Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management.

ABSTRACT: Platelet demand management (PDM) is a resource-consuming task for physicians and transfusion managers of large hospitals. Inpatient numbers and institutional standards play significant roles in PDM. However, reliance on these factors alone commonly results in platelet shortages. Using data from multiple sources, we developed, validated, tested, and implemented a patient-specific approach to support PDM that uses a deep learning-based risk score to forecast platelet transfusions for each hospitalized patient in the next 24 hours. The models were developed using retrospective electronic health record data of 34 809 patients treated between 2017 and 2022. Static and time-dependent features included demographics, diagnoses, procedures, blood counts, past transfusions, hematotoxic medications, and hospitalization duration. Using an expanding window approach, we created a training and live-prediction pipeline with a 30-day input and 24-hour forecast. Hyperparameter tuning determined the best validation area under the precision-recall curve (AUC-PR) score for long short-term memory deep learning models, which were then tested on independent data sets from the same hospital. The model tailored for hematology and oncology patients exhibited the best performance (AUC-PR, 0.84; area under the receiver operating characteristic curve [ROC-AUC], 0.98), followed by a multispecialty model covering all other patients (AUC-PR, 0.73). The model specific to cardiothoracic surgery had the lowest performance (AUC-PR, 0.42), likely because of unexpected intrasurgery bleedings. To our knowledge, this is the first deep learning-based platelet transfusion predictor enabling individualized 24-hour risk assessments at high AUC-PR. Implemented as a decision-support system, deep-learning forecasts might improve patient care by detecting platelet demand earlier and preventing critical transfusion shortages.

Primary acute lymphoblastic leukemia cells are susceptible to microtubule depolymerization in G1 and M phases through distinct cell death pathways.

Microtubule targeting agents (MTAs) are widely used cancer chemotherapeutics which conventionally exert their effects during mitosis, leading to mitotic or postmitotic death. However, accumulating evidence suggests that MTAs can also generate death signals during interphase, which may represent a key mechanism in the clinical setting. We reported previously that vincristine and other microtubule destabilizers induce death not only in M phase but also in G1 phase in primary acute lymphoblastic leukemia cells. Here, we sought to investigate and compare the pathways responsible for phase-specific cell death. Primary acute lymphoblastic leukemia cells were subjected to centrifugal elutriation, and cell populations enriched in G1 phase (97%) or G2/M phases (80%) were obtained and treated with vincristine. We found death of M phase cells was associated with established features of mitochondrial-mediated apoptosis, including Bax activation, loss of mitochondrial transmembrane potential, caspase-3 activation, and nucleosomal DNA fragmentation. In contrast, death of G1 phase cells was not associated with pronounced Bax or caspase-3 activation but was associated with loss of mitochondrial transmembrane potential, parylation, nuclear translocation of apoptosis-inducing factor and endonuclease G, and supra-nucleosomal DNA fragmentation, which was enhanced by inhibition of autophagy. The results indicate that microtubule depolymerization induces distinct cell death pathways depending on during which phase of the cell cycle microtubule perturbation occurs. The observation that a specific type of drug can enter a single cell type and induce two different modes of death is novel and intriguing. These findings provide a basis for advancing knowledge of clinical mechanisms of MTAs.

FHIR-PYrate: a data science friendly Python package to query FHIR servers.

BACKGROUND: We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks. METHODS: The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant. RESULTS: As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases. CONCLUSIONS: FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors.

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

Liraglutide Counteracts Endoplasmic Reticulum Stress in Palmitate-Treated Hypothalamic Neurons without Restoring Mitochondrial Homeostasis.

One feature of high-fat diet-induced neurodegeneration in the hypothalamus is an increased level of palmitate, which is associated with endoplasmic reticulum (ER) stress, loss of CoxIV, mitochondrial fragmentation, and decreased abundance of MC4R. To determine whether antidiabetic drugs protect against ER and/or mitochondrial dysfunction by lipid stress, hypothalamic neurons derived from pre-adult mice and neuronal Neuro2A cells were exposed to elevated palmitate. In the hypothalamic neurons, palmitate exposure increased expression of ER resident proteins, including that of SERCA2, indicating ER stress. Liraglutide reverted such altered ER proteostasis, while metformin only normalized SERCA2 expression. In Neuro2A cells liraglutide, but not metformin, also blunted dilation of the ER induced by palmitate treatment, and enhanced abundance and expression of MC4R at the cell surface. Thus, liraglutide counteracts, more effectively than metformin, altered ER proteostasis, morphology, and folding capacity in neurons exposed to fat. In palmitate-treated hypothalamic neurons, mitochondrial fragmentation took place together with loss of CoxIV and decreased mitochondrial membrane potential (MMP). Metformin, but not liraglutide, reverted mitochondrial fragmentation, and both liraglutide and metformin did not protect against either loss of CoxIV abundance or MMP. Thus, ER recovery from lipid stress can take place in hypothalamic neurons in the absence of recovered mitochondrial homeostasis.

