Recent Advances in the Control of Clinically Important Biofilms
Bibliographic record
Abstract
Biofilms are complex structures formed by bacteria, fungi, or even viruses on biotic and abiotic surfaces, and they can be found in almost any part of the human body. The prevalence of biofilm-associated diseases has increased in recent years, mainly because of the frequent use of indwelling medical devices that create opportunities for clinically important bacteria and fungi to form biofilms either on the device or on the neighboring tissues. As a result of their resistance to antibiotics and host immunity factors, biofilms have been associated with the development or persistence of several clinically important diseases. The inability to completely eradicate biofilms drastically increases the burden of disease on both the patient and the healthcare system. Therefore, it is crucial to develop innovative ways to tackle the growth and development of biofilms. This review focuses on dental- and implant-associated biofilm infections, their prevalence in humans, and potential therapeutic intervention strategies, including the recent advances in pharmacology and biomedical engineering. It lists current strategies used to control the formation of clinically important biofilms, including novel antibiotics and their carriers, antiseptics and disinfectants, small molecule anti-biofilm agents, surface treatment strategies, and nanostructure functionalization, as well as multifunctional coatings particularly suitable for providing antibacterial effects to the surface of implants, to treat either dental- or implant-related bacterial 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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".