Limitation in Controlling the Morphology of Mammalian Vero Cells Induced by Cell Division on Asymmetric Tungsten-Silicon Oxide Nanocomposite
Bibliographic record
Abstract
Engineered nanomaterials are often used in tissue engineering applications to influence and manipulate the behavior of cells. Recently, a number of tungsten-silicon oxide nanocomposite devices containing equal width (symmetric) tungsten and silicon oxide parallel line comb structures were developed and used by our group. The devices induced over 90% of seeded cells (Vero) to align within ±20° of the axes of 10 µm wide tungsten lines. Furthermore, a mathematical model was successfully developed to predict this alignment behavior and forecast the minimum width of isolated tungsten lines required to induce such behavior. However, the mechanism by which the widths of the symmetrical tungsten and silicon oxide lines induce the alignment behavior is still unknown. Furthermore, the model was never tested on more complex asymmetrical structures. Herewith, experiments were conducted with mammalian cells on complex asymmetrical structures with unequal tungsten and silicon oxide line widths. Results showed that the model could be extended to more complex pattern structures. In addition, cell morphology on the patterned structures reset during cell division because of mitotic rounding, which reduced the population of cells that elongated and aligned on the tungsten lines. Ultimately, we concluded that it was impossible to achieve a 100% alignment with cells having unsynchronized cell cycles because cell rounding during mitosis took precedence over cell alignment; in other words, internal chemical cues had a stronger role in cell morphology than external cues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".