Effects of titanium‐coated micromachined grooved substrata on orienting layers of osteoblast‐like cells and collagen fibers in culture
Bibliographic record
Abstract
Osteogenic cells from newborn rat calvariae were cultured on titanium surfaces on which cell orientation could be manipulated. Substrata included smooth surfaces and substrata with smooth regions (gaps) flanked by grooves of 47-microm pitch and 3-, 10-, or 30-microm depth. Orientation angles of the cells were measured over time using propidium-iodide staining and confocal laser scanning microscopy. In addition, collagen fibers were identified using picro-sirius staining and reflected light polarization microscopy. Grooves proved effective in orienting cells, but their orienting ability decreased above the ridge level. Cells on the smooth surface showed no preferred orientation. Cells in the gaps became oriented as a result of cell-cell interactions with the cells on the flanking grooves. Cells in grooves produced oriented collagen fibers, but in the gaps, fibers could be parallel, perpendicular, or diagonal to the grooves. Collagen fibers on the smooth surfaces formed arrays of parallel fibers in a crisscross pattern. In long-term cultures, bone-like nodules were formed, but mostly above the ridge level. These data demonstrate that grooved surfaces can influence cell orientation both in cell populations above the cells in contact with the grooves and in cell populations adjacent to the grooves.
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 | 0.001 |
| 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".