Experiences of health tracking in mobile apps for multiple sclerosis: A qualitative content analysis of user reviews.

Background: Mobile health applications (apps) are promising condition self-management tools for people living with multiple sclerosis (MS). However, most existing apps do not include health tracking features. This gap has been raised as a priority research topic, but the development of new self-management apps will require designers to understand the context and needs of those living with MS.

Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews.

Background: Tracking and visualizing health data using mobile apps can be an effective self-management strategy for mental health conditions. However, little evidence is available to guide the design of mental health-tracking mechanisms.

Objective: The aim of this study was to analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms for future apps.

Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives.

Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions.

Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering.

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic, and industrial processes by modification of naturally occurring proteins or by invention of proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy.

A deep learning genome-mining strategy for biosynthetic gene cluster prediction.

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved.

Novel genes in Human Asthma Based on a Mouse Model of Allergic Airway Inflammation and Human Investigations.

Purpose: Based on a previous gene expression study in a mouse model of asthma, we selected 60 candidate genes and investigated their possible roles in human asthma.

Methods: In these candidate genes, 90 SNPs were genotyped using MassARRAY technology from 311 asthmatic children and 360 healthy controls of the Hungarian (Caucasian) population. Moreover, gene expression levels were measured by RT PCR in the induced sputum of 13 asthmatics and 10 control individuals.

Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy.

Despite famous serendipitous drug repositioning success stories, systematic projects have not yet delivered the expected results. However, repositioning technologies are gaining ground in different phases of routine drug development, together with new adaptive strategies. We demonstrate the power of the compound information pool, the ever-growing heterogeneous information repertoire of approved drugs and candidates as an invaluable catalyzer in this transition.

Drug repositioning for treatment of movement disorders: from serendipity to rational discovery strategies.

Movement disorders are a heterogeneous group of both common and rare neurological conditions characterized by abnormalities of motor functions and movement patterns. This work overviews recent successes and ongoing studies of repositioning relating to this disease group, which underscores the challenge of integrating the voluminous and heterogeneous findings required for making suitable drug repositioning decisions. In silico drug repositioning methods hold the promise of automated fusion of heterogeneous information sources, but the controllable, flexible and transparent incorporation of the expertise of medicinal chemists throughout the repositioning process remains an open challenge.

Complex analysis of multiple single nucleotide polymorphisms as putative risk factors of tooth agenesis in the Hungarian population.

Objectives: The role was studied of multiple single nucleotide polymorphisms in tooth agenesis in the Hungarian population using a complex approach.

Methods: Eight SNPs, PAX9 -912 C/T, PAX9 -1031 A/G, MSX1 3755 A/G, FGFR1 T/C rs881301, IRF6 T/C rs764093, AXIN2-8150 A/G, AXIN2-8434 A/G and AXIN2-30224 C/T, were studied in 192 hypodontia and 17 oligodontia cases and in 260 healthy volunteers. Case-control analysis was performed to test both allelic and genotypic associations as well as associations at the level of haplotypes.

Evaluation of a partial genome screening of two asthma susceptibility regions using bayesian network based bayesian multilevel analysis of relevance.

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA).