Measuring Amyloid‐β Peptide Concentrations in Murine Brain with Improved ELISA Assay
Bibliographic record
Abstract
The amyloid-β (Aβ) peptides of 40 and 42 amino acids that are implicated in Alzheimer's disease may potentially aggregate into toxic oligomers and form neuritic plaques. The enzyme-linked immunosorbent assay (ELISA) is a facile method used for the determination of Aβ concentrations in biological matrices, namely plasma, cerebrospinal fluid, and brain. The method is mostly used for the measurement of Aβ concentrations in transgenic mice, but it is unknown whether the ELISA method is suitable for measuring low, endogenous levels of Aβ in the brains of wild-type mice. The Aβ ELISA kit manufacturer recommends use of 5 M guanidine hydrochloride (GuHCl), a protein-denaturing agent, for homogenization of the brain tissue, followed by dilution back down to 0.1 M to avoid quenching by GuHCl. Components of brain matrices and GuHCl that could interfere with the quantitation have not been investigated. In this article, we describe an improved method involving homogenization of mouse brain with 1 M instead of 5 M GuHCl, reducing the dilution factor by 5× to provide a higher sensitivity. The modified ELISA assay is improved for the quantitation of brain Aβ peptides in wild-type mice, where Aβ peptide levels are much lower than those in transgenic mouse models. © 2021 Wiley Periodicals LLC.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".