IL-17 inhibitors are a class of biological agents used in the treatment of moderate-to-severe plaque psoriasis. We report five cases initially treated for psoriasis with IL-17 inhibitors that subsequently showed disease progression and were ultimately diagnosed with mycosis fungoides (MF), and provide a summarized analysis of their clinical features. We hypothesize that IL-17 inhibitors may promote the progression of MF by impairing anti-tumor immune responses and disrupting the Treg/Th17 balance. In psoriasis patients with atypical clinical manifestations or poor response to biologic therapy, the possibility of MF should be considered, and a skin biopsy is recommended prior to treatment when necessary to establish a definitive diagnosis.

The diagnosis and treatment of cutaneous T-cell lymphoma (CTCL), a rare and serious skin malignancy, remain challenging due to limited understanding of its pathogenesis. Recently, increasing attention has been directed toward a possible link between CTCL development and the use of biologic therapies for inflammatory skin diseases, such as psoriasis and atopic dermatitis. These biologics include tumor necrosis factor-α inhibitors, interleukin (IL)-4/IL-13 inhibitors, IL-17 inhibitors, and IL-12/IL-23 inhibitors. The precise relationship between CTCL onset and biologic exposure remains unclear, highlighting an important and evolving clinical concern. This review examined potential associations between CTCL development and commonly used biologic therapies for inflammatory skin diseases, with a particular emphasis on underlying immunological and molecular mechanisms. This review was intended to offer insights that can guide future research regarding molecular pathways involved in CTCL pathogenesis after biologic treatment, thus refining therapeutic strategies and improving patient care.

BackgroundCutaneous T-cell lymphomas (CTCL) are a group of non-Hodgkin T-cell lymphomas, with mycosis fungoides and Sézary syndrome being the most common subtypes. Advanced-stage CTCL are typically aggressive, exhibiting interpatient heterogeneity in treatment response and prognosis. The underlying pathogenesis remains incompletely elucidated, posing challenges for the selection of appropriate therapies.MethodsWe obtained tumor cell-enriched regions of 33 advanced CTCL samples from 31 patients using laser capture microdissection, followed by integrated proteomic and transcriptomic profiling. Selected biomarkers were further validated via immunohistochemistry.ResultsWe identified three molecular subtypes of advanced CTCL, which exhibited significant differences in clinical phenotypes, signature proteins, and pathways. These subtypes were designated as intracellular signaling subtype, metabolic subtype, and extracellular matrix remodeling subtype, respectively. Within intracellular signaling subtype, the PI3K-AKT-mTOR pathway was characteristically upregulated, and we found the expression level of phospho-AKT was associated with response to PI3Kδ inhibitor therapy. Comparative proteomic analysis of patients with varying treatment responsiveness and disease progression identified CTSB, GSTO1, and WDFY4 as potential biomarkers for predicting treatment responsiveness, and GOLGA1 and STIP1 as potential biomarkers for progression prediction.ConclusionThis study explored a potential molecular subtyping framework for advanced-stage CTCL associated with different clinical phenotypes. Our findings provided preliminary evidence suggesting that certain biomarkers may be associated with treatment response to PI3K inhibitors. Additionally, we screened and preliminarily identified candidate biomarkers that may be associated with treatment responsiveness and progression risk, which may assist clinicians in the management of advanced-stage CTCL. Notably, these molecular differences may also correlate with clinical characteristics and require validation in larger cohorts.

