ReviewRSC medicinal chemistry2024
Current and promising applications of MOF composites in the healing of diabetes wounds.
Review in RSC medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Bridging traditional Chinese medicine theory with advanced MOFs delivery for synergistic drug-resistant infected-chronic wound therapy.Materials today. Bio · 2026Article
- Advancing biomedical technology through multifunctional porphyrin-based MOFs: design principles, applications, and biosafety evaluations.Materials today. Bio · 2026Review
- Cu-based metal-organic frameworks: synthesis, biomedical applications, and beyond.Discover nano · 2026Review
- Intelligent hydrogel-based dressings for treatment of chronic diabetic wounds.World journal of diabetes · 2025Review
- The Trilogy of Skin Regeneration via Metal-Organic Frameworks Nanomedicine: Precision Management of Refractory Wounds, Pathological Scarring, and Hair Follicle Reactivation.International journal of nanomedicine · 2025Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes mellitus is an exponentially growing chronic metabolic disease identified by prolonged hyperglycemia that leads to a plethora of health problems. It is well established that the skin of diabetic patients is more prone to injury, and hence, wound healing is an utmost critical restorative process for injured skin and other tissues. Diabetes patients have problems with wound healing at all stages, which ultimately results in delays in the healing process. Therefore, it is vital to find new medications or techniques to hasten the healing of wounds. Metal-organic frameworks (MOFs), an assorted class of porous hybrid materials comprising metal ions coordinated to organic ligands, can display great potential in accelerating diabetic wound healing due to their good physicochemical properties. The release of metal ions during the degradation of MOFs can promote the differentiation of fibroblasts into myofibroblasts and subsequently angiogenesis. Secondly, similar to enzyme-like active substances, they can eliminate reactive oxygen species (ROS) overproduction (secondary to the bio-load of wound bacteria), which is conducive to accelerating diabetic wound healing. Subsequently, MOFs can support the slow release of drugs (molecular or gas therapeutics) in diabetic wounds and promote wound healing by regulating pathological signaling pathways in the wound microenvironment or inhibiting the expression of inflammatory factors. In addition, the combination of photodynamic and photothermal therapies using photo-stimulated porphyrin-based MOF nanosystems has brought up a new idea for treating complicated diabetic wound microenvironments. In this review, recent advances affecting diabetic wound healing, current means of rapid diabetic wound healing, and the limitations of traditional approaches are discussed. Further, the diabetic wound healing applications of MOFs have been discussed followed by the future challenges and directions of MOF materials in diabetic wound healing.
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.