Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics.

BACKGROUND AND PURPOSE: Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation. MATERIALS AND METHODS: Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model. RESULTS: Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96). CONCLUSIONS: This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.

A Single-Tube HNB-Based Loop-Mediated Isothermal Amplification for the Robust Detection of the Ostreid herpesvirus 1.

The Ostreid herpesvirus 1 species affects shellfish, contributing significantly to high economic losses during production. To counteract the threat related to mortality, there is a need for the development of novel point-of-care testing (POCT) that can be implemented in aquaculture production to prevent disease outbreaks. In this study, a simple, rapid and specific colorimetric loop-mediated isothermal amplification (LAMP) assay has been developed for the detection of Ostreid herpesvirus1 (OsHV-1) and its variants infecting Crassostrea gigas (C. gigas). The LAMP assay has been optimized to use hydroxynaphthol blue (HNB) for visual colorimetric distinction of positive and negative templates. The effect of an additional Tte UvrD helicase enzyme used in the reaction was also evaluated with an improved reaction time of 10 min. Additionally, this study provides a robust workflow for optimization of primers for uncultured viruses using designed target plasmid when DNA availability is limited.