Antibiofilm Activity of GlmU Enzyme Inhibitors against Catheter-Associated Uropathogens
Bibliographic record
Abstract
The colonization of uropathogenic bacteria on urinary catheters resulting in biofilm formation frequently leads to the infection of surrounding tissue and often requires removal of the catheter. Infections associated with biofilms are difficult to treat since they may be more than 1,000 times more resistant to antibiotics than their planktonic counterparts. We have developed an antibiofilm composition comprising an N-acetyl-D-glucosamine-1-phosphate acetyltransferase (GlmU) inhibitor and protamine sulfate, a cationic polypeptide. The antibiofilm activity of GlmU inhibitors, such as iodoacetamide (IDA), N-ethyl maleimide (NEM), and NEM analogs, including N-phenyl maleimide, N,N'-(1,2-phenylene)dimaleimide (oPDM), and N-(1-pyrenyl)maleimide (PyrM), was tested against that of catheter-associated uropathogens. Both IDA and NEM inhibited biofilm formation in Escherichia coli. All NEM analogs showed antibiofilm activity against clinical isolates of E. coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus epidermidis, and Enterococcus faecalis. The combination of oPDM with protamine sulfate (PS) enhanced its antibiofilm activity and reduced its effective concentration to as low as 12.5 microM. In addition, we found that the in vitro inhibitory activity of oPDM-plus-PS-coated silicone catheters against P. aeruginosa and S. epidermidis colonization was superior to that of catheters coated with silver hydrogel. Confocal scanning laser microscopy further confirmed that the oPDM-plus-PS-coated silicone catheters were almost free from bacterial colonization. Thus, a broad-spectrum antibiofilm composition comprising a GlmU inhibitor and protamine sulfate shows promise for use in anti-infective coatings for medical devices, including urinary catheters.
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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) | 0.000 | 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 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".