Editorial: Natural products as drivers in drug development for neurodegenerative disorders
Bibliographic record
Abstract
Editorial on the Research Topic Natural products as drivers in drug development for neurodegenerative disordersThe prevalence of neurological disorders (NDs) is a large and increasing health burden worldwide and is one of the emerging reasons for morbidity, mortality, and cognitive impairment in aging.Impact of NDs is expected to significantly increase in the next decades due to the progressive aging of the world society (Feigin and Vos, 2019).In the specific case of neurodegenerative process, it leads to malfunctions and cellular death, which seems to be triggered by a set of complex biological mechanisms, such as protein aberrant aggregation, mitochondrial dysfunction, oxidative stress, defective protein quality control, and degradation pathways, stress granules, and maladaptive immune response (Moujalled et al., 2021).Despite the advances achieved, the effectiveness of current drugs to control, delay, or block the NDs progression is still limited (Durães et al., 2018).Thus, the discovery and development of new therapeutic agents that can improve the currently therapeutic regimes are of utmost importance.Accordingly, a large focus has been placed on the potential of natural products (NPs) as new natural neuroprotective agents, essentially due to their scaffold diversity, structural complexity, and ability to activate several intracellular signaling pathways through distinct mechanisms of action while presenting fewer side-effects (Alghamdi et al., 2022).Over the last decades, nature has revealed to be a prolific source of unparalleled structurally active metabolites, with a broad spectrum of biological activities, including antibacterial, antifungal, anticancer, antifouling, antioxidant, antiinflammatory, and neuroprotective properties (Grkovic et al.,
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".