Attachment, spreading, and matrix formation by human gingival fibroblasts on porous‐structured titanium alloy and calcium polyphosphate substrates
Bibliographic record
Abstract
Porous calcium polyphosphate (CPP) structures represent promising resorbable implant systems that can promote anchorage to connective tissues. Previous studies focused on chondrocyte interactions with CPP, but there are limited data on interactions of soft connective tissue cells with these materials. We studied attachment, spreading, and matrix formation by human gingival fibroblasts when cultured on amorphous and crystalline CPP. Comparison with porous Ti6Al4V substrates of similar volume percent, porosity, and pore size distribution provided evaluations of fibroblast interactions with rapid, moderate, and nonbiodegradable systems, respectively. Cells were incubated on substrates in medium containing ascorbic acid and evaluated at 3, 24, 48, 72, and 96 h after plating. Attached cell counts, cytoplasmic actin filament area, and immunostained extracellular type 1 collagen or fibronectin were quantified by morphometric analyses using epifluorescence microscopy. Cell morphology and substrate interactions were evaluated by scanning electron microscopy. Spreading, attachment, and matrix production were similar for both CPP substrates. In contrast, titanium alloy substrates exhibited threefold more attachment and twofold more spreading than CPP substrates. The area per cell of immunostained extracellular collagen and fibronectin was similar for the three different substrates. The results indicate that the crystallinity and, hence, degradation rate of CPP substrates does not substantially affect the interactions of fibroblasts with CPP materials but that compared with titanium alloy substrates, spreading and attachment are inhibited.
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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) | 0.000 | 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".