Autopsy‐confirmed minimally invasive biomarker identifies Alzheimer’s Disease even in the presence of co‐morbid pathologies
Bibliographic record
Abstract
Abstract Background There are currently no FDA‐approved tests that are highly sensitive and highly specific for Alzheimer’s disease (AD). Diagnostic Accuracy has been even more difficult due to recent findings that >50% of AD brains show co‐morbid pathologies such as multi‐infarct dementia and Frontal Lobe Dementia. Here, we describe an autopsy‐confirmed AD Biomarker that identifies AD pathology even with co‐morbid brain pathology. Method A Morphometric Imaging (MI) assay was previously shown to correlate the dementia and presence of AD pathology in the brains of AD patients (Chirila et al., 2013) with abnormalities of skin fibroblasts isolated with routine punch biopsies (∼3mm). Cells were cultured on a thick layer of 3‐D Matrigel matrix and subjected to image analysis. AD cell lines formed large aggregates in contrast to non‐AD dementia (non‐ADD) or non‐demented control (NDC) cell samples. Typically skin fibroblasts formed “networks” analogous to networks formed by neurons isolated in cell culture and AD networks were slower and less connected Quantitative image analyses enabled the calculation of average unit aggregate area (A) in terms of ln(A/N). Samples were collected with a double‐blind protocol for demented patients > 55 years old who eventually reached blinded autopsy examination. NDC samples were collected for Biomarker assay only. Result The total fibroblast patient sample (N = 74) consisted of AD (N = 26) patients and non‐ADD (N = 21) patients (all were autopsy confirmed and had blinded biomarker data); and NDC, N = 27 that had biomarker data. The cut‐off value of ln(A/N) = 6.98 was determined from the biomarker values for NDC patient samples. For the Biomarker AD vs. Non‐ADD data, True Positive = 26, False Negative = 0, False Positive = 0, with Sensitivity and Specificity calculated as 100 and 100, respectively. (AD vs. Non‐ADD, p < .000001). The NDC MI assay values closely superimposed with the non‐ADD sample values. Conclusion In these autopsy‐confirmed results, the MI Biomarker distinguished AD from Non‐ADD patients and correctly diagnosed AD even in the presence of other co‐morbid pathologies at autopsy. This highly, accurate, minimally invasive AD biomarker, therefore, was validated by rigorous reference to the NIH gold standard criteria for an AD Diagnosis, dementia in life and the presence of plaques and hyperphosphorylated tau at autopsy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".