Poly(propylene fumarate) Composite Scaffolds for Bone Tissue Engineering: Innovation in Fabrication Techniques and Artificial Intelligence Integration

Polymers, Journal Year: 2025, Volume and Issue: 17(9), P. 1212 - 1212

Published: April 28, 2025

Over the past three decades, biodegradable polymer known as poly(propylene fumarate) (PPF) has been subject of numerous research due to its unique properties. Its biocompatibility and controllable mechanical properties have encouraged scientists manufacture produce a wide range PPF-based materials for biomedical purposes. Additionally, ability tailor degradation rate scaffold material match new bone tissue formation is particularly relevant in engineering, where synchronized regeneration are critical effective healing. This review thoroughly summarizes advancements different approaches PPF composite preparation engineering. challenges faced by each approach, such biocompatibility, degradation, features, crosslinking, were emphasized, noteworthy benefits most pertinent synthesis strategies highlighted. Furthermore, synergistic outcome between engineering artificial intelligence (AI) was addressed, along with advantages brought implication machine learning (ML) well revolutionary impact on regenerative medicines. Future advances could be facilitated enormous potential individualized successful treatments that arise from combination intelligence. By assessing patient's reaction certain drug choosing best course action depending genetic clinical characteristics, AI can also assist treatment illnesses. used discovery, target identification, trial design, predicting safety effectiveness novel medications. Still, there ethical issues including data protection requirement reliable management systems. adoption healthcare sector expensive, involving staff facility investments training professionals application.

Language: Английский

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Computational Design of Peptides for Biomaterials Applications

ACS Applied Bio Materials, Journal Year: 2023, Volume and Issue: 7(2), P. 617 - 625

Published: March 27, 2023

Computer-aided molecular design and protein engineering emerge as promising active subjects in bioengineering biotechnological applications. On one hand, due to the advancing computing power past decade, modeling toolkits force fields have been put use for accurate multiscale of biomolecules including lipid, protein, carbohydrate, nucleic acids. other machine learning emerges a revolutionary data analysis tool that promises leverage physicochemical properties structural information obtained from order build quantitative structure–function relationships. We review recent computational works utilize state-of-the-art methods engineer peptides proteins various emerging biomedical, antimicrobial, antifreeze also discuss challenges possible future directions toward developing roadmap efficient biomolecular engineering.

Language: Английский

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Application of Digital Methods in Polymer Science and Engineering

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(8)

Published: Oct. 27, 2023

Abstract The development of new polymer materials is an emerging field for more than 100 years. However, it currently facing major challenges and the application digital methods can help to develop processes, discover and, thus, contribute today future. Though, in science very limited, when compared other classes such as small molecules or inorganic high‐performance materials. Nevertheless, there are already first, promising approaches. current review article focuses on these different aspects research including design, synthesis, characterization. Furthermore, discovery engineering highlighted detail showing broad range potential applications science. Finally, future possibilities opportunities derived from state‐of‐the‐art perspectives a evolution provided.

Language: Английский

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Novel deep recurrent neural structure with Bayesian distributed backpropagation for biomaterial model involving ferro–copper/blood nanofluids

The European Physical Journal Plus, Journal Year: 2024, Volume and Issue: 139(1)

Published: Jan. 11, 2024

Language: Английский

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Mapping Biomaterial Complexity by Machine Learning

Tissue Engineering Part A, Journal Year: 2024, Volume and Issue: 30(19-20), P. 662 - 680

Published: Aug. 13, 2024

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design methods is largely inefficient for discovery complex biomaterials. More recently, high-throughput experimentation coupled with machine learning has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As result, we now opportunity strategically utilize all available experiments train efficacious models map behavior biomaterials discovery. Herein, discuss necessary shift data-driven determination as highlight how leveraged in identifying physicochemical cues tissue engineering, gene delivery, drug protein stabilization, antifouling materials. We also data-mining approaches are biomaterial functions reduce load on experimental faster Ultimately, harnessing prowess will lead accelerated development optimal designs.

Language: Английский

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