Thykamine Extracts from Spinach Reduce Acute Inflammation In Vivo and Downregulate Phlogogenic Functions of Human Blood Neutrophils In Vitro
Bibliographic record
Abstract
The anti-inflammatory and antioxidant role of Thykamine, a botanical extract of thylakoides obtained from spinach leaves, has been investigated in animal and cellular models. The oxidative properties have been proven by inhibiting NO production (>98%) in J774A.1 cells and by protecting a linoelic acid emulsion subjected to lipid peroxidation caused by AAPH. Thykamine injected intraperitoneally to rats reduced the inflammatory process of (TNBS)-induced colitis and carrageenan-induced paw edema. As neutrophils are the first cells to migrate to inflammatory sites, the influence of Thykamine on the primary neutrophil functions were studied. Thykamine dose-dependent reduced neutrophil chemiotaxis, phagocytosis, and degranulation. No change in the release of LDH by neutrophils on Thykamine was recorded. Thykamine inhibited by 85% the neutrophil production of O2−. A superoxide recovery activity was observed on a zymography demonstrating a SOD-like enzyme on Thykamine extracts. Spontaneous fluorescence provided by carotenoid and chlorophyll pigments (488/675 nm) detected Thykamine on the surface, in the cytoplasm (mainly central where Golgi are present) and weakly in the nucleus of neutrophils. The results argue that SOD and pigments found in Thykamine are part of its antioxidant and anti-inflammatory properties shown in in vivo and in vitro models of inflammation.
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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".