Umbelliferone modulates the quorum sensing and biofilm of Gram − ve bacteria: <i>in vitro</i> and <i>in silico</i> investigations
Bibliographic record
Abstract
In last two decades, the world has seen an exponential increase in the antimicrobial resistance (AMR), making the issue a serious threat to human health. The mortality caused by AMR is one of the leading causes of human death worldwide. Till the end of the twentieth century, a tremendous success in the discovery of new antibiotics was seen, but in last two decades, there is negligible progress in this direction. The increase in AMR combined with slow progress of antibiotic drug discovery has created an urgent demand to search for newer methods of intervention to combat infectious diseases. One of such approach is to look for biofilm and quorum sensing (QS) inhibitors. Plants are excellent source of wide class compounds that can be harnessed to look for the compounds with such properties. This study proves a broad-spectrum biofilm and QS inhibitory potential of umbelliferone. More than 85% reduction in violacein production Chromobacterium violaceum 12472 was found. All tested virulent traits of Pseudomonas aeruginosa PAO1 and Serratia marcescens MTCC 97 were remarkably inhibited that ranged from 56.62% to 86.24%. Umbelliferone also successfully prevented the biofilm of test bacteria at least by 67.68%. Umbelliferone interacted at the active site of many proteins of QS circuit, which led to the mitigation of virulent traits. The stable nature of complexes of umbelliferone with proteins further strengthens in vitro results. After examining the toxicological profile and other drug-like properties, umbelliferone could be potentially developed as new drug to target the infections caused by Gram − ve bacteria.Communicated by Ramaswamy H. Sarma
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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) | 0.002 | 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".