Quantifying neuronal biomarkers in aqueous humor in individuals undergoing routine cataract surgery and correlating with age and cognition
Bibliographic record
Abstract
Abstract Background Plasma and cerebrospinal fluid (CSF) levels of neuronal biomarkers have been associated with Alzheimer’s disease (AD). The aqueous humor, which can be collected during routine cataract surgery, may have similar concentrations of neuronal biomarkers to CSF. Method This proof‐of‐concept study prospectively enrolled adults without a diagnosis of cognitive impairment undergoing cataract surgery at a tertiary academic medical center. Participants underwent Montreal Cognitive Assessment (MoCA blind version 7.1, “abnormal” < 18) at the time of study enrollment. Aqueous samples were collected during cataract surgery through a surgical paracentesis. Samples were analyzed using an ultrasensitive single‐molecule array (SiMoA). Assessed biomarkers included phosphorylated tau (pTau)‐181, amyloid‐beta (Aß)‐42, Aß‐40, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). Correlations were assessed using Spearman’s rank correlation (rho). Result Sixteen eyes of 16 patients (average MoCA 19.2) underwent aqueous sampling to assess pTau‐181 and 7 eyes of 7 separate patients (average MoCA 20.0) underwent aqueous and plasma sampling to assess Aß‐42, Aß‐40, NfL, and GFAP. Aqueous pTau‐181 increased with increasing age (rho = 0.67, p = 0.0046). No aqueous or plasma biomarkers were significantly correlated with MoCA scores (all p > 0.05). Aqueous and plasma levels of biomarkers were not significantly correlated. Conclusion Neuronal biomarkers are measurable in aqueous humor using SiMoA analysis; however, these biomarkers did not significantly correlate with MoCA scores in a cognitively normal population. Further study in individuals with a known diagnosis of Alzheimer’s disease or mild cognitive impairment is indicated.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".