Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images. Using a ResNet 50 architecture pretrained on ImageNet and a previously successful strategy, we trained deep learning (DL)-based image analysis algorithms to predict sentinel status on hematoxylin/eosin-stained images of predominantly luminal, primary breast tumours from the INSEMA trial and three additional, independent cohorts (The Cancer Genome Atlas (TCGA) and cohorts from the University hospitals of Mannheim and Regensburg), and compared their performance with that of a logistic regression using clinical data only. Performance on an INSEMA hold-out set was investigated in a blinded manner. None of the generated image analysis algorithms yielded significantly better than random areas under the receiver operating characteristic curves on the test sets, including the hold-out test set from INSEMA. In contrast, the logistic regression fitted on the Mannheim cohort retained a better than random performance on INSEMA and Regensburg. Including the image analysis model output in the logistic regression did not improve performance further on INSEMA. Employing DL-based image analysis on histological slides, we could not predict SLN status for unseen cases in the INSEMA trial and other predominantly luminal cohorts.

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.

Background: Mitochondria play a crucial role in adapting to fluctuating energy demands, particularly in various heart diseases. This study investigates mitochondrial morphology near intercalated discs in left ventricular (LV) heart tissues, comparing samples from patients with sinus rhythm (SR), atrial fibrillation (AF), dilated cardiomyopathy (DCM), and ischemic cardiomyopathy (ICM). Methods: Transmission electron microscopy was used to analyze mitochondria within 0–3.5 μm and 3.5–7 μm of intercalated discs in 9 SR, 10 AF, 9 DCM, and 8 ICM patient samples. Parameters included mean size in µm² and elongation, count, percental mitochondrial area in the measuring frame, and a conglomeration score. Results: AF patients exhibited higher counts of small mitochondria in the LV myocardium, resembling SR. DCM and ICM groups had fewer, larger, and often hydropic mitochondria. Accumulation rates and percental mitochondrial area were similar across groups. Significant positive correlations existed between other defects/size and hydropic mitochondria and between count/area and conglomeration score, while negative correlations between count and size/other defects and between hydropic mitochondria and count could be seen as well. Conclusion: Mitochondrial parameters in the LV myocardium of AF patients were similar to those of SR patients, while DCM and ICM displayed distinct changes, including a decrease in number, an increase in size, and compromised mitochondrial morphology. Further research is needed to fully elucidate the pathophysiological role of mitochondrial morphology in different heart diseases, providing deeper insights into potential therapeutic targets and interventions.

Mitochondria play a crucial role in adapting to fluctuating energy demands, particularly in various heart diseases. In addition to functional analyses such as the measurement of ROS or ATP, analysis of mitochondrial ultrastructure can be used to draw further conclusions about their functions and effects in tissue. In this protocol, we introduce a set of measurements to compare the ultrastructural and functional characteristics of human left ventricular mitochondria, using transmission electron microscopy (TEM). Measured parameters included mean size in µm², elongation, count, percental mitochondrial area in the measuring frame, and a conglomeration score. We also introduce a novel method of defining hydropic mitochondria as a comparable evaluation standard. With this cluster of measurement parameters, we aim to contribute a protocol for studying human mitochondrial morphology, distribution, and functionality.

Neutrophils are central executors of innate immunity. Yet murine models are inherently limited by low baseline neutrophil counts. NSG mice are among the most widely used models for xenotransplantation and studies on the humanized immune system. Although G-CSF is known to stimulate granulopoiesis, the dose- and schedule-dependent effects of intraperitoneal G-CSF administration have not been systematically characterized in this immunodeficient background. Male NSG mice received intraperitoneal G-CSF according to one of five regimens (n = 6 per group): group 0 served as the saline control, group 1 received a single dose of 250 µg/kg G-CSF administered at 48 h; group 2 received a single dose of 250 µg/kg G-CSF administered at 24 h; group 3 received three doses of 250 µg/kg administered G-CSF at 0 h, 24 h, and 48 h and group 4 received a single dose of 500 µg/kg G-CSF administered at 48 h. All animals were sacrificed at 72 h. Circulating neutrophils were then quantified by flow cytometry, bone marrow neutrophil proportions by panoptic smear analysis, and splenic neutrophil abundance by Ly6G immunofluorescence. Systemic neutrophil activation was assessed via plasma neutrophil elastase (NE) activity and cell-free DNA (cfDNA) levels. Repeated G-CSF administration (Group 3) induced an approximately 13-fold expansion of circulating neutrophils, approaching the human physiological range, with significant increases also observed in bone marrow and a trend towards increased neutrophil abundance in the spleen. A single dose of 250 µg/kg administered at 24 h (group 2), produced significant neutrophil expansion in peripheral blood and bone marrow but not in the spleen, while all other single-dose regimens failed to induce significant expansion in any compartment. NE activity and cfDNA concentrations and a selected cytokine panel remained unaltered across all groups. This systematic comparison establishes repeated intraperitoneal G-CSF administration as a reproducible strategy to achieve human-like neutrophil levels in NSG mice without inducing systemic inflammation. This provides a validated protocol with direct utility in translational models of neutrophil-dependent diseases.