Quantitative Forecasting of Risk for PTSD Using Ecological Factors: A Deep Learning Application

Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning a large population possible. An exhaustive literature review was conducted to identify multiple ecological risk factors for PTSD. A questionnaire assessing these factors was designed and distributed among residents of southern Israel who have been exposed to terror attacks; data were collected from 1,290 residents. A neural network classification algorithm was used to predict the likelihood of a PTSD diagnosis. Assessed by cross-validation, the prediction of PTSD diagnostic status yielded a mean area under receiver operating characteristics curve of .91 (F score = .83). This study is a novel attempt to implement a neural network classification algorithm using ecological risk factors to predict potential risk for PTSD. Preexposure identification of future soldiers and other individuals at risk for PTSD from a large population of candidates is feasible using machine learning methods and readily collected ecological factors

Nanoprodrugs of NSAIDs: Preparation and Characterization of Flufenamic Acid Nanoprodrugs

We demonstrated that hydrophobic derivatives of the nonsteroidal anti-inflammatory drug (NSAID)flufenamic acid (FA), can be formed into stable nanometer-sized prodrugs (nanoprodrugs) that inhibit the growth of glioma cells, suggesting their potential application as anticancer agent. We synthesized highly hydrophobic monomeric and dimeric prodrugs of FA via esterification and prepared nanoprodrugs using spontaneous emulsification mechanism. The nanoprodrugs were in the size range of 120 to 140 nm and physicochemically stable upon long-term storage as aqueous suspension, which is attributed to the strong hydrophobic interaction between prodrug molecules. Importantly, despite the highly hydrophobic nature and water insolubility, nanoprodrugs could be readily activated into the parent drug by porcine liver esterase, presenting a potential new strategy for novel NSAID prodrug design. The nanoprodrug inhibited the growth of U87-MG glioma cells with IC50 of 20 μM, whereas FA showed IC50 of 100 μM, suggesting that more efficient drug delivery was achieved with nanoprodrugs

Kinematics of Mass Transport Deposits revealed by magnetic fabrics

This study was supported by the Israel Science Foundation (ISF grants No. 1245/11 and 1436/14). We thank the Editor A. V. Newman and the reviewers M. Jackson and B. Almqvist, whose comments and suggestions improved the quality of our manuscript. The laboratory assistance of Ran Issachar and Daniel Zvi is highly acknowledged. All data used in this analysis is presented in Figures 1-4 and Table 1. Correspondence and requests for materials should be addressed to R.W. ([email protected]).Peer reviewedPublisher PD

Brain energy rescue:an emerging therapeutic concept for neurodegenerative disorders of ageing

The brain requires a continuous supply of energy in the form of ATP, most of which is produced from glucose by oxidative phosphorylation in mitochondria, complemented by aerobic glycolysis in the cytoplasm. When glucose levels are limited, ketone bodies generated in the liver and lactate derived from exercising skeletal muscle can also become important energy substrates for the brain. In neurodegenerative disorders of ageing, brain glucose metabolism deteriorates in a progressive, region-specific and disease-specific manner — a problem that is best characterized in Alzheimer disease, where it begins presymptomatically. This Review discusses the status and prospects of therapeutic strategies for countering neurodegenerative disorders of ageing by improving, preserving or rescuing brain energetics. The approaches described include restoring oxidative phosphorylation and glycolysis, increasing insulin sensitivity, correcting mitochondrial dysfunction, ketone-based interventions, acting via hormones that modulate cerebral energetics, RNA therapeutics and complementary multimodal lifestyle changes

A co-condensation model for in-flight synthesis of metal-carbide nanoparticles in thermal plasma jet

We present a theoretical analysis of the formation, growth, and transport of two-component nanoparticles in thermal plasma jet. The approach of the aerosol science and the idea of multicomponent co-condensation are employed for the analysis. The processes of homogeneous nucleation, heterogeneous growth, and coagulations due to Brownian collisions are considered in combination with the convective and diffusive transport of particles and the reacting gases within an axisymmetric domain. As a particular example, the authors study multicomponent co-condensation of metal-carbide nanoparticles from various precursors in a DC plasma gun operating in an argon atmosphere

Carbon nanotube based composite membranes for water desalination by membrane distillation

New technologies are required to improve desalination efficiency and increase water treatment capacities. One promising low energy technique to produce potable water from either sea or sewage water is membrane distillation (MD). However, to be competitive with other desalination processes, membranes need to be designed specifically for the MD process requirements. Here we report on the design of carbon nanotube (CNT) based composite material membranes for direct contact membrane distillation (DCMD). The membranes were characterized and tested in a DCMD setup under different feed temperatures and test conditions. The composite CNT structures showed significantly improved performance compared to their pure self-supporting CNT counterparts. The best composite CNT membranes gave permeabilities as high as 3.3×(10 to the 12th power)kg/m s Pa) with an average salt rejection of 95% and lifespan of up to 39 h of continuous testing, making them highly promising candidates for DCMD