Background: Within the colorectal cancer (CRC) tumour microenvironment, tumour infiltrating lymphocytes (TILs) and tumour cell density (TCD) are recognised prognostic markers. Measurement of TILs and TCD using deep-learning (DL) on haematoxylin and eosin (HE) whole slide images (WSIs) could aid management.
Methods: HE WSIs from the primary tumours of 127 CRC patients were included.
Context: Anterior cruciate ligament reconstruction (ACLR) is a known risk factor for knee osteoarthritis (OA). Since no disease-modifying treatments for OA exist, it is critical to understand joint responses to physical activity following an ACLR. Understanding knee cartilage deformation through ultrasound may provide a better understanding of how knee cartilage responds to running, and how this may contribute to OA pathophysiology and risk.
View Article and Find Full Text PDFPurpose: Plasma-based liquid biopsy tests can detect tumor-specific genetic alterations and offer many advantages that complement tissue-based Comprehensive Genomic Profiling (CGP). However, age-related clonal hematopoiesis (CH) mutations can confound liquid biopsy results and potentially lead to incorrect therapy choice.
Experimental Design: We assessed the landscape of 16,812 liquid profiles across 49 cancer types using the Caris Assure assay, a whole exome and whole transcriptome NGS workflow that independently sequences both plasma-derived cell-free total nucleic acids (cfTNA) as well as the white blood cell DNA and RNA from the buffy coat.
Background: Regional lymph node (LN) status is a key prognostic factor in oesophageal cancer (OeC). Tumour-derived antigens can activate immune reactions in LNs, potentially reflecting the host's anti-tumour immune response. It remains unclear whether this response is homogeneous across all tumour negative LNs (LNneg) within individual OeC patients.
View Article and Find Full Text PDFObjective: We aim to demonstrate the versatility of the All of Us database as an important source of rare and undiagnosed disease (RUD) data, because of its large size and range of data types.
Materials And Methods: We searched the public data browser, electronic health record (EHR), and several surveys to investigate the prevalence, mental health, healthcare access, and other data of select RUDs.
Results: Several RUDs have participants in All of Us [eg, 75 of 100 rare infectious diseases (RIDs)].