Bibliographic record
Abstract
BACKGROUND: Glucosinolates (GSLs) are the most controversial yet ignored class of phytochemicals. These are the middleman phytochemicals that have low bioactivity. But once there is any injury in the plant-manmade, insect caused, or natural-magic happens. The compound is broken down into smaller phytochemicals referred to as glucosinolate hydrolytic products (GHPs; nitriles, isothiocyanates [ITCs], and thiocyanates). These hydrolytic products are like a showstopper of the fashion industry. These compounds have some of the highest bioactivity in nature. They have been associated with a varied range of bioactivities (anticancer, antioxidant, insecticidal, weedicide, etc.) by researchers across the globe. OBJECTIVE: The objective of the current article is to provide a critical review to highlight some of the important bioactivities of these ignored compounds and for promoting researchers to at least give these compounds a chance-to glow in the dark. METHODS: This review has been written from analysis of accessible literature, mostly from the last 5 years (2018-2023), with some critically essential exceptions. RESULTS: The review highlighted a brief background of GSLs and its hydrolysis. Efforts were made to include most of the biological properties of the compound. Special emphasis has been given to the anticancer activities of the compound with details of the involved mechanism. CONCLUSIONS: Considering the wide array of bioactivities of GHPs, it is essential to consider it as a prospective medicinal compound. More GHPs-in a similar manner as sulforaphane-can be proceeded to phase trials. HIGHLIGHTS: The mechanistic pathway for production of GHPs and related biological activities have been discussed in detail. The bioactivities have been further explained using the involved mechanism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".