The serum levels of sE-selectin are increased in patients with bullous pemphigoid or pemphigus vulgaris. Correlation with the number of skin lesions and recovery after corsticosteroid therapy

Pemphigus vulgaris (PV) is an autoimmune disease characterized by the presence of IgG autoantibodies against desmoglein-3. Despite the variety of findings, the chemokine and cytokine profiles that characterize the immune response in the disease are still poorly explored. Thus, 20 PV patients and 20 controls were grouped according to gender, ethnicity, place of residence, and clinical parameters of the disease. Then, the levels of chemokines and of Th1/Th2/Th17/Treg/Th9/Th22-related cytokines were assessed in the serum. PV patients had higher levels of inflammatory Th1/Th17 cytokines (IFN-γ, IL-17, and IL-23), as well as higher levels of CXCL8 and reduced levels of Th1/Th2-related chemokines (IP-10 and CCL11). However, no differences in the levels of IL-2, IL-6, TNF-α, IL-1β, IL-4, IL-9, IL-12, TGF-β, IL-33, MCP-1, RANTES, and MIP-1α were found between PV patients and their control counterparts. Furthermore, PV patients with skin lesions had higher serum levels of IL-6 and CXCL8 when compared to PV patients without lesions. Taken together, our findings describe the role of cytokines and chemokines associated with Th1/Th17 immune response in PV patients. Finally, these data are important for better understanding of the immune aspects that control disease outcome, and they may also provide important information about why patients develop autoantibodies against desmogleins.

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Faculty Opinions recommendation of Serum levels of BAFF are increased in bullous pemphigoid but not in pemphigus vulgaris.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1109179.565177 ◽

2008 ◽

Author(s):

Cassian Sitaru

Keyword(s):

Bullous Pemphigoid ◽

Pemphigus Vulgaris ◽

Serum Levels

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Statistical Study of Patients with pemphigus Vulgaris and Bullous Pemphigoid at Saga Medical School Hospital.

Nishi Nihon Hifuka ◽

10.2336/nishinihonhifu.55.1092 ◽

1993 ◽

Vol 55 (6) ◽

pp. 1092-1095 ◽

Cited By ~ 1

Author(s):

Tatsurou TANAKA ◽

Kiyohisa MOTOKI ◽

Takahisa NISHI ◽

Yutaka NARISAWA ◽

Hiromu KOHDA

Keyword(s):

Medical School ◽

Bullous Pemphigoid ◽

Statistical Study ◽

Pemphigus Vulgaris

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Faculty Opinions recommendation of Accelerating effects of epidermal growth factor on skin lesions of pemphigus vulgaris: a double-blind, randomized, controlled trial.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1065847.518790 ◽

2007 ◽

Author(s):

Aimee Payne

Keyword(s):

Randomized Controlled Trial ◽

Growth Factor ◽

Epidermal Growth Factor ◽

Controlled Trial ◽

Pemphigus Vulgaris ◽

Skin Lesions ◽

Double Blind ◽

Randomized Controlled ◽

Epidermal Growth

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AI-Provided Instant Differential Diagnosis of Pemphigus Vulgaris and Bullous Pemphigoid: Qualitative Study (Preprint)

10.2196/preprints.24845 ◽

2020 ◽

Author(s):

Xiaoyu He ◽

Juan Su ◽

Guangyu Wang ◽

Kang Zhang ◽

Navarini Alexander ◽

...

Keyword(s):

Artificial Intelligence ◽

Early Diagnosis ◽

Image Classification ◽

Bullous Pemphigoid ◽

Skin Diseases ◽

Pemphigus Vulgaris ◽

Classification Model ◽

Clinical Image ◽

Clinical Images ◽

Multimodal Classification

BACKGROUND Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are two rare but severe inflammatory dermatoses. Due to the regional lack of trained dermatologists, many patients with these two diseases are misdiagnosed and therefore incorrectly treated. An artificial intelligence diagnosis framework would be highly adaptable for the early diagnosis of these two diseases. OBJECTIVE Design and evaluate an artificial intelligence diagnosis framework for PV and BP. METHODS The work was conducted on a dermatological dataset consisting of 17,735 clinical images and 346 patient metadata of bullous dermatoses. A two-stage diagnosis framework was designed, where the first stage trained a clinical image classification model to classify bullous dermatoses from five common skin diseases and normal skin and the second stage developed a multimodal classification model of clinical images and patient metadata to further differentiate PV and BP. RESULTS The clinical image classification model and the multimodal classification model achieved an area under the receiver operating characteristic curve (AUROC) of 0.998 and 0.942, respectively. On the independent test set of 20 PV and 20 BP cases, our multimodal classification model (sensitivity: 0.85, specificity: 0.95) performed better than the average of 27 junior dermatologists (sensitivity: 0.68, specificity: 0.78) and comparable to the average of 69 senior dermatologists (sensitivity: 0.80, specificity: 0.87). CONCLUSIONS Our diagnosis framework based on clinical images and patient metadata achieved expert-level identification of PV and BP, and is potential to be an effective tool for dermatologists in remote areas in the early diagnosis of these two diseases.

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