Evidence map›Paper›PMID 40384373›Full record

ArticleCell proliferation2025

Ellagic Acid-Loaded sEVs Encapsulated in GelMA Hydrogel Accelerate Diabetic Wound Healing by Activating EGFR on Skin Repair Cells.

Lige Tian, Zihao Wang, Shengqiu Chen, Kailu Guo, Yaying Hao, Liqian Ma, Kui Ma, Junli Chen, Xi Liu, Linlin Li and 2 more

Abstract read
In one paragraph

Article in Cell proliferation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Lige TianCollege of Graduate, Tianjin Medical University, Tianjin, China.
Zihao WangMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Shengqiu ChenMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Kailu GuoCollege of Graduate, Tianjin Medical University, Tianjin, China.
Yaying HaoMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Liqian MaMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Kui MaMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Junli ChenMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Xi LiuMedical Innovation Research Department, PLA General Hospital, Beijing, China.ORCID https://orcid.org/0000-0002-7968-0461
Linlin LiBeijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, China.
Xiaobing FuMedical Innovation Research Department, PLA General Hospital, Beijing, China.
Cuiping ZhangMedical Innovation Research Department, PLA General Hospital, Beijing, China.ORCID https://orcid.org/0000-0003-0320-6226

Funding

National Key Research and Development Program of China 2022YFA1104303National Natural Science Foundation of China 22205260National Natural Science Foundation of China 82172211National Natural Science Foundation of China 92268206National Natural Science Foundation of China 92468303Natural Science Foundation of Beijing Municipality 7242129
6 · The paper itself

Abstract

Delayed diabetic wound healing is partially attributed to the functional disorder of skin repair cells caused by high glucose (HG). Small extracellular vehicles (sEVs) loaded with small-molecule drugs represent a highly promising therapeutic strategy. This study aims to evaluate the therapeutic efficacy of ellagic acid-encapsulated small extracellular vesicles (EA-sEVs) in diabetic wound regeneration and to unravel related mechanisms. Cytotoxicity tests of ellagic acid (EA) as liposomal small molecules (LSMs) were performed with the CCK8 assay. EA was incorporated into sEVs obtained from chorionic plate-mesenchymal stem cells (CP-MSCs) to construct EA-engineered sEVs. The protective effects of EA-sEVs on human dermal fibroblasts (HDFs) and human epidermal keratinocytes (HEKs) induced by high glucose (HG) were assessed through the evaluation of their proliferative, migrative and differentiative capabilities. Furthermore, to illustrate the underlying mechanism, the specific biological targets of EA were predicted and confirmed. Finally, EA-sEVs were encapsulated in GelMA hydrogel for investigating the pro-healing effects on diabetic wounds. EA was harmless to cell viability, increasing the possibility and safety of drug development. EA-engineered sEVs were fabricated by loading EA in sEVs. In vitro, EA-sEVs promoted the proliferation, migration, and transdifferentiation of HG-HDFs and the proliferation and migration of HG-HEKs. Mechanism analysis elucidated that epidermal growth factor receptor (EGFR) was the specific biological target of EA. EA interacting with EGFR was responsible for the functional improvement of HG-HDFs and HG-HEKs. In vivo, EA-sEVs encapsulated in GelMA promoted the healing of diabetic wounds by improving re-epithelialisation, collagen formation and the expression of EGFR. Gel-EA-sEVs promoted diabetic wound healing by improving biological functions of HDFs and HEKs. EGFR was first identified as the specific biological target of EA and was responsible for the functional improvement of HG-HDFs and HG-HEKs by Gel-EA-sEVs. Hence, Gel-EA-sEVs can serve as a new promising active dressing for diabetic wound treatment.

Indexed as

Diabetes Mellitus, ExperimentalEllagic AcidHydrogelsSkinWound HealingAnimalsCell MovementCell ProliferationErbB ReceptorsFibroblastsHumansKeratinocytesMaleMesenchymal Stem CellsMiceEGFR protein, humanEllagic AcidErbB ReceptorsHydrogelsdiabetic wound healingellagic acidfibroblastsGelMA hydrogelkeratinocytessmall extracellular vesicles

Identifiers

PMID40384373
PMCPMC12508689

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Registered trials

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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.