Chronic tophaceous gout presenting as acute arthritis during an acute illness: a case report

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Cytological diagnostic features of late breast implant seromas. From reactive to anaplastic large cell lymphoma

Late breast implant seroma may be the presentation of a breast implant-associated anaplastic large cell lymphoma (BI-ALCL), which claims for a prompt recognition. However, BI-ALCL diagnosis on fine-needle aspiration (FNA) might be challenging for pathologists lacking experience with peri-implant breast effusions. Sixty-seven late breast implant seromas collected by FNA from 50 patients were evaluated by Papanicolaou smear stain and immunocytochemistry on cell blocks. A diagnostic algorithm based on the cellular composition, cell morphology and percentage of CD30+ cells was developed. Histological evaluation of the corresponding peri-prosthetic capsules was also performed. Most of the effusions (91% of the samples) were classified as reactive and 9% as BI-ALCL. In the BI-ALCL cases, medium-to-large atypical cells expressing CD30 represented more than 70% of the cellularity, whereas in in the reactive effusions CD30+ elements were extremely rare (<5%) and consisted of non-atypical elements. The reactive effusions were categorized into three patterns: i) acute infiltrate with prominent neutrophilic component (33% of the samples); ii) mixed infiltrate characterized by a variable number of neutrophils, lymphocytes and macrophages (30% of the samples); iii) chronic infiltrate composed predominantly of T lymphocytes or macrophages with only sporadic granulocytes (37% of the samples). The inflammatory cytological patterns were consistent with the histology of the corresponding capsules. Our results indicate that cytological analysis of late breast implant effusions, supported by the knowledge of the heterogeneous cytomorphological spectrum of late seromas, is a valuable approach for the early recognition of BI-ALCL

Ribavirin-Induced Anemia in Hepatitis C Virus Patients Undergoing Combination Therapy

The current standard of care for hepatitis C virus (HCV) infection – combination therapy with pegylated interferon and ribavirin – elicits sustained responses in only ∼50% of the patients treated. No alternatives exist for patients who do not respond to combination therapy. Addition of ribavirin substantially improves response rates to interferon and lowers relapse rates following the cessation of therapy, suggesting that increasing ribavirin exposure may further improve treatment response. A key limitation, however, is the toxic side-effect of ribavirin, hemolytic anemia, which often necessitates a reduction of ribavirin dosage and compromises treatment response. Maximizing treatment response thus requires striking a balance between the antiviral and hemolytic activities of ribavirin. Current models of viral kinetics describe the enhancement of treatment response due to ribavirin. Ribavirin-induced anemia, however, remains poorly understood and precludes rational optimization of combination therapy. Here, we develop a new mathematical model of the population dynamics of erythrocytes that quantitatively describes ribavirin-induced anemia in HCV patients. Based on the assumption that ribavirin accumulation decreases erythrocyte lifespan in a dose-dependent manner, model predictions capture several independent experimental observations of the accumulation of ribavirin in erythrocytes and the resulting decline of hemoglobin in HCV patients undergoing combination therapy, estimate the reduced erythrocyte lifespan during therapy, and describe inter-patient variations in the severity of ribavirin-induced anemia. Further, model predictions estimate the threshold ribavirin exposure beyond which anemia becomes intolerable and suggest guidelines for the usage of growth hormones, such as erythropoietin, that stimulate erythrocyte production and avert the reduction of ribavirin dosage, thereby improving treatment response. Our model thus facilitates, in conjunction with models of viral kinetics, the rational identification of treatment protocols that maximize treatment response while curtailing side effects