The response of mineralizing culture systems to microtextured and polished titanium surfaces
Bibliographic record
Abstract
The surface texture of titanium has a predictable effect on peri-implant tissue formation in vivo. When implanted in an osseous environment, smooth surfaces (R(a) < 0.5 mm) are generally apposed by fibrous tissue and textured surfaces (R(a) > 1.0 mm) are generally apposed by osseous tissue. Thus in vitro study assessed the mineralization and proliferation response of TF274, MC3T3-E1, murine femoral stromal cells and canine stromal cells to tissue culture plastic (R(a) = 0.001 mm), polished (R(a) = 0.01 mm) and irregularly textured (R(a) = 3.26 mm) titanium surfaces. Amongst all culture systems, proliferation was significantly decreased on textured vs. smooth surfaces. Midway through the culture of the canine marrow cells, the cell layer detached from the tissue culture plastic and polished titanium surfaces. The TF274, MC3T3-E1, murine femoral stromal cell systems formed a mineralized matrix on the tissue culture plastic and polished titanium surfaces which was not observed with the canine stromal cell system. Compared to the tissue culture plastic and polished titanium surfaces, matrix mineralization was significantly reduced on the textured titanium surfaces for the TF274, MC3T3-E1, murine femoral stromal systems, a result which was differed significantly in comparison to the canine stromal system. These results were surprising given the large number of reports concerning the in vivo response to titanium in clinical and pre-clinical studies. Further work is required to determine if the TF274, MC3T3-E1 and murine femoral stromal systems are suitable for the in vitro investigation of the effects of titanium surface texture on osteoblast activity.
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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 | 0.001 | 0.001 |
| 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".