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BackgroundUpadacitinib, a specific JAK1 inhibitor, has minimal effect on other JAK subtypes. It influences the inflammatory process in various ways. Upadacitinib has been approved for conditions such as rheumatoid arthritis, psoriatic arthritis, atopic dermatitis, and ulcerative colitis in various countries. The purpose of this study is to assess the clinical efficacy and safety of upadacitinib in patients with refractory palmoplantar pustulosis who have not responded to conventional treatments (e.g., Acitretin, Tripterygium wilfordii Hook F, cyclosporine, methotrexate).MethodsWe conducted a retrospective collection of clinical data from 28 patients who received upadacitinib treatment at the Department of Dermatology, First Affiliated Hospital of Suzhou University, from July 2022 to December 2023. We evaluated the Palmoplantar Pustulosis Area and Severity Index (PPPASI) scores, Dermatology Life Quality Index (DLQI) scores, and Physician’s Global Assessment (PGA) scores before and after treatment. We also recorded any adverse events during the treatment process.ResultsA total of 28 patients were diagnosed with PPP, including 10 males and 18 females, and 8 patients (3 males and 5 females) were diagnosed with SAPHO syndrome. The mean age was (36.3 ± 10.5) years. After 12 weeks of treatment, PPPASI scores decreased from baseline (13.86 ± 2.76) to (5.56 ± 1.08), with a statistically significant difference (p < 0.05). DLQI scores decreased from (12.55 ± 4.56) to (2.03 ± 1.13), also showing a statistically significant difference (p < 0.05). Additionally, 20 patients achieved a PGA score of 0/1. No severe adverse events were reported during the treatment and follow-up period.ConclusionUpadacitinib could be an additional safe and effective treatment for treating refractory palmoplantar pustulosis with a potential benefit on patients’ quality of life. Further studies are needed to assess its short-and long-term efficacy and safety in larger sample sizes in randomized double-blind controlled trials.

Lichen sclerosus et atrophicus (LSA) is a chronic inflammatory skin lesion with an undefined cause. It is more commonly found in the genital area, particularly in adolescents, premenopausal women and postmenopausal women. LSA is difficult to treat and often recurs. The primary treatment for LSA involves the administration of potent topical corticosteroids. Dupilumab is increasingly being used for the treatment of itching in non-atopic dermatitis patients but there are few reports on its use for the treatment of LSA. Here, we present a case of LSA in a 61-year-old woman with extensive vulvar itching. Over four months of dupilumab therapy, significant therapeutic effects were observed, including vulvar skin thinning and pruritus relief without adverse reactions.

Psoriasis and atopic dermatitis (AD) are prevalent inflammatory skin disorders, each stemming from diverse factors, and characterized by recurring episodes. In certain complex cases, the clinical and pathological features exhibit overlapping and atypical characteristics, making accurate clinical diagnosis and targeted treatment a challenge. Psoriasiform dermatitis is the term used to describe such cases. Moreover, when patients have a history of malignancy, the situation becomes even more intricate, resulting in limited treatment options. Biologic therapies have transformed the management of immune-mediated inflammatory diseases, including psoriasis and AD. Meanwhile, the safety of biologics in special populations, especially among patients with a history of malignancy, should be underlined. The selective Janus kinase 1 (JAK1) inhibitor abrocitinib has been approved for the treatment of AD and has showed satisfying efficacy and safety in the treatment of psoriasis in clinical trials. Although unreported, JAK1 inhibitors are thought to have the potential to increase the risk of potential tumors. Apremilast, an oral phosphodiesterase (PDE)-4 inhibitor, is approved for moderate to severe plaque psoriasis. It has been investigated for its efficacy in AD, and is not contraindicated in malignancy. This report presents three cases of psoriasiform dermatitis in patients with a history of malignancy, showcasing significant improvement following treatment with systemic glucocorticoid, abrocitinib, or apremilast.

Automatic segmentation of brain tumors from multi-modalities magnetic resonance image data has the potential to enable preoperative planning and intraoperative volume measurement. Recent advances in deep convolutional neural network technology have opened up an opportunity to achieve end-to-end segmenting the brain tumor areas. However, the medical image data used in brain tumor segmentation are relatively scarce and the appearance of brain tumors is varied, so that it is difficult to find a learnable pattern to directly describe tumor regions. In this paper, we propose a novel cross-modalities interactive feature learning framework to segment brain tumors from the multi-modalities data. The core idea is that the multi-modality MR data contain rich patterns of the normal brain regions, which can be easily captured and can be potentially used to detect the non-normal brain regions, i.e., brain tumor regions. The proposed multi-modalities interactive feature learning framework consists of two modules: cross-modality feature extracting module and attention guided feature fusing module, which aim at exploring the rich patterns cross multi-modalities and guiding the interacting and the fusing process for the rich features from different modalities. Comprehensive experiments are conducted on the BraTS 2018 benchmark, which show that the proposed cross-modality feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions for learning multiple-spatial-frequency features, thus can better capture tumors with varying sizes and shapes. The proposed network takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. Finally, we integrate octave convolutions into the encoder-decoder architecture of UNet, which can generate high resolution tumor segmentation in one single forward feeding without post-processing steps. Both architectures are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge. The proposed approach is shown to significantly outperform other networks in terms of various accuracy measures and processing speed.