Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides.

Background: Molecular profiling of estrogen receptor (ER), progesterone receptor (PR), and ERBB2 (also known as Her2) is essential for breast cancer diagnosis and treatment planning. Nevertheless, current methods rely on the qualitative interpretation of immunohistochemistry and fluorescence in situ hybridization (FISH), which can be costly, time-consuming, and inconsistent. Here we explore the clinical utility of predicting receptor status from digitized hematoxylin and eosin-stained (H&E) slides using machine learning trained and evaluated on a multi-institutional dataset.

Unravelling a Latent Pathobiome Across Coral Reef Biotopes.

Previous studies on disease in coral reef organisms have neglected the natural distribution of potential pathogens and the genetic factors that underlie disease incidence. This study explores the intricate associations between hosts, microbial communities, putative pathogens, antibiotic resistance genes (ARGs) and virulence factors (VFs) across diverse coral reef biotopes. We observed a substantial compositional overlap of putative bacterial pathogens, VFs and ARGs across biotopes, consistent with the 'everything is everywhere, but the environment selects' hypothesis.

Using artificial intelligence to prioritize pathology samples: report of a test drive.

The digital transformation of pathology, through automation and computational tools, addresses current challenges in the field. This study evaluates Paige Pan Cancer, a novel artificial intelligence tool based on the Virchow foundation model, designed to flag invasive cancer in haematoxylin and eosin-stained slides from 16 primary tissue types. Using 62 cases from the Ipatimup Pathology Laboratory, we found the tool had a sensitivity of 93.

Development and validation of an experimental life support system to study coral reef microbial communities.

In the present study, we developed and validated an experimental life support system (ELSS) designed to investigate coral reef associated bacterial communities. The microcosms in the ELSS consisted of coral reef sediment, synthetic seawater, and specimens of five benthic reef species. These included two hard corals Montipora digitata and Montipora capricornis, a soft coral Sarcophyton glaucum, a zoanthid Zoanthus sp.

Image analysis for bright-field HER2 in situ hybridization: validation for clinical use.

The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio.