Serum p97 levels as an aid to identifying Alzheimer's disease
Bibliographic record
Abstract
Background: The application of formal clinical diagnostic criteria for the identification of Alzheimer's Disease (AD) has improved diagnostic sensitivity. However, there remains a need for non-invasive biological markers and laboratory tests, which can facilitate case identification, and the assessment of treatment response. The p97 protein is a secreted protein specifically expressed by amyloid plaque associated reactive microglia that may have AD diagnostic ability. Methods: A quantitative radioimmunoassay was developed to measure serum p97. This study, under a double blind protocol, evaluated the utility of serum p97 as diagnostic test for AD. All subjects were referred to the UBC Clinic for Alzheimer's Disease and Related Disorders (CADRD) for clinical assessment of dementia. A serum p97 sample was obtained at the time of assessment but diagnosis of disease was determined independently of p97 examination. Results: "Possible" and "probable" AD cases (n = 41) and cognitively normal controls (n = 64) showed a highly significant difference in mean p97 concentration (41 vs. 20 ng/ml, p<0.001). There was some overlap in p97 distributions between AD cases and control subjects. The area under the curve (AUC) for the receiver operator curve (ROC) was 0.812. Conclusions: These results further support the specificity of high serum p97 levels in AD and its potential utility as a biological marker in AD. The reproducible elevation of serum p97 in AD underlines the need to further determine its role as a biological marker and diagnostic adjunct for AD.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".