Prevention and treatment of biofilms by hybrid- and nanotechnologies
Bibliographic record
Abstract
Ramanathan K Kasimanickam,1 Ashish Ranjan,2 GV Asokan,3 Vanmathy R Kasimanickam,1 John P Kastelic41Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA; 2Department of Physiological Sciences, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA; 3College of Health Sciences, University of Bahrain, Salmaniya Medical Complex, Manama, Bahrain; 4Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, CanadaAll authors contributed equally to this workAbstract: Bacteria growing as adherent biofilms are difficult to treat and frequently develop resistance to antimicrobial agents. To counter biofilms, various approaches, including prevention of bacterial surface adherence, application of device applicators, and assimilation of antimicrobials in targeted drug delivery machinery, have been utilized. These methods are also combined to achieve synergistic bacterial killing. This review discusses various multimodal technologies, presents general concepts, and describes therapies relying on the principles of electrical energy, ultrasound, photodynamics, and targeted drug delivery for prevention and treatment of biofilms.Keywords: biofilm, antimicrobial, drug carrier, hybrid technology, nanotechnology
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".