Fluoxetine-induced hepatic lipid accumulation is mediated by prostaglandin endoperoxide synthase 1 and is linked to elevated 15-deoxy-Δ12,14PGJ2

Major depressive disorder and other neuropsychiatric disorders are often managed with long-term use of antidepressant medication. Fluoxetine, an SSRI antidepressant, is widely used as a first-line treatment for neuropsychiatric disorders. However, fluoxetine has also been shown to increase the risk of metabolic diseases such as non-alcoholic fatty liver disease. Fluoxetine has been shown to increase hepatic lipid accumulation in vivo and in vitro. In addition, fluoxetine has been shown to alter the production of prostaglandins which have also been implicated in the development of non-alcoholic fatty liver disease. The goal of this study was to assess the effect of fluoxetine exposure on the prostaglandin biosynthetic pathway and lipid accumulation in a hepatic cell line (H4-II-E-C3 cells). Fluoxetine treatment increased mRNA expression of prostaglandin biosynthetic enzymes (Ptgs1, Ptgs2, and Ptgds), PPAR gamma (Pparg), and PPAR gamma downstream targets involved in fatty acid uptake (Cd36, Fatp2, and Fatp5) as well as production of 15-deoxy-Δ^12,14PGJ₂ a PPAR gamma ligand. The effects of fluoxetine to induce lipid accumulation were attenuated with a PTGS1 specific inhibitor (SC-560), whereas inhibition of PTGS2 had no effect. Moreover, SC-560 attenuated 15-deoxy-Δ^12,14PGJ₂ production and expression of PPAR gamma downstream target genes. Taken together these results suggest that fluoxetine-induced lipid abnormalities appear to be mediated via PTGS1 and its downstream product 15d-PGJ₂ and suggest a novel therapeutic target to prevent some of the adverse effects of fluoxetine treatment.     

Correction: Thrombotic and bleeding events,mortality, and anticoagulant use among 546,656 hospitalized patients with COVID‑19 in the United States: a retrospective cohort study

Journal of Thrombosis and Thrombolysis -     

Measuring Psychological Inflexibility of Suicidal Thoughts: The Acceptance and Action Questionnaire for Suicidal Ideation (AAQ-SI)

Cognitive Therapy and Research - Despite interest in psychological inflexibility as a marker of suicide risk, no measure of psychological inflexibility specific to SI exists. The present study...     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.