Injury to hypothalamic Sim1 neurons is a common feature of obesity by exposure to high-fat diet in male and female mice.

The hypothalamus is essential for regulation of energy homeostasis and metabolism. Feeding hypercaloric, high-fat (HF) diet induces hypothalamic arcuate nucleus injury and alters metabolism more severely in male than in female mice. The site(s) and extent of hypothalamic injury in male and female mice are not completely understood. In the paraventricular nucleus (PVN) of the hypothalamus, single-minded family basic helix-loop helix transcription factor 1 (Sim1) neurons are essential to control energy homeostasis. We tested the hypothesis that exposure to HF diet induces injury to Sim1 neurons in the PVN of male and female mice. Mice expressing membrane-bound enhanced green fluorescent protein (mEGFP) in Sim1 neurons (Sim1-Cre:Rosa-mEGFP mice) were generated to visualize the effects of exposure to HF diet on these neurons. Male and female Sim1-Cre:Rosa-mEGFP mice exposed to HF diet had increased weight, hyperleptinemia, and developed hepatosteatosis. In male and female mice exposed to HF diet, expression of mEGFP was reduced by > 40% in Sim1 neurons of the PVN, an effect paralleled by cell apoptosis and neuronal loss, but not by microgliosis. In the arcuate nucleus of the Sim1-Cre:Rosa-mEGFP male mice, there was decreased alpha-melanocyte-stimulating hormone in proopiomelanocortin neurons projecting to the PVN, with increased cell apoptosis, neuronal loss, and microgliosis. These defects were undetectable in the arcuate nucleus of female mice exposed to the HF diet. Thus, injury to Sim1 neurons of the PVN is a shared feature of exposure to HF diet in mice of both sexes, while injury to proopiomelanocortin neurons in arcuate nucleus is specific to male mice. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Lipid stress inhibits endocytosis of melanocortin-4 receptor from modified clathrin-enriched sites and impairs receptor desensitization.

Melanocortin-4 receptor (MC4R) is a G-protein-coupled receptor expressed in the brain's hypothalamus where it regulates energy homeostasis. MC4R agonists function to lower food intake and weight. In this respect, although obesity promotes hyperlipidemia and hypothalamic injury, MC4R agonists are nevertheless more effective to reduce food intake within hours of administration in overweight, rather than lean, mice. MC4R undergoes constitutive internalization and recycling to the plasma membrane with agonist binding inducing receptor retention along the intracellular route and, under prolonged exposure, desensitization. Here, we found that, in neuronal cells, lipid stress by exposure to elevated palmitate leaves unchanged the rate by which MC4R and transferrin receptor are constitutively excluded from the cell surface. However, lipid stress disrupted later steps of MC4R and transferrin receptor internalization to endosomes as well as traffic of agonist-occupied MC4R to lysosomes and MC4R desensitization. In the lipid-stressed cells, MC4R and clathrin were redistributed to the plasma membrane where they colocalized to sites that appeared by super-resolution microscopy to be modified and to have higher clathrin content than those of cells not exposed to elevated palmitate. The data suggest that lipid stress disrupts steps of endocytosis following MC4R localization to clathrin-coated sites and exclusion of the receptor from the extracellular medium. We conclude that increased effectiveness of MC4R agonists in obesity may be an unexpected outcome of neuronal injury with disrupted clathrin-dependent endocytosis and impaired receptor desensitization.

Evidence That G-quadruplex DNA Accumulates in the Cytoplasm and Participates in Stress Granule Assembly in Response to Oxidative Stress.

