ArticleGels (Basel, Switzerland)2025
Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting.
Article in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Smart Bioinks for 4D Bioprinting: Requirements, Design, and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- After the Nozzle: Post-Printing Maturation, Failure Modes, and Use-Point Assessment of Cell-Laden Extrusion-Bioprinted Hydrogel Constructs.Gels (Basel, Switzerland) · 2026Review
- Multiscale biomimetic design for peripheral nerve repair: Beyond passive bridging.Materials today. Bio · 2026Review
- The Next Phase of 3D Bioprinting: AI-Native Systems-A Narrative Review.Journal of functional biomaterials · 2026Review
- From Technological Innovation to Clinical Translation: Progress and Challenges in 3D Bioprinting for the Development of Breast Cancer Bone Metastasis Models.Advanced healthcare materials · 2026Review
- Soft Robotics and Advanced Technologies for Minimally Invasive Bioprinting: The Future of Internal Organ Repair.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Living Tissues by Design: The Rise of Hybrid Models in Biofabrication.Journal of functional biomaterials · 2026Review
- Unconventional bioprinting modalities for advanced tissue biofabrication.Biomaterials · 2026Review
- A Formal Optimization-Oriented Design Framework for Predictive Extrusion-Based 3D Bioprinting.Biomimetics (Basel, Switzerland) · 2026Article
- Quantitative Assessment of Hydrogel Printability in Extrusion Bioprinting.Gels (Basel, Switzerland) · 2026Review
- Artificial intelligence-assisted smart hydrogel bioinks in 3D bioprinting: design, optimization, and construct validation for functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- 3D Bioprinting and Artificial Intelligence for Tumor Microenvironment Modeling: A Scoping Review of Models, Methods, and Integration Pathways.Molecular pharmaceutics · 2025Article
- Data-Driven Optimization of Bioink Formulations for Extrusion-Based Bioprinting: A Predictive Modeling Approach.Journal of manufacturing science and engineering · 2025Article
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2-5.25%), gelatin (2-5.25%), and TEMPO-Nano fibrillated cellulose (0.5-1%) at shear rates from 0.1 to 100 s
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What OpenQuestion holds
Registered trials
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.