Elevated Serum Neuropeptide FF Levels Are Associated with Cognitive Decline in Patients with Spinal Cord Injury
Bibliographic record
Abstract
Background. Spinal cord injury (SCI) has high incidence globally and is frequently accompanied by subsequent cognitive decline. Accurate early risk-categorization of SCI patients for cognitive decline using biomarkers can enable the timely application of appropriate neuroprotective measures and the development of new agents for the management of SCI-associated cognitive decline. Neuropeptide FF is an endogenous neuropeptide with a multitude of functions and is associated with neuroinflammatory processes. This prospective study investigated the predictive value of serum neuropeptide FF levels measured after acute SCI for subsequent cognitive decline. Methods. 88 patients presenting with acute SCI without preexisting neurological injury, brain trauma, or severe systemic illness and 60 healthy controls were recruited. Serum neuropeptide FF levels, clinical, and routine laboratory variables including low-density lipoprotein, high-density lipoprotein, fasting blood glucose, total triiodothyronine (TT3), total thyroxine (TT4), and thyroid-stimulating hormone (TSH) levels collected from all subjects were assessed. Montreal cognitive assessment (MoCA) was performed 3 months after enrollment. SCI patients were grouped according to quartile of serum neuropeptide FF level and MoCA scores were compared using ANOVA. Additionally, multivariate linear regression with clinical and laboratory variables was performed to predict MoCA scores. Results. SCI patients displayed significantly higher baseline serum neuropeptide FF levels than healthy controls ( 38.5 ± 4.1 versus 23.4 ± 2.0 pg / ml , p < 0.001 ∗ ∗ ). SCI patients in higher quartiles of baseline serum neuropeptide FF displayed significantly lower MoCA scores at 3 months. Linear regression analysis indicated serum neuropeptide FF levels as a significant independent predictor of worse MoCA scores after SCI ( r = 0.331 , p = 0.034 ∗ ). Conclusion. Early serum neuropeptide FF levels significantly and independently predicted cognitive decline after acute SCI among patients without preexisting neurological 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".