Coprecipitated Keratin/hydroxyapatite nanocomposites assisted by microwave/ultrasound irradiation, and its cytotoxic evaluation on NIH/3T3 fibroblast cells
Bibliographic record
Abstract
This work is aimed to synthesize Hydroxyapatite (Hap) and Keratin/Hydroxyapatite (KerHap) nanocomposites by coprecipitation using the simultaneous microwave/ultrasound (Mw/Us) method to confer specific textural and morphological properties to the materials using keratin (Ker) extracted from human hair. Four materials were synthesized applying different powers of Mw/Us irradiation: Hap (1100 W Mw, 40 kHz Us), KerHap A (700 W Mw, 40 kHz Us), KerHap B (900 W Mw, 40 kHz Us) and KerHap C (1100 W Mw, 40 kHz Us) at 120 ºC for 10 min. The materials were characterized according to their structure (FTIR, XDR, Raman spectroscopy), morphology and size (TEM), and thermal properties (TGA). Their cytotoxic effects were studied at different concentrations (25, 50, 100, 200, and 400 μg/mL) using the Fibroblast cell line (NIH/3T3) by MTT assay at 24, 48, and 72 h. The Ker extract exhibited a major molecular weight band between 11-10 kDa in SDS-PAGE (Sodium dodecyl sulfate-polyacrylamide gel electrophoresis). Morphology, particle, and crystallite sizes were dependent on the Mw irradiation power applied and keratin added during the synthesis. The MTT assay at 48 h demonstrated a significant increase in cell viability of 39% for Hap at 50 µg/mL, for KerHap A did not show a significant difference, while KerHap B-C showed a significant increase at 200 µg/mL of 30% in cell viability. It means that fibroblast presented better cell adhesion on Hap than on KerHap A-C composites; it may be due to keratin blocking Hap pores because of electrostatic interactions between keratin and Hap.
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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".