Esc(1-21) a novel antimicrobial peptide for microbial keratitis?
Bibliographic record
Abstract
Purpose: To investigate the antimicrobial efficacy of a novel\namphibian antimicrobial peptide, Esculentin 1-21 (Esc 1-21) in vitro.\nMethods: Microbroth dilution assays were used to determine the\nminimal inhibitory concentration (MIC) of Esc(1-21) against PA\nstrain ATCC 27853 and the effects of salt and tears (basal and reflex) on peptide antimicrobial activity. Esc(1-21) activity on a pre-formed biofilm was also tested using the Calgary Biofilm device and evaluated by analysis of re-growth, viable cell (CFU counts and MTT assay) and biomass.\nResults: The MIC for Esc(1-21) was 4uM (n=3). Esc(1-21), 1uM,\nretained the ability to kill 100% of ATCC 27853, within 20 min, in\n150mM NaCl (n=3). When tested in the presence of 50 and 70% v/v reflex human tears, killing of ATCC 27853 by 20uM Esc(1-21) was 100% and 94% respectively, after 90 min of peptide treatment. When tested in the presence of 50 and 70% v/v basal human tears, killing was 98% and 70% respectively. The minimum biofilm eradication concentration (concentration inhibiting re-growth of bacteria from peptide treated biofilm) was 6uM (n=4) and the minimum bactericidal concentration (concentration required to reduce the number of viable biofilm cells by ≥3 log10) was 12uM (n=3). Biofilm biomass, evaluated by crystal violet staining, was 15% to 32% for 48-12uM peptide.\nConclusions: Esc(1-21) is effective against both the free-living and\nsessile forms of PA in vitro and importantly retains significant\nbactericidal activity in the presence of human tears. As antimicrobial peptides are recognized to induce minimal pathogen resistance Esc(1-21) is a very promising candidate for a novel therapeutic for the treatment of microbial keratitis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".