"The doctors were more knowledgeable about what I had": patient views on the value of point-of-care tests for managing respiratory infections in European primary care.

Background: Point-of-care tests (POCT) can support diagnosis of patients with community acquired acute respiratory tract infections (CA-RTI) in primary care and thereby reduce uncertainty whether antibiotics may benefit patients. However, successful roll out of POCTs need to be built on a deep understanding of patients' perspectives on the place of POCTs in patient centred care.

Aim: To explore patients' perceptions of the value of POCTs during consultations for CA-RTI.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI.

Automated curation of large-scale cancer histopathology image datasets using deep learning.

Background: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements.

Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4).

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload.

Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer.

Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally.

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology.

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types.

Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study.

Background: Homologous Recombination Deficiency (HRD) is a pan-cancer predictive biomarker that identifies patients who benefit from therapy with PARP inhibitors (PARPi). However, testing for HRD is highly complex. Here, we investigated whether Deep Learning can predict HRD status solely based on routine Hematoxylin & Eosin (H&E) histology images in ten cancer types.

An overview and a roadmap for artificial intelligence in hematology and oncology.

Background: Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals.

Characterisation of tumour-immune phenotypes and PD-L1 positivity in squamous bladder cancer.

Aims: Immune checkpoint inhibitor (ICI) therapy has become a viable treatment strategy in bladder cancer. However, treatment responses vary, and improved biomarkers are needed. Crucially, the characteristics of immune cells remain understudied especially in squamous differentiated bladder cancer (sq-BLCA).

Dereplication of Gambierdiscusbalechii extract by LC-HRMS and in vitro assay: First description of a putative ciguatoxin and confirmation of 44-methylgambierone.

Marine toxins have a significant impact on seafood resources and human health. Up to date, mainly based on bioassays results, two genera of toxic microalgae, Gambierdiscus and Fukuyoa have been hypothesized to produce a suite of biologically active compounds, including maitotoxins (MTXs) and ciguatoxins (CTXs) with the latter causing ciguatera poisoning (CP) in humans. The global ubiquity of these microalgae and their ability to produce (un-)known bioactive compounds, necessitates strategies for screening, identifying, and reducing the number of target algal species and compounds selected for structural elucidation.

Impact of Fecal Microbiota Transplantation on Gut Bacterial Bile Acid Metabolism in Humans.

Fecal microbiota transplantation (FMT) is a promising therapeutic modality for the treatment and prevention of metabolic disease. We previously conducted a double-blind, randomized, placebo-controlled pilot trial of FMT in obese metabolically healthy patients in which we found that FMT enhanced gut bacterial bile acid metabolism and delayed the development of impaired glucose tolerance relative to the placebo control group. Therefore, we conducted a secondary analysis of fecal samples collected from these patients to assess the potential gut microbial species contributing to the effect of FMT to improve metabolic health and increase gut bacterial bile acid metabolism.

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label.

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types.

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types.

Molecular Profiling Reveals Characteristic and Decisive Signatures in Patients after Allogeneic Stem Cell Transplantation Suffering from Invasive Pulmonary Aspergillosis.

Despite available diagnostic tests and recent advances, diagnosis of pulmonary invasive aspergillosis (IPA) remains challenging. We performed a longitudinal case-control pilot study to identify host-specific, novel, and immune-relevant molecular candidates indicating IPA in patients post allogeneic stem cell transplantation (alloSCT). Supported by differential gene expression analysis of six relevant in vitro studies, we conducted RNA sequencing of three alloSCT patients categorized as probable IPA cases and their matched controls without infection (66 samples in total).

Classical mathematical models for prediction of response to chemotherapy and immunotherapy.

Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real world-data of human patients. In this study, we fitted a range of differential equation models to tumor volume measurements of patients undergoing chemotherapy or cancer immunotherapy for solid tumors.

Crown-Cut Endobronchial Ultrasound Guided Transbronchial Aspiration Needle: First Real-World Experiences.

Advancements in personalized medicine have increased the demand for quantity and preservation of tissue architecture of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) samples. These demands may be addressed by the SonoTip TopGain needle, which has a 3-point crown-cut design that contrasts with the standard single bevel design of the ViziShot 2. The objective was to compare the SonoTip TopGain and ViziShot 2 needles by considering biopsy sample characteristics, diagnostic accuracy, and patient safety.

Ciguatoxin in Hawai'i: Fisheries forecasting using geospatial and environmental analyses for the invasive Cephalopholis argus (Epinephelidae).

Invasive species can precede far-reaching environmental and economic consequences. In the Hawai'ian Archipelago Cephalopholis argus (family Serranidae) is an established invasive species, now recognized as the dominant local reef predator, negatively impacting the native ecosystem and local fishery. In this region, no official C.

Extraction and LC-MS/MS Analysis of Ciguatoxins: A Semi-Targeted Approach Designed for Fish of Unknown Origin.

Ciguatoxins (CTXs) are polyether marine biotoxins that can cause ciguatera poisoning (CP) after the consumption of fish or invertebrates containing sub ppb levels; concentrations that present a challenge for current extraction and analysis methods. Here, a newly developed and (partly) validated single-day extraction protocol is presented. First, the fish sample is broken-down by enzymatic digestion, followed by extraction and extract clean-up by defatting and two solid-phase extractions.