Lentigo maligna (LM) is a subtype of cutaneous melanoma in situ that develops on chronically sun-damaged skin in elderly individuals. Its diagnosis and management remain challenging due to its slow progression, frequent occurrence on cosmetically sensitive areas in frail individuals, and tendency for subclinical peripheral extension. Despite its potential for invasive transformation, the natural history of LM remains incompletely understood and evidence-based management strategies for this specific entity remain limited. This work aims to bridge the gap between scarce high-quality evidence and the clinicians' need for practical guidance on LM management by formulating evidence-based and expert consensus-driven recommendations for diagnosis, treatment and follow-up. Under the coordination of the International Dermoscopy Society, a global, multidisciplinary consortium of 53 experts-including dermatologists, dermato-oncologists, dermatologic surgeons, radiologists, radiotherapists, pathologists and epidemiologists-formulated recommendations through structured consensus, informed by a comprehensive review of current scientific evidence and clinical practice standards. Optimal management of LM requires accurate diagnosis and individualized treatment planning. Non-invasive skin imaging techniques, especially dermoscopy and reflectance confocal microscopy, aid in diagnosis, biopsy orientation, lesion delineation and post-treatment monitoring. Multiple partial biopsies help confirm diagnosis and rule out invasion. Complete surgical excision remains the first treatment option. No definitive safety margins can be recommended for standard surgery; margin-controlled techniques are preferable for large or ill-defined lesions. Topical imiquimod and radiotherapy are effective alternatives where surgery is unsuitable. Topical imiquimod is useful as primary, adjuvant or neoadjuvant therapy. Blind destructive methods should be avoided. Close clinical and imaging follow-up is needed. Patient-centred care, shared decision-making, and a multidisciplinary approach are critical for optimal outcomes. These international consensus recommendations summarize current best practices for LM management, offering practical, evidence-informed guidance to clinicians while acknowledging the potential of future research in refining these conclusions.

Folliculotropic mycosis fungoides (FMF) is a rare subtype of MF, characterized by prominent folliculotropism in histopathology. Clinically, FMF exhibits polymorphic presentations, mainly including follicular papules, plaques, alopecia, and other nonspecific lesions, with a predilection for the head and neck region, leading to frequent misdiagnosis. Historically, FMF was perceived as an aggressive subtype with an unfavorable prognosis, often regarded as advanced-stage MF requiring aggressive combination therapies. However, recent studies have identified a subset of FMF with indolent progression and favorable prognosis, which can achieve remission through skin-directed therapies (SDTs). Therefore, FMF treatment strategies should follow the stage-adapted principles such as classical MF, with individualized regimens based on disease staging. This review comprehensively elaborates the diagnostic criteria and clinicopathological staging system of FMF, with a focus on stage-based therapeutic principles, aiming to guide clinical practice.

Atopic dermatitis (AD) is a common chronic, recurrent inflammatory disease, yet its accompanying nail abnormalities have long received insufficient attention. The clinical characteristics, underlying mechanisms, and assessment systems for nail dystrophy in AD remain unclear. AD-associated nail dystrophy can manifest in various forms, including Beau’s lines, nail pitting, koilongchia, trachyonychia, leukonychia, brachyonychia, melanoychia, onychomadesis, onychoschizia, onycholysis, and paronychia. Current treatments face limitations such as slow onset of action and uncertain efficacy with traditional therapies, particularly with limited drug options for the pediatric populations. With the deepening of research into the Th2 inflammatory pathway, biologics such as dupilumab have shown therapeutic potential. Through retrospective analysis, this paper presents the effectiveness and safety of dupilumab in five pediatric AD patients with nail dystrophy under 12 years old. After at least 12 weeks of treatment, their skin lesions and nail dystrophy both showed marked improvement. Additionally, we reviewed four reported cases in the literature of adult AD patients with nail dystrophy who experienced significant improvement in nail changes after dupilumab treatment. These results suggest that dupilumab may be an effective treatment for nail dystrophy in AD. This case series provides the first evidence demonstrating the significant efficacy of dupilumab for nail dystrophy in pediatric AD patients. However, further large-scale prospective studies are still needed to better guide clinical practice.

Primary cutaneous T-cell lymphoma (CTCL) comprises a group of rare, aggressive non-Hodgkin lymphomas, of which mycosis fungoides (MF) and Sézary syndrome are the most common subtypes. In the absence of a universally accepted standard of care for advanced stages, allogeneic hematopoietic stem cell transplantation (allo-HSCT) offers curative potential; however, post-transplant relapses remains the principle cause of treatment failure, making effective maintenance strategies crucial. This report describes the case of a young woman with stage IVA MF who, following failure of multiple conventional therapies, underwent haploidentical allo-HSCT and initiated chidamide maintenance upon achieving complete remission. This approach successfully consolidated remission for 10 months; however, the patient relapsed at 11 months post-transplant and ultimately died of neutropenic septic shock. This case suggests that post-transplant chidamide maintenance may have value in delaying disease progression in advanced MF; however, treatment-related toxicities and the risk of relapse remain significant clinical challenges requiring further investigation.

Background and Objectives: Artificial intelligence (AI) has transitioned to an integral part of dermatology in only few years, yet perceptions of its use vary widely, reflecting diverse hopes, concerns, and perceived clinical utility. Materials and Methods: In this study, 300 dermatologists from 13 countries, representing a range of experience levels and AI usage statuses, were surveyed regarding the characteristics and applications of AI in dermatology. Results: Among respondents, 61.33% reported having used AI tools in clinical practice. Adoption of AI was observed across all age groups, countries, and experience levels. Analysis of the types of AI tools used revealed a strong reliance on general-purpose large language models (LLMs), with chatbots being the most frequently cited category, utilized by 58.15% of users. Younger clinicians demonstrated a significant preference for chatbots (p < 0.05). Country-specific patterns in AI adoption were also noted. The most highly rated expected benefit of AI in dermatology was improved diagnostic accuracy, while the primary concern centered on regulatory and ethical limitations, suggesting that the “AI revolution” in dermatology is currently constrained less by technical barriers and more by regulation considerations. Use of consent forms when AI use takes place was more frequently reported as mandatory by dermatologists who had never used AI, reflecting heightened caution among non-users (p = 0.03). Additionally, 75% of respondents agreed that formal training in AI is necessary, highlighting a significant gap in traditional medical education regarding emerging technologies.

Cutaneous T-cell lymphoma (CTCL) represents a heterogeneous group of non-Hodgkin lymphomas that originate in the skin, and are often challenging to manage at advanced stages. In recent years, the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway has garnered significant attention due to its established roles in the pathogenesis of various inflammatory diseases and tumors. However, whether the JAK/STAT pathway can serve as a therapeutic target for CTCL remains inconclusive. Notably, some clinical reports have suggested that JAK inhibitors may potentially increase the risk of CTCL onset. This review summarizes current clinical research progress regarding the role of the JAK/STAT pathway in the development and progression of CTCL, as well as the dual potential of JAK inhibitors to either treat or trigger the disease.

Significance Genital lichen sclerosus (GLS) treatment remains challenging. Both photodynamic therapy (PDT) and retinoids exhibit efficacy, and dermoscopy shows promise for evaluating responses. Approach This study aims to investigate the efficacy and safety of PDT combined with acitretin in GLS. Patients were divided into PDT monotherapy (n=10) and combination groups (n=10). Symptoms, lesion characteristics, and dermoscopic features were scored at baseline, week 6, and week 12. Results By week 12, 40% of patients in the monotherapy group and 60% in the combination group achieved an Investigator's Global Assessment (IGA) score of ≤1. Combination therapy showed earlier IGA reduction at week 6, with decreased dermoscopic white structureless areas and white shiny streaks. Both groups demonstrated significant symptom and lesion improvement by week 12. Conclusions Combining PDT with acitretin shows enhanced efficacy in GLS treatment, and dermoscopy is a valuable tool for assessing therapeutic response. Further large-scale trials are warranted.

BACKGROUND:Mycosis fungoides (MF) is the most common type of cutaneous T-cell lymphoma, and early-stage MF is difficult to differentiate from erythematous inflammatory disease. With the exception of biopsy, noninvasive information such as a patient's medical history and clinical and dermoscopic images is of great significance for early diagnosis of MF. However, there is a lack of diagnostic models based on convolutional neural networks that can use multimodal information. OBJECTIVES:To develop an artificial intelligence (AI) deep learning model based on multimodal information, to verify its classification efficiency and to construct an AI-aided early diagnostic model of MF and inflammatory skin diseases for dermatologists. METHODS:This was a single-centre retrospective study based on multimodal information, including clinical information, clinical images and dermoscopic images. A total of 1157 cases of MF and inflammatory diseases were collected, including 2452 clinical images, 6550 dermoscopic images and corresponding clinical data. To assess the practicality of using AI models to help with clinical diagnoses, we carried out a comparative study involving three distinct groups: (i) dermatologists, (ii) the AI model and (iii) dermatologists + AI model. The dermatologist group comprised 23 dermatologists with a certain level of expertise and more than 10 h of systematic dermoscopy training. We used RegNetY400MF as the backbone network to extract features from the dermoscopic and clinical images. RESULTS:The AI model demonstrated higher levels of total accuracy, precision, sensitivity and specificity in the classification of MF and other inflammatory skin diseases than participating dermatologists. A significant enhancement was noticed in the average accuracy, sensitivity and specificity for MF and inflammatory diseases in the 'dermatologist + AI' group, with values of 82.9%, 86.2% and 96.5%, respectively, compared with 71.5%, 74.6% and 94.1%, respectively, in the 'dermatologist-only' group. A more accurate diagnosis of each disease was also achieved by the multiclassification model. CONCLUSIONS:The results indicate that our AI model has a significantly strong discriminative ability to assist dermatologists with improving diagnostic accuracy in early-stage MF and common inflammatory skin diseases.

