[P2–255]: SALIVARY AMYLOID‐BETA PROTEIN LEVELS CAN DIAGNOSE ALZHEIMER DISEASE AND PREDICT ITS FUTURE ONSET
Bibliographic record
Abstract
Peripheral diagnostics for AD continue to be in development. Biomarkers derived from plasma, serum and urine have been explored. Saliva is appealing as it is relatively easy to acquire and is non-invasive. The test measures salivary levels of amyloid beta-protein terminating at position 42 (Abeta42). It was found to be produced in all organs tested, thus establishing the generality of its production. Saliva levels were first stabilized by adding thioflavin S as an anti-aggregation agent and sodium azide as an anti-bacterial agent. We quantitated the Abeta42 in a series of samples with ELISA type tests. Seven AD subjects (4M, 3F, mean age79.57+/-6.13, mean MMSE19.29+/-3.54) and four NC subjects (1M, 3F, mean age 55.25+/-8.66, mean MMSE 29+/-1.41) were enrolled and provided samples. AD subjects were significantly older and more impaired on the MMSE compared to NC. The saliva Ab42 levels were significantly higher in AD than in NC (53.95+/-7.41 vs 20.63+/-0.72, p<0001). We report results of a simple, non-invasive test to potentially be used as an adjunct to diagnose Alzheimer's disease (AD). We will report a larger sample size. Future studies will assess the accuracy of the test in MCI and PD and other conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".