Diagnostic of Novel Plasma Biomarkers in Controls, SCD and MCI Subjects
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is the most common cause of dementia, and a challenging disease for the development of plasma biomarkers. Blood samples are the most easily collectable samples for medical diagnosis. This study evaluates the potential of plasma biomarkers in identifying early stages of AD. Method 189 participants were recruited at the NYU Alzheimer Disease Research Center. This cohort includes n = 75 with normal cognition (NL), n = 77 with subjective cognitive decline (SCD), n = 37 with mild cognitive impairment (MCI). Comprehensive neuropsychological and magnetic resonance imaging evaluations were conducted for all patients. Plasma biomarkers assays (total tau [t‐tau], neurofilament light [NfL], glial fibrillary acid protein [GFAP], ubiquitin carboxyl‐terminal hydrolase L1 [UCH‐L1], Aβ1‐42, Aβ1‐40 and pTau181) were measured using the SIMOA SR‐X, a novel technology that employs highly sensitive immunoassays with a limit of detection (LOD) under 100 fg/ml. Result The levels of NfL, t‐Tau, GFAP and UCH‐L1 were measured using the Neurology 4‐plex A. NfL levels showed a significant difference between NL and SCD (one tailed t‐ test p = 0.0459). GFAP showed statistically significant differences between NL and MCI, (one tailed t‐test p = 0.0369). Aβ1‐42 showed a significant difference between SCD and MCI (one tailed t‐test p = 0.0493). pTau181 levels showed a significant difference between NL, SCD and NL, MCI (p = 0.0447 and p = 0.0439, respectively). The ratio of pTau181/Aβ1‐42 shows a significant difference between NL and MCI (one tailed t‐test p = 0.0187). Correlation of these biomarkers with brain imaging and cognitive measures is underway. Conclusion Biomarker levels of NfL, GFAP, Aβ1‐42, pTau181 and Ratio pTau181/Aβ1‐42 in plasma samples showed several significant differences between the three groups of subjects. Plasma biomarkers can be useful for the diagnosis of AD related pathology at early stages.
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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 | 0.002 |
| 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 | 0.001 | 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".