Article3D printing and additive manufacturing2023
Three-Dimensional Printed Cellulose for Wound Dressing Applications.
Article in 3D printing and additive manufacturing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Natural Bioactive-Based Advanced Wound Dressings for Diabetic Wound Healing: A Systematic Review of Emerging Biomaterial Platforms.International journal of nanomedicine · 2026Pooled it
- Magnetic Cellulose Nanocrystal Composites: Synthesis, Properties, Applications, and Opportunities.Nanomaterials (Basel, Switzerland) · 2026Review
- Biopolymer-Based Electrospun Nanofibers for Wound Healing, Regeneration, and Therapeutics.Materials (Basel, Switzerland) · 2026Review
- Advances in 3D Bioprinting: Materials, Processes, and Emerging Applications.Micromachines · 2026Review
- Herbal bioactive-loaded biopolymeric formulations for wound healing applications.RSC advances · 2025Review
- Review
- Effect of Diabetes on Wound Healing: A Bibliometrics and Visual Analysis.Journal of multidisciplinary healthcare · 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
9 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wounds are skin tissue damage due to trauma. Many factors inhibit the wound healing phase (hemostasis, inflammation, proliferation, and alteration), such as oxygenation, contamination/infection, age, effects of injury, sex hormones, stress, diabetes, obesity, drugs, alcoholism, smoking, nutrition, hemostasis, debridement, and closing time. Cellulose is the most abundant biopolymer in nature which is promising as the main matrix of wound dressings because of its good structure and mechanical stability, moisturizes the area around the wound, absorbs excess exudate, can form elastic gels with the characteristics of bio-responsiveness, biocompatibility, low toxicity, biodegradability, and structural similarity with the extracellular matrix (ECM). The addition of active ingredients as a model drug helps accelerate wound healing through antimicrobial and antioxidant mechanisms. Three-dimensional (3D) bioprinting technology can print cellulose as a bioink to produce wound dressings with complex structures mimicking ECM. The 3D printed cellulose-based wound dressings are a promising application in modern wound care. This article reviews the use of 3D printed cellulose as an ideal wound dressing and their properties, including mechanical properties, permeability aspect, absorption ability, ability to retain and provide moisture, biodegradation, antimicrobial property, and biocompatibility. The applications of 3D printed cellulose in the management of chronic wounds, burns, and painful wounds are also discussed.
Indexed as
Identifiers
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.