Cells engage numerous signaling pathways in response to oxidative stress that together repair macromolecular damage or direct the cell toward apoptosis. As a result of DNA damage, mitochondrial DNA or nuclear DNA has been shown to enter the cytoplasm where it binds to "DNA sensors," which in turn initiate signaling cascades. Here we report data that support a novel signaling pathway in response to oxidative stress mediated by specific guanine-rich sequences that can fold into G-quadruplex DNA (G4DNA). In response to oxidative stress, we demonstrate that sequences capable of forming G4DNA appear at increasing levels in the cytoplasm and participate in assembly of stress granules. Identified proteins that bind to endogenous G4DNA in the cytoplasm are known to modulate mRNA translation and participate in stress granule formation. Consistent with these findings, stress granule formation is known to regulate mRNA translation during oxidative stress. We propose a signaling pathway whereby cells can rapidly respond to DNA damage caused by oxidative stress. Guanine-rich sequences that are excised from damaged genomic DNA are proposed to enter the cytoplasm where they can regulate translation through stress granule formation. This newly proposed role for G4DNA provides an additional molecular explanation for why such sequences are prevalent in the human genome.

Exposure of MC4R to agonist in the endoplasmic reticulum stabilizes an active conformation of the receptor that does not desensitize.

Melanocortin-4 receptor (MC4R) is a G protein-coupled receptor expressed in neurons of the hypothalamus where it regulates food intake. MC4R responds to an agonist, α-melanocyte-stimulating hormone (α-MSH) and to an antagonist/inverse agonist, agouti-related peptide (AgRP), which are released by upstream neurons. Binding to α-MSH leads to stimulation of receptor activity and suppression of food intake, whereas AgRP has opposite effects. MC4R cycles constantly between the plasma membrane and endosomes and undergoes agonist-mediated desensitization by being routed to lysosomes. MC4R desensitization and increased AgRP expression are thought to decrease the effectiveness of MC4R agonists as an antiobesity treatment. In this study, α-MSH, instead of being delivered extracellularly, is targeted to the endoplasmic reticulum (ER) of neuronal cells and cultured hypothalamic neurons. We find that the ER-targeted agonist associates with MC4R at this location, is transported to the cell surface, induces constant cAMP and AMP kinase signaling at maximal amplitude, abolishes desensitization of the receptor, and promotes both cell-surface expression and constant signaling by an obesity-linked MC4R variant, I316S, that otherwise is retained in the ER. Formation of the MC4R/agonist complex in the ER stabilizes the receptor in an active conformation that at the cell surface is insensitive to antagonism by AgRP and at the endosomes is refractory to routing to the lysosomes. The data indicate that targeting agonists to the ER can stabilize an active conformation of a G protein-coupled receptor that does not become desensitized, suggesting a target for therapy.

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging.

OBJECTIVES: Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). MATERIALS AND METHODS: This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). RESULTS: For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. CONCLUSIONS: The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.

CellViT: Vision Transformers for precise cell segmentation and classification.

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.

SAROS: A dataset for whole-body region and organ segmentation in CT imaging.

The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso). Case selection was based on the DICOM series description, gender, and imaging protocol, resulting in 882 patients (438 female) for a total of 900 CTs. Manual review and correction of proposals were conducted in a continuous quality control cycle. Only every fifth axial slice was annotated, yielding 20150 annotated slices from 28 data collections. For the reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. The SAROS dataset serves as an open-access resource for training and evaluating novel segmentation models, covering various scanner vendors and diseases.

AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump.

Introduction: Perihilar cholangiocarcinoma (PHCC) is a rare malignancy with limited survival prediction accuracy. Artificial intelligence (AI) and digital pathology advancements have shown promise in predicting outcomes in cancer. We aimed to improve prognosis prediction for PHCC by combining AI-based histopathological slide analysis with clinical factors. Methods: We retrospectively analyzed 317 surgically treated PHCC patients (January 2009-December 2018) at the University Hospital of Essen. Clinical data, surgical details, pathology, and outcomes were collected. Convolutional neural networks (CNN) analyzed whole-slide images. Survival models incorporated clinical and histological features. Results: Among 142 eligible patients, independent survival predictors were tumor grade (G), tumor size (T), and intraoperative transfusion requirement. The CNN-based model combining clinical and histopathological features demonstrates proof of concept in prognosis prediction, limited by histopathological complexity and feature extraction challenges. However, the CNN-based model generated heatmaps assisting pathologists in identifying areas of interest. Conclusion: AI-based digital pathology showed potential in PHCC prognosis prediction, though refinement is necessary for clinical relevance. Future research should focus on enhancing AI models and exploring novel approaches to improve PHCC patient prognosis prediction.

Constitutive cholesterol-dependent endocytosis of melanocortin-4 receptor (MC4R) is essential to maintain receptor responsiveness to α-melanocyte-stimulating hormone (α-MSH).

