Lactobacillus acidophilus impairs the establishment of pathogens in a subgingival multispecies biofilm
Bibliographic record
Abstract
The present study evaluated the antibiofilm effects of Lactobacillus acidophilus within a subgingival multispecies biofilm. Lactobacillus acidophilus (La5) at 1 × 102, 1 × 104, and 1 × 106 were included at the beginning of biofilm formation, which lasted 7 days. The biofilms comprised 33 periodontitis-related bacterial species and the Calgary Biofilm device was used. At the end, DNA–DNA hybridization (checkerboard) was performed. A Kruskal–Wallis test followed by a Dunn post hoc test were performed (p ≤ 0.05). La5 at 1 × 104 and 1 × 106 reduced the total counts of biofilm and the proportions of red and green complexes when compared to the control biofilm without La5 (p ≤ 0.05). La5 at 1 × 104 increased the proportions of Actinomyces complex compared to the controls (p ≤ 0.05). Both La5 at 1 × 104 and 1 × 106 decreased levels of 20 and 14 distinct species, respectively, including Porphyromonas gingivalis, Prevotella intermedia, Fusobacterium nucleatum polymorphum, and Parvimonas micra compared to the control (p ≤ 0.05). Only La5 at 1 × 104 reduced the levels of Tannerella forsythia, Fusobacterium periodonticum, and Aggregatibacter actinomycetencomytans compared to the control (p ≤ 0.05). L. acidophilus inhibited establishing periodontic pathogens from red complex such as P. gingivalis and T. forsythia in a subgingival multispecies biofilm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".