Br. J. Dermatol. [JOURNAL]

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Cutaneous angiosarcoma.

Bai J, Qiao J

Br J Dermatol · 2026 Apr · PMID 41999276 · Publisher ↗

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Localized miliaria crystallina on the hands of a paediatric patient.

Teng YS, Wang HJ

Br J Dermatol · 2026 May · PMID 41996471 · Publisher ↗

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Response to "Public health advice on sun exposure and sunbed user should not be disregarded" and its discussion of our paper "Ultraviolet light exposure is associated with lower mortality".

Dibben C, Stevenson AC

Br J Dermatol · 2026 Apr · PMID 41995696 · Publisher ↗

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Nerve growth factor receptor p75NTR interacts with Toll-like receptor 4 and the alarmins high mobility group box 1 and nucleophosmin: a novel inflammatory mechanism in psoriasis.

Sileno S, D'Agostino M, Farina L … +18 more , Kosta A, Manni L, Soligo M, Mercurio L, Madonna S, Albanesi C, Pallotta S, Moretta G, Palese E, Cirielli C, Melillo G, Avitabile D, Nardini C, Tieri P, De Benedetti F, Longo FM, Magenta A, Bracci-Laudiero L

Br J Dermatol · 2026 Apr · PMID 41995608 · Publisher ↗

BACKGROUND: Nerve growth factor (NGF) is upregulated in psoriatic skin, and increasing evidence indicates that its receptor, p75 neurotrophin receptor (p75NTR), promotes inflammatory responses. However, the contribution... BACKGROUND: Nerve growth factor (NGF) is upregulated in psoriatic skin, and increasing evidence indicates that its receptor, p75 neurotrophin receptor (p75NTR), promotes inflammatory responses. However, the contribution of p75NTR and its ligand, pro-nerve growth factor (proNGF), in psoriasis inflammation remains unclear. OBJECTIVES: To investigate the role of p75NTR in the inflammatory response of skin fibroblasts derived from plaques from patients with psoriasis, to unravel possible novel interactions. METHODS: Plasma levels of proNGF and p75NTR extracellular domain (ECD) were measured in 54 patients with psoriasis and 25 healthy donors (HDs). p75NTR and tropomyosin receptor kinase A expression were evaluated in skin biopsies and dermal fibroblasts from patients with psoriasis and from HDs. Protein–protein interaction (PPI) analysis was used to prioritize potential p75NTR interactors. The effects of cytokines related to psoriasis [interferon (IFN)-γ, tumour necrosis factor (TNF)-α, interleukin (IL)-22, IL-17A] on receptor expression were examined in skin biopsies and fibroblasts. Pharmacological inhibition of p75NTR with LM11A-31 (p75i) or RNA interference was used to assess effects on intracellular signalling, inflammatory gene expression (IL6, PTGS2, CCL2, CCL20) and molecular interactions using reverse transcription quantitative polymerase chain reaction, Western blotting, enzyme-linked immunosorbent assay and proximity ligation assay (PLA). RESULTS: p75NTR expression was increased in the dermal layer of skin biopsies from psoriasis plaques, and plasma levels of p75NTR ECD and proNGF were enhanced in patients with psoriasis and correlated with disease severity. Psoriasis fibroblasts showed high basal p75NTR expression, which was inducible by the cytokine mix (IFN-γ, TNF-α, IL-22, IL-17A) in HD fibroblasts and blocked by LM11A-31. In fibroblasts from HDs and from patients with psoriasis, p75NTR inhibition significantly reduced cytokine-induced inflammatory gene expression and prevented nuclear translocation of nuclear factor-κB. Similar effects were seen after inhibition of Toll-like receptor 4 (TLR4). PPI analysis identified the alarmins high mobility group box 1 (HMGB1) and nucleophosmin as potential connectors between p75NTR and TLR4. These interactions were confirmed by PLA. Treatment with the cytokine mix enhanced p75NTR–TLR4–alarmin interactions, which were prevented by the p75i. Moreover, nucleophosmin and HMGB1 inhibition decreased p75NTR–TLR4 interaction and inflammatory gene expression. In psoriasis fibroblasts, p75NTR inhibition consistently reduced cytokine mix or lipopolysaccharide-induced extracellular release of nucleophosmin and HMGB1. CONCLUSION: p75NTR upregulation and signalling contribute to alarmin release and amplification of the TLR4–NF-κB inflammatory pathway in psoriasis. p75NTR can be considered a sensor of inflammation required for full activation of TLR4-dependent inflammatory signalling. Thus, p75NTR targeting may represent a novel therapeutic strategy to counteract chronic inflammation in psoriasis.

