ArticleActa biomaterialia2026
Human cell-derived dermal-specific interwoven extracellular matrix for diabetic wound healing.
Article in Acta biomaterialia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Zinc ion-releasing immunomodulatory hydrogels for enhanced wound healing.Regenerative biomaterials · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Diabetic wounds present significant clinical challenges due to their multifactorial and complex pathological characteristics. Although commercially available allogenic and xenogeneic tissue-derived acellular dermal matrices have been employed in chronic wound management, their utility is constrained by limited donor availability, potential immunogenicity, mismatched biomechanical properties, and batch-to-batch variability. Consequently, these limitations often result in incomplete wound closure and inconsistent therapeutic outcomes across patient populations. To overcome these challenges, we designed a completely biological and human dermis-specific acellular interwoven extracellular matrix (iECM). To closely replicate the ECM complexity and collagen bundle architecture of native dermis, a soft lithography-based approach was employed to guide human dermal fibroblast organization, enabling subsequent ECM deposition in an interwoven pattern. Post decellularization, the iECM retained both the compositional and structural features of native dermis, with a bulk elastic modulus (4 MPa) comparable to human dermal tissue (3-18 MPa). Upon implantation in a full-thickness diabetic rat wound model, iECM significantly accelerated healing rates by 80% compared to non-structured ECM by promoting granulation tissue formation, enhancing angiogenesis, accelerating resolution of inflammation, supporting uniform collagen deposition, and enabling complete re-epithelialization. By mimicking the architectural, compositional, and mechanical properties of native dermis, the highly biomimetic iECM presents a promising strategy for diabetic wound management. STATEMENT OF SIGNIFICANCE: Chronic wounds, like diabetic wounds, are a clinical challenge due to complex pathophysiology, impaired healing mechanisms, and limited effect of numerous treatment approaches like skin substitutes and wound dressings. This research work presents a completely biological, human cell-derived acellular dermal matrix fabricated using soft lithography, to mimic the interwoven architecture and mechanical properties of native extracellular matrix. Unlike traditional dermal matrices, the bioengineered iECM exhibits biomimicry, robustness, and regenerative potential, promoting collagen organization, angiogenesis, and re-epithelialization in chronic diabetic wound healing. This work introduces a bioinspired matrix, offering a promising advancement with the potential to transform chronic wound management.
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.