Melanocortin-4 receptor (MC4R) is a G-protein-coupled receptor expressed in the hypothalamus where it controls feeding behavior. MC4R cycles constitutively and is internalized at the same rate in the presence or absence of stimulation by the agonist, melanocyte-stimulating hormone (α-MSH). This is different from other G-protein-coupled receptors, such as ß(2)-adrenergic receptor (ß(2)AR), which internalizes more rapidly in response to agonist stimulation. Here, it is found that in immortalized neuronal Neuro2A cells expressing exogenous receptors, constitutive endocytosis of MC4R and agonist-dependent internalization of ß(2)AR were equally sensitive to clathrin depletion. Inhibition of MC4R endocytosis by clathrin depletion decreased the number of receptors at the cell surface that were responsive to the agonist, α-MSH, by 75%. Mild membrane cholesterol depletion also inhibited constitutive endocytosis of MC4R by ∼5-fold, while not affecting recycling of MC4R or agonist-dependent internalization of ß(2)AR. Reduced cholesterol did not change the MC4R dose-response curve to α-MSH, but it decreased the amount of cAMP generated per receptor number indicating that a population of MC4R at the cell surface becomes nonfunctional. The loss of MC4R function increased over time (25-50%) and was partially reversed by mutations at putative phosphorylation sites (T312A and S329A). This was reproduced in hypothalamic GT1-7 cells expressing endogenous MC4R. The data indicate that constitutive endocytosis of MC4R is clathrin- and cholesterol-dependent. MC4R endocytosis is required to maintain MC4R responsiveness to α-MSH by constantly eliminating from the plasma membrane a pool of receptors modified at Thr-312 and Ser-329 that have to be cycled to the endosomal compartment to regain function.

Design of a modular and compliant wrist module for upper limb prosthetics.

In the last decades, there have been great efforts in the development of advanced polyarticulated prosthetic hands; in contrast, prosthetic wrists have drawn less interest. Nevertheless, increasing the dexterity of the wrist improves handling skills because the wrist allows the prepositioning of the hand before carrying out a task, avoiding the onset of unwanted trunk or shoulders compensatory movements and potential onset or exacerbation of articular injuries. This study presents a novel 2-degree-of-freedom prosthetic wrist module with active pronation/supination and passive elastic flexion/extension. This system is suitable to be included in hand prostheses to improve anthropomorphism and produce a more physiological motion. The first submodule within the socket is able to rotate a prosthetic hand and an external load of 3 kg at 2.6 rad/s. The second one can guarantee a range of motion of ±75° with a centering elastic torque (compliant mode) or it can keep firms grasps (fixed mode). Compliant mode is based on a Scotch-Yoke mechanism converting wrist flexion/extension into the linear motion of a crossbeam acting on compression springs, while fixed mode is achieved by means of a piston that can be engaged/disengaged. The whole module fits with anthropometry and the modular design ensures the proposed system can be used in a stand-alone way and adapted to different hand prostheses. This device is expected to favor a more physiological dexterity with respect to simpler fixed prostheses that can potentially induce harmful motion of body districts not naturally involved in the reaching and grasping tasks.

BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care.

PURPOSE: The study aimed to develop the open-source body and organ analysis (BOA), a comprehensive computed tomography (CT) image segmentation algorithm with a focus on workflow integration. METHODS: The BOA combines 2 segmentation algorithms: body composition analysis (BCA) and TotalSegmentator. The BCA was trained with the nnU-Net framework using a dataset including 300 CT examinations. The CTs were manually annotated with 11 semantic body regions: subcutaneous tissue, muscle, bone, abdominal cavity, thoracic cavity, glands, mediastinum, pericardium, breast implant, brain, and spinal cord. The models were trained using 5-fold cross-validation, and at inference time, an ensemble was used. Afterward, the segmentation efficiency was evaluated on a separate test set comprising 60 CT scans. In a postprocessing step, a tissue segmentation (muscle, subcutaneous adipose tissue, visceral adipose tissue, intermuscular adipose tissue, epicardial adipose tissue, and paracardial adipose tissue) is created by subclassifying the body regions. The BOA combines this algorithm and the open-source segmentation software TotalSegmentator to have an all-in-one comprehensive selection of segmentations. In addition, it integrates into clinical workflows as a DICOM node-triggered service using the open-source Orthanc research PACS (Picture Archiving and Communication System) server to make the automated segmentation algorithms available to clinicians. The BCA model's performance was evaluated using the Sørensen-Dice score. Finally, the segmentations from the 3 different tools (BCA, TotalSegmentator, and BOA) were compared by assessