Investigation of the antimicrobial activity of soy peptides by developing a high throughput drug screening assay
Bibliographic record
Abstract
Antimicrobial resistance is a great concern in the medical community, as well as food industry. Soy peptides were tested against bacterial biofilms for their antimicrobial activity. A high throughput drug screening assay was developed using microfluidic technology, RAMAN spectroscopy, and optical microscopy for rapid screening of antimicrobials and rapid identification of pathogens. Synthesized PGTAVFK and IKAFKEATKVDKVVVLWTA soy peptides were tested against Pseudomonas aeruginosa and Listeria monocytogenes using a microdilution assay. Microfluidic technology in combination with Surface Enhanced RAMAN Spectroscopy (SERS) and optical microscopy was used for rapid screening of soy peptides, pathogen identification, and to visualize the impact of selected peptides. The PGTAVFK peptide did not significantly affect P. aeruginosa, although it had an inhibitory effect on L. monocytogenes above a concentration of 625 µM. IKAFKEATKVDKVVVLWTA was effective against both P. aeruginosa and L. monocytogenes above a concentration of 37.2 µM. High throughput drug screening assays were able to reduce the screening and bacterial detection time to 4 h. SERS spectra was used to distinguish the two bacterial species. PGTAVFK and IKAFKEATKVDKVVVLWTA soy peptides showed antimicrobial activity against P. aeruginosa and L. monocytogenes. Development of high throughput assays could streamline the drug screening and bacterial detection process. The results of this study show that the antimicrobial properties, biocompatibility, and biodegradability of soy peptides could possibly make them an alternative to the ineffective antimicrobials and antibiotics currently used in the food and medical fields. High throughput drug screening assays could help hasten pre-clinical trials in the medical field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".