Intelligent Scaffolds for Advanced Bone Regeneration
Bibliographic record
Abstract
Copyright © 2025 A9.com LLC or its affiliates. Published Heritage Branch, Library and Archives Canada. All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations used in critical reviews or scholarly articles.This book is a work of academic and scientific research for Doctoral thesis as Development and Optimization of a Chitosan-Based Nanocomposite Scaffold with Magnesium-Doped Hydroxyapatite and Bioactive Agents for Enhanced Bone Augmentation. While every effort has been made to ensure the accuracy and reliability of the information presented herein, the authors and publisher accept no responsibility for any errors or omissions or for any consequences arising from the use of this material.Bone tissue engineering (BTE) integrates materials science, cellular biology, and biomedical engineering to address the challenges of regenerating bone in critical-sized defects caused by trauma, disease, or surgical resection. Traditional bone grafts face limitations such as donor site morbidity and insufficient vascularization, necessitating advanced biomimetic solutions. This research introduces a novel chitosan-based nanocomposite scaffold doped with magnesium-enriched hydroxyapatite (Mg-HA) and enhanced with bioactive agents—Icariin, Lithium Chloride, and Naringin—to improve bone augmentation. The scaffold aims to mimic the extracellular matrix (ECM) of bone, providing mechanical support, osteoinductivity, osteoconductivity, and vascularization. By leveraging advanced fabrication techniques and proposing targeted enhancements, this study seeks to increase the quality and quantity of regenerated bone, offering a sustainable alternative to conventional grafts. The research will validate the scaffold’s efficacy through in vitro and in vivo studies, with the ultimate goal of advancing clinical translation for maxillofacial and orthopedic applications. The primary aim of this research is to develop and optimize a chitosan-based nanocomposite scaffold incorporating magnesium-doped hydroxyapatite (Mg-HA) and bioactive molecules (Icariin, Lithium Chloride, and Naringin) to enhance bone augmentation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".