ArticleRoyal Society open science2020
Correlating
Article in Royal Society open science, 2020. 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.
- Advancements in Functional Polymeric Scaffolds for Scar-Free Skin Regeneration.Polymer science & technology (Washington, D.C.) · 2026Review
- Biomaterial Strategies for Three-Dimensional Bioprinting and Drug Delivery Application.Materials (Basel, Switzerland) · 2026Review
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- Bioengineered Constructs as a Tissue Engineering-Based Therapy for Volumetric Muscle Loss.Tissue engineering. Part B, Reviews · 2026Review
- Identification of Biomarker in Kidney Stone Disease by Integrating Transcriptomics and Olink Proteomics: A Case-Control Study.Journal of inflammation research · 2026Article
- Cell condensation initiates organogenesis: the role of actin dynamics in supracellular self-organizing process.Cell & bioscience · 2025Review
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
- Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters.RSC advances · 2024Article
- Harnessing the power of machine learning into tissue engineering: current progress and future prospects.Burns & trauma · 2024Review
- Development of Bioactive Scaffolds for Orthopedic Applications by Designing Additively Manufactured Titanium Porous Structures: A Critical Review.Biomimetics (Basel, Switzerland) · 2023Review
- Machine learning and 3D bioprinting.International journal of bioprinting · 2023Article
- Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach.Materials (Basel, Switzerland) · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Artificial Intelligence Aided Design of Tissue Engineering Scaffolds Employing Virtual Tomography and 3D Convolutional Neural Networks.Materials (Basel, Switzerland) · 2021Article
- 3D bioprinting of skin equivalents: Towards functional wound healing models.Journal of tissue engineeringReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell-material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental
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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.