Harmonizing HeLa cell cytoskeleton behavior by multi-Ti oxide phased nanostructure synthesized through ultrashort pulsed laser
Bibliographic record
Abstract
Knowledge about cancer cell behavior on heterogeneous nanostructures is relevant for developing a distinct biomaterial that can actuate cancer cells. In this manuscript, we have demonstrated a harmonized approach of forming multi Ti-oxide phases in a nanostructure (MTOP nanostructure) for its unique cancer cell controlling behavior.Conventionally, single phases of TiO2 are used for targeted therapy and as drug carrier systems.In this research, we have shown a biomaterial that can control HeLa cells diligently using a combination of TiO, Ti3O and TiO2 phases when compared to fibroblast (NIH3T3) cells.MTOP-nanostructures are generated by varying the ionization energy in the vapor plume of the ultrashort pulse laser; this interaction with the material allows accurate tuning and composition of phases within the nanostructure. In addition, the lattice spacing of MTOP-nanostructures was analyzed as shown by HR-TEM investigations. An FESEM investigation of MTOP-nanostructures revealed a greater reduction of HeLa cells relative to fibroblast cells. Altered cell adhesion was followed by modulation of HeLa cell architecture with a significant reduction of actin stress fibers.The intricate combination of MTOP-nanostructures renders a biomaterial that can precisely alter HeLa cell but not fibroblast cell behavior, filling a void in the research for a biomaterial to modulate cancer cell behavior.
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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".