Exploratory Testing of Diatom Silica to Map the Role of Material Attributes on Cell Fate
Bibliographic record
Abstract
Porous silica is an attractive biomaterial in many applications, including drug-delivery systems, bone-graft fillers and medical devices. The issue with porous silica biomaterials is the rate at which they resorb and the significant role played by interfacial chemistry on the host response in vivo. This paper explores the potential of diatom-biosilica as a model tool to assist in the task of mapping and quantifying the role of surface topography and chemical cues on cell fate. Diatoms are unicellular microalgae whose cell walls are composed of, amorphous nanopatterned biosilica that cannot be replicated synthetically. Their unique nanotopography has the potential to improve understanding of interface reactions between materials and cells. This study used Cyclotella meneghiniana as a test subject to assess cytotoxicity and pro-inflammatory reactions to diatom-biosilica. The results suggest that diatom-biosilica is non-cytotoxic to J774.2 macrophage cells, and supports cell proliferation and growth. The addition of amine and thiol linkers have shown a significant effect on cytotoxicity, growth and cytokine response, thus warranting further investigation into the interfacial effects of small chemical modifications to substrate surfaces. The overall findings suggest diatom-biosilica offers a unique platform for in-depth investigation of the role played by nanotopography and chemistry in biomedical applications.
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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) | 0.001 | 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".