Characterization of anti-inflammatory effect and possible mechanism of action of <i>Tibouchina granulosa</i>
Bibliographic record
Abstract
OBJECTIVES: Tibouchina granulosa, popularly known as 'quaresmeira', belong to a genus widely used in the traditional medicine as infusions from their leaves. Other species of Tibouchina are used as antibacterial, antioxidant or antileishmanial. In this work, our objectives were to investigate the biological effects of T. granulosa in models of acute inflammation. METHODS: Chemical analysis showed the presence of proanthocyanidins and flavonoids. Infusions from leaves of T. granulosa (1, 3, 10, 30 or 100 mg/kg) were orally administered to mice, and the anti-inflammatory effects were evaluated by the formalin-induced licking response, inhibition of carrageenan-induced cell migration into subcutaneous air pouch (SAP) and inhibition of inflammatory mediator production in inflammatory exudate collected from SAP. KEY FINDINGS: Our data indicate that tested doses of T. granulosa infusion reduced cell migration, protein extravasated to SAP and cytokine production (i.e. TNF-α and IL-10). All doses also inhibited the first and second phase of formalin-induced licking response. CONCLUSIONS: Taken together, our results indicate that leaves of T. granulosa present anti-inflammatory effect and can be useful in the preparation of new phytomedicines.
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) | 0.001 | 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".