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Record W2921269373 · doi:10.1093/jbcr/irz013.411

521 Adipose Derived Stem Cells Populated Matrix Promotes Wound Healing in Mice

2019· article· en· W2921269373 on OpenAlexaff
Diana Forbes, Mohammadreza Pakyari, Ruhangiz T. Kilani, Aziz Ghahary, Reza B. Jalili

Bibliographic record

VenueJournal of Burn Care & Research · 2019
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWound healingMedicineStem cellMesenchymal stem cellRegeneration (biology)Adipose tissueIn vivoMatrix (chemical analysis)SurgeryRegenerative medicinePathologyCell biologyBiologyInternal medicineChemistryBiotechnology

Abstract

fetched live from OpenAlex

Wound repair and regeneration is a multidisciplinary field of research with considerable value to the treatment of deep and large burn injuries. These injuries lack an appropriate tissue scaffold and pro-healing cells making them difficult to heal. An alternative to the often limited autologous skin is a therapy that would restore the essential matrix and cellular components for rapid healing. Over the last decade, mesenchymal stem cells have become the focus of research in regenerative medicine owing to their ability to provide the essential building blocks for skin regeneration. Herein, we utilize a validated method of wound splinting in a delayed-healing murine model to investigate the pro-healing effects of adipose-derived stem cells (ASCs) in a novel dermal matrix in the healing of complex wounds. To ensure ASC survival within the gel matrix, cells were incubated with the matrix for 14 days prior to in-vivo studies. Viability was tested at days 3,7 and 14. With ethics approval, full-thickness 8 mm diameter excisional wounds were created and splinted on the dorsum of genetically diabetic mice. Eighteen animals were randomized into 3 groups: 1) occlusive dressing only (control), 2) gel, 3) gel + ASCs. Wounds were photographed at days 0, 7, 10, 14 and wound area was calculated using Image J Software. Histologic samples were examined for architecture and collagen content. Capillary formation was quantified using immunofluorescence. GFP labelling of ASCs was used to track the fate of these cells within the wound. The gel matrix supported the survival of ASCs. In-vivo testing showed that treatment groups had accelerated epithelialization. Wounds treated with gel + ASCs had a significant reduction in wound size after Day 10 (p<0.001). Histology showed earlier re-epithelialization in both treatment groups. GFP staining showed co-localization of ASCs with capillaries. There was an increased number of capillaries in the wound site for groups treated with ASCs compared to control (p <0.001). ASCs are a viable source of pro-healing cells in deep wounds. The cell populated matrix accelerated wound healing, decreased wound size and increased capillary formation in a delayed-healing murine model. The dermal gel matrix combined with ASCs is a feasible treatment strategy for complex or large burn wounds. The dermal gel matrix component has since been shown to be non-toxic to human cells. The protocol can be modified for human use by using autologous ASCs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.400
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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