Acute Inflammatory Response to Laser‐Induced Micro‐ and Nano‐Sized Titanium Surface Features
Bibliographic record
Abstract
BACKGROUND: The inflammatory process induced by implant surfaces is an important component of the tissue response, where limited knowledge is available regarding the role of surface topography. With laser ablation, a combined micro- and nanoscale surface modification could be created, which have been shown to enhance bone growth and biomechanical stability in vivo. PURPOSE: The aim of this article was to evaluate the early in vivo inflammatory response to laser-modified titanium disks, with machined titanium disks and sham operation sites serving as controls. MATERIALS AND METHODS: Circular disks were installed in a subcutaneous rat model for 24 and 72 hours, where the cell number, cell types, and cytokine levels were evaluated. RESULTS: The results revealed that significantly fewer inflammatory cells (mononuclear and polymorphonuclear) were attracted to the sites with the laser-modified implants compared with the machined titanium implants. Similar concentrations of pro-inflammatory cytokines (TNF-a and MCP-1), together with slightly higher cell viability, were observed around the laser-modified surface compared with the machined surface. CONCLUSIONS: The results in the present study suggest that the combination of surface micro and nano features of the laser-treated surface contributes to the downregulation of early inflammatory events.
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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) | 0.002 | 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".