Titania‐polymeric powder coatings with nano‐topography support enhanced human mesenchymal cell responses
Bibliographic record
Abstract
Titanium implant osseointegration is dependent on the cellular response to surface modifications and coatings. Titania-enriched nanocomposite polymeric resin coatings were prepared through the application of advanced ultrafine powder coating technology. Their surfaces were readily modified to create nano-rough (<100 nm) surface nano-topographies that supported human embryonic palatal mesenchymal cell responses. Energy dispersive x-ray spectroscopy confirmed continuous and homogenous coatings with a similar composition and even distribution of titanium. Scanning electron microscopy (SEM) showed complex micro-topographies, and atomic force microscopy revealed intricate nanofeatures and surface roughness. Cell counts, mitochondrial enzyme activity reduction of yellow 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) to dark purple, SEM, and inverted fluorescence microscopy showed a marked increase in cell attachment, spreading, proliferation, and metabolic activity on the nanostructured surfaces. Reverse Transcription- Polymerase Chain Reaction (RT-PCR) analysis showed that type I collagen and Runx2 expression were induced, and Alizarin red staining showed that mineral deposits were abundant in the cell cultures grown on nanosurfaces. This enhancement in human mesenchymal cell attachment, growth, and osteogenesis were attributed to the nanosized surface topographies, roughness, and moderate wetting characteristics of the coatings. Their dimensional similarity to naturally occurring matrix proteins and crystals, coupled with their increased surface area for protein adsorption, may have facilitated the response. Therefore, this application of ultrafine powder coating technology affords highly biocompatible surfaces that can be readily modified to accentuate the cellular response.
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 | 0.000 |
| 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".