Influence of Nanotopography on Early Bone Healing during Controlled Implant Loading
Bibliographic record
Abstract
Nanoscale surface modifications influence peri-implant cell fate decisions and implant loading generates local tissue deformation, both of which will invariably impact bone healing. The objective of this study is to determine how loading affects healing around implants with nanotopography. Implants with a nanoporous surface were placed in over-sized osteotomies in rat tibiae and held stable by a system that permits controlled loading. Three regimens were applied: (a) no loading, (b) one daily loading session with a force of 1.5N, and (c) two such daily sessions. At 7 days post implantation, animals were sacrificed for histomorphometric and DNA microarray analyses. Implants subjected to no loading or only one daily loading session achieved high bone implant contact (BIC), bone implant distance (BID) and bone formation area near the implant (BFAt) values, while those subjected to two daily loading sessions showed less BFAt and BIC and more BID. Gene expression profiles differed between all groups mainly in unidentified genes, and no modulation of genes associated with inflammatory pathways was detected. These results indicate that implants with nanotopography can achieve a high level of bone formation even under micromotion and limit the inflammatory response to the implant surface.
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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".