Definition of a concentration and RNA extraction protocol for optimal whole genome sequencing of SARS-CoV-2 in wastewater (ANRS0160).

Monitoring the presence of RNA from emerging pathogenic viruses, such as SARS-CoV-2, in wastewater (WW) samples requires suitable methods to ensure an effective response. Genome sequencing of WW is one of the crucial methods, but it requires high-quality RNA in sufficient quantities, especially for monitoring emerging variants. Consequently, methods for viral concentration and RNA extraction from WW samples have to be optimized before sequencing.

Highly efficient and sensitive membrane-based concentration process allows quantification, surveillance, and sequencing of viruses in large volumes of wastewater.

Wastewater-based epidemiology is experiencing exponential development. Despite undeniable advantages compared to patient-centered approaches (cost, anonymity, survey of large populations without bias, detection of asymptomatic infected peoples…), major technical limitations persist. Among them is the low sensitivity of the current methods used for quantifying and sequencing viral genomes from wastewater.

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study.

Background: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers.

Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model.

Background And Aims: Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps.

Methods: We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies.

Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases.

Purpose: To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs).

Materials And Methods: This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material-enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard.