Approaches for the inhibition and elimination of microbial biofilms using macromolecular agents
Bibliographic record
Abstract
Biofilms are complex three-dimensional structures formed at interfaces by the vast majority of bacteria and fungi. These robust communities have an important detrimental impact on a wide range of industries and other facets of our daily lives, yet their removal is challenging owing to the high tolerance of biofilms towards conventional antimicrobial agents. This key issue has driven an urgent search for new innovative antibiofilm materials. Amongst these emerging approaches are highly promising materials that employ aqueous-soluble macromolecules, including peptides, proteins, synthetic polymers, and nanomaterials thereof, which exhibit a range of functionalities that can inhibit biofilm formation or detach and destroy organisms residing within established biofilms. In this Review, we outline the progress made in inhibiting and removing biofilms using macromolecular approaches, including a spotlight on cutting-edge materials that respond to environmental stimuli for "on-demand" antibiofilm activity, as well as synergistic multi-action antibiofilm materials. We also highlight materials that imitate and harness naturally derived species to achieve new and improved biomimetic and biohybrid antibiofilm materials. Finally, we share some speculative insights into possible future directions for this exciting and highly significant field of research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".