Sonication of proteins causes formation of aggregates that resemble amyloid
Bibliographic record
Abstract
Despite the widespread use of sonication in medicine, industry, and research, the effects of sonication on proteins remain poorly characterized. We report that sonication of a range of structurally diverse proteins results in the formation of aggregates that have similarities to amyloid aggregates. The formation of amyloid is associated with, and has been implicated in, causing of a wide range of protein conformational disorders including Alzheimer's disease, Huntington's disease, Parkinson's disease, and prion diseases. The aggregates cause large enhancements in fluorescence of the dye thioflavin T, exhibit green-gold birefringence upon binding the dye Congo red, and cause a red-shift in the absorbance spectrum of Congo red. In addition, circular dichroism reveals that sonication-induced aggregates have high beta-content, and proteins with significant native alpha-helical structure show increased beta-structure in the aggregates. Ultrastructural analysis by electron microscopy reveals a range of morphologies for the sonication-induced aggregates, including fibrils with diameters of 5-20 nm. The addition of preformed aggregates to unsonicated protein solutions results in accelerated and enhanced formation of additional aggregates upon heating. The dye-binding and structural characteristics, as well as the ability of the sonication-induced aggregates to seed the formation of new aggregates are all similar to the properties of amyloid. These results have important implications for the use of sonication in food, biotechnological and medical applications, and for research on protein aggregation and conformational disorders.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".