Anti-Inflammatory Activity of Haskap Cultivars is Polyphenols-Dependent
Bibliographic record
Abstract
Haskap (Lonicera caerulea L.) berries have long been used for their health promoting properties against chronic conditions. The current study investigated the effect of Canadian haskap berry extracts on pro-inflammatory cytokines using a human monocytic cell line THP-1 derived macrophages stimulated by lipopolysaccharide. Methanol extracts of haskap from different growing locations in Canada were prepared and characterized for their total phenolic profile using colorimetric assays and liquid chromatography-Mass spectrometry (UPLC-MS/MS). Human THP-1 monocytes were seeded in 24-well plates (5 × 10⁵/well) and treated with phorbol 12-myristate 13-acetate (PMA, 0.1 μg/mL) for 48 h to induce macrophage differentiation. After 48 h, the differentiated macrophages were washed with Hank's buffer and treated with various concentrations of test compounds for 4 h, followed by the lipopolysaccharide (LPS)-stimulation (18 h). Borealis cultivar showed the highest phenolic content, flavonoid content and anthocyanin content (p < 0.05). A negative correlation existed between the polyphenol concentration of the extracts and pro-inflammatory cytokines: Interleukin-6 (IL-6), tumour necrosis factor-alpha (TNF-α), prostaglandin (PGE2), and cyclooxygenase-2 (COX-2) enzyme. Borealis exhibited comparable anti-inflammatory effects to COX inhibitory drug, diclofenac. The results showed that haskap berry polyphenols has the potential to act as an effective inflammation inhibitor.
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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".