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Multiple annular erythema on the face.

Luo S, Li Y

Br J Dermatol · 2026 Apr · PMID 41992550 · Publisher ↗

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Lichen sclerosus GWAS meta-analysis reveals immune pathways and drug repurposing opportunities.

Simmonds F, Petukhova L

Br J Dermatol · 2026 Apr · PMID 41987490 · Full text

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The prognostic impact of prior atopic dermatitis on cutaneous T-cell lymphoma: a single-center retrospective cohort study.

Khoshniyati S, Fleischli A, Niknejad K … +3 more , Weiner J, Pierog O, Rozati S

Br J Dermatol · 2026 Apr · PMID 41987349 · Publisher ↗

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Assessment of mental health disorders among patients with lichen simplex chronicus.

Haq Z, Abdi P, Tran JT … +4 more , Diaz MJ, Mirza FN, Qureshi AA, Libby TJ

Br J Dermatol · 2026 Apr · PMID 41968095 · Publisher ↗

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Low dose methotrexate and superpotent topical steroids in the management of bullous pemphigoid.

Mackie L, Tull TJ

Br J Dermatol · 2026 Apr · PMID 41968094 · Full text

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More than a symptom: itch as an independent clinical contributor in atopic dermatitis - a global-scale study on 15,223 patients.

Silverberg JI, Dodiuk-Gad R, Takaoka R … +22 more , Tan J, Gu C, Luger T, Halioua B, Pretti Aslanian F, Prakoeswa CRS, Demessant Flavigny A, Le Floc'h C, Kerrouche N, Merhand S, Begolka WS, Smith ÁLT, Burstein S, Tempark T, Getachew A, Kerob D, Taieb C, Skayem C, Stratigos A, Steinhoff M, Seneschal J, Misery L

Br J Dermatol · 2026 Apr · PMID 41968093 · Publisher ↗

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Genetics vs clinical risk scores for melanoma prediction.

Wang H, Ghajari H, Jayasinghe GJMSR … +8 more , Seviiri M, Gordon SD, Neale RE, Pandeya N, Olsen CM, MacGregor S, Whiteman DC, Law MH

Br J Dermatol · 2026 Apr · PMID 41968088 · Publisher ↗

BACKGROUND: Cutaneous melanoma is a common cancer, for which risk stratification has been proposed to aid early detection. OBJECTIVES: To assess the performance of a polygenic risk score (PRS) in predicting risk for inva... BACKGROUND: Cutaneous melanoma is a common cancer, for which risk stratification has been proposed to aid early detection. OBJECTIVES: To assess the performance of a polygenic risk score (PRS) in predicting risk for invasive cutaneous melanoma, alone and combined with clinical risk factors. METHODS: The PRS was derived from the most recent genome-wide association study (GWAS) meta-analysis of cutaneous melanoma, which included 28,849 melanoma cases and 78,922 controls from 20 studies from United Kingdom, United States, Australia, and Europe. Then it was tuned in the Canadian Longitudinal Studying on Aging (CLSA) cohort, which included 528 melanoma cases and 17,787 controls. The PRS was then tested independently against 14 self-reported clinical factors and a clinical prediction model (the MP16 model) in the QSkin prospective cohort, which included 16,282 participants with genetic data aged 40-69 years at baseline, among whom 359 participants were identified with new invasive melanomas during 10 years of follow up. RESULTS: The PRS outperformed any other single clinical risk factor in QSkin (c-index 0.643). The baseline risk model (age, sex, first 10 principal components) had a c-index of 0.603; adding PRS to the baseline model increased the c-index to 0.670 (likelihood ratio test (LRT) p=1.68 × 10-21). Adding PRS to the MP16 model significantly enhanced discrimination (MP16 c-index: 0.713, MP16 + PRS c-index: 0.729 (median LRT p = 5.56×10-11)), and predicted more true cases in the 1st and 2nd top deciles (111 for MP16 + PRS vs 104 for MP16 in the 1st decile, and 72 for MP16 + PRS vs 63 for MP16 in the 2nd decile). Across various screening thresholds (10% to 50%), the sensitivity and/or specificity is higher, and the net reclassification improvements (NRIs) were in the range of 0.01 to 0.05, comparing the MP16 + PRS model with the MP16 model. CONCLUSIONS: Incorporating genetic risk information into existing clinical risk tools significantly improves prediction performance for melanoma.

