Objective: This single-center clinical study identifies clusters of different phenotypes and pathophysiology subtypes of patients with gout and associated comorbidities.Methods: Patients clinically diagnosed with gout were enrolled between January 2018 and December 2019. Hierarchical cluster analyses were performed using clinical data or biological markers, inflammatory markers, and oxidative stress pathway metabolites assayed from serum and plasma samples. Subgroup clusters were compared using ANOVA for continuous data and chi-square tests for categorical data.Results: Hierarchical cluster analysis identified 3 clusters. Cluster 1 (C1; n = 24) comprised dyslipidemia, hypertension, and early-onset gout, without tophi. Cluster 2 (C2; n = 25) comprised hypertension, dyslipidemia, nephrolithiasis, and obesity. Cluster 3 (C3; n = 39) comprised multiple comorbidities and tophi. Post hoc comparisons of data obtained from samples of patients in C1, C2, and C3 revealed significant differences in the levels of oxidative stress and inflammation-related markers, including 3-nitrotyrosine, tumor necrosis factor, C-reactive protein, interleukin (IL) 1β, IL-6, platelet-derived growth factor (PDGF)–AA, and PDGF-BB. Reclustering patients based on all markers as well as on the biological markers that significantly differed among the initial clusters identified similar clusters.Conclusion: Oxidative stress and inflammatory marker levels may affect the development and clinical manifestations (ie, clinical phenotypes) of gout. Measuring oxidative stress and levels of inflammatory cytokines is a potential adjunctive tool and biomarker for early identification and management of gout.

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by integrating various diagnostic tools. Through a comprehensive literature review, we analyzed studies on AI applications in melanoma immunotherapy, focusing on predictive modeling, biomarker identification, and treatment response prediction. Key findings highlight the efficacy of AI in improving ICI outcomes. Machine learning models successfully identified prognostic cytokine signatures linked to nivolumab clearance. The combination of AI with RNAseq analysis had the potential for the development of personalized treatment with ICIs. A machine learning-based approach was able to assess the risk-benefit ratio for the prediction of immune-related adverse events (irAEs) using the electronic health record (EHR) data. Deep learning algorithms demonstrated high accuracy in tumor microenvironment analysis, including tumor region identification and lymphocyte detection. AI-assisted quantification of tumor-infiltrating lymphocytes (TILs) proved prognostically valuable in primary melanoma and predictive of anti-PD-1 therapy response in metastatic cases. Integrating multiple diagnostic modalities, such as CT imaging and laboratory data, modestly enhanced predictive performance for 1-year survival in advanced cancers treated with immunotherapy. These findings underscore the potential of AI-driven approaches to refine biomarker identification, treatment prediction, and patient stratification in melanoma immunotherapy. While promising, clinical validation and implementation challenges remain.

Extracorporeal photopheresis (ECP) is an apheresis procedure that is conventionally used as a first-line treatment for cutaneous and leukemic subtypes of T-cell lymphoma, such as Sezary’s syndrome and mycosis fungoides. Over the past three decades, its immunotherapeutic properties have been tested on a variety of autoimmune conditions, including many dermatologic diseases. There is ample evidence of ECP’s ability to modify leukocytes and alter cytokine production for certain dermatologic diseases that have been refractory to first-line treatments, such as atopic dermatitis. However, the evidence on the efficacy of ECP for the treatment of these dermatologic diseases is unclear and/or lacks sufficient evidence. The purpose of this paper is to review the literature on the utilization and clinical efficacy of ECP in the treatment of several [autoimmune] dermatologic diseases and discuss its applications, guidelines, recommendations, and future implementation for dermatologic diseases.

Towarzystwo o profilu zawodowo-naukowym, którego działalność skupia się głównie na wspieraniu rozwoju nauk farmaceutycznych oraz reprezentacji zawodu farmaceuty, założone w 1947 z siedzibą w Warszawie.

Introduction: Androgen receptor (AR) has emerged as a potential molecular target for therapeutic agents due to its crucial role in the pathogenesis of prostate cancer (CaP) of both androgen-dependent (AD-CaP) as well as androgen-independent (AI-CaP) type. Upon activation by androgen or by constitutive activation, AR triggers the induction of signaling pathways which help in the proliferation and survival of CaP cells. Dismal outcome of conventional therapy has necessitated a need to identify agents those could target AR in both types of patients with AD-CaP and AI-CaP disease. Objectives: We determined the mechanism-based efficacy of Lupeol triterpene as a potent AR inhibitor for both AD-CaP and AI-CaP. Methods and Results: We selected a series of cells representing (i) AD-CaP (LAPC4-wild type AR; LNCaP-mutant/functional AR), (ii) transition state from AD-CaP to AI-CaP (22Rv1-mutant/functional AR) and (iii) AI-CaP (LNCaP-derived C4-2b cells-mutant/functional AR). By employing MTT, 3[H]thymidine uptake and soft−agar assays, we show that Lupeol (10−50 μM/L) significantly inhibits the growth and proliferation of LAPC4, LNCaP, 22Rv1 and C4-2b cells. Androgen is known to induce AR and AR-responsive genes such as PSA in CaP cells. By employing RT-PCR, immunoblot, reporter and ChiP assays, Lupeol was observed to (1) reduce the growth-inducing effects of R1881, an androgen analogue, in LNCaP and LAPC4 cells, (2) inhibit the activation of AR and PSA, and (3) decrease the occupancy of AR on the ARE sequence of PSA gene in all CaP cells. To understand the mechanism underlying AR inhibition, we asked if Lupeol acts as a (A) competitive inhibitor of androgen or (B) transcriptional/translational inhibitor of AR. By employing drug-receptor interaction in silico and cell-based radio-ligand competitive binding assays, we show that Lupeol (1) binds to AR at its ligand-binding site and (2) antagonistically binds to both mutant and wild type AR in CaP cells. Interestingly, Lupeol was observed to reduce mRNA and protein levels of AR and PSA in all CaP cells. Lupeol exhibited more AR inhibitory potential than Curcumin, Lycopene, EGCG and Fisetin. Lupeol was also observed to sensitize CaP cells for Bicalutamide treatment. Finally, intraperitoneal administration of Lupeol (40mg/Kg; 3 days/week) inhibited the growth of LNCaP and C4-2b cell-originated tumors and reduced serum-PSA levels in xenograft mouse models. Immunohitochemical analysis of tumors showed a decreased AR and PSA levels in Lupeol treated mice. Conclusion: These data show that due to its dual role as a competitive ligand-binding inhibitor and a transcriptional inhibitor, Lupeol qualifies as a potent AR inhibitor. We suggest that Lupeol alone, or as an adjuvant to chemotherapeutic agents could be developed as a novel therapeutic agent to treat human CaP patients with hormone-sensitive as well as hormone-refractory disease type. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 943. doi:10.1158/1538-7445.AM2011-943