Bone healing response in cyclically loaded implants: Comparing zero, one, and two loading sessions per day
Bibliographic record
Abstract
When bone implants are loaded, they are inevitably subjected to displacement relative to bone. Such micromotion generates stress/strain states at the interface that can cause beneficial or detrimental sequels. The objective of this study is to better understand the mechanobiology of bone healing at the tissue-implant interface during repeated loading. Machined screw shaped Ti implants were placed in rat tibiae in a hole slightly bigger than the implant diameter. Implants were held stable by a specially-designed bone plate that permits controlled loading. Three loading regimens were applied, (a) zero loading, (b) one daily loading session of 60 cycles with an axial force of 1.5 N/cycle for 7 days, and (c) two such daily sessions with the same axial force also for 7 days. Finite element analysis was used to characterize the mechanobiological conditions produced by the loading sessions. After 7 days, the implants with surrounding interfacial tissue were harvested and processed for histological, histomorphometric and DNA microarray analyses. Histomorphometric analyses revealed that the group subjected to repeated loading sessions exhibited a significant decrease in bone-implant contact and increase in bone-implant distance, as compared to unloaded implants and those subjected to only one loading session. Gene expression profiles differed during osseointegration between all groups mainly with respect to inflammatory and unidentified gene categories. The results indicate that increasing the daily cyclic loading of implants induces deleterious changes in the bone healing response, most likely due to the accumulation of tissue damage and associated inflammatory reaction at the bone-implant interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".