LOMIX, a Mixture of Flaxseed Linusorbs, Exerts Anti-Inflammatory Effects through Src and Syk in the NF-κB Pathway
Bibliographic record
Abstract
Although flax (Linum usitatissimum L.) has long been used as Ayurvedic medicine, its anti-inflammatory role is still unclear. Therefore, we aimed to investigate the anti-inflammatory role of a linusorb mixture (LOMIX) recovered from flaxseed oil. Effects of LOMIX on inflammation and its mechanism of action were examined using several in vitro assays (i.e., NO production, real-time PCR analysis, luciferase-reporter assay, Western blot analysis, and kinase assay) and in vivo analysis with animal inflammation models as well as acute toxicity test. Results: LOMIX inhibited NO production, cell shape change, and inflammatory gene expression in stimulated RAW264.7 cells through direct targeting of Src and Syk in the NF-κB pathway. In vivo study further showed that LOMIX alleviated symptoms of gastritis, colitis, and hepatitis in murine model systems. In accordance with in vitro results, the in vivo anti-inflammatory effects were mediated by inhibition of Src and Syk. LOMIX was neither cytotoxic nor did it cause acute toxicity in mice. In addition, it was found that LOB3, LOB2, and LOA2 are active components included in LOMIX, as assessed by NO assay. These in vitro and in vivo results suggest that LOMIX exerts an anti-inflammatory effect by inhibiting the inflammatory responses of macrophages and ameliorating symptoms of inflammatory diseases without acute toxicity and is a promising anti-inflammatory medication for inflammatory diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".