A PET-CT study on neuroinflammation in Huntington’s disease patients participating in a randomized trial with laquinimod
Bibliographic record
Abstract
Abstract Microglia activation, an indicator of central nervous system inflammation, is believed to contribute to the pathology of Huntington’s disease. Laquinimod is capable of regulating microglia. By targeting the translocator protein, 11C-PBR28 PET-CT imaging can be used to assess the state of regional gliosis in vivo and explore the effects of laquinimod treatment. This study relates to the LEGATO-HD, multi-centre, double-blinded, Phase 2 clinical trial with laquinimod (US National Registration: NCT02215616). Fifteen patients of the UK LEGATO-HD cohort (mean age: 45.2 ± 7.4 years; disease duration: 5.6 ± 3.0 years) were treated with laquinimod (0.5 mg, N = 4; 1.0 mg, N = 6) or placebo (N = 5) daily. All participants had one 11C-PBR28 PET-CT and one brain MRI scan before laquinimod (or placebo) and at the end of treatment (12 months apart). PET imaging data were quantified to produce 11C-PBR28 distribution volume ratios. These ratios were calculated for the caudate and putamen using the reference Logan plot with the corpus callosum as the reference region. Partial volume effect corrections (Müller–Gartner algorithm) were applied. Differences were sought in Unified Huntington’s Disease Rating Scale scores and regional distribution volume ratios between baseline and follow-up and between the two treatment groups (laquinimod versus placebo). No significant change in 11C-PBR28 distribution volume ratios was found post treatment in the caudate and putamen for both those treated with laquinimod (N = 10) and those treated with placebo (N = 5). Over time, the patients treated with laquinimod did not show a significant clinical improvement. Data from the 11C-PBR28 PET-CT study indicate that laquinimod may not have affected regional translocator protein expression and clinical performance over the studied period.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".