Effects of Photodynamic Process (<scp>PDP</scp>) in Implant Osseointegration: A Histologic and Histometric Study in Dogs
Bibliographic record
Abstract
BACKGROUND: The combination between photosensitivity substances with laser or light-emitting diode (LED) form the photodynamic therapy basis that consists of photosensitivity drug activated by low-frequency light. This mechanism is used in soft tissue healing process to improve the oxygen tension leading to a fast revascularization. PURPOSE: The objective of this study was to evaluate the effects of photosensitivity drugs activated through LED on osseointegration process. MATERIALS AND METHODS: Eight mongrel dogs underwent implant therapy in four mandibular bone defects using 5.0 mm trephine drill on each side of the mandible. The defects were randomly filled up with (1) Nano emulsion, (2) liposome, (3) blood clot, and (4) autogenous bone. LED with visible and infrared light were applied after 48/72 postoperative hours on four dogs and after 96/120 postoperative hours in the other four dogs. All the animals were euthanized at 15 days after surgery. Ground sections slides were prepared from the experimental site for histomorphometry and histological analysis. RESULTS: No difference was detected in the following parameters: bone-implant contact, bone inside the defect and crest level on LED 48/72. Significant difference was detected inside the defect when filled with autogenous bone (p = .0238) on LED 96/120. When LED 48/72 and LED 96/120 were compared, significant higher bone formation was detected when autogenous bone on bone-implant contact (p = .0043) and bone inside the defect (p = .0008) was used. CONCLUSION: The use of photosensitivity drugs activated by LED demonstrated a tendency to stimulate bone formation, similar to autogenous bone graft on later time point.
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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 | 0.001 |
| 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".