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Enregistrement W4407838659 · doi:10.33137/cpoj.v8i1.43717

WOUND MANAGEMENT, HEALING, AND EARLY PROSTHETIC REHABILITATION: PART 3 - A SCOPING REVIEW OF CHEMICAL BIOMARKERS

2025· review· en· W4407838659 sur OpenAlexvenueaboutno aff
Hannnelore Williams-Reid, Anton Johannesson, Arjan Buis

Notice bibliographique

RevueCanadian Prosthetics & Orthotics Journal · 2025
Typereview
Langueen
DomaineEngineering
ThématiqueProsthetics and Rehabilitation Robotics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAmputationMEDLINECINAHLCochrane LibraryRandomized controlled trialPhysical therapyIntensive care medicineSurgeryPsychological intervention

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Poor post-amputation healing delays prosthetic fitting, adversely affecting mortality, quality of life, and cardiovascular health. Current residual limb assessments are subjective and lack standardized guidelines, emphasizing the need for objective biomarkers to improve healing and prosthesis readiness assessments. OBJECTIVE(S): This review aimed to identify predictive, diagnostic, and indicative chemical biomarkers of healing of the tissues and structures found in the residual limbs of adults with amputation. METHODOLOGY: This scoping review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. Searches using the terms “biomarkers,” “wound healing,” and “amputation” were performed across Web of Science, Ovid Medline, Ovid Embase, Scopus, Cochrane, PubMed, and CINAHL databases. Inclusion criteria were: 1) References to chemical biomarkers and healing; 2) Residuum tissue healing; 3) Repeatable methodology with ethical approval. Included articles were evaluated for quality of evidence (QualSyst tool) and level of evidence (JBI classification). Sources were categorized by study (e.g., randomized controlled trial or bench research), wound (diabetic, amputation, other), and model (human, murine, other) type. Chemical biomarkers repeated across study categories, and quantification methods were reported on. FINDINGS: From 3,306 titles and abstracts screened, 646 underwent full-text review, and 203 met the criteria for data extraction, with 76% classified as strong quality. 38 chemical biomarkers were identified across 4 to 50 sources, with interleukins (predictive, indicative, and diagnostic) and HbA1c (predictive) most prevalent, appearing in 50 and 48 sources, respectively. Other biomarkers included predictive blood markers (e.g., cholesterol, white blood cell counts), indicative growth factors, bacteria presence (predictive), proteins (predictive, indicative, and diagnostic, e.g., matrix metalloproteinases), and cellular markers (indicative and diagnostic, e.g., Ki-67, alpha-smooth muscle actin [α-SMA]). CONCLUSION: Predictive biomarkers identify comorbidities that may hinder healing, aiding in pre-amputation risk assessment for poor recovery. Indicative biomarkers monitor key biological healing processes, such as angiogenesis (the formation of new blood vessels), wound contraction, and inflammation. Diagnostic biomarkers provide direct insights into tissue composition and cellular-level healing. Integrating these biomarkers into post-amputation assessments enables continuous monitoring of the healing process while accounting for comorbidities, enhancing the objectivity of post-surgical healing management and ensuring more effective, personalized rehabilitation strategies. Layman's Abstract Poor healing after amputation can delay prosthetic fitting, negatively impacting health, and quality of life, and increasing the risk of heart problems and death. Currently, the assessment of residual limb health is subjective, with no standardized guidelines, creating a need for more reliable measures. This review explored chemical biomarkers (biological markers like those found in blood or tissue) that can indicate, predict, or diagnose tissue healing in adults with amputation. A scoping review was conducted using multiple databases, following established guidelines. Studies were included if they connected chemical biomarkers to healing, focused on residual limb tissue, and used ethical, repeatable methods. The studies were assessed for quality and classified based on research type, wound type (e.g., amputation or diabetic), and model (human or animal). Chemical biomarkers repeated across study categories, and methods used to measure them were reported on. From 3,306 titles and abstracts screened, 646 underwent full-text review, and 203 met the criteria for data extraction, with 76% classified as strong quality. 38 different biomarkers were identified, with two types, interleukins (involved in inflammation) and a blood sugar control marker (predicting healing), being the most common. Other biomarkers included blood tests (cholesterol, white blood cell counts) and bacteria levels that predict healing, growth factors that indicate healing progress, and markers that diagnose tissue changes at a cellular level. Biomarkers that predict healing can identify issues like infections or poor nutrition that might slow healing, useful for assessing non-healing risks before amputation. Markers that indicate healing show how the healing process is progressing by tracking changes like decreases in inflammation or increases in tissue growth. Diagnostic biomarkers provide detailed information about the healing tissue at a cellular level. Using a range of these biomarkers helps track every stage of healing and considers factors like other health conditions, leading to a more accurate way to manage recovery after amputation. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/43717/33685 How To Cite: Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 Corresponding Author: Professor Arjan Buis, PhDDepartment of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, Scotland.E-Mail: arjan.buis@strath.ac.ukORCID ID: https://orcid.org/0000-0003-3947-293X

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,332
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,279
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission2
Résumé présentoui

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