Alternative strategies for the study and treatment of clinical bacterial biofilms
Bibliographic record
Abstract
Biofilms represent an adaptive lifestyle where microbes grow as structured aggregates in many different environments, e.g. on body surfaces and medical devices. They are a profound threat in medical (and industrial) settings and cause two-thirds of all infections. Biofilm bacteria are especially recalcitrant to common antibiotic treatments, demonstrating adaptive multidrug resistance. For this reason, novel methods to eradicate or prevent biofilm infections are greatly needed. Recent advances have been made in exploring alternative strategies that affect biofilm lifestyle, inhibit biofilm formation, degrade biofilm components and/or cause dispersal. As such, naturally derived compounds, molecules that interfere with bacterial signaling systems, anti-biofilm peptides and phages show great promise. Their implementation as either stand-alone drugs or complementary therapies has the potential to eradicate resilient biofilm infections. Additionally, altering the surface properties of indwelling medical devices through bioengineering approaches has been examined as a method for preventing biofilm formation. There is also a need for improving current biofilm detection methods since in vitro methods often do not accurately measure live bacteria in biofilms or mimic in vivo conditions. We propose that the design and development of novel compounds will be enabled by the improvement and use of appropriate in vitro and in vivo models.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".