D06 Platelet Bdnf In Huntington's Disease: Evidence From A Preliminary Study
Bibliographic record
Abstract
Background Brain Derived Neurotrophic Factor (BDNF) is a neurotrophin implicated in the pathogenesis of neurodegenerative diseases and mood disorders; highly expressed in central nervous system, it can be detected in peripheral tissues such as blood, mainly stored in platelets. Wild type huntingtin (HTT) is believed to activate BDNF transcription and transport, mutated HTT is responsible for reduction of BDNF levels by inhibiting both processes. Several studies investigated BDNF in HD patients demostrating lower values in brain, blood and plasma with respect to controls; however results were not always confirmed and this lack of concordance could be due to extreme variability of sampling collection and laboratory techniques. Aims To evaluate BDNF levels in HD and possible correlations with psychiatric and motor scales by using platelet dosage. Methods 12 patients with symptomatic HD (S&F stage II) and 10 controls, matched for age and educational level were investigated by means of neuropsychological tests (MoCa, MMSE, FAB), psychiatric scales (BDI, PBA) and motor evaluation (UHDRS). Blood samples were collected to investigate BDNF platelet levels determined by ELISA. Results HD patients showed statistically significant lower performances at MoCa (p < 0.01) and FAB (p < 0.01). Platelet BDNF was higher in patients (2950.5 ± 749.1 pg/ml) with respect to controls (2481.6 ± 1063.8 pg/ml) but the difference was not statistically significant. Further analysis demonstrated a direct correlation between disease duration and BDNF platelet levels (p < 0.05). Conclusions Our finding of a lack of significant difference in platelet BDNF between HD and controls could suggest along with recent studies on human blood that peripheral levels of BDNF are not informative as HD biomarkers. However, the correlation between BDNF platelet levels and disease duration might indicate that the peripheral BDNF levels can fluctuate in different phases of the 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".