New Perspectives in Biofilm Eradication
Bibliographic record
Abstract
Microbial biofilms, which are elaborate and highly resistant microbial aggregates formed on surfaces or medical devices, cause two-thirds of infections and constitute a serious threat to public health. Immunocompromised patients, individuals who require implanted devices, artificial limbs, organ transplants, or external life support and those with major injuries or burns, are particularly prone to become infected. Antibiotics, the mainstay treatments of bacterial infections, have often proven ineffective in the fight against microbes when growing as biofilms, and to date, no antibiotic has been developed for use against biofilm infections. Antibiotic resistance is rising, but biofilm-mediated multidrug resistance transcends this in being adaptive and broad spectrum and dependent on the biofilm growth state of organisms. Therefore, the treatment of biofilms requires drug developers to start thinking outside the constricted "antibiotics" box and to find alternative ways to target biofilm infections. Here, we highlight recent approaches for combating biofilms focusing on the eradication of preformed biofilms, including electrochemical methods, promising antibiofilm compounds and the recent progress in drug delivery strategies to enhance the bioavailability and potency of antibiofilm agents.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".