Preferential recruitment of interferon‐γ–expressing T<sub>H</sub>17 cells in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: There is substantial evidence supporting the role of interferon (IFN)-gamma-producing T helper (T(H)) 1 and interleukin (IL)-17-expressing T(H)17 lymphocytes in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). However, to date little is known about the potential cooperative interplay between these 2 cytokines. In the current study, we sought to evaluate the frequency of IFN-gamma-expressing T(H)17 lymphocytes in MS and EAE, and study their recruitment into the central nervous system (CNS). METHODS: Human T(H)17 lymphocytes were expanded in vitro from the blood of healthy controls and relapsing MS patients using IL-23. Immune cell migration to the CNS was assessed in vitro with primary cultures of human blood-brain barrier (BBB)-derived endothelial cells, and in vivo in EAE mice. RESULTS: We demonstrate that in response to IL-23, human memory lymphocytes expand into a T(H)17 phenotype, with a subpopulation of cells simultaneously expressing IFN-gamma and IL-17. We note that lymphocytes obtained from the blood of relapsing MS patients have an increased propensity to expand into IFN-gamma-producing T(H)17 cells and identify numerous T lymphocytes coexpressing IL-17 and IFN-gamma in brain tissue of MS patients. We also find lymphocytes expressing both the T(H)1- and the T(H)17-associated transcription factors ROR gamma t and T-bet, in situ and in vitro. We further provide in vitro and in vivo evidence that IFN-gamma(+) T(H)17 lymphocytes preferentially cross the human BBB and accumulate in the CNS of mice during the effector phase of EAE. INTERPRETATION: Our data underscore the involvement of IFN-gamma(+) T(H)17 lymphocytes in the pathology of MS and EAE and their preferential recruitment into the CNS during inflammatory events.
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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 | 0.001 |
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".