Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning.

Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.

The Chromosome Passenger Complex (CPC) Components and Its Associated Pathways Are Promising Candidates to Differentiate Between Normosensitive and Radiosensitive ATM-Mutated Cells.

Background: Sensitivity to ionizing radiation differs between individuals, but there is a limited understanding of the biological mechanisms that account for these variations. One example of such mechanisms are the mutations in the ATM (mutated ataxia telangiectasia) gene, that cause the rare recessively inherited disease Ataxia telangiectasia (AT). Hallmark features include chromosomal instability and increased sensitivity to ionizing radiation (IR).

Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon.

Objectives: To generate sagittal T1-weighted fast spin echo (T1w FSE) and short tau inversion recovery (STIR) images from sagittal T2-weighted (T2w) FSE and axial T1w gradient echo Dixon technique (T1w-Dixon) sequences.

Materials And Methods: This retrospective study used three existing datasets: "Study of Health in Pomerania" (SHIP, 3142 subjects, 1.5 Tesla), "German National Cohort" (NAKO, 2000 subjects, 3 Tesla), and an internal dataset (157 patients 1.

Virtual reality-empowered deep-learning analysis of brain cells.

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches.

Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy.

Objectives: To study the changes in vessel densities (VD) stratified by vessel diameter in the retinal superficial and deep vascular complexes (SVC/DVC) using optical coherence tomography angiography (OCTA) images obtained from people with diabetes and age-matched healthy controls.

Methods: We quantified the VD based on vessel diameter categorized as <10, 10-20 and >20 μm in the SVC/DVC obtained on 3 × 3 mm OCTA scans using a deep learning-based segmentation and vascular graph extraction tool in people with diabetes and age-matched healthy controls.

Results: OCTA images obtained from 854 eyes of 854 subjects were divided into 5 groups: healthy controls (n = 555); people with diabetes with no diabetic retinopathy (DR, n = 90), mild and moderate non-proliferative DR (NPDR) (n = 96), severe NPDR (n = 42) and proliferative DR (PDR) (n = 71).

Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization.

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task.

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations.

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio.

Whole-body cellular mapping in mouse using standard IgG antibodies.

Whole-body imaging techniques play a vital role in exploring the interplay of physiological systems in maintaining health and driving disease. We introduce wildDISCO, a new approach for whole-body immunolabeling, optical clearing and imaging in mice, circumventing the need for transgenic reporter animals or nanobody labeling and so overcoming existing technical limitations. We identified heptakis(2,6-di-O-methyl)-β-cyclodextrin as a potent enhancer of cholesterol extraction and membrane permeabilization, enabling deep, homogeneous penetration of standard antibodies without aggregation.

Differentially Private Graph Neural Networks for Whole-Graph Classification.

Graph Neural Networks (GNNs) have established themselves as state-of-the-art for many machine learning applications such as the analysis of social and medical networks. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection.

A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images.

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by arterial obstruction. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determining the choice of a stroke care model. Though many imaging and grading methods exist for quantifying collateral blood flow, the actual grading is mostly done through manual inspection.

Spatial proteomics in three-dimensional intact specimens.

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling.

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g.

Can high COVID-19 vaccination rates in adults help protect unvaccinated children? Evidence from a unique mass vaccination campaign, Schwaz/Austria, March 2021.

BackgroundAfter an outbreak of the SARS-CoV-2 Beta variant in the district of Schwaz/Austria, vaccination with Comirnaty vaccine (BNT162b2 mRNA, BioNTech-Pfizer) had been offered to all adult inhabitants (≥ 16 years) in March 2021. This made Schwaz one of the most vaccinated regions in Europe at that time (70% of the adult population took up the offer). In contrast, all other Austrian districts remained with low vaccine coverage.

SRflow: Deep learning based super-resolution of 4D-flow MRI data.

Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible.

Impacts of rapid mass vaccination against SARS-CoV2 in an early variant of concern hotspot.

We study the real-life effect of an unprecedented rapid mass vaccination campaign. Following a large outbreak of the Beta variant in the district of Schwaz/Austria, 100,000 doses of BNT162b2 (Pfizer/BioNTech) were procured to mass vaccinate the entire adult population of the district between the 11th and 16th of March 2021. This made the district the first widely inoculated region in Europe.

Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles.

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles.

Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models.

Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute.

Labor market effects of COVID-19 in Sweden and its neighbors: Evidence from administrative data.

This paper studies the labor market effects of non-pharmaceutical interventions (NPIs) to combat the COVID-19 pandemic. We focus on the Nordic countries which showed one of the highest variations in NPIs despite having similar community spread of COVID-19 at the onset of the pandemic: While Denmark, Finland and Norway imposed strict measures ('lockdowns'), Sweden decided for much lighter restrictions. Empirically, we use novel administrative data on weekly new unemployment and furlough spells from all 56 regions of the Nordic countries to compare the labor market outcomes of Sweden with the ones of its neighbors.

A 1024-Channel 10-Bit 36- μW/ch CMOS ROIC for Multiplexed GFET-Only Sensor Arrays in Brain Mapping.

This paper presents a 1024-channel neural read-out integrated circuit (ROIC) for solution-gated GFET sensing probes in massive μECoG brain mapping. The proposed time-domain multiplexing of GFET-only arrays enables low-cost and scalable hybrid headstages. Low-power CMOS circuits are presented for the GFET analog frontend, including a CDS mechanism to improve preamplifier noise figures and 10-bit 10-kS/s A/D conversion.

Persistence of immunity to SARS-CoV-2 over time in the ski resort Ischgl.

Background In early March 2020, a SARS-CoV-2 outbreak in the ski resort Ischgl in Austria triggered the spread of SARS-CoV-2 throughout Austria and Northern Europe. In a previous study, we found that the seroprevalence in the adult population of Ischgl had reached 45% by the end of April, representing an exceptionally high level of local seropositivity in Europe. We performed a follow-up study in Ischgl, which is the first to show persistence of immunity and protection against SARS-CoV-2 and some of its variants at a community level.

An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset.

It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord).