Anti‐Inflammatory and Antioxidant Properties of Black Mulberry (<i>Morus nigra</i> L.) in a Model of LPS‐Induced Sepsis
Bibliographic record
Abstract
Sepsis is a complex disease and is the cause of many deaths worldwide. Sepsis pathogenesis involves a dysregulated inflammatory response with consequent production of inflammatory mediators and reactive species. The production and excessive release of these substances into the systemic circulation trigger various cellular and metabolic alterations that are observed during the disease evolution. Thus, more studies have been carried out to investigate the therapeutic potential of plants such as Morus nigra L., popularly known as black mulberry. Studies have shown that plants belonging to the Morus genus are rich in secondary metabolites such as flavonoids which are associated with important biological activities as antioxidant and anti‐inflammatory actions. Based on this context, the objective of our study was to evaluate the anti‐inflammatory and antioxidant properties of Morus nigra L. in a sepsis model induced by LPS. Male C57BL/6 mice were distributed in four groups: control, sepsis, sepsis treated with leaf extract of mulberry, and sepsis treated with mulberry pulp. The animals were treated with 100 μL of their respective treatments for twenty‐one days. Sepsis was induced at the 21st day with lipopolysaccharide (LPS) by intraperitoneal injection. The animals were euthanized 24 hours after receiving the LPS injection. The data obtained were analyzed in GraphPad Prism 6.0 software. Our results showed that treatment with either extract significantly decreased the number of leukocytes in the bronchoalveolar lavage fluid and serum levels of TNF in septic animals. Regarding the redox status, the treatments significantly decreased the antioxidant activity of the enzyme glutathione peroxidase. Regarding metalloproteinase type 2, it was observed that the treatment with black mulberry pulp was able to significantly reduce the activity of this enzyme concerning the sepsis group. Finally, these results together promoted an increase in the animal’s survival that received the black mulberry leaf or pulp extract.
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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) | 0.001 | 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 | 0.001 |
| 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".