Haskap Berry Phenolic Subclasses Differentially Impact Cellular Stress Sensing in Primary and Immortalized Dermal Fibroblasts
Bibliographic record
Abstract
It is generally accepted that dietary phenolics from fruits are of significant importance to human health. Unfortunately, there is minimal published data on how differences in phenolic structure(s) impact biological pathways at cellular and molecular levels. We observed that haskap berry extracts isolated with ethanol:formic acid:water or phenolic subclass fractions separated using different concentrations of ethanol (40% and 100%) impacted cell growth in a positive manner. All fractions and extracts significantly increased population doubling times. All extracts and fractions reduced intracellular free radicals; however, there were differences in these effects, indicating different abilities to scavenge free radicals. The extracts and fractions also exhibited differing impacts on transcripts encoding the antioxidant enzymes (CAT, SOD1, GPX1, GSS and HMOX1) and the phosphorylation state of nuclear factor-κB (NF-κB). We further observed that extracts and fractions containing different phenolic structures had divergent impacts on the mammalian target of rapamycin (mTOR) and sirtuin 1 (SIRT1). siRNA-mediated knockdown of SIRT1 transcripts demonstrated that this enzyme is key to eliciting haskap phenolic(s) impact on cells. We postulate that phenolic synergism is of significant importance when evaluating their dietary impact.
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 | 0.000 | 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) | 0.002 | 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".