Ecologically sustainable benchmarking of AI models for histopathology.

Deep learning (DL) holds great promise to improve medical diagnostics, including pathology. Current DL research mainly focuses on performance. DL implementation potentially leads to environmental consequences but approaches for assessment of both performance and carbon footprint are missing.

Surgical pathology and sustainable development: international landscape and prospects.

The healthcare sector significantly contributes to global greenhouse gas emissions, with surgical pathology (SP) playing a notable role. This review explores the ecological transformation of SP, offering a global overview of existing challenges and sustainable initiatives worldwide.While some countries, such as the UK and France, have developed national strategies to reduce the carbon footprint of healthcare, including SP, many regions remain at an early stage of implementing green practices.

Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study.

Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated data. However, achieving this requires robust collaboration among pathologists, computer scientists, and other researchers to ensure data quality and consistency.

ADAMTS12 promotes fibrosis by restructuring extracellular matrix to enable activation of injury-responsive fibroblasts.

Fibrosis represents the uncontrolled replacement of parenchymal tissue with extracellular matrix (ECM) produced by myofibroblasts. While genetic fate-tracing and single-cell RNA-Seq technologies have helped elucidate fibroblast heterogeneity and ontogeny beyond fibroblast to myofibroblast differentiation, newly identified fibroblast populations remain ill defined, with respect to both the molecular cues driving their differentiation and their subsequent role in fibrosis. Using an unbiased approach, we identified the metalloprotease ADAMTS12 as a fibroblast-specific gene that is strongly upregulated during active fibrogenesis in humans and mice.

Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images.

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions.

3D Humanized Bioprinted Tubulointerstitium Model to Emulate Renal Fibrosis In Vitro.

Chronic kidney disease (CKD) leads to a gradual loss of kidney function, with fibrosis as pathological endpoint, which is characterized by extracellular matrix (ECM) deposition and remodeling. Traditionally, in vivo models are used to study interstitial fibrosis, through histological characterization of biopsy tissue. However, ethical considerations and the 3Rs (replacement, reduction, and refinement) regulations emphasizes the need for humanized 3D in vitro models.

Renal IL-23-Dependent Type 3 Innate Lymphoid Cells Link Crystal-induced Intrarenal Inflammasome Activation with Kidney Fibrosis.

Chronic inflammasome activation in mononuclear phagocytes (MNPs) promotes fibrosis in various tissues, including the kidney. The cellular and molecular links between the inflammasome and fibrosis are unclear. To address this question, we fed mice lacking various immunological mediators an adenine-enriched diet, which causes crystal precipitation in renal tubules, crystal-induced inflammasome activation, and renal fibrosis.

Robust, credible, and interpretable AI-based histopathological prostate cancer grading.

Background: Prostate cancer (PCa) is among the most common cancers in men and its diagnosis requires the histopathological evaluation of biopsies by human experts. While several recent artificial intelligence-based (AI) approaches have reached human expert-level PCa grading, they often display significantly reduced performance on external datasets. This reduced performance can be caused by variations in sample preparation, for instance the staining protocol, section thickness, or scanner used.

DNA-binding protein-A: a multitool in tubular epithelial cells.

Acute kidney injury is still associated with high morbidity and mortality. Reichardt et al. investigated DNA-binding protein-A (Ybx3) in acute kidney injury induced by ischemia-reperfusion injury and found that mice lacking Ybx3 have altered mitochondrial function and increased antioxidant activity, making them more resistant to ischemia-reperfusion injury-acute kidney injury.

Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative.

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges.

Asciminib antagonizes transplantable BCR::ABL1-positive lymphoid blast crisis in vivo by targeting malignant stem cells.

Philadelphia chromosome-positive (Ph+) lymphoid blast crisis (BC), emanating from chronic myeloid leukemia (CML), is a fatal disease with limited treatment options. Asciminib (ABL001) is a novel selective allosteric inhibitor of the ABL kinase with high efficacy against TKI-resistant BCR::ABL1. In this study, we demonstrate significant suppression of an aggressive B-lymphoblastic disease and restoration of normal hematopoiesis in an inducible transgenic mouse model of p210-BCR::ABL1-positive CML-BC.

Grainyhead-like 2 Deficiency and Kidney Cyst Growth in a Mouse Model.

Key Points: Our study reveals segment-specific mechanisms in cystic kidney disease and suggests as a modifier of collecting duct–derived cyst progression. Our data demonstrate that genetic deletion of accelerates disease progression in a cystic mouse model.

Background: The transcription factor grainyhead-like 2 (GRHL2) plays a crucial role in maintaining the epithelial barrier properties of the kidney collecting duct and is important to osmoregulation.

Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models.

Objectives: Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl 4 )-induced CLD models.

Materials And Methods: Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl 4 for 8 weeks.

[Artificial intelligence in kidney transplant pathology].

Background: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology.

Aim: Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook.

Materials And Methods: Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney".

Histopathological biomarkers for predicting the tumour accumulation of nanomedicines.

The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features.

tRigon: an R package and Shiny App for integrative (path-)omics data analysis.

Background: Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis.

[Development and progress of the National Autopsy Network (NATON)].

Background: Autopsies have long been considered the gold standard for quality assurance in medicine, yet their significance in basic research has been relatively overlooked. The COVID-19 pandemic underscored the potential of autopsies in understanding pathophysiology, therapy, and disease management. In response, the German Registry for COVID-19 Autopsies (DeRegCOVID) was established in April 2020, followed by the DEFEAT PANDEMIcs consortium (2020-2021), which evolved into the National Autopsy Network (NATON).