To the Editor: Basal cell carcinoma (BCC) is the most prevalent skin malignancy, with an increasing incidence and economic burden worldwide.[1] Various histopathological subtypes of BCC have been well described, and subtype confirmation is essential for BCC classification according to the risk of recurrence.[2] Early diagnosis and intervention are important, especially considering that the incidence of aggressive subtypes of BCC is increasing faster than that of indolent subtypes.[3] Initial screening for BCC relies mainly on clinical observation and dermoscopy, which can further assist in distinguishing between different histopathological subtypes. Recently, artificial intelligence (AI), especially convolutional neural networks (CNNs), has been successfully applied to classify BCC.[4] However, the diagnostic performance of models based on single-modal light-skinned images is limited, with a risk of impaired accuracy when these models are applied to patients with darker skin. Therefore, we aimed to fine-tune a multimodal neural network using dermoscopic and clinical images of Chinese patients to assist in the differentiation between BCC and common benign tumors, including melanocytic nevus (MN), seborrheic keratosis (SK), and dermatofibroma (DF), and further predict the subtypes of BCC. This study was approved by the Medical Ethics Committee of Peking Union Medical College Hospital (No. I-23PJ360). All the included patients or their parents (for the minors) signed written informed consent. Clinical and dermoscopic images of patients diagnosed with BCC, MN, SK, and DF that were captured from June 2018 to January 2024 were collected from the skin imaging database of our department, screened, and annotated by two experienced dermatologists independently. All the patients with BCC were further divided according to their histopathological subtypes. Two datasets were formed, with 2675 images (four classes, BCC vs. benign) and 594 images (six classes, BCC subtype distinction) [Supplementary Table 1, https://links.lww.com/CM9/C334]. The second dataset exhibited a pronounced long-tail distribution [Supplementary Figure 1, https://links.lww.com/CM9/C334] and addressing this issue is also a key focus of our proposed method. Our approach involved three main components. First, an information complementary block (ICB) improved classification by integrating unique features for better extraction efficiency [Supplementary Figure 2, https://links.lww.com/CM9/C334]. Second, we fine-tuned a large contrastive language-image pretraining (CLIP) model on our dataset, freezing most parameters and adjusting a smaller subset to enhance task-specific performance. Third, the logit-adjusted loss (LA loss) was used to address long-tail dataset imbalance by adjusting the Softmax output probabilities based on category distribution. We used ResNet as the feature extractor, which provided comprehensive lesion characterization. Features from shallow to deep layers were fused in the ICB module, with convolutional layers and a linear classification head for multiclass output. Experimental comparisons revealed that the performance was the best when features from Stages 2 and 4 were used. To address the long-tail distribution, we fine-tuned a CLIP-based model with an image encoder and a text encoder. The image encoder captured feature information, whereas the text encoder extracted categorical information. Using contrastive loss, we aligned image and category features. We selectively updated the image encoder parameters and introduced LA loss to increase the model's focus on underrepresented categories, improving performance on tail categories [Supplementary Figure 3, https://links.lww.com/CM9/C334]. The performance of the proposed model is shown in detail in Supplementary Figure 4 and Supplementary Table 2, https://links.lww.com/CM9/C334. In the first task, the model exhibited near-perfect sensitivity and specificity across the four categories. In the second task, the model demonstrated better performance in discerning nodular (NOD), superficial (SUP), and mixed (MIX) BCC, with both sensitivity and specificity higher than 0.90, whereas its performance in distinguishing micronodular (MIC) and infiltrative (INF) subtypes and basosquamous carcinoma (BSQ) was weaker. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) results are shown in Figure 1A. Confusion matrices are shown in Figure 1B. We compared our multimodal approach with other single-modal models [Supplementary Table 3, https://links.lww.com/CM9/C334]. The results showed that our multimodal model outperformed the single-modal models based on either clinical or dermoscopic images alone, highlighting the robustness and superior reliability of multimodal integration and leveraging the strengths of each modality. As the saliency map in Figure 1C shows, our model employs CNNs combined with gradient-weighted class activation mapping (Grad-CAM) to visualize the key regions associated with disease classification. The results demonstrate that the model has learned the correct lesion features. Figure 1D shows t-distributed stochastic neighbor embedding (t-SNE) visualizations of the internal features from our framework, revealing clear clusters of skin images by clinical category and demonstrating the model's effectiveness in distinguishing different skin tumors and BCC subtypes.Figure 1: Performance and visualization of our proposed model. (A) ROC curves and the AUC results. Curves on the right demonstrate zoomed-in views between the abscissa 0–0.2. (B) Confusion matrices for the four-class classification task (left) and the six-class classification task (right). (C) Saliency map. (D) The t-SNE visualization for the four-class classification task (left) and the six-class classification task (right). AUC: Area under the curve; BCC: Basal cell carcinoma; BSQ: Basosquamous carcinoma; DF: Dermatofibroma; INF: Infiltrative BCC; MIC: Micronodular BCC; MIX: Mixed forms of BCC subtypes; MN: Melanocytic nevus; t-SNE: t-distributed stochastic neighbor embedding; NOD: Nodular BCC; ROC: Receiver operating characteristic; SK: Seborrheic keratosis; SUP: Superficial BCC.Given that the global population is aging persistently and that actinic damage accumulates, an accurate tool that helps with the rapid and convenient screening of suspicious BCC lesions is greatly needed, especially in rural areas where the insufficiency of dermatologists is critical. Multimodal fusion models aim to integrate data from various modalities to provide a more comprehensive understanding of complex scenarios. However, challenges such as long-tail distributions leading to biased performance on underrepresented classes and difficulties in aligning and fusing different modalities effectively exist. In this study, we propose a multimodal architecture on the basis of a CLIP-based model with LA loss, which utilizes clinical and dermoscopic images integrated with ICBs. The approach achieved rather high accuracy in the detection of BCC. In addition, innovatively, our model was able to predict different histopathological subtypes of BCC cases with an overall accuracy of 0.899, providing an important reference to clinicians for precise, individualized evaluation. Our model attempts to classify BCC cases into different histopathological subtypes. The BCC subtype of the patient, in combination with clinical information, is essential for predicting the risk of recurrence, staging, and making management plans.[5] However, several limitations exist. First, we could not include all rare cases, such as morpheaform BCC and fibroepithelial BCC. Second, we did not include other benign or malignant skin tumors, such as hemangioma and melanoma. Third, we could not validate our model with interactions with dermatologists or with external data. The inclusion of more types of skin tumors, external validation with images captured in various settings, and evaluation in real-world clinical settings in subsequent studies will be essential for further assessing the application value of our model. Funding This work was supported by grants from CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2022-I2M-C&T-A-007), the National Natural Science Foundation of China (No. 92354307), and the Fundamental Research Funds for the Central Universities (No. 2023RC09). Conflicts of interest None.

