TGF‐β activity in acid bone lysate adsorbs to titanium surface
Bibliographic record
Abstract
OBJECTIVES: Osteoblasts lay down new bone on implant surfaces. The underlying cellular mechanism and the spatio-temporal mode of action, however, remain unclear. It can be proposed that growth factors released upon acidification by osteoclasts adsorb to the implant surface and control the early stages of osseointegration. METHODS: To simulate bone lysis by osteoclasts, titanium discs were exposed to acid bone lysate (ABL) followed by vigorous washing and seeding of oral fibroblasts. The expression of TGF-β target genes interleukin 11 (IL11) and NADPH oxidase 4 (NOX4) was evaluated by reverse transcriptase polymerase chain reaction and IL11 ELISA. TGF-β signaling activation was assessed via Smad2/3 immunofluorescence. The impact of ABL on osteogenic differentiation was determined with murine ST2 mesenchymal stromal cells. RESULTS: We report here that ABL-conditioned titanium discs, independent of turned or rough surface, increased the expression of IL11 and NOX4. This increase was blocked by the TGF-β receptor 1 antagonist SB431542. Further support for the TGF-β signaling activation came from the translocation of Smad2/3 into the nucleus of oral fibroblasts. Moreover, titanium discs exposed to ABL decreased alkaline phosphatase and osteopontin in ST2 cells. CONCLUSIONS: These in vitro findings suggest that titanium can adsorb TGF-β from ABLs. The data provide a strong impetus for studies on the protein adsorption on implant surfaces in vitro and in vivo, specifically for growth factors including bone-derived TGF-β during successful and failed osseointegration.
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 | 0.000 |
| 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".