Antibiofilm Peptides: Potential as Broad-Spectrum Agents
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
The treatment of bacterial diseases is facing twin threats, with increasing bacterial antibiotic resistance and relatively few novel compounds or strategies under development or entering the clinic. Bacteria frequently grow on surfaces as biofilm communities encased in a polymeric matrix. The biofilm mode of growth is associated with 65 to 80% of all clinical infections. It causes broad adaptive changes; biofilm bacteria are especially (10- to 1,000-fold) resistant to conventional antibiotics and to date no antimicrobials have been developed specifically to treat biofilms. Small synthetic peptides with broad-spectrum antibiofilm activity represent a novel approach to treat biofilm-related infections. Recent developments have provided evidence that these peptides can inhibit even developed biofilms, kill multiple bacterial species in biofilms (including the ESKAPE [Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species] pathogens), show strong synergy with several antibiotics, and act by targeting a universal stress response in bacteria. Thus, these peptides represent a promising alternative treatment to conventional antibiotics and work effectively in animal models of biofilm-associated infections.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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