Analyses of peripheral blood dendritic cells and magnetic resonance spectroscopy support dysfunctional neuro‐immune crosstalk in Tourette syndrome
Bibliographic record
Abstract
BACKGROUND: Evidence supports that neurodevelopmental diseases, such as Tourette syndrome (TS), may involve dysfunctional neural-immune crosstalk. This could lead to altered brain maturation and differences in immune and stress responses. Dendritic cells (DCs) play a major role in immunity as professional antigen-presenting cells; changes in their frequency have been observed in several autoimmune conditions. METHODS: In 18 TS patients (15 on stable pharmacological treatment, three unmedicated) and 18 age-matched healthy volunteers (HVs), we explored circulating blood-derived DCs and their relationship with clinical variables and brain metabolites, measured via proton magnetic resonance spectroscopy (1H-MRS). DC subsets, including plasmacytoid and myeloid type 1 and 2 dendritic cells (MDC1, MDC2), were studied with flow cytometry. 1H-MRS was used to measure total choline, glutamate plus glutamine, total creatine (tCr), and total N-acetylaspartate and N-acetylaspartyl-glutamate levels in frontal white matter (FWM) and the putamen. RESULTS: We did not observe differences in absolute concentrations of DC subsets or brain inflammatory metabolites between patients and HVs. However, TS patients manifesting anxiety showed a significant increase in MDC1s compared to TS patients without anxiety (p = 0.01). We also found a strong negative correlation between MDC1 frequency and tCr in the FWM of patients with TS (p = 0.0015), but not of HVs. CONCLUSION: Elevated frequencies of the MDC1 subset in TS patients manifesting anxiety may reflect a proinflammatory status, potentially facilitating altered neuro-immune crosstalk. Furthermore, the strong inverse correlation between brain tCr levels and MDC1 subset frequency in TS patients suggests a potential association between proinflammatory status and metabolic changes in sensitive brain regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".