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Record W4285084826 · doi:10.1002/lsm.23579

Relative perfusion index: An objective, quantitative and noninvasive method for evaluating the severity of keloids

2022· article· en· W4285084826 on OpenAlexaboutno aff
Shuo Li, Mingzi Zhang, Xiao Long, Xiaojun Wang

Bibliographic record

VenueLasers in Surgery and Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsKeloidMedicinePerfusionVascular endothelial growth factorHypoxia (environmental)Visual analogue scalePathologyInternal medicineVEGF receptorsSurgeryChemistry

Abstract

fetched live from OpenAlex

Abstract Background Keloids are the result of abnormal wound healing, and they differ from the normal skin of the patient in the level of blood perfusion and the degrees of inflammation, hypoxia, regeneration of vessels, and expression of sensory receptors. However, there is no objective assessment method to accurately characterize the severity of keloids. Objectives The purpose of this study was to evaluate the perfusion levels of keloids and the expression levels of various internal cytokines, including hypoxia‐induced factor‐1α (HIF‐1α), vascular endothelial growth factor (VEGF), interleukin‐17 (IL‐17), HT 2A receptor subtype (5‐HT 2A R), and H1R, in keloids and nonadjacent normal skin and to propose a laser speckle contrast imaging (LSCI)‐based relative perfusion index (RPI), through which keloids can be divided into five grades to objectively characterize their severity. Methods This population‐based cross‐sectional study included 70 untreated keloid patients who each had only one keloid on the chest. LSCI was used to measure the area of each patient's keloid () and the perfusion level of each patient's keloid () and normal skin (). The Vancouver Scar Scale (VSS) and Visual Analog Scale (VAS) for pain and pruritus were also used to assess each keloid. Immunohistochemistry and Western blot were used to detect the expression levels of various internal cytokines in keloids and normal skin. We compared the perfusion and expression levels of intrinsic cytokines between keloids and normal skin. We established the RPI to grade the severity of keloids and applied different methods to test the utility of the RPI. Results The mean perfusion level of keloids was significantly higher than that of normal skin ( p < 0.001). The expression levels of HIF‐1α, VEGF, IL‐17, 5‐HT 2A R, and H1R in keloids were significantly higher than those in normal skin ( p < 0.05). RPI was defined as: The severity of keloids could be divided into five grades based on RPI. The RPI had a higher correlation with the pain‐VAS, pruritus‐VAS, and the expression levels of internal cytokines in keloids than blood perfusion levels and the VSS. T‐SNE (t‐distributed stochastic neighbor embedding) was also used to verify the clinical discriminatory abilities of this RPI model. Conclusions The proposed RPI based on LSCI showed the highest accuracy, unlike the VSS and assessment of perfusion, and can be utilized as a reliable, objective, quantitative, and noninvasive tool to evaluate the severity of keloids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.106
GPT teacher head0.444
Teacher spread0.338 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2022
Admission routes1
Has abstractyes

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