Transcriptome assembly and profiling of Candida auris reveals novel insights into biofilm-mediated resistance

Candida auris has emerged as a significant global nosocomial pathogen. This is primarily due to its antifungal resistance profile but also its capacity to form adherent biofilm communities on a range of clinically important substrates. While we have a comprehensive understanding of how other Candida species resist and respond to antifungal challenge within the sessile phenotype, our current understanding of C. auris biofilm-mediated resistance is lacking. In this study, we are the first to perform transcriptomic analysis of temporally developing C. auris biofilms, which were shown to exhibit phase- and antifungal class-dependent resistance profiles. A de novo transcriptome assembly was performed, where sequenced sample reads were assembled into an ~11.5-Mb transcriptome consisting of 5,848 genes. Differential expression (DE) analysis demonstrated that 791 and 464 genes were upregulated in biofilm formation and planktonic cells, respectively, with a minimum 2-fold change. Adhesin-related glycosylphosphatidylinositol (GPI)-anchored cell wall genes were upregulated at all time points of biofilm formation. As the biofilm developed into intermediate and mature stages, a number of genes encoding efflux pumps were upregulated, including ATP-binding cassette (ABC) and major facilitator superfamily (MFS) transporters. When we assessed efflux pump activity biochemically, biofilm efflux was greater than that of planktonic cells at 12 and 24 h. When these were inhibited, fluconazole sensitivity was enhanced 4- to 16-fold. This study demonstrates the importance of efflux-mediated resistance within complex C. auris communities and may explain the resistance of C. auris to a range of antimicrobial agents within the hospital environment

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine

Caenorhabditis elegans as a model system for studying drug induced mitochondrial toxicity

Today HIV-1 infection is recognized as a chronic disease with obligatory lifelong treatment to keep viral titers below detectable levels. The continuous intake of antiretroviral drugs however, leads to severe and even life-threatening side effects, supposedly by the deleterious impact of nucleoside-analogue type compounds on the functioning of the mitochondrial DNA polymerase. For detailed investigation of the yet partially understood underlying mechanisms, the availability of a versatile model system is crucial. We therefore set out to develop the use of Caenorhabditis elegansto study drug induced mitochondrial toxicity. Using a combination of molecular-biological and functional assays, combined with a quantitative analysis of mitochondrial network morphology, we conclude that anti-retroviral drugs with similar working mechanisms can be classified into distinct groups based on their effects on mitochondrial morphology and biochemistry. Additionally we show that mitochondrial toxicity of antiretroviral drugs cannot be exclusively attributed to interference with the mitochondrial DNA polymerase

Teen Risk-Taking: Promising Prevention Programs and Approaches

To help close the knowledge gap and to help program directors, practitioners, and community leaders enlarge the network of effective programs and approaches for at-risk youth, Urban Institute researchers reviewed what is known about successful prevention interventions and their dissemination. They identified 51 problem behavior prevention interventions whose initial effectiveness has been demonstrated through scientific evaluation. A subset of 21 programs was selected on the basis of the rigor of their evaluations or the strength of their results for closer examination of the program elements and/or delivery modes that appeared to be associated with their effectiveness. The researchers also explored with the assistance of experienced prevention scientists and school-based practitioners what might be the essential elements of schools' and other community organizations' readiness to undertake research-based problem behavior prevention programming. This guidebook to promising programs and approaches offers the fruits of that research. It is our hope that it will provide a helpful starting point for the development of a larger, more sustainable network of effective prevention programs and approaches for at-risk teens.In the booklet you will find:An Update on Adolescent Risk-Taking -- what is known about the level and characteristics of teen risk-taking today and why it is both necessary and an opportune time to improve and expand the network of effective prevention programs for at-risk preteens and teens.The Common Elements of Successful Prevention Programs, briefly summarized, along with an explanation of the criteria used to select the 51 programs profiled in this guidebook.Moving from Research to Practice -- a discussion of the challenges facing practitioners seeking to replicate promising intervention programs or approaches, with some suggestions for ways to meet these challenges.A Prevention Readiness Questionnaire to help program directors and planners identify and assess factors necessary to create favorable conditions and circumstances for successful adaptation or replication of the programs or their salient components in new settings.Profiles of 51 Prevention Programs whose behavioral evaluations demonstrate their effectiveness. The profiles provide general information about the program, highlight unique features, summarize evaluation results, and give general contact information. The 21 (most) rigorously evaluated programs also have curriculum, training, and contact information included.A Handy Reference Chart for quick comparison of the 51 programs

Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years) and most of them (83.3%) were females. As a prerequisite for admission to college, students are required to pass a series of mandatory core classes and are expected to complete them in two semesters. Delay in completing the curriculum is considered low academic achievement. Parenting was assessed taking into account the mother and the father and considering two dimensions: responsiveness and demandingness. Perceived social support was analyzed considering four sources: parents, teachers, classmates, and best friend or boyfriend/girlfriend. Path analysis showed that, as hypothesized, responsiveness had a positive indirect effect on the perception of social support and enhanced achievement. Demandingness had a different effect in the case of the mother as compared to the father. In the mother model, demandingness had a positive direct effect on achievement. In the case of the father, however, the effect of demandingness had a negative and indirect impact on the perception of social support. Teachers were the only source of perceived social support that significantly predicted achievement. The pathway that belongs to teachers as a source of support was positive and direct. Implications for possible interventions are discussed.Fil: de la Iglesia, Guadalupe. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Freiberg Hoffmann, Agustín. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Family and parenting characteristics associated with marijuana use by Chilean adolescents

OBJECTIVE: Family involvement and several characteristics of parenting have been suggested to be protective factors for adolescent substance use. Some parenting behaviors may have stronger relationships with adolescent behavior while others may have associations with undesirable behavior among youth. Although it is generally acknowledged that families play an important role in the lives of Chilean adolescents, scant research exists on how different family and parenting factors may be associated with marijuana use and related problems in this population which has one of the highest rates of drug use in Latin America. METHODS: Using logistic regression and negative binomial regression, we examined whether a large number of family and parenting variables were associated with the possibility of Chilean adolescents ever using marijuana, and with marijuana-related problems. Analyses controlled for a number of demographic and peer-related variables. RESULTS: Controlling for other parenting and family variables, adolescent reports of parental marijuana use showed a significant and positive association with adolescent marijuana use. The multivariate models also revealed that harsh parenting by fathers was the only family variable associated with the number of marijuana-related problems youth experienced. CONCLUSION: Of all the family and parenting variables studied, perceptions of parental use of marijuana and harsh parenting by fathers were predictors for marijuana use, and the experience of marijuana-related problems. Prevention interventions need to continue emphasizing the critical socializing role that parental behavior plays in their children's development and potential use of marijuana.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3109755/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3109755/Accepted manuscrip

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2