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Record W6893175487 · doi:10.5281/zenodo.15436856

Comparing The Role of Mesenchymal Stem Cell Glue (MSCS) With Platelet Rich Fibrin (PRF) Gel in Treating Abrasions

2024· article· en· W6893175487 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPlatelet-rich fibrinFibrinMesenchymal stem cellFibrin glueWound healingScarsPlateletElastinStem cell

Abstract

fetched live from OpenAlex

Abstract Introduction: Abrasions are minor injuries to the body's skin and inner linings that can be seen on any part of the body, but are most commonly found on the forehead, nose tip, cheek, and chin region. Though, facial abrasions are superficial injuries, but it can also lead to unacceptable scars and appearances. Hence, using the autologous medium as a dressing material over these abrasions can substantially reduce the tendency of scar and hyperpigmentation. A blood clot serves as the focal point for healing, with 95% red blood cells and 5% platelets making up a normal blood clot. PRF membranes prevent epithelial cells from migrating away from its surface, stimulate the development of micro vascularization, and protect open wounds. Natural stem cell therapy is the latest advancement in treatment of facial wounds and has shown to increase the level of collagen and elastin in skin and reduce scars. This study aims to compare and evaluate the outcomes of dressing materials over abrasive wounds and their role in soft tissue healing. Purpose: The study aims to evaluate the healing potential of mesenchymal stem cells and platelet rich fibrin in treating skin abrasions. Study Design: A total number of 32 patients requiring intervention in abrasive wounds were randomly divided into two groups: Group A-16 treated with PRF and Group B-16 treated with MSCs. Results: Our study found that both PRF and PRFM induces a dense fibrous matrix and stimulates angiogenesis and epithelialization, leading to the conversion of soluble fibrinogen into insoluble fibrin that polymerizes with thrombin. The PRF membrane consists of a fibrin 3D mesh polymerized structure, platelets, leukocytes, growth factors, and circulating stem cells. Post-operative pain, clinical appearance of wound bed, size of the wound along with H2O2 epithelial test and Vancouver scar scale showed no significant difference (p>0.05). Conclusion: PRF and MSCs have improved soft tissue healing, decreased discomfort, increased wound contraction and epithelization, and decreased scarring of healing wound sites. This is the first time that the efficacy of both dressing materials is compared, but additional clinical studies are needed to compare them. Future studies are needed to better understand the platelet concentrates' capacity for regeneration.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.034
GPT teacher head0.259
Teacher spread0.225 · 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 designNon-randomized trial
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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