ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Next-generation smart polymeric hydrogels for chronic wound management: integrating antimicrobial, immunomodulatory and regenerative strategies.RSC advances · 2026Review
- Next-Generation Cartilage Repair: Clinical Use of Wharton's Jelly MSCs and the Emerging Role of AI-Assisted Bioprinting.Bioengineering (Basel, Switzerland) · 2026Review
- After the Nozzle: Post-Printing Maturation, Failure Modes, and Use-Point Assessment of Cell-Laden Extrusion-Bioprinted Hydrogel Constructs.Gels (Basel, Switzerland) · 2026Review
- Bioprinting Organs-Science or Fiction?-A Review From Students to Students.Advanced healthcare materials · 2026Review
- Additive manufacturing - an antidote to bottlenecks in tissue engineering and regenerative medicine.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Bioprinting revolution: Innovative design of 3D bioactive scaffolds for living organs and transdermal tissues.Bioengineering & translational medicine · 2025Review
- A Critical Review: Gel-Based Edible Inks for 3D Food Printing: Materials, Rheology-Geometry Mapping, and Control.Gels (Basel, Switzerland) · 2025Review
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- Machine Learning in Predicting and Optimizing Polymer Printability for 3D Bioprinting.Polymers · 2025Review
- Traceless Photopolymerization with Non-Pulsed Red Light Enables 3D-Printable Cell-Laden Hydrogels.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- 4D printing polymeric biomaterials for adaptive tissue regeneration.Bioactive materials · 2025Review
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Advances and Challenges in 3D Bioprinted Cancer Models: Opportunities for Personalized Medicine and Tissue Engineering.Polymers · 2025Review
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study.Polymers · 2025Article
- Artificial intelligence-assisted organoid construction in congenital heart disease: current applications and future prospects.Frontiers in bioengineering and biotechnology · 2025Review
- A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface.Journal of nanobiotechnology · 2024Article
- Recent advances and applications of artificial intelligence in 3D bioprinting.Biophysics reviews · 2024Review
- POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles.Molecular pharmaceutics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Hydrogel ink formulations based on rheology additives are becoming increasingly popular as they enable 3-dimensional (3D) printing of non-printable but biologically relevant materials. Despite the widespread use, a generalized understanding of how these hydrogel formulations become printable is still missing, mainly due to their variety and diversity. Employing an interpretable machine learning approach allows the authors to explain the process of rendering printability through bulk rheological indices, with no bias toward the composition of formulations and the type of rheology additives. Based on an extensive library of rheological data and printability scores for 180 different formulations, 13 critical rheological measures that describe the printability of hydrogel formulations, are identified. Using advanced statistical methods, it is demonstrated that even though unique criteria to predict printability on a global scale are highly unlikely, the accretive and collaborative nature of rheological measures provides a qualitative and physically interpretable guideline for designing new printable materials.
Indexed as
Identifiers
What Socratic 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 Socratic graph.