Bibliographic record
Abstract
Considering rising antibiotic resistance issues of bacterias, systemic toxicity and drug resistance in chemotherapy, discovering new bio-active compounds is essential.Therefore, introducing new bio-active compounds for treating patients of infectious diseases and cancer can be considered.In this study, the stability of antimicrobial metabolite produced by Pseudomonas sp.UW4 against lipase enzyme and the anti-cancer effect of it was evaluated through the rate of HT29 cell line proliferation and apoptosis.In order to achieve this goal, Pseudomonas sp.UW4 was isolated from Isfahan soil samples and it was evaluated for production of active biocompounds and stability of antimicrobial metabolite Pseudomonas sp.UW4 against the lipase enzyme.The evaluation of the stability of the produced metabolite against the lipase enzyme showed that this metabolite is persistent against the enzyme.Also, treatment with the metabolite showed that by increasing the concentration of the metabolite depended on dose and time, the biotic potential of the cells decreased.The highest effect was observed at the concentration of 20 mg/ml and 72 hours after treatment of the cells.Induction of apoptosis was also dose-dependent and the concentration of 20 mg / ml supernatant induced the highest percentage of apoptosis in HT29 cells.Therefore, bioactive compounds produced by Pseudomonas sp.UW4 may be used to eliminate infections and treat HT29 breast cancer.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.880 | 0.856 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".