Development, Optimization, and Evaluation of a Duplex Droplet Digital PCR Assay To Quantify the T-nos/hmg Copy Number Ratio in Genetically Modified Maize.

Certified reference materials (CRMs) are required to guarantee the reliability of analytical measurements. The CRMs available in the field of genetically modified organisms (GMOs) are characterized using real-time polymerase chain reaction (qPCR). This technology has limited application, because of its dependence on a calibrant. The objective of this study was to obtain a method with higher metrological quality, to characterize the CRMs for their contents of T-nos/hmg copy number ratio in maize. A duplex droplet digital PCR (ddPCR) assay was developed and optimized by a central composite design. The developed method achieved an absolute limit of detection (LOD) of 11 cP T-nos, a relative LOD of 0.034%, a limit of quantification (LOQ) of 23 cP (relative LOQ of 0.08%), and a dynamic range of 0.08%-100% T-nos/hmg ratio. The specificity and applicability of the assay were established for the analysis of low T-nos concentrations (0.9%) in several corn varieties. The convenience of DNA digestion to reduce measurement bias in the case of multiple-copy binding was confirmed through an enzymatic restriction assay. Given its overall performance, this method can be used to characterize CRM candidates for their contents of T-nos/hmg ratio.

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01).

Draft Genome Sequences of 18 Salmonella enterica subsp. enterica Serovar Oranienburg Strains Isolated from Rivers in Northwestern Mexico.

Salmonella enterica subsp. enterica serovar Oranienburg is recognized as a foodborne pathogen widely distributed in the environment. Here, we report 18 draft genomes of S Oranienburg strains isolated from rivers in the northwestern region of Mexico.