Evidence map›Paper›PMID 40536605›Full record

Article3D printing in medicine2025

Early stage prediction of bone regeneration using FEA and cell differentiation algorithms with 3D-printed PLA and PCL scaffolds: modeling and application to dorsal double-plating in distal radius fractures.

Hsuan Chih Liu, Ya-Han Chan, Shao-Fu Huang, Wei-Che Tsai, Yen Cheng, Chun-Li Lin

Abstract read
In one paragraph

Article in 3D printing in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hsuan Chih LiuChi-mei Medical Center, Department of Orthopedic, Liouying, Tainan, Taiwan.
Ya-Han ChanDepartment of Biomedical Engineering, National Yang Ming Chaio Tung University, Hsinchu, Taiwan.
Shao-Fu HuangDepartment of Biomedical Engineering, National Yang Ming Chaio Tung University, Hsinchu, Taiwan.
Wei-Che TsaiDepartment of Biomedical Engineering, National Yang Ming Chaio Tung University, Hsinchu, Taiwan.
Yen ChengDepartment of Biomedical Engineering, National Yang Ming Chaio Tung University, Hsinchu, Taiwan.
Chun-Li LinDepartment of Biomedical Engineering, National Yang Ming Chaio Tung University, Hsinchu, Taiwan. cllin2@nycu.edu.tw.

Funding

National Science and Technology Council 113-2327-B-A49 -001
6 · The paper itself

Abstract

This study introduces an advanced framework that integrates biphasic cell differentiation bone remodeling theory with finite element (FE) analysis and multi-remodeling simulation to evaluate the performance of 3D-printed biodegradable scaffolds for bone defect repair. The program incorporates a time-dependent cell differentiation stimulus (S), accounting for fluid-phase shear stress and solid-phase shear strain, to dynamically predict bone cell behavior. The study focuses on polylactic acid (PLA) and polycaprolactone (PCL) scaffolds with diamond (DU) and random (YM) lattice designs, applied to a dorsal double-plating (DDP) fixation model for distal radius fractures. Material testing reveals that PLA provides higher rigidity and strength, while PCL offers superior ductility. Mechanical strength tests confirm the superior performance of DU lattice structures under compression, shear, and torsion forces. The bone remodeling program, applied to 36 model combinations of fracture gaps, materials, and lattice designs, computes the total percentage of cell differentiation (TPCD), identifying scaffold material as the key factor, with PLA significantly enhancing TPCD values. Biomechanical analysis after 50 remodeling iterations in a 5.4 mm fracture gap shows that the PLA + DU scaffold reduces displacement by 35%/39%/75%, bone stress by 19%/16%/67%, and fixation plate stress by 77%/66%/93% under axial/bending/torsion loads, respectively, compared to the PCL + YM scaffold. This study highlights the critical role of dynamic remodeling programs in optimizing scaffold material properties and lattice architectures, establishing a robust platform for patient-specific bone repair solutions in regenerative medicine.

Indexed as

3D printingBiomechanicalBone remodelingLatticePLA/PCLScaffold

Identifiers

PMID40536605
PMCPMC12177956

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.