Follicle on the Roof: Tertiary Lymphoid Structures in Central Nervous System Autoimmunity
Bibliographic record
Abstract
Leptomeningeal tertiary lymphoid structures (TLS) have emerged as a relatively common pathological feature of autoimmune disease, including multiple sclerosis (MS) and particularly in people with progressive and nonremitting MS. These ectopic lymphoid aggregates, observed in the leptomeninges adjacent to so-called "Type 3" sub-pial cortical lesions, are associated with more severe gray matter damage and worse clinical outcomes. Mouse models of MS that recapitulate TLS formation in the central nervous system (CNS) have provided critical insights into the mechanisms driving their development and maintenance. In these models of experimental autoimmune encephalomyelitis (EAE) initiation of TLS is facilitated by Th17 cells, which promote chronic inflammation via cytokines such as IL-17 and GM-CSF. The cell surface expression of lymphotoxin-α and lymphotoxin-β heterotrimers (LTαβ) on lymphocytes, including Th17 cells, elaborates the organization of ectopic lymphoid tissues via LTβR signaling on radio-resistant stromal cells and resident fibroblasts. Ultimately a pro-inflammatory environment characterized by cytokines such as IL-17 and GM-CSF promotes the recruitment of neutrophils which produce proteases and chemokines that sustain a pro-inflammatory milieu. Emerging EAE data suggest that disrupting TLS organization or targeting key pathways involved in their maintenance could represent promising strategies for modulating chronic CNS inflammation in MS. Understanding the cellular and molecular mechanisms regulating TLS dynamics is therefore critical for the development of therapies aimed at halting or reversing nonremitting MS 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".