Potential Treatment of Parkinson's Disease, Using Last-Generation Carbon Nanotubes: A Bimolecular In-Silico Study
Bibliographic record
Abstract
Abstract Background: Parkinson's disease (PD) is one of the most common neurodegenerative disorders. One of the underlying mechanisms of the disease is the accumulation of α-synuclein protein aggregates, including amyloids and Lewy bodies in the brain, resulting in the death of dopaminergic cells in the substantia nigra. The current treatments for PD are mainly focused on replacing dopamine. However, if these medications are stopped, the severity of PD will increase. Moreover, the drugs used for the treatment of PD are associated with considerable side effects and dietary restrictions. Therefore, necessary studies to develop more effective medications for PD seem to be indispensable. To prevent the progression of PD, avoiding the development of α-synuclein amyloids could be proposed. Methods: In this study, the effects of three last-generation nanotube-based structures on α-synuclein amyloid formation were investigated for the first time employing Molecular Dynamics (MD) simulation tools. Molecular dynamics provide a deep insight into atomic interactions and can well study α-synuclein amyloid formation at the atomic and molecular scales.Results: The molecular study results indicated that all of the nanotubes studied in this work, had strong energy interactions with α-synuclein. Therefore, nanotubes using phosphorus, nitrogen and boron dopants, have great potential to prevent α-synuclein amyloid formation. Among these nanotubes, phosphorus-doped carbon nanotube (P-CNT) has the most substantial interactions with α-synuclein. The P-CNT caused more hydrogen bonds to be formed between water and α-synuclein molecules. This phenomenon leads to a decrease in the compactness, stability, and contact area of α-synuclein proteins, which results in considerable changes in the secondary structure of α-synuclein.Conclusions: Doping nanotubes especially P-CNT could be very effective for preventing the α-synuclein amyloid formation and hence, halting the progression of PD. This molecular study paves the way for the use of the Doping nanotubes in the treatment of PD. These structures are highly tunable and flexible. Therefore, the results of this work can be developed to computational, experimental and clinical levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".