New highly sensitive rodent and human tests for soluble amyloid precursor protein alpha quantification: preclinical and clinical applications in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Amyloid precursor protein (APP), a key molecule in Alzheimer's disease (AD), is metabolized in two alternative cleavages, generating either the amyloidogenic peptides involved in AD pathology or the soluble form of APP (sAPPα). The level of amyloidogenic peptides in human cerebrospinal fluid (CSF) is considered to be a biomarker of AD, whereas the level of sAPPα in CSF as a biomarker has not been clearly established. sAPPα has neurotrophic and neuroprotective properties. Stimulating its formation and secretion is a promising therapeutic target in AD research. To this end, very sensitive tests for preclinical and clinical research are required. METHODS: The tests are based on homogenous time-resolved fluorescence and require no washing steps. RESULTS: We describe two new rapid and sensitive tests for quantifying mouse and human sAPPα. These 20 μl-volume tests quantify the levels of: i) endogenous mouse sAPPα in the conditioned medium of mouse neuron primary cultures, as well as in the CSF of wild-type mice, ii) human sAPPα in the CSF of AD mouse models, and iii) human sAPPα in the CSF of AD and non-AD patients. These tests require only 5 μl of conditioned medium from 5 × 10(4) mouse primary neurons, 1 μl of CSF from wild-type and transgenic mice, and 0.5 μl of human CSF. CONCLUSIONS: The high sensitivity of the mouse sAPPα test will allow high-throughput investigations of molecules capable of increasing the secretion of endogenous sAPPα in primary neurons, as well as the in vivo validation of molecules of interest through the quantification of sAPPα in the CSF of treated wild-type mice. Active molecules could then be tested in the AD mouse models by quantifying human sAPPα in the CSF through the progression of the disease. Finally, the human sAPPα test could strengthen the biological diagnosis of AD in large clinical investigations. Taken together, these new tests have a wide field of applications in preclinical and clinical studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".