) tend to be lower. Conclusion Our preliminary results suggest an increased frequency of pro-inflammatory Th17/1 lymphocytes and their related cytokines in DRE. Pro-inflammatory CD4 T cells could play a role in DRE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".