Molecular mechanisms associated with the interaction of external electromagnetic fields in protein dynamics and aggregation: a focus on amyloid-<i>β</i> peptide
Bibliographic record
Abstract
Abstract Transcranial stimulation has emerged as a non-invasive treatment that applies electrical currents and magnetic fields to regulate brain functions. Previous studies have shown that magnetic stimulation modulates the dynamics of charged molecules in biological systems. In some pathologies, once the electrical or magnetic field is applied directly to subjects, it can interact with, and alter, abnormally folded proteins, including amyloid-β peptides and their aggregates, reducing cognitive impairments. While our understanding of the molecular mechanisms underlying the interaction between amyloid-β peptide and the physical forces generated by electrical or magnetic stimulation remains unclear, observations show that these stimuli exert attractive and repulsive forces while interacting with the charged groups of peptide side chains as well as lipids. These interactions influence hydrophobic packing and secondary structure, ultimately inducing alterations in aggregation kinetics. The study of structural models of amyloidogenic proteins aids in understanding the mechanisms involved in the protein aggregation process and suggests possible therapeutic applications. This review examines proposed molecular mechanisms to explain the modulatory effects of external electromagnetic fields on the dynamics of proteins and their complexes that regulate pathological processes associated with amyloid-β peptide fibrillation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".