Pegylated interferon alfa-2a for polycythemia vera or essential thrombocythemia resistant or intolerant to hydroxyurea

Prior studies have reported high response rates with recombinant interferon-a (rIFN-a) therapy in patients with essential thrombocythemia (ET) and polycythemia vera (PV). To further define the role of rIFN-a,we investigated the outcomes of pegylated-rIFN-a2a (PEG) therapy in ET and PV patients previously treated with hydroxyurea (HU). The Myeloproliferative Disorders Research Consortium (MPD-RC)-111 study was an investigator-initiated, international, multicenter, phase 2 trial evaluating the ability of PEG therapy to induce complete (CR) and partial (PR) hematologic responses in patients with high-risk ET or PVwho were either refractory or intolerant to HU. The study included 65 patients with ET and 50 patients with PV. The overall response rates (ORRs; CR/PR) at 12 monthswere 69.2%(43.1% and 26.2%) in ET patients and 60% (22% and 38%) in PV patients. CR rates were higher in CALR-mutated ET patients (56.5% vs 28.0%; P 5 .01), compared with those in subjects lacking a CALR mutation. The median absolute reduction in JAK2V617F variant allele fraction was 26% (range, 284%to 47%) in patients achieving a CR vs 14%(range, 218% to 56%) in patients with PR or nonresponse (NR). Therapy was associated with a significant rate of adverse events (AEs); most were manageable, and PEG discontinuation related to AEs occurred in only 13.9% of subjects. We conclude that PEG is an effective therapy for patients with ET or PV who were previously refractory and/or intolerant of HU

Selektivno određivanje Fe(III) u uzorcima Fe(II) UV-spektrofotometrijom pomoću kvercetina i morina

Selective UV-spectrophotometric methods for determination of iron(III) in iron(II) samples have been developed. The methods are based on the interaction of Fe(III) with quercetin and morin, compounds of the flavonoid group. Redox reactions occurring between Fe(III) ions and the reagents used make the basis for the detection. Iron(II) does not react with quercetin and morin under the conditions applied [aqueous-methanolic (3 : 2) solutions, 0.3 mol L1 HCl, and 1.2 × 10-4 mol L1 quercetin (morin)] and does not interfere with the determination of Fe(III). Iron(III) can be determined up to 15 μg mL1 using both the examined systems. The detection limits are 0.06 and 0.38 μg mL1 when using quercetin or morin, respectively. The method with quercetin was applied to the determination of Fe(III) (ca. 0.2%) in a Fe(II) pharmaceutical product.U radu je opisan razvoj selektivnih UV-spektrofotometrijskih metoda za određivanje željeza(III) u uzorku željeza(II). Metode se temelje na redoks reakciji Fe(III) sa spojevima iz skupine flavonoida kvercetinom i morinom u reakcijskim uvjetima u kojima željezo(II) ne reagira (vodeno/metanolna otopina 3:2, 0,3 mol L1 HCl, 1,2 x 104 mol L1 kvercetin ili morin). Najniža koncentracija željeza(III) koja se može odrediti je 15 μg mL1 u oba ispitivana sustava. Granice detekcije su 0,06 i 0,38 μg mL1 ako se koristi kvercetin, odnosno morfin. Metoda s kvercetinom primijenjena je za određivanje Fe(III (približno 0,2%) u farmaceutskom produktu Fe(II)

Synergistic combination of cytotoxic chemotherapy and cyclin-dependent kinase 4/6 inhibitors in biliary tract cancers

Background and aims: Biliary tract cancers (BTCs) are uncommon, but highly lethal, gastrointestinal malignancies. Gemcitabine/cisplatin is a standard-of-care systemic therapy, but has a modest impact on survival and harbors toxicities, including myelosuppression, nephropathy, neuropathy, and ototoxicity. Whereas BTCs are characterized by aberrations activating the cyclinD1/cyclin-dependent kinase (CDK)4/6/CDK inhibitor 2a/retinoblastoma pathway, clinical use of CDK4/6 inhibitors as monotherapy is limited by lack of validated biomarkers, diffident preclinical efficacy, and development of acquired drug resistance. Emerging studies have explored therapeutic strategies to enhance the antitumor efficacy of CDK4/6 inhibitors by the combination with chemotherapy regimens, but their mechanism of action remains elusive.Approach and results: Here, we report in vitro and in vivo synergy in BTC models, showing enhanced efficacy, reduced toxicity, and better survival with a combination comprising gemcitabine/cisplatin and CDK4/6 inhibitors. Furthermore, we demonstrated that abemaciclib monotherapy had only modest efficacy attributable to autophagy-induced resistance. Notably, triplet therapy was able to potentiate efficacy through elimination of the autophagic flux. Correspondingly, abemaciclib potentiated ribonucleotide reductase catalytic subunit M1 reduction, resulting in sensitization to gemcitabine.Conclusions: As such, these data provide robust preclinical mechanistic evidence of synergy between gemcitabine/cisplatin and CDK4/6 inhibitors and delineate a path forward for translation of these findings to preliminary clinical studies in advanced BTC patients.</p

GENESIS: Generative Scene Inference and Sampling of Object-Centric Latent Representations

Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative models do not explicitly capture the compositional nature of visual scenes. Two recent exceptions, MONet and IODINE, decompose scenes into objects in an unsupervised fashion. Their underlying generative processes, however, do not account for component interactions. Hence, neither of them allows for principled sampling of novel scenes. Here we present GENESIS, the first object-centric generative model of rendered 3D scenes capable of both decomposing and generating scenes by capturing relationships between scene components. GENESIS parameterises a spatial GMM over images which is decoded from a set of object-centric latent variables that are either inferred sequentially in an amortised fashion or sampled from an autoregressive prior. We train GENESIS on several publicly available datasets and evaluate its performance on scene generation, decomposition, and semi-supervised learning