Detection of Amyloid Beta (Aβ) Oligomeric Composition Using Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI MS)
Bibliographic record
Abstract
The use of MALDI MS as a fast and direct method to detect the Aβ oligomers of different masses is examined in this paper. Experimental results suggest that Aβ oligomers are ionized and detected as singly charged ions, and thus, the resulting mass spectrum directly reports the oligomer size distribution. Validation experiments were performed to verify the MS data against artifacts. Mass spectra collected from modified Aβ peptides with different propensities for aggregation were compared. Generally, the relative intensities of multimers were higher from samples where oligomerization was expected to be more favorable, and vice versa. MALDI MS was also able to detect the differences in oligomeric composition before and after the incubation/oligomerization step. Such differences in sample composition were also independently confirmed with an in vitro Aβ toxicity study on primary rat cortical neurons. An additional validation was accomplished through removal of oligomers from the sample using molecular weight cutoff filters; the resulting MS data correctly reflected the removal at the expected cutoff points. The results collectively validated the ability of MALDI MS to assess the monomeric/multimeric composition of Aβ samples. Graphical Abstract ᅟ.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".