In-vitro Pharmacological activity of Endophyte Aspergillus austwickii isolated from leaves of Premna serratifolia
Bibliographic record
Abstract
Diabetes mellitus is one of the most widely occurring non communicable diseases spreading rapidly worldwide that occurs as a result of non-regulation of glucose in the blood stream or because of improper functioning of the enzymes α- amylase and α-glucosidase. Endophytes are the microorganisms that reside symbiotically inside the living tissues of plants. Endophytic fungi have the ability to synthesize various important bioactive metabolites. The current study was aimed to explore the less reported endophytic fungus Aspergillus austwickii isolated from ethnobotanical medicinal plant Premna serratifolia L. The methanolic extract of the endophytic fungus was subjected to activities like in vitro antioxidant assays, anti-inflammatory assay, antidiabetic assay in addition to exploring their total phenolic and total flavonoid content along with the phytochemicals present. The results revealed that Aspergillus austwickii exhibited phytoconstituents like alkaloids, phenols, flavonoids, tannins and carbohydrates. The total phenolic content and total flavonoid content of the fungus was found to be 22.048 µg GAE/g and 18.828 µg GAE/g. The crude extract showed 46.20+0.53% of antioxidant activity with an IC50 value of 128.69 µg/mL for radical scavenging by DPPH. It also exhibited 71.86+0.34% of anti-inflammatory activity by protein denaturation assay. Notably, it revealed, antidiabetic activity for both alpha amylase and alpha glucosidase with the percentage of inhibition of 68.22+0.17% and 73.72+0.18% with an IC50 value of 178.10 µg/mL and 166.16µg/mL respectively. The current study shows that the methanolic extract of Aspergillus austwickii possesses considerable antioxidant, anti-inflammatory and antidiabetic capabilities. The findings of the current research can be explored for the future research to find out new set of natural drugs which can be used against several disorders.
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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".