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Background Psoriasis is a common chronic inflammatory disease with an unclear aetiology. Keratinocytes in psoriasis are susceptible to exogenous triggers that induce inflammatory cell death. Objectives To investigate whether gasdermin E (GSDME)-mediated pyroptosis in keratinocytes contributes to the pathogenesis of psoriasis. Methods Skin samples from patients with psoriasis and from healthy controls were collected to evaluate the expression of GSDME, cleaved caspase-3 and inflammatory factors. We then analysed the data series GSE41662 to further compare the expression of GSDME between lesional and nonlesional skin samples in those with psoriasis. In vivo, a caspase-3 inhibitor and GSDME-deficient mice (Gsdme–/–) were used to block caspase-3/GSDME activation in an imiquimod-induced psoriasis model. Skin inflammation, disease severity and pyroptosis-related proteins were analysed. In vitro , tumour necrosis factor (TNF)-α-induced caspase-3/GSDME-mediated pyroptosis in the HACAT cell line was explored. Results Our analysis of the GSE41662 data series found that GSDME was upregulated in psoriasis lesions vs. normal skin. High levels of inflammatory cytokines such as interleukin (IL)-1β, IL-6 and TNF-α were also found in psoriasis lesions. In mice in the Gsdme –/– and caspase-3 inhibitor groups, the severity of skin inflammation was attenuated and GSDME and cleaved caspase-3 levels decreased after imiquimod treatment. Similarly, IL-1β, IL-6 and TNF-α expression was decreased in the Gsdme –/– and caspase-3 inhibitor groups. In vitro , TNF-α induced HACAT cell pyroptosis through caspase-3/GSDME pathway activation, which was suppressed by blocking caspase-3 or silencing Gsdme. Conclusions Our study provides a novel explanation of TNF-α/caspase-3/GSDME-mediated keratinocyte pyroptosis in the initiation and -acceleration of skin inflammation and the progression of psoriasis. [ABSTRACT FROM AUTHOR]

Background: Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients., Methods: We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians., Results: NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01)., Conclusions: The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-729/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objective: To evaluate the value of judging for the activity in AS by diffusion-weighted sequences (DWI) and enhance sequences MRI(DCE-MRI), to explore the correlation of Apparent diffusion coefficient(ADC) values, intensive rate and the clinical activity index in sacroiliitis (SIJ) of ankylosing spondylitis (AS)., Methods: 56 patients prospectively choiced and diagnosed were divided into two groupsas, active group (n = 32) and chronic group (n = 24) by rheumatologist according to BASDAI and laboratory parameters. Twenty healthy adults were as control group. The values of ADC and intensive rate of all sacroiliac joints (SIJs) were measured on MRI. BASDAI score were got by Bath ankylosing spondylitis disease activity index. ESR, CPR and were got by laboratory. Statisticaly to analysis whether the parameters were significantly different amang AS active, chronic, and the control group. To assess the correlation of the values of ADC, intensive rate and BASDAI score, ESR, CPR and in SIJ., Results: the values of ADC and intensive rate were significantly different among AS active, chronic and the control group. There were the significant correlation between the values of ADC, intensive rate and BASDAI score., Conclusion: Diffusion-weighted sequences and Contrast-enhanced sequences is superior to other methods in judging the activity in AS.combined with clinical activity index, the accuracy can significantly be improved to explore whether the activities of AS are.