Developmental Biology-Based 3D Bioprinting: An Off-the-Shelf Strategy to Induce Tissue Regeneration

再生(生物学) 3D生物打印 现成的 发育生物学 组织工程 细胞生物学 生物 生物医学工程 医学 工程类 制造工程
作者
Juhi Chakraborty,Sourabh Ghosh
出处
期刊:ACS Biomaterials Science & Engineering [American Chemical Society]
标识
DOI:10.1021/acsbiomaterials.5c00213
摘要

The inability of simplistic tissue engineering approaches to produce cell-based treatments has led to a growing understanding of the necessity to recapitulate the natural mechanisms that govern cell fate and differentiation. Converging developments in developmental biology are poised to transform tissue engineering, a hitherto primarily empirical field, into a rigorous discipline founded on widely recognized engineering principles of quality by design. Lately, the tissue engineering research community has experienced a paradigm shift to reconsider research directions in the field to address challenges in clinical translation. This resulted in the need to rely on the in vivo tissue development processes to utilize cells' innate, evolutionarily programmed ability to self-organize into native tissue-like structures, which may increase the chances of clinical success. The present review emphasizes the potential directions for tissue regeneration that combine off-the-shelf strategies, namely, 3D bioprinting and organoids, following the developmental biology route. The most promising approach in next-generation tissue engineering would be to recapitulate it after gaining a thorough understanding of the embryonic level. Modern developmental re-engineering techniques focus on mimicking the embryonic stages of tissue development rather than concentrating on adult tissue traits. One significant step in this direction is the regulation of many signaling pathways by combining developmental re-engineering with 3D bioprinting. This can aid in bridging the gap between the two disciplines, which may help in the fabrication of mini-models for transplantation or the development of an organ-on-chip platform as a drug screening platform.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
aajhajkahna应助ks采纳,获得10
刚刚
天天快乐应助正义的伙伴采纳,获得10
刚刚
科研通AI6.2应助小大林采纳,获得10
刚刚
1秒前
小杨发布了新的文献求助10
1秒前
淡淡新竹完成签到,获得积分10
1秒前
徐国发发布了新的文献求助10
3秒前
隐形曼青应助Ytwo采纳,获得10
3秒前
英勇水云发布了新的文献求助10
3秒前
舒心睿渊发布了新的文献求助10
3秒前
4秒前
徐华佳发布了新的文献求助10
4秒前
秀丽的无施完成签到,获得积分10
4秒前
杨晓钢发布了新的文献求助10
4秒前
楊書銘发布了新的文献求助10
5秒前
领导范儿应助zyj采纳,获得10
5秒前
5秒前
从雪发布了新的文献求助20
5秒前
Kobe完成签到,获得积分10
5秒前
徐徐完成签到,获得积分10
6秒前
JAYGOD发布了新的文献求助10
6秒前
sunny完成签到 ,获得积分10
6秒前
6秒前
yym发布了新的文献求助10
6秒前
7秒前
7秒前
7秒前
8秒前
Sakura发布了新的文献求助10
8秒前
Yuqi发布了新的文献求助10
8秒前
maizencrna完成签到,获得积分10
8秒前
8秒前
xgwfr完成签到,获得积分10
9秒前
leaf完成签到,获得积分10
9秒前
9秒前
9秒前
9秒前
李爱国应助徐华佳采纳,获得10
9秒前
gwy完成签到,获得积分10
9秒前
Tina完成签到,获得积分10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
Synthesis of P-Chiral Phosphine Ligands and Their Applications in Asymmetric Catalysis 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7622703
求助须知:如何正确求助?哪些是违规求助? 9198136
关于积分的说明 19717446
捐赠科研通 7194146
什么是DOI,文献DOI怎么找? 3273075
关于科研通互助平台的介绍 2435430
邀请新用户注册赠送积分活动 2268515