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Record W4308075637 · doi:10.1088/1758-5090/ac9ff4

Bioscaffold developed with decellularized human amniotic membrane seeded with mesenchymal stromal cells: assessment of efficacy and safety profiles in a second-degree burn preclinical model

2022· article· en· W4308075637 on OpenAlexaff
Liliana Ivet Sous Naasani, Luiza Pretto, Carla Zanatelli, Thaís Casagrande Paim, Aline Francielle Damo Souza, Pablo Fagundes Pase, Marilda da Cruz Fernandes, Jean Sévigny, Márcia Rosângela Wink

Bibliographic record

VenueBiofabrication · 2022
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversité Laval
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsDecellularizationMesenchymal stem cellBiomedical engineeringStromal cellMaterials scienceRegenerative medicineMembraneThird-Degree BurnAmnionMedicineTissue engineeringPathologySurgeryCell biologyChemistryStem cellFetusBiologyPregnancy

Abstract

fetched live from OpenAlex

Abstract Therapies to deep burn injuries remain a global challenge. Human amniotic membrane (hAM) is a biomaterial that has been increasingly explored by the field of regenerative medicine. A decellularized hAM (DhAM) can be used as scaffold for mesenchymal stromal cells (MSCs) to grow without the loss of their stemness potential, allowing its application as cell therapy for wound healing. In this work, we associated DhAM with adipose-derived MSCs (DhAM + AD-MSCs), as a therapy strategy for second-degree burns in a preclinical model. Animals with induced second-degree burns were divided into four groups: control, which consists of a non-adherent gauze; a synthetic commercial dressing as the positive control (Control+); DhAM; and DhAM plus rat AD-MSCs (DhAM + AD-MSCs), followed by detailed and long term analysis (5 weeks). The macroscopical analysis showed the healing improvement in the wound area after the DhAM + AD-MSC treatment. Histological analysis also showed no alteration in the animal organs and a regular epithelial progression in comparison to the control. This observation was also confirmed by the analysis of suprabasal layers in the neoepidermis with CK10, showing a stratified and differentiated epithelium, when compared to Control and Control+. A strong CD73 (ecto-5′-nucleotidase) labeling was observed in the first 2 weeks postburn in dermis and epidermis. The expression in dermis was stronger in the second week in the middle of the wound, when comparing the Control+ with DhAM + AD-MSCs ( p = 0.0238). In the epidermis the expression of CD73 was increased in all regions when compared to the control. This data suggests the involvement of this protein on wound healing. A low CD11b labeling was observed in DhAM + AD-MSCs treatment group mainly in the last treatment week, in comparison to Control and Control+ ( p < 0.0001), which indicates a reduction in the inflammatory process. MSCs through CD73 can release high concentrations of adenosine, an immunosuppressive molecule, suggesting that this could be the mechanism by which the inflammation was better modulated in the DhAM + AD-MSCs group. The results obtained with this preclinical model confirm the effectiveness and safety of this low-cost and highly available dressing for future clinical application as a therapy for burn treatments.

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)0.000
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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.310
Teacher spread0.267 · 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".

Quick stats

Citations19
Published2022
Admission routes1
Has abstractyes

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