Electrochemical surface-enhanced Raman spectroscopy (EC-SERS) study of the interaction between protein aggregates and biomimetic membranes
Bibliographic record
Abstract
Human diseases characterized by the uncontrolled deposition of insoluble extracellular protein aggregates are collectively referred to as amyloidoses. Such diseases include Alzheimer's, Parkinson's, Huntington's, and prion disease. In Alzheimer's disease, it is believed that amyloid-β proteins may be responsible for pore and defect formation within cellular membranes, leading to a breakdown of cellular homeostasis causing eventual neuronal death. This theory is referred to as the amyloid pore hypothesis of Alzheimer's disease. In this work, the interaction between a model amyloid-forming protein (insulin) and a biomimetic membrane was studied at the molecular level. Protein at different stages of aggregation was allowed to interact with a biomimetic membrane formed on a nanostructured substrate using Langmuir-Blodgett/Langmuir-Schaefer deposition. Electrochemical surface-enhanced Raman spectroscopy (EC-SERS) was used to monitor the molecular level changes occurring as a result of this interaction. Based on the results it was observed that oligomers and protofibrils caused the most significant membrane deterioration whilst native protein appeared to play a protective role. To the best of our knowledge, this work represents the first EC-SERS investigation of protein aggregate-biomembrane interactions, and highlights the usefulness of this tool for studying complex biomolecular interactions.
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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) | 0.000 | 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 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".