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Autopsy examination of lungs from seven patients who died from Covid-19 showed intussusceptive angiogenesis in greater profusion than was found in lungs from patients who died from influenza or in uninfected lungs that were rejected for transplantation.

SARS-CoV-2 infection poses a major threat to the lungs and multiple other organs, occasionally causing death. Until effective vaccines are developed to curb the pandemic, it is paramount to define the mechanisms and develop protective therapies to prevent organ dysfunction in patients with COVID-19. Individuals that develop severe manifestations have signs of dysregulated innate and adaptive immune responses. Emerging evidence implicates neutrophils and the disbalance between neutrophil extracellular trap (NET) formation and degradation plays a central role in the pathophysiology of inflammation, coagulopathy, organ damage, and immunothrombosis that characterize severe cases of COVID-19. Here, we discuss the evidence supporting a role for NETs in COVID-19 manifestations and present putative mechanisms, by which NETs promote tissue injury and immunothrombosis. We present therapeutic strategies, which have been successful in the treatment of immunο-inflammatory disorders and which target dysregulated NET formation or degradation, as potential approaches that may benefit patients with severe COVID-19.

For the first time, we have used phase-contrast X-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopathological examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross-section of 3.5mm were scanned at a laboratory setup as well as at a parallel beam setup at a synchrotron radiation facility the synchrotron in a parallel beam configuration. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by high-resolution cone beam X-ray tomography and scanning electron microscopy. Furthermore, we implemented a fully automated analysis of the tissue structure in the form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis.

Nintedanib, a tyrosine kinase inhibitor approved for the treatment of idiopathic pulmonary fibrosis, has anti-fibrotic, anti-inflammatory, and anti-angiogenic activity. We explored the impact of nintedanib on microvascular architecture in a pulmonary fibrosis model. Lung fibrosis was induced in C57Bl/6 mice by intratracheal bleomycin (0.5 mg/kg). Nintedanib was started after the onset of lung pathology (50 mg/kg twice daily, orally). Micro-computed tomography was performed via volumetric assessment. Static lung compliance and forced vital capacity were determined by invasive measurements. Mice were subjected to bronchoalveolar lavage and histologic analyses, or perfused with a casting resin. Microvascular corrosion casts were imaged by scanning electron microscopy and synchrotron radiation tomographic microscopy, and quantified morphometrically. Bleomycin administration resulted in a significant increase in higher-density areas in the lungs detected by micro-computed tomography, which was significantly attenuated by nintedanib. Nintedanib significantly reduced lung fibrosis and vascular proliferation, normalized the distorted microvascular architecture, and was associated with a trend toward improvement in lung function and inflammation. Nintedanib resulted in a prominent improvement in pulmonary microvascular architecture, which outperformed the effect of nintedanib on lung function and inflammation. These findings uncover a potential new mode of action of nintedanib that may contribute to its efficacy in idiopathic pulmonary fibrosis.

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1–2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segmentation requires the effective propagation and integration of information across different scales. However, most existing segmentation methods are optimised for single-scale imaging modalities, limiting their ability to capture and analyse small functional units throughout complete human organs. To facilitate multiscale biomedical image segmentation, we utilised Hierarchical Phase-Contrast Tomography (HiP-CT), an advanced imaging modality that can generate 3D multiscale datasets from high-resolution volumes of interest (VOIs) at ca. 1 μ m /voxel to whole-organ scans at ca. 20 μ m /voxel. Building on these hierarchical multiscale datasets, we developed a deep learning-based segmentation pipeline that is initially trained on manually annotated high-resolution HiP-CT data and then extended to lower-resolution whole-organ scans using pseudo-labels generated from high-resolution predictions and multiscale image registration. As a case study, we focused on glomeruli in human kidneys, benchmarking four 3D deep learning models for biomedical image segmentation on a manually annotated high-resolution dataset extracted from VOIs, at 2.58 to ca. 5 μ m /voxel, of four human kidneys. Among them, nnUNet demonstrated the best performance, achieving an average test Dice score of 0.906, and was subsequently used as the baseline model for multiscale segmentation in the pipeline. Applying this pipeline to two low-resolution full-organ data at ca. 25 μ m /voxel, the model identified 1,019,890 and 231,179 glomeruli in a 62-year-old donor without kidney diseases and a 94-year-old hypertensive donor, enabling comprehensive morphological analyses, including cortical spatial statistics and glomerular distributions, which aligned well with previous anatomical studies. Our results highlight the effectiveness of the proposed pipeline for segmenting small functional units in multiscale bioimaging datasets and suggest its broader applicability to other organ systems.

