Inhibition of Bacterial Biofilm Formation and Swarming Motility by a Small Synthetic Cationic Peptide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biofilms cause up to 80% of infections and are difficult to treat due to their substantial multidrug resistance compared to their planktonic counterparts. Based on the observation that human peptide LL-37 is able to block biofilm formation at concentrations below its MIC, we screened for small peptides with antibiofilm activity and identified novel synthetic cationic peptide 1037 of only 9 amino acids in length. Peptide 1037 had very weak antimicrobial activity, but at 1/30th the MIC the peptide was able to effectively prevent biofilm formation (>50% reduction in cell biomass) by the Gram-negative pathogens Pseudomonas aeruginosa and Burkholderia cenocepacia and Gram-positive Listeria monocytogenes. Using a flow cell system and a widefield fluorescence microscope, 1037 was shown to significantly reduce biofilm formation and lead to cell death in biofilms. Microarray and follow-up studies showed that, in P. aeruginosa, 1037 directly inhibited biofilms by reducing swimming and swarming motilities, stimulating twitching motility, and suppressing the expression of a variety of genes involved in biofilm formation (e.g., PA2204). Comparison of microarray data from cells treated with peptides LL-37 and 1037 enabled the identification of 11 common P. aeruginosa genes that have a role in biofilm formation and are proposed to represent functional targets of these peptides. Peptide 1037 shows promise as a potential therapeutic agent against chronic, recurrent biofilm infections caused by a variety of bacteria.
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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 | 0.000 | 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) | 0.000 | 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 it