Cancer cell cytoskeleton behavior on titanium oxides synthesized through ultrafast pulsed laser irradiation
Bibliographic record
Abstract
Conventionally, single phases of TiO2 are used for targeted therapy and a drug carrier systems. In this research a harmonized approach in synthesizing multi-Ti oxide phases in a nanostructure and its ability to control cancer cell cytoskeleton behavior. This modulation of HeLa cancer cell cytoskeleton behaviour including shape of the cell, surface area of the cell, alignment of the cell is diligent by using the combination of TiO, Ti3O, Ti2O phases. Field emission scanning electron microscope investigation (FESEM) revealed that multi-Ti oxide nanostructure revealed a greater reduction of HeLa cell relative to fibroblast cell. This altered cell adhesion was followed by modulation of HeLa cell architecture with significant reduction in actin stress fibers. The intricate combination of multi-Ti oxide nanostructures renders a biomaterial that can precisely alter HeLa cell but not the fibroblast cell behaviour has the potential application of creating a multi-Ti oxide nanostructure for targeted cancer therapy, developing nano patterning devices. This unique interaction of HeLa cancer cell with multi-Ti oxide nanostructure has provided an insight of cell-cell signalling which is the fundamental mechanism in regulating their proliferative characteristics.
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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".