Is Nephrology More at Ease Than Oncology with Erythropoiesis-Stimulating Agents? Treatment Guidelines and an Update on Benefits and Risks

Abstract Erythropoiesis-stimulating agents (ESAs), which promote RBC production, have been extensively used to reduce transfusion requirements and improve quality of life (QoL) in both cancer patients and those with chronic kidney disease (CKD). However, the likelihood of response and duration of treatment differ in the two settings. In renal anemia, ESAs act straightforwardly as hormone-replacement therapy. The anemia of cancer, however, relates not to a lack of endogenous erythropoietin production but to diverse aspects of the disease (including a relevant inflammatory component) and chemotherapy. Response to ESAs is slower and less certain than in nephrology. In both settings, early studies showed that reversal of severe anemia was accompanied by substantial improvement in QoL. However, again in both settings, subsequent studies indicated that efforts to normalize hemoglobin might worsen outcome. In the context of cancer, this concern was reinforced by the suggestion that malignant cells had erythropoietin receptors and that its administration might therefore accelerate tumor growth, and moreover that cancer patients are more susceptible to venous thrombosis. The absence of these concerns for nephrologists, and their greater experience in managing ESAs and patients' iron status, may make them more at ease with ESAs than their counterparts in oncology. However, both groups of specialists have had to deal with reversals in recommended thresholds for intervention and restrictions imposed by regulatory authorities. In both specialties, the broad consensus now emerging is that the optimum balance of benefits and risks lies in using ESAs aimed at a hemoglobin level in the range of 11–12 g/dl, although for CKD patients there is still room for an individualized approach

Influence of ellagitannins extracted by pomegranate fruit on disulfide isomerase PDIA3 activity

Pomegranate fruit is a functional food of high interest for human health due to its wide range of phytochemicals with antioxidant properties are implicated in the prevention of inflammation and cancer. Ellagitannins, such as punicalagin and ellagic acid, play a role as anti-atherogenic and neuroprotective molecules in the complex fighting against the degenerative diseases. The aim of this work was to evaluate the composition in punicalagins and ellagic acid of differently obtained extracts from whole fruit, peels and juices, prepared by squeezing or by centrifugation, of pomegranate belonging to different cultivars. Moreover, a wider phenolic fingerprint was also determined. The bioactivity of the extracts was tested on the redox activity of PDIA3 disulfide isomerase, an enzyme involved in the regulation of several cellular functions and associated with different diseases such as cancer, prion disorders, Alzheimer’s and Parkinson’s diseases. The results demonstrate that the different ratios between punicalagin and ellagic acid modulate the enzyme activity and other ellagitannins could interfere with this activity

Clinical benefits of slowing the progression of renal failure

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Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing

Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks. This limitation led to a resurgence of methods that model alignments between sentences and their corresponding meaning representations, either implicitly through latent variables or explicitly by taking advantage of alignment annotations. We take the second direction and propose TPol, a two-step approach that first translates input sentences monotonically and then reorders them to obtain the correct output. This is achieved with a modular framework comprising a Translator and a Reorderer component. We test our approach on two popular semantic parsing datasets. Our experiments show that by means of the monotonic translations, TPol can learn reliable lexico-logical patterns from aligned data, significantly improving compositional generalization both over conventional seq2seq models, as well as over a recently proposed approach that exploits gold alignments.Comment: 8 pages, 4 figures, 4 table