Blood Biomarkers for Alzheimer's Disease: Much Promise, Cautious Progress

Biomarkers in Alzheimer's disease (AD) have the potential to allow early and more accurate diagnosis, predict disease progression, stratify individuals and track response to candidate therapies in drug trials. The first fluid biomarkers reflecting aspects of AD neuropathology were identified in cerebrospinal fluid (CSF) in the 1990s. Three CSF biomarkers (amyloid-β 1-42, total tau and phospho-tau) have consistently been shown to have diagnostic utility and are incorporated into the new diagnostic criteria for AD. These markers have also been shown in longitudinal studies to predict conversion of mild cognitive impairment to AD. However, a key issue with the use of CSF biomarkers as a screening test is the invasiveness of lumbar puncture. Over the last 20 years there has been an active quest for blood biomarkers, which could be easily acquired and tested repeatedly throughout the disease course. One approach to identifying such markers is to attempt to measure candidates that have already been identified in CSF. Until recently, this approach has been limited by assay sensitivity, but newer platforms now allow single molecule-level detection. Another approach is identification of candidates in large multiplex panels that allow for multiple analytes to be quantified in parallel. While both approaches show promise, to date no blood-based biomarker or combination of biomarkers has sufficient predictive value to have utility in clinical practice. In this review, an overview of promising blood protein candidates is provided, and the challenges of validating and converting these into practicable tests are discussed

Thermal and pressure stability of myrosinase enzymes from black mustard (Brassica nigra L. W.D.J Koch. var. nigra), brown mustard (Brassica juncea L. Czern. var. juncea) and yellow mustard (Sinapsis alba L. Subsp Maire) seeds

This study investigates the effects of temperature and pressure on inactivation of myrosinase extracted from black, brown and yellow mustard seeds. Brown mustard had higher myrosinase activity (2.75 un/mL) than black (1.50 un/mL) and yellow mustard (0.63 un/mL). The extent of enzyme inactivation increased with pressure (600-800 MPa) and temperature (30-70 °C) for all the mustard seeds. However, at combinations of lower pressures (200-400 MPa) and high temperatures (60-80 °C), there was less inactivation. For example, application of 300 MPa and 70 °C for 10 minutes retained 20%, 80% and 65% activity in yellow, black and brown mustard, respectively, whereas the corresponding activity retentions when applying only heat (70 °C, 10min) were 0%, 59% and 35%. Thus, application of moderate pressures (200-400 MPa) can potentially be used to retain myrosinase activity needed for subsequent glucosinolate hydrolysis

Bilateral optic neuritis as a first presentation of lymph node tuberculosis

Tuberculosis (TB) may affect the nervous system in many ways. We describe an immunocompetent teenage girl with lymph node TB who had first presented with bilateral optic neuritis. Detailed history identified features inconsistent with immune-mediated optic neuritis. Several unusual features prompted further investigation, including transient visual obscurations without raised intracranial pressure, prominent disc swelling and absence of laboratory findings to support an immune-mediated cause. Whole body PET/MR imaging identified widespread mediastinal and supraclavicular lymphadenopathy. Despite no known TB contacts, a negative interferon gamma release assay and a normal chest X-ray, a targeted lymph node biopsy confirmed TB

Lipidomics Reveals Early Metabolic Changes in Subjects with Schizophrenia: Effects of Atypical Antipsychotics

There is a critical need for mapping early metabolic changes in schizophrenia to capture failures in regulation of biochemical pathways and networks. This information could provide valuable insights about disease mechanisms, trajectory of disease progression, and diagnostic biomarkers. We used a lipidomics platform to measure individual lipid species in 20 drug-naïve patients with a first episode of schizophrenia (FE group), 20 patients with chronic schizophrenia that had not adhered to prescribed medications (RE group), and 29 race-matched control subjects without schizophrenia. Lipid metabolic profiles were evaluated and compared between study groups and within groups before and after treatment with atypical antipsychotics, risperidone and aripiprazole. Finally, we mapped lipid profiles to n3 and n6 fatty acid synthesis pathways to elucidate which enzymes might be affected by disease and treatment. Compared to controls, the FE group showed significant down-regulation of several n3 polyunsaturated fatty acids (PUFAs), including 20:5n3, 22:5n3, and 22:6n3 within the phosphatidylcholine and phosphatidylethanolamine lipid classes. Differences between FE and controls were only observed in the n3 class PUFAs; no differences where noted in n6 class PUFAs. The RE group was not significantly different from controls, although some compositional differences within PUFAs were noted. Drug treatment was able to correct the aberrant PUFA levels noted in FE patients, but changes in re patients were not corrective. Treatment caused increases in both n3 and n6 class lipids. These results supported the hypothesis that phospholipid n3 fatty acid deficits are present early in the course of schizophrenia and tend not to persist throughout its course. These changes in lipid metabolism could indicate a metabolic vulnerability in patients with schizophrenia that occurs early in development of the disease. © 2013 McEvoy et al

Stability of blood-based biomarkers of Alzheimer's disease over multiple freeze-thaw cycles.

Introduction: Freeze-thaw instability may contribute to preanalytical variation in blood-based biomarker studies. We investigated the effects of up to four freeze-thaw cycles on single molecule array immunoassays of serum neurofilament light chain and plasma total tau, amyloid β 1-40 (Aß40), and Aβ 1-42 (Aβ42). Methods: Individuals who had peripheral venepuncture during investigation of suspected neurodegenerative disease were recruited. After standardized preprocessing, 200 μL of plasma and serum aliquots were stored at -80°C within 60 minutes. Aliquots underwent one to four freeze-thaw cycles. Results: There was no significant difference across four freeze-thaw cycles for serum neurofilament light chain (n = 12), plasma total tau (n = 11), or plasma Aβ42 (n = 12). For plasma Aβ40 (n = 14), there were significant median reductions by ratios of .96 and .92 at the third and fourth cycles, respectively. Discussion: Up to four freeze-thaw cycles do not influence single molecule array blood biomarkers of neurofilament light chain, total tau, or Aβ42, with at most minor reductions in Aβ40

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders

Purpose of ReviewSleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions.Recent FindingsTo date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes.SummarySuccess of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.Peer reviewe

Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC

A comparative study of the characteristics of French Fries produced by deep fat frying and air frying

Air frying is being projected as an alternative to deep fat frying for producing snacks such as French Fries. In air frying, the raw potato sections are essentially heated in hot air containing fine oil droplets, which dehydrates the potato and attempts to impart the characteristics of traditionally produced French fries, but with a substantially lower level of fat absorbed in the product. The aim of this research is to compare: 1) the process dynamics of air frying with conventional deep fat frying under otherwise similar operating conditions, and 2) the products formed by the two processes in terms of color, texture, microstructure, calorimetric properties and sensory characteristics Although, air frying produced products with a substantially lower fat content but with similar moisture contents and color characteristics, it required much longer processing times, typically 21 minutes in relation to 9 minutes in the case of deep fat frying. The slower evolution of temperature also resulted in lower rates of moisture loss and color development reactions. DSC studies revealed that the extent of starch gelatinization was also lower in the case of air fried product. In addition, the two types of frying also resulted in products having significantly different texture and sensory characteristics

Inferring short-term volatility indicators from Bitcoin blockchain

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume