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头皮浅表毛囊炎管理的实用算法:10年临床及皮肤镜检查经验

A Practical Algorithm for the Management of Superficial Folliculitis of the Scalp: 10 Years of Clinical and Dermoscopy Experience.

作者信息

Starace Michela, Yamagata João Paulo, Cortez de Almeida Rita Fernanda, Frattini Simone, Bruni Francesca, Alessandrini Aurora, Iorizzo Matilde, Melo Daniel Fernandes, Neri Iria, Piraccini Bianca Maria

机构信息

Dermatology - IRCCS Azianda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.

出版信息

Dermatol Pract Concept. 2023 Jul 1;13(3):e2023131. doi: 10.5826/dpc.1303a131.

Abstract

INTRODUCTION

Superficial folliculitis of the scalp (SFS) is a common complaint in clinical practice, and initial presentation may be difficult to differentiate as they may appear very similar to each other.

OBJECTIVES

The aim of this thesis is to describe the pathologies that occur clinically as folliculitis of the scalp, identify their causes and characteristics and create a standardized classification.

METHODS

This is a retrospective clinical, dermoscopic and histopathological study over 10 years of dermatologic consultations. Only individuals with a confirmed diagnosis of SFS (updated diagnostic criteria or biopsy) were included.

RESULTS

In this review, we describe the various clinical features of different causes of SFS in ninety-nine cases and divided into infectious due to fungus, bacteria, or virus and inflammatory conditions such as rosacea, acneiform eruption and Ofuji syndrome.

CONCLUSIONS

The clinician must differentiate SFS from other underlying scarring disorders to prevent poorer outcomes. We created an algorithm to help the clinician reach a proper diagnosis.

摘要

引言

头皮浅表性毛囊炎(SFS)是临床实践中常见的病症,其初始表现可能难以区分,因为它们彼此看起来可能非常相似。

目的

本论文的目的是描述临床上表现为头皮毛囊炎的病理情况,确定其病因和特征,并创建一个标准化分类。

方法

这是一项对10年皮肤科会诊病例进行的回顾性临床、皮肤镜和组织病理学研究。仅纳入确诊为SFS(更新的诊断标准或活检)的个体。

结果

在本综述中,我们描述了99例不同病因的SFS的各种临床特征,并将其分为由真菌、细菌或病毒引起的感染性疾病以及诸如玫瑰痤疮、痤疮样皮疹和Ofuji综合征等炎症性疾病。

结论

临床医生必须将SFS与其他潜在的瘢痕性疾病区分开来,以防止出现更差的结果。我们创建了一种算法来帮助临床医生做出正确的诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4b5/10412046/29045132b394/dp1303a131g001.jpg

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