ReviewTissue engineering. Part B, Reviews2026
Bioengineered Constructs as a Tissue Engineering-Based Therapy for Volumetric Muscle Loss.
Review in Tissue engineering. Part B, Reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Engineered decellularized muscle extracellular matrix fabrics enable functional volumetric muscle loss repair.Bioactive materials · 2026Article
- Woven dECM yarn scaffolds enable volumetric muscle loss regeneration via immunomodulation.Nature communications · 2026Article
- Neuromuscular Mechanisms and Oxidative Stress in Skeletal Muscle Atrophy: Emerging Stem Cell and Gene-Based Therapeutic Strategies.Muscles (Basel, Switzerland) · 2026Review
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
Severe skeletal muscle injuries involving substantial tissue loss can significantly impair muscle strength and functionality, reducing the quality of life for affected individuals. Such injuries, termed volumetric muscle loss, require extensive clinical intervention, as the body's innate healing mechanisms are insufficient to regenerate functional muscle. The current standard of care primarily involves autologous muscle tissue transfer, with some consideration of acellular synthetic constructs. However, both approaches have limited therapeutic efficacy, presenting challenges such as donor-site morbidity, infection risks, and suboptimal functional recovery. Over the past decade, skeletal muscle tissue engineering (SMTE) has emerged as a promising strategy for regenerating functional muscle through bioengineered constructs. Advanced biofabrication techniques, including bioprinting, have further enabled the development of synthetic constructs that closely mimic native muscle architecture. Given these advancements, a critical review of recent therapeutic strategies, their achievements, and limitations is necessary. This review examines the spectrum of bioengineered constructs developed from various biomaterials and evaluates their therapeutic potential. Special emphasis is placed on 3D bioprinting strategies and their role in creating physiologically relevant constructs for functional muscle restoration. In addition, the integration of machine learning in optimizing construct design, predicting cellular behavior, and enhancing tissue integration is discussed. The review indicates that despite significant progress in SMTE, key challenges remain, including replicating the complex structural organization of muscle tissue, minimizing fibrosis, and achieving vascularization and innervation to regenerate functional, strengthened muscle. Future research should address these barriers while prioritizing the development of translational, clinically relevant regenerative constructs. In addition, efforts should focus on advancing scalable, construct-based regenerative treatments that are readily available at the point of care and easily managed in surgical settings.
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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.