PREDICTION OF PATHOLOGICAL REGENERATION OF INTEGUMENTARY TISSUES AFTER SURGICAL INTERVENTIONS (LITERATURE REVIEW)

Introduction. In the clinical practice of a surgeon, an important place is occupied by the process of restoration of integumentary tissues after surgery. Knowledge of the epidemiology of the processes of restoration of covering tissues, risk factors for complications and modern tissue markers for the prediction of pathological regeneration are key to the practice of a modern surgeon. Aim. Summarize scientific data on the general principles of the course of the regeneration of integumentary tissues in the area of postoperative wounds, causes and variants of the course of pathological regeneration of integumentary tissues after surgical interventions, risk factors complicating recovery processes, prognostic markers of complications of regenerative processes. Materials and methods. Bibliosemantic, comparative and system analysis method. The literature review was developed on the basis of the analysis of modern literature, the results of randomized studies and meta-analyses devoted to the study of the problem of epidemiology of wound surface regeneration processes and tissue biomarkers for the prediction of pathological repair of covering tissues. Results. Large-scale scientific studies of the mechanism of wound regeneration have shown that traditional recommendations for determining the reparative potential of a wound based on measuring the erythrocyte sedimentation rate (ESR) and C-reactive protein in the blood do not provide sufficient reliability of the forecast. Instead of existing scientific evidence, the focus has shifted to cytokines, chemokines, and proteases, which have the greatest potential as biomarkers. Conclusions. The problem of predicting regeneration of covering tissues after surgical interventions remains relevant to this day, as traditional markers of regenerative potential have an insufficient level of reliability. Histological examination of tissue samples with the determination of biomarkers of regeneration has the greatest potential. The following tissue biomarkers are the most studied – β-catenin, c-myc, cathelicidin, keratin 15, ADAM12 (disintegrin and metalloproteinase 12), EGFR (epidermal growth factor receptor), tPA (tissue polypeptide antigen), uPA (urokinase-type plasminogen activator), PAI-1 (plasminogen activator inhibitor-1), TGFβ (transforming growth factor β) I, II, III.

Authors

Institutions

Publication Details

Journal
Clinical and Preventive Medicine
Published
2024-11-28
DOI
https://doi.org/10.31612/2616-4868.7.2024.18
Primary Topic
Wound Healing and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PREDICTION OF PATHOLOGICAL REGENERATION OF INTEGUMENTARY TISSUES AFTER SURGICAL INTERVENTIONS (LITERATURE REVIEW)

Mariia V. Lohvynenko, Ponomarenko Ov
Clinical and Preventive Medicine
Wound Healing and Treatments
article

PREDICTION OF PATHOLOGICAL REGENERATION OF INTEGUMENTARY TISSUES AFTER SURGICAL INTERVENTIONS (LITERATURE REVIEW)

Mariia V. Lohvynenko, Ponomarenko Ov
article en

Abstract

Introduction. In the clinical practice of a surgeon, an important place is occupied by the process of restoration of integumentary tissues after surgery. Knowledge of the epidemiology of the processes of restoration of covering tissues, risk factors for complications and modern tissue markers for the prediction of pathological regeneration are key to the practice of a modern surgeon. Aim. Summarize scientific data on the general principles of the course of the regeneration of integumentary tissues in the area of postoperative wounds, causes and variants of the course of pathological regeneration of integumentary tissues after surgical interventions, risk factors complicating recovery processes, prognostic markers of complications of regenerative processes. Materials and methods. Bibliosemantic, comparative and system analysis method. The literature review was developed on the basis of the analysis of modern literature, the results of randomized studies and meta-analyses devoted to the study of the problem of epidemiology of wound surface regeneration processes and tissue biomarkers for the prediction of pathological repair of covering tissues. Results. Large-scale scientific studies of the mechanism of wound regeneration have shown that traditional recommendations for determining the reparative potential of a wound based on measuring the erythrocyte sedimentation rate (ESR) and C-reactive protein in the blood do not provide sufficient reliability of the forecast. Instead of existing scientific evidence, the focus has shifted to cytokines, chemokines, and proteases, which have the greatest potential as biomarkers. Conclusions. The problem of predicting regeneration of covering tissues after surgical interventions remains relevant to this day, as traditional markers of regenerative potential have an insufficient level of reliability. Histological examination of tissue samples with the determination of biomarkers of regeneration has the greatest potential. The following tissue biomarkers are the most studied – β-catenin, c-myc, cathelicidin, keratin 15, ADAM12 (disintegrin and metalloproteinase 12), EGFR (epidermal growth factor receptor), tPA (tissue polypeptide antigen), uPA (urokinase-type plasminogen activator), PAI-1 (plasminogen activator inhibitor-1), TGFβ (transforming growth factor β) I, II, III.

Clinical and Preventive Medicine(7)
Zaporizhzhia State Medical and Pharmaceutical University (UA)
Good health and well-being
Openalex Percentile: Top 70%
Wound Healing and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.