Targeting PINK1 using phytochemicals: exploring molecular insights into Parkinson's disease therapeutics
Bibliographic record
Abstract
Parkinson's disease (PD) is one of the most commonly affecting neurodegenerative disorder prevalent in our society. The inherited autosomal recessive PD/parkinsonism occurs due to mutations in six genes including, the gene for PTEN (phosphatase and tensin homologue)-induced putative kinase1 (PINK1). The pathophysiology and development of disorders associated with the mitochondria occur simultaneously with the dysregulation of PINK1. The activation/regulation of PINK1 through autophagy regulators can reduce PD condition. This study focused on exploring the possibility of 2062 phytochemicals as autophagy regulators. In silico docking and simulation studies are performed to identify their binding with the PINK1. Our studies highlight the phytochemicals like Proanthocyanidin A-6, Withanolide Q, and pseudo-ginsenoside F11 that showed higher binding energy and stable interactions during the course of simulation. This study opens avenues for testing these compounds as positive modulators of PINK1 kinase activity using in vitro and in vivo methods and use of these compounds as phytotherapeutic for the treatment of PD.
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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".