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Genotype- dermatological phenotype correlations in CDKN2A, POT1, POLE, BAP1 variant carriers using 3D total-body-imaging.

Maas EJ, Kahler S, Betz-Stablein B … +17 more , Gillis MC, Primiero C, Portony M, Mothershaw A, Berkman J, De Bortoli E, Pham T, Weir V, Kurtansk N, Serra-García L, Carrera C, Aguilera P, Malvehy J, Puig S, Rotemberg V, Soyer HP, McInerney-Leo AM

Br J Dermatol · 2026 Apr · PMID 41968077 · Publisher ↗

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Spectacles Cleaning Products: a hidden source of direct isothiazolinone exposure on facial skin.

Phillips PF, Bourke JF

Br J Dermatol · 2026 Apr · PMID 41967137 · Publisher ↗

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Self-supervised AI system for differentiating mycosis fungoides and benign inflammatory dermatoses.

Zhao J, Bai J, Li G … +6 more , Sun Q, Li S, Wang C, Li H, Xue R, Qiao J

Br J Dermatol · 2026 Apr · PMID 41967134 · Publisher ↗

BACKGROUND: Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma, is challenging to diagnose in its early stages because clinical and histopathologic features often overlap with benign inflammatory dermatose... BACKGROUND: Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma, is challenging to diagnose in its early stages because clinical and histopathologic features often overlap with benign inflammatory dermatoses (BIDs), leading to misclassification and delayed treatment. OBJECTIVE: To evaluate whether a self-supervised AI system can support diagnostic decision-making among MF and common inflammatory mimics. METHODS: This retrospective two-center study included patients with confirmed diagnoses of MF or BIDs. A self-supervised multimodal system integrating whole-slide histopathology and routine clinical variables was developed to perform multiclass differential classification across MF and common BIDs. Cases were partitioned into training, internal validation, and independent external validation cohorts at the patient level. Clinical utility was assessed in a reader study in which dermatopathologists reviewed cases with and without system assistance. RESULTS: Across 786 WSIs from 532 patients, the multimodal model achieved macro-AUCs greater than 0.85 in both validation sets, with macro-balanced accuracy of 0.837 (95% CI, 0.778-0.897; n=106) internally and 0.762 (95% CI, 0.724-0.802; n=260) externally, representing improved performance over the unimodal histopathology model. In the reader study, AI assistance was associated with improved macro-balanced accuracy among both junior (0.778 to 0.818) and senior (0.805 to 0.859) dermatopathologists, accompanied by consistent improvements in macro-averaged sensitivity and specificity across all diagnostic categories. Interpretability analyses generated heatmaps that were generally consistent with recognized histopathologic features of both MF and BIDs, aligning with dermatopathologist interpretation. CONCLUSIONS: This multimodal, self-supervised system may support dermatopathologists by providing interpretable, probability-based guidance for the classification of MF and its common inflammatory mimics.

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