The relationship between inflammatory markers in cerebrospinal fluid in healthy controls and in patients with severe essential tremor before and after deep brain stimulation
Bibliographic record
Abstract
Objective Essential tremor (ET) is the most common movement disorder, with a prevalence of approximately 5% in individuals over 65 years old. The pathophysiology behind ET is still largely unknown but emerging evidence indicates that ET can be a neurodegenerative disorder. Our aim in this pilot study was to evaluate alterations in inflammatory cytokines in ET patients undergoing deep brain stimulation (DBS) compared to healthy controls. Methods Ten patients with severe ET were included in this study. Cerebrospinal fluid was analyzed in healthy controls and in ET patients before and after deep brain stimulation. The samples were analyzed with a U-PLEX assay, based on an electrochemiluminescent detection method. Results Several cytokines were downregulated or upregulated in patients with ET compared to the healthy control group. Interestingly, macrophage migration inhibitory factor (MIF) was one of the significantly upregulated inflammatory mediators. There was no difference in the analyzed cytokines before and after DBS. Conclusion Inflammatory proteins are altered in patients with ET compared to healthy individuals. The finding of an upregulation of MIF, an interesting cytokine that plays a role in other neurodegenerative disorders, suggests evidence for a neurodegenerative pathophysiology in ET. Inflammatory biomarkers might be promising to be future biomarkers and targets of therapeutics against several neurodegenerative disorders.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".