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Inflammatory biomarker profile in optimal and suboptimal responder psoriasis patients treated with ustekinumab
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Psoriasis is a chronic autoimmune inflammatory skin condition characterized by uncontrolled keratinocyte proliferation and potential systemic manifestations. Its pathogenesis involves activation of both innate and adaptive immune responses, leading to an imbalance in inflammatory cytokines. Interleukin (IL)-12 and IL-23 are key cytokines in the pathophysiology of psoriasis and sustain chronic skin inflammation. Biologic therapies, such as ustekinumab (UST), have been developed to induce long-term remission in moderate-to-severe psoriasis. The objective of this study was to identify differences in serum levels of inflammatory biomarkers [erythrocyte sedimentation rate, high-sensitivity C-reactive protein (hs-CRP), IL-12, IL-17, IL-22, and IL-23] between optimal and suboptimal responder Iraqi patients with moderate-to-severe plaque-type psoriasis treated with UST. Clinical response was assessed using the Psoriasis Area and Severity Index (PASI) score, and patients were divided into two groups: Group 1, patients with an optimal response (PASI ≤ 3); and Group 2, patients with a suboptimal response (PASI > 3). Optimal responders demonstrated significantly greater improvement in PASI and body surface area percent change compared to suboptimal responders (p = 0.001 for both). In contrast, suboptimal responders exhibited significantly higher levels of hs-CRP, IL-17, IL-22, and IL-23, indicating a greater inflammatory burden among individuals with an inadequate clinical response. These findings suggest that patients with persistent disease activity have an elevated pro-inflammatory cytokine environment, which may contribute to their reduced therapeutic response. Cytokine levels may serve as crucial indicators that optimal responders achieve not only skin clearance but also deeper, systemic control of the inflammatory disease process.

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Publication Date
Sun May 01 2022
Journal Name
Expert Systems With Applications
Novel large scale brain network models for EEG epileptic pattern generations
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Background: Unlike normal EEG patterns, the epileptiform abnormal pattern is characterized by different mor phologies such as the high-frequency oscillations (HFOs) of ripples on spikes, spikes and waves, continuous and sporadic spikes, and ploy2 spikes. Several studies have reported that HFOs can be novel biomarkers in human epilepsy study. S) Method: To regenerate and investigate these patterns, we have proposed three large scale brain network models (BNM by linking the neural mass model (NMM) of Stefanescu-Jirsa 2D (S-J 2D) with our own structural con nectivity derived from the realistic biological data, so called, large-scale connectivity connectome. These models include multiple network connectivity of brain regions at different

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