226 research outputs found
Evolution and Impact of High Content Imaging
Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990â˛s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:⢠Evolution and impact of high content imaging: An academic perspective⢠Evolution and impact of high content imaging: An industry perspective⢠Evolution of high content image analysis⢠Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications⢠The role of data integration and multiomics⢠The role and evolution of image data repositories and sharing standards⢠Future perspective of high content imaging hardware and softwar
Development and preliminary testing of the psychosocial adjustment to hereditary diseases scale
Background: The presence of Lynch syndrome (LS) can bring a lifetime of uncertainty to an entire family as
members adjust to living with a high lifetime cancer risk. The research base on how individuals and families adjust
to genetic-linked diseases following predictive genetic testing has increased our understanding of short-term
impacts but gaps continue to exist in knowledge of important factors that facilitate or impede long-term
adjustment. The failure of existing scales to detect psychosocial adjustment challenges in this population has led researchers to question the adequate sensitivity of these instruments. Furthermore, we have limited insight into the role of the family in promoting adjustment.
Methods: The purpose of this study was to develop and initially validate the Psychosocial Adjustment to Hereditary
Diseases (PAHD) scale. This scale consists of two subscales, the Burden of Knowing (BK) and Family Connectedness (FC). Items for the two subscales were generated from a qualitative data base and tested in a sample of 243 participants from families with LS.
Results: The Multitrait/Multi-Item Analysis Program-Revised (MAP-R) was used to evaluate the psychometric
properties of the PAHD. The findings support the convergent and discriminant validity of the subscales. Construct
validity was confirmed by factor analysis and Cronbachâs alpha supported a strong internal consistency for BK (0.83)
and FC (0.84).
Conclusion: Preliminary testing suggests that the PAHD is a
psychometrically sound scale capable of assessing
psychosocial adjustment. We conclude that the PAHD may be a valuable monitoring tool to identify individuals and
families who may require therapeutic interventions
A Machine Learning Classifier Trained on Cancer Transcriptomes Detects NF1 Inactivation Signal in Glioblastoma
We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM)
Elaboration and properties of plasticised chitosan-based exfoliated nano-biocomposites
A series of plasticised chitosan-based materials and nanocomposites were successfully prepared by thermomechanical kneading. During the processing, the montmorillonite (MMT) platelets were fully delaminated. The nanoclay type and content and the preparation method were seen to have an impact on the crystallinity, morphology, glass transition temperature, and mechanical properties of the samples. When higher content (5%) of MMTâNa+ or either content (2.5% or 5%) of chitosan-organomodified MMT (OMMTâCh) was used, increases in crystallinity and glass transition temperature were observed. Compared to the neat chitosan, the plasticised chitosan-based nano-biocomposites showed drastically improved mechanical properties, which can be ascribed to the excellent dispersion and exfoliation of nanoclay and the strong affinity between the nanoclay and the chitosan matrix. The best mechanical properties obtained were Young's modulus of 164.3 MPa, tensile strength of 13.9 MPa, elongation at break of 62.1%, and energy at break of 0.671 MPa. While the degree of biodegradation was obviously increased by the presence of glycerol, a further increase might be observed especially by the addition of unmodified nanoclay. This could surprisingly contribute to full (100%) biodegradation after 160 days despite the well-known antimicrobial property of chitosan. The results in this study demonstrate the great potential of plasticised chitosan-based nano-biocomposites in applications such as e.g., biodegradable packaging materials
Managing Soybean Insects
36 pp., 3 tables, 18 illustrations, 28 photosThis publication details integrated pest management principles for managing soybean insects. Topics include variety selection, inspecting fields for insects and damage, soybean insect pests, and insecticide application methods. A table lists products registered for controlling soybean insects
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Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.
Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer
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