Calibration and Analysis of a Multimodal Micro-CT and Structured Light Imaging System for the Evaluation of Excised Breast Tissue.

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 ?m for a 1.2?min scan. The SLI system, spanning wavelengths 490?nm to 800?nm and spatial frequencies up to 1.37 , was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, and phase function parameter, ?, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7?mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages

BACKGROUND: With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. RESULTS: We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. CONCLUSIONS: By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/]

Therapeutic treatment with an oral prodrug of the remdesivir parental nucleoside is protective against SARS-CoV-2 pathogenesis in mice

The coronavirus disease 2019 (COVID-19) pandemic remains uncontrolled despite the rapid rollout of safe and effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines, underscoring the need to develop highly effective antivirals. In the setting of waning immunity from infection and vaccination, breakthrough infections are becoming increasingly common and treatment options remain limited. Additionally, the emergence of SARS-CoV-2 variants of concern, with their potential to escape neutralization by therapeutic monoclonal antibodies, emphasizes the need to develop second-generation oral antivirals targeting highly conserved viral proteins that can be rapidly deployed to outpatients. Here, we demonstrate the in vitro antiviral activity and in vivo therapeutic efficacy of GS-621763, an orally bioavailable prodrug of GS-441524, the parent nucleoside of remdesivir, which targets the highly conserved virus RNA-dependent RNA polymerase. GS-621763 exhibited antiviral activity against SARS-CoV-2 in lung cell lines and two different human primary lung cell culture systems. GS-621763 was also potently antiviral against a genetically unrelated emerging coronavirus, Middle East Respiratory Syndrome CoV (MERS-CoV). The dose-proportional pharmacokinetic profile observed after oral administration of GS-621763 translated to dose-dependent antiviral activity in mice infected with SARS-CoV-2. Therapeutic GS-621763 administration reduced viral load and lung pathology; treatment also improved pulmonary function in COVID-19 mouse model. A direct comparison of GS-621763 with molnupiravir, an oral nucleoside analog antiviral which has recently received EUA approval, proved both drugs to be similarly efficacious in mice. These data support the exploration of GS-441524 oral prodrugs for the treatment of COVID-19

On the reproducibility of airborne sound insulation measurements of noise barriers; a case study

status: Published onlin

Batch effect removal methods for microarray gene expression data integration: A survey

Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called 'batch effects'. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis; and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB .http://insilico.ulb.ac.be. © The Author 2012. Published by Oxford University Press.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Sequential application of feature selection and extraction for predicting breast cancer aggressiveness

Breast cancer is a heterogenous disease with a large variance in prognosis of patients. It is hard to identify patients who would need adjuvant chemotherapy to survive. Using microarray based technology and various feature selection techniques, a number of prognostic gene expression signatures have been proposed recently. It has been shown that these signatures outperform traditional clinical guidelines for estimating prognosis. This paper studies the applicability of state-of-the-art feature extraction methods together with feature selection methods to develop more powerful prognosis estimators. Feature selection is used to remove features not related with the clinical issue investigated. If the resulted dataset is still described by a high number of probes, feature extraction methods can be applied to further reduce the dimension of the data set. In addition we derived six new signatures using three independent data sets, containing in total 610 samples. Additional information: http://como.vub.ac.be/~jtaminau/CSBio2010/© 2010 Springer-Verlag Berlin Heidelberg.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Validation of Merging Techniques for Cancer Microarray Data Sets

info:eu-repo/semantics/publishe

inSilicoDb: An R/bioconductor package for accessing human Affymetrix expert-curated datasets from GEO

Microarray technology has become an integral part of biomedical research and increasing amounts of datasets become available through public repositories. However, re-use of these datasets is severely hindered by unstructured, missing or incorrect biological samples information; as well as the wide variety of preprocessing methods in use. The inSilicoDb R/Bioconductor package is a command-line front-end to the InSilico DB, a web-based database currently containing 86 104 expert-curated human Affymetrix expression profiles compiled from 1937 GEO repository series. The use of this package builds on the Bioconductor project's focus on reproducibility by enabling a clear workflow in which not only analysis, but also the retrieval of verified data is supported. © The Author 2011. Published by Oxford University Press. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe