P3‐223: AMIRASPEC: BLOOD CELL FLUORESCENCE FOR THE DIAGNOSIS OF ALZHEIMER'S DISEASE
Bibliographic record
Abstract
In Alzheimer's disease, the accumulation of toxic Aβ peptide aggregates throughout the extracellular space and walls of blood vessels in the brain results in increased permeability and immune cell activation. As blood cells contact these aggregates, they may be changed in ways that are detectable once they return to circulation. Previously, we have demonstrated that both erythrocytes and leukocytes, when stained with an amyloid sensitive probe, display distinct spectral changes in subjects with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). We have continued to optimize our novel method for early diagnosis of AD using blood. We obtained leukocytes and cerebrospinal fluid (CSF) from subjects with a variety of neurological conditions, including AD and MCI, along with aged controls (total 93 subjects). CSF-ELISA negative (CSF) or positive (CSF) subjects (54 subjects) were pre-selected for our spectral amyloid detection assay (Oboudiyat et al., 2017), and leukocytes were labelled with a conformationally-sensitive probe, and imaged with a spectral fluorescence microscope. Comparing subjects with CSF AD/MCI (n=24) to CSF healthy controls (n=10), our technique identifies significant differences (P<0.0002, Fig. 1). The resulting Receiver Operating Characteristics curve (ROC) has an area under the curve (AUC) of 0.899 (Fig. 2). Examination of the scores of subjects with Transient Ischemic Attack (TIA; Fig. 1), but tested CSF, interestingly, indicates highly diverse scores, many in the range of AD/MCI samples (Fig. 1).
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".