Background:Atopic dermatitis (AD) is a chronic, recurrent, inflammatory skin disease. Although dupilumab has demonstrated favorable efficacy in the treatment of patients with moderate-to-severe atopic dermatitis, data on its recurrence after discontinuation remain limited. Objective:To explore the recurrence rate, time to recurrence, and factors influencing recurrence in patients with moderate-to-severe AD after discontinuing dupilumab, to bridge the existing knowledge gap and provide a reference for promoting long-term standardized management of the disease in AD patients to reduce AD recurrence. Methods:Patients with moderate-to-severe AD treated with dupilumab between January 2021 and December 2023 at Sichuan Provincial People's Hospital were included. All patients started from the time of drug discontinuation, and baseline characteristics of patients were collected from all enrolled patients, and follow-up visits were conducted every 2 weeks after drug discontinuation utilizing telephone or medical records. Descriptive statistics summarized the relapse rate and time to relapse, and the Cox proportional hazards model was applied to determine the predictive factors of relapse after discontinuing dupilumab. Results:By the follow-up cut-off time, the median follow-up time was 49 weeks (24-85 weeks), and 141 AD patients were finally included in the statistical analysis. Of the 141 patients, 33 patients relapsed, with a relapse rate of 23.4% (95% CI, 16-30%), and the median time to relapse was 29 weeks. Predictors with a significant effect on recurrence included allergic conjunctivitis (HR = 7.912, 95% CI, 1.280-48.895, p = 0.026), duration of treatment <16 weeks (HR = 5.871, 95% CI, 2.154-16.003, p = 0.001), BMI ≥ 28 (HR = 5.653, 95% CI, 2.331-13.713, p < 0.001), male (HR = 5.634, 95% CI, 1.727-18.373, p = 0.004), and positive familial predisposition to allergy (HR = 3.438, 95% CI, 1.351-8.747, p = 0.01). Conclusion:The cumulative recurrence rate in 141 AD patients was 23.4%; the median time to recurrence in 33 AD recurrence patients was 29 weeks (22-59 weeks); comorbid allergic conjunctivitis, treatment duration shorter than 16 weeks, obesity, male patients, and positive familial predisposition to allergy were independent risk factors for AD recurrence. These findings confirm the disease characteristic of AD's susceptibility to relapse and emphasize the need for individualized treatment, post-discontinuation monitoring, and long-term standardized management of AD patients with different risk factors for relapse.

