Pistachio leaf waste transformed into a gut-targeted bioactive phytocomplex
Bibliographic record
Abstract
This dataset supports the study "Pistachio leaf waste transformed into a gut-targeted bioactive phytocomplex" published in iScience Journal (https://doi.org/10.1016/j.isci.2025.113345). The human gut microbiota is integral to host physiology, influencing immune responses, metabolic regulation, and digestion. Disruptions in this microbial community are linked to inflammatory and metabolic disorders. In this study, we explored the potential of Pistachio Leaf Extract (PLE), a polyphenol-rich natural product, to support gut health. The extract, obtained from Pistacia vera leaves harvested in Sicily, was chemically characterized and assessed for prebiotic-like, antimicrobial, antioxidant, and anti-inflammatory effects. It promoted the growth of beneficial gut bacteria, inhibited pathogenic strains, and exhibited marked antioxidant activity. In patient-derived intestinal organoids, the extract reduced inflammation and enhanced antioxidant defenses, with no detectable cytotoxicity in human colon cells. These results suggested that PLE is a safe and multifunctional compound that may contribute to intestinal homeostasis, offering promise for its inclusion in future functional food formulations aimed at promoting gastrointestinal health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".