Peripheral blood BDNF and soluble CAM proteins as possible markers of prolonged disorders of consciousness: a pilot study
Bibliographic record
Abstract
Although clinical examination still represents the gold standard for the differential diagnosis of prolonged disorders of consciousness (pDoC), the introduction of innovative markers is essential for diagnosis and prognosis, due to the problem of covert cognition. We evaluated the brain-derived neurotrophic factor protein (BDNF) and the soluble cell adhesion molecules proteins (CAMs) in a cohort of prolonged disorders of consciousness patients to identify a possible application in the clinical context. Furthermore, peripheral blood determinations were correlated with imaging parameters such as white matter hyperintensities (WMH), cranial standardized uptake value (cSUV), electroencephalography (EEG) data and clinical setting. Our results, although preliminary, identify BDNF as a possible blood marker for the diagnosis of pDoC (p value 0.001), the soluble CAMs proteins CD44, Vcam-1, E-selectin (p value < 0.01) and Icam-3 (p value < 0.05) showed a higher peripheral blood value in pDoC compared with control. Finally, soluble Ncam protein could find useful applications in the clinical evolution of the pDoC, showing high levels in the MCS and EMCS subgroups (p value < 0. 001) compared to VS/UWS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".