Response to ‘From benchmark to bedside: can multimodal artificial intelligence withstand real-world dermatology?’ by Zhihao Lei.

Importance:Patients with relapsed or refractory (r/r) cutaneous T-cell lymphoma (CTCL) have limited treatment options. Combining agents that target complementary oncogenic pathways may enhance efficacy while maintaining tolerability. Objective:To evaluate the safety and efficacy of linperlisib, a PI3Kδ inhibitor, combined with chidamide, a histone deacetylase inhibitor, in patients with r/r CTCL. Design, Setting, and Participants:This prospective, single-arm, phase 1 nonrandomized clinical trial with a 3 + 3 dose-escalation phase followed by dose expansion was conducted at a tertiary referral hospital in China from May 1, 2023, to March 6, 2025, with a median follow-up of 8.9 months (range, 1-21 months). It included patients with histologically confirmed advanced CTCL. All had an Eastern Cooperative Oncology Group performance status of 0 to 2 and received a median (range) of 3 (1-7) prior systemic therapies. Patients were enrolled consecutively based on eligibility. Interventions:Oral linperlisib administered once daily in escalating doses (40 mg, 60 mg, or 80 mg) plus chidamide, 20 mg, twice weekly. Treatment continued until progression, unacceptable toxic effects, or withdrawal. Main Outcomes and Measures:Primary outcomes were dose-limiting toxic effects, maximum tolerated dose, and objective response rate. Secondary outcomes included safety, progression-free survival, and disease control rate. Results:Of 22 patients (19 [86.4%] with mycosis fungoides, 3 [13.6%] with Sézary syndrome), 10 were female individuals (45.5%), and the median (range) age was 44 (27-71) years. No dose-limiting toxic effects were observed. The recommended phase 2 dose of linperlisib was 80 mg. The most common treatment-related adverse events were nausea (8 [36.4%]), pruritus (7 [31.8%]), and skin rash (6 [27.3%]), mostly grade 1 to 2. Grade 3 adverse events occurred in 5 patients (22.7%); no grade 4 to 5 events were reported. The objective response rate was 59.1% (13 of 22; 95% CI, 38.7%-76.7%), including 2 complete responses and 11 partial responses. The disease control rate was 86.4% (19 of 22), and the median progression-free survival was 5.4 months. Conclusions and Relevance:This nonrandomized clinical trial found that plus chidamide showed a manageable safety profile and promising activity in r/r CTCL. This all-oral combination may represent a new therapeutic option for advanced CTCL, particularly in mycosis fungoides. Trial Registration:ClinicalTrials.gov Identifier: NCT06037239.

