Lethal photosensitization of periodontal pathogens by a red‐filtered Xenon lamp <i>in vitro</i>
Bibliographic record
Abstract
BACKGROUND: The ability of Helium-Neon (He-Ne) laser irradiation of a photosensitizer to induce localized phototoxic effects that kill periodontal pathogens is well documented and is termed photodynamic therapy (PDT). OBJECTIVES: We investigated the potential of a conventional light source (red-filtered Xenon lamp) to activate toluidine blue O (TBO) in vitro and determined in vitro model parameters that may be used in future in vivo trials. MATERIALS AND METHODS: Porphyromonas gingivalis 381 was used as the primary test bacterium. RESULTS: Treatment with a 2.2 J/cm2 light dose and 50 micro g/ml TBO concentration resulted in a bacterial kill of 2.43 +/- 0.39 logs with the He-Ne laser control and 3.34 +/- 0.24 logs with the lamp, a near 10-fold increase (p = 0.028). Increases in light intensity produced significantly higher killing (p = 0.012) that plateaued at 25 mW/cm2. There was a linear relationship between light dose and bacterial killing (r2 = 0.916); as light dose was increased bacterial survival decreased. No such relationship was found for the drug concentrations tested. Addition of serum or blood at 50% v/v to the P. gingivalis suspension prior to irradiation diminished killing from approximately 5 logs to 3 logs at 10 J/cm2. When serum was washed off, killing returned to 5 logs for all species tested except Bacteroides forsythus (3.92 +/- 0.68 logs kill). CONCLUSIONS: The data indicate that PDT utilizing a conventional light source is at least as effective as laser-induced treatment in vitro. Furthermore, PDT achieves significant bactericidal activity in the presence of serum and blood when used with the set parameters of 10 J/cm2, 100 mW/cm2 and 12.5 micro g/ml TBO.
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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 | 0.000 | 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 | 0.000 |
| 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".