Mucus plugging is a hallmark of muco-obstructive lung diseases; however, the spatiotemporal dynamics of plug formation remain poorly understood. We used a multimodal high-resolution imaging approach to study mucus plugging in newborn (0–1 days), juvenile (13–16 days), and adult (49–59 days) βENaC-transgenic mice (βENaC-tg, n  = 24), a model of muco-obstructive lung disease, in comparison to age-matched wild-type mice ( n  = 28). Micro-computed tomography (µCT) enabled whole lung mucus scoring on a scale from 0 (no obstruction) to 2 (> 50% obstruction) using an extended murine airway tree nomenclature. A subset of βENaC-tg lungs (5 juvenile, 5 adult) underwent localised synchrotron radiation-based computed tomography (SRCT) for segmentation-based calculation of mucus area and contact ratios as indicators of luminal obstruction and airway wall adherence. µCT-guided scanning electron microscopy (SEM) provided nanoscale visualisation of mucus plugs on selected βENaC-tg lung samples (3 juvenile). µCT revealed age-dependent, heterogeneous mucus plug formation, with significantly increased mucus scores in juvenile (0.45 ± 0.26) and adult (0.30 ± 0.17) βENaC-tg compared to wild-type mice (0.01 ± 0.02 with P <  0.01, and 0.01 ± 0.02 with P  < 0.05, respectively). SRCT analysis showed a strong positive correlation between mucus area and contact ratios ( r  ≥ 0.80), while SEM revealed the mucus plug ultrastructure. This multimodal imaging approach highlights the spatiotemporal heterogeneity of mucus plugging, forming a basis for future research on targeted therapeutic strategies for muco-obstructive lung diseases.

The following case report details the case of a 40-year-old Caucasian patient who presented with dyspnea following a serologically confirmed mild-to-severe pulmonary infection with SARS-CoV-2. Chest computer tomography revealed a solitary ground-glass pulmonary nodule in the lower right lobe, measuring 2.1 cm in diameter. Video-assisted thoracoscopic surgery wedge resection revealed well-circumscribed lymphoid aggregates adjacent to the round, smaller airways, bronchioles, and blood vessels. IgKappa B exhibited a monoclonal polyclonal pattern, in contrast to the behavior exhibited by IgKappa A and IgLambda. In the following discussion, the lymphoid lesion was considered in the context of lymphoid hyperplasia, accompanied by an early infiltration of low-grade extranodal B cell lymphoma of the bronchus-associated lymphoid tissue (BALToma).

A drug encapsulation/delivery system using a novel principle is described that is based on an intra-particle migration of calcium ions between a central Ca 2+ -enriched nanoparticle core and the surrounding shell compartment. The supply of Ca 2+ is needed for the formation of a coacervate shell around the nanoparticles, acting as the core of drug-loadable core–shell particles, using the physiological inorganic polymer polyphosphate (polyP). This polyanion has the unique property to form, at an alkaline pH and in the presence of a stoichiometric surplus of calcium ions, water-insoluble and stabile amorphous nanoparticles. At neutral pH a coacervate, the biologically active form of the polymer, is obtained that is composed of polyP and Ca 2+ . The drug-loaded core–shell particles, built from the Ca–polyP core and the surrounding Ca–polyP shell, were fabricated in two successive steps. First, the formation of the nanoparticle core at pH 10 and a superstoichiometric 2:1 molar ratio between CaCl 2 and Na–polyP into which dexamethasone, as a phosphate derivative, was incorporated. Second, the preparation of the coacervate shell, loaded with ascorbic acid, by exposure of the Ca–polyP core to soluble Na–polyP and L-ascorbate (calcium salt). EDX analysis revealed that during this step the Ca 2+ ions required for coacervate formation migrate from the Ca–polyP core (with a high Ca:P ratio) to the shell. Electron microscopy of the particles show an electron-dense 150–200 nm sized core surrounded by a less sharply delimited electron-sparse shell. The core–shell particles exhibited strong osteogenic activity in vitro, based on the combined action of polyP and of dexamethasone and ascorbic acid, which reversibly bind to the anionic polyP via ionic Ca 2+ bonds. Drug release from the particles occurs after contact with a peptide/protein-containing serum, a process which is almost complete after 10 days and accompanied by the conversion of the nanoparticles into a coacervate. Human osteosarcoma SaOS-2 cells cultivated onto or within an alginate hydrogel matrix showed increased growth/viability and mineralization when the hybrid particles containing dexamethasone and ascorbic acid were embedded in the matrix. The polyP-based core–shell particles have the potential to become a suitable, pH-responsive drug encapsulation/release system, especially for bone, cartilage and wound healing.