Background:Treatment methods for pruritus in patients with chronic kidney disease (CKD) are lacking. Exploring the therapeutic potential of dupilumab in alleviating pruritus in CKD patients has good clinical value. Objectives:This retrospective study aims to analyze the effectiveness and safety of dupilumab in atopic dermatitis (AD) patients with CKD and uremic pruritus (UP) patients. Methods:Demographic and clinical data from AD patients with CKD stages 3-5 and UP patients who received dupilumab treatment were retrospectively analyzed. Improvements in pruritus were assessed via Peak Pruritus Numerical Rating Scale (PP-NRS) and 5-D itch scale (5-D IS) at weeks 2, 4, 12, and 16. Eczema Area and Severity Index (EASI) and Atopic Dermatitis Control Tool (ADCT) scores were also recorded at week 16 in AD patients with CKD. Safety during treatment was observed. Results:After dupilumab treatment, the PP-NRS and 5D-IS scores of 12 AD patients with CKD and 10 UP patients were significantly decreased. The percentages of UP patients who achieved PP-NRS ≥ 4-point improvement and 5D-IS ≤ 10-point at week 4, 12, and 16 did not significantly differ from those of AD patients with CKD (p > 0.05). At week 16, the skin symptoms in AD patients significantly improved (66.67% achieved EASI-75). No significant adverse effects were found. Conclusion:Dupilumab safely and effectively reduced pruritus in UP patients in the short term and achieved a comparable anti-pruritus effect to AD patients with CKD.

Mobocertinib (TAK-788) is an oral irreversible tyrosine kinase inhibitor specifically developed to target EGFR exon 20 insertion (ex20ins) mutant non-small cell lung cancer (NSCLC); however, acquired resistance inevitably develops, limiting its clinical efficacy. This study aims to elucidate mechanisms of mobocertinib resistance through multiple clinical and preclinical approaches. Through targeted Next-Generation Sequencing on tumor samples from a patient with EGFR ex20ins (A767_S768insSVD) mutant NSCLC treated with mobocertinib, we identified secondary KRAS Q61H mutation as a potential resistance mechanism. To recapitulate clinical resistance, we successfully established a cell line (TH937) derived from this patient's pre-treatment tumor. In vivo mobocertinib-resistant tumors (mobo-R) were generated by continuous oral administration of mobocertinib to TH937 xenograft nude mice. Although no actionable secondary EGFR or KRAS mutations were identified in whole exome sequencing, transcriptomic analyses revealed upregulation of MAPK and RAS-related signaling in mobo-R tumors. In vitro analyses also showed reactivation of ERK signaling after mobocertinib treatment, while phosphorylation of 49 major receptor tyrosine kinases (RTKs), including EGFR and other ErbB family members, was not observed. These finding indicates that RTK-independent MAPK reactivation, potentially driven by RAS signaling, may serve as a resistance mechanism. In conclusion, we found that genomic or transcriptomic alterations of MAPK/RAS signaling mediate mobocertinib resistance. Further validation and exploration are needed to improve the therapy of EGFR ex20insmutant NSCLC. Gaku Yamamoto, Yu Tanaka, Yoshitaka Zenke, Jie Liu, Tetsuya Sakai, Hiroki Izumi, Eri Sugiyama, Shigeki Umemura, Shingo Matsumoto, Kiyotaka Yoh, Koichi Goto, Susumu S. Kobayashi, Hibiki Udagawa. Elucidating mechanisms of acquired resistance to mobocertinib in non-small cell lung cancer harboring EGFR exon 20 insertion mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4714.

Psoriasis is a chronic inflammatory disease with significant physical and psychological impacts. To overcome the limitations of single-modality artificial intelligence models in diagnosing inflammatory skin diseases, we propose a multimodal framework-spatial alignment multimodal contrastive learning-integrating dermoscopic and clinical photographs and trained on the developed Peking Union Medical College Hospital - Inflammatory Skin Diseases dataset, which includes 8 inflammatory skin diseases. On the Peking Union Medical College Hospital - Inflammatory Skin Diseases dataset, our model achieved classification accuracies of 0.822 for an 8-class classification task and 0.911 for a binary classification task, outperforming single-modality models and simple fusion approaches. Furthermore, on the publicly available Derm7pt dataset, the proposed model surpassed 11 state-of-the-art multimodal methods, achieving an accuracy of 0.807. When used for diagnostic assistance, the model significantly improved the diagnostic accuracy of 20 dermatologists, increasing from 0.775 to 0.890 (P < .05). Among 110 test cases, 87 (79.1%) showed improved diagnostic accuracy. The model demonstrated notable enhancements in diagnosing specific conditions, such as lichen planus, acne, rosacea, and morphea. Validation using heatmaps confirmed that the model's attention aligned with key lesion features in most cases. This study introduces a multimodal model that bridges scale discrepancies between dermoscopic and clinical photographs, offering an efficient tool for diagnosing psoriasis and other inflammatory skin diseases.