Neuropeptide Y as a risk factor for cardiorenal disease and cognitive dysfunction in chronic kidney disease: translational opportunities and challenges

Neuropeptide Y (NPY) is a 36-amino-acid peptide member of a family also including peptide YY and pancreatic polypeptide, which are all ligands to Gi/Go coupled receptors. NPY regulates several fundamental biologic functions including appetite/satiety, sex and reproduction, learning and memory, cardiovascular and renal function and immune functions. The mesenteric circulation is a major source of NPY in the blood in man and this peptide is considered a key regulator of gut-brain cross talk. A progressive increase in circulating NPY accompanies the progression of chronic kidney disease (CKD) toward kidney failure and NPY robustly predicts cardiovascular events in this population. Furthermore, NPY is suspected as a possible player in accelerated cognitive function decline and dementia in patients with CKD and in dialysis patients. In theory, interfering with the NPY system has relevant potential for the treatment of diverse diseases from cardiovascular and renal diseases to diseases of the central nervous system. Pharmaceutical formulations for effective drug delivery and cost, as well as the complexity of diseases potentially addressable by NPY/NPY antagonists, have been a problem until now. This in part explains the slow progress of knowledge about the NPY system in the clinical arena. There is now renewed research interest in the NPY system in psychopharmacology and in pharmacology in general and new studies and a new breed of clinical trials may eventually bring the expected benefits in human health with drugs interfering with this system

Cognitive disorders in patients with chronic kidney disease: Approaches to prevention and treatment

Background: Cognitive impairment is common in patients with chronic kidney disease (CKD), and early intervention may prevent the progression of this condition. Methods: Here, we review interventions for the complications of CKD (anemia, secondary hyperparathyroidism, metabolic acidosis, harmful effects of dialysis, the accumulation of uremic toxins) and for prevention of vascular events, interventions that may potentially be protective against cognitive impairment. Furthermore, we discuss nonpharmacological and pharmacological methods to prevent cognitive impairment and/or minimize the latter's impact on CKD patients' daily lives. Results: A particular attention on kidney function assessment is suggested during work-up for cognitive impairment. Different approaches are promising to reduce cognitive burden in patients with CKD but the availabe dedicated data are scarce. Conclusions: There is a need for studies assessing the effect of interventions on the cognitive function of patients with CKD

A machine learning algorithm for stock picking built on information based outliers

Stock picking based on regularities in time series is one of the most studied topics in the financial industry. Various machine learning techniques have been employed for this task. We build a trading strategy algorithm that receives as input indicators defined through outliers in the time series of stocks (return, volume, volatility, bid-ask spread). The procedure identifies the most relevant subset of indicators for the prediction of stock returns by combining an heuristic search strategy, guided from the Information Gain Criterium, with the Naive-Bayes classification algorithm. We apply the methodology to two sets of stocks belonging respectively to the EUROSTOXX50 and the DOW JONES index. Performance is smoother than in the Buy&Hold strategy and yields a higher risk-adjusted return, in particular in a turbulent period. However, outperformance vanishes when transaction costs (5–15 basis points) are inserted. Asset return and return/volume serial correlation turn out to be the most relevant indicators to build the trading algorithm