High-throughput method to test antimicrobial gels against a multispecies oral biofilm
Bibliographic record
Abstract
ABSTRACT Periodontitis, characterized by the damage of the periodontium can eventually lead to tooth loss. Moreover, severe forms of periodontitis are associated with several systemic disorders. The evolution of the disease is linked to the pathogenic switch of the oral microbiota comprising of commensal colonizers and anaerobic pathogens. Treatment with antimicrobial gels has the potential to help eradicate periodontal pathogens. Testing antibacterial gels against in vitro biofilm models is complicated. Recovery of detached and sessile bacteria from in vitro biofilms treated with gel formulations using conventional methods (microtiter plates, μ-slides, flow cells etc.,) may prove arduous. To overcome this challenge, we optimised a simple method using the principle of the Calgary Biofilm Device (CBD) for testing antimicrobial gels against multispecies oral biofilms. First, we established three-species oral biofilms consisting of two periodontal pathogens ( Porphyromonas gingivalis , Treponema denticola ) and a primary colonizer of the dental plaque ( Streptococcus gordonii ) on the surface of pegs. Next, a protocol to test gels against oral biofilms was implemented using commercially available gels with different active products. This method enables the analysis of the composition of biofilm and detached/planktonic cells to measure the effect of topical gel formulations/antibacterial gels for the treatment of periodontitis. However, the method is not restricted to oral biofilms and can be adapted for other biofilm-related studies.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".