Histopathology and proteomics are synergistic for high-grade serous ovarian cancer platinum response prediction.

Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E)-stained pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained whole slide images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts.

ViralPrimer: a web server to monitor viral nucleic acid amplification tests' primer efficiency during pandemics, with emphasis on SARS-CoV-2 and Mpox.

Summary: Accurate pathogen identification is crucial during outbreaks, especially with the emergence of new variants requiring frequent primer updates. However, resources for maintaining up-to-date verification of primer sequences are often limited, which poses challenges for reliable diagnosis and hinders potential monitoring efforts based on genome sequencing. To address this, we introduce ViralPrimer, a web server facilitating primer design, SARS-CoV-2 and Mpox variant monitoring, and adaptation to future threats.

Frequent CHD1 deletions in prostate cancers of African American men is associated with rapid disease progression.

We analyzed genomic data from the prostate cancer of African- and European American men to identify differences contributing to racial disparity of outcome. We also performed FISH-based studies of Chromodomain helicase DNA-binding protein 1 (CHD1) loss on prostate cancer tissue microarrays. We created CHD1-deficient prostate cancer cell lines for genomic, drug sensitivity and functional homologous recombination (HR) activity analysis.

Time-series sewage metagenomics distinguishes seasonal, human-derived and environmental microbial communities potentially allowing source-attributed surveillance.

Sewage metagenomics has risen to prominence in urban population surveillance of pathogens and antimicrobial resistance (AMR). Unknown species with similarity to known genomes cause database bias in reference-based metagenomics. To improve surveillance, we seek to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time.