Journal Of The American Academy Of Dermatology[JOURNAL]

BACKGROUND: Palmoplantar pustulosis (PPP) carries substantial burden with limited treatments. Selective JAK1 inhibition is promising, yet prospective, immune-integrated data remain scarce. OBJECTIVES: To evaluate the eff... BACKGROUND: Palmoplantar pustulosis (PPP) carries substantial burden with limited treatments. Selective JAK1 inhibition is promising, yet prospective, immune-integrated data remain scarce. OBJECTIVES: To evaluate the efficacy, safety, and immunomodulatory effects of ivarmacitinib in PPP and to explore baseline predictors of response. METHODS: Prospective, single-arm, 12-week study across three centers. Sixty patients received oral ivarmacitinib 4 mg daily; 58 (96.7%) completed follow-up. PRIMARY ENDPOINT: PPPASI change at week 12. Exploratory endpoints included peripheral T lymphocyte subsets, neutrophil phenotypes, and plasma proteomics. RESULTS: Mean PPPASI decreased from 11.3 (95%CI: 9.9-12.6) to 3.9 (3.4-4.4) (P < 0.001); 85.0% achieved PPPASI50 and 13.3% achieved PPPASI75 after 12 weeks. Pruritus, pain, and quality of life improved by week 2 (all P < 0.05). Treatment partially normalized Th2/Th17-skewed polarization and activated neutrophil phenotypes. Early response correlated with lower baseline CD4/CD8 ratio, Th2/Th1 ratio, and plasma IL10RA/IL2RB; sustained response with lower CD4+ effector memory and CD8+ central memory proportions, and higher GDNF. Twenty-two grade 1-2 adverse events occurred and 2 patients discontinued treatment. LIMITATIONS: Single-arm design. CONCLUSIONS: Ivarmacitinib achieved rapid improvement with favorable safety and partial immune remodeling in PPP. Baseline immune profiles may stratify response, supporting selective JAK1 inhibition and biomarker-guided patient selection.

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Nemolizumab for treatment-refractory cutaneous adverse events in patients with cancer: A retrospective multi-institutional case series.

Karagenova R, Lee E, Fuksman L … +4 more , Mu A, Lu C, Lin C, Semenov YR

J Am Acad Dermatol · 2026 Jun · PMID 42341932 · Publisher ↗

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JAAD Game Changers: "Incidence and profile of skin cancers in patients following ultraviolet phototherapy without psoralens: A retrospective cohort study".

Grant-Kels JM

J Am Acad Dermatol · 2026 Jun · PMID 42341930 · Publisher ↗

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A retrospective observational study of oral ivarmacitinib and narrow-band ultraviolet-B in refractory progressive vitiligo.

Ou Y, Shao X, Chang L … +4 more , Chen Z, Wang R, Wan X, Chen J

J Am Acad Dermatol · 2026 Jun · PMID 42340296 · Publisher ↗

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Sepsis-3 Improves Risk Stratification in Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis With Infection.

Wu Y, Dong M, Li J … +3 more , Qiu Y, Li C, Wang R

J Am Acad Dermatol · 2026 Jun · PMID 42336218 · Publisher ↗

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Improved Detection of Lentigo Maligna with AI-Assisted Dermoscopy: A Reader Study in Facial Pigmented Lesions.

Yilmaz A, Erol Mart HM, Temelkuran B … +1 more , Akay BN

J Am Acad Dermatol · 2026 Jun · PMID 42336217 · Publisher ↗

BACKGROUND: Differentiating lentigo maligna (LM) from benign facial pigmented lesions remains difficult due to substantial clinical and dermoscopic overlap, particularly on chronically sun-damaged skin, a setting underre... BACKGROUND: Differentiating lentigo maligna (LM) from benign facial pigmented lesions remains difficult due to substantial clinical and dermoscopic overlap, particularly on chronically sun-damaged skin, a setting underrepresented in existing AI studies. OBJECTIVE: To develop and evaluate a deep learning-based model for facial pigmented lesions and assess its impact on dermatology resident diagnostic performance. METHODS: In this retrospective study, 722 lesions (894 dermoscopic images) were analyzed (LM: 190; PAK: 230; SL/SK: 302). Twenty percent of lesions were reserved for testing; the remainder underwent 5-fold stratified cross-validation. An Xception-based convolutional neural network was trained for binary and 3-class classification. A reader study with 26 residents compared diagnostic accuracy before and after AI assistance. RESULTS: The model achieved a mean accuracy of 84.2% ± 2.5%, sensitivity of 90.8% ± 11.5%, and specificity of 81.9% ± 1.2%. Resident accuracy improved from 64.9% to 74.0% with AI support (p < 0.0001), with the largest gain observed in LM detection (+16.5%). LIMITATIONS: Retrospective design, lack of multimodal clinical data, and resident-only reader study. CONCLUSION: AI-assisted dermoscopy improves diagnostic performance in a challenging facial lesion setting, particularly for LM, supporting its role as an adjunct tool in clinical decision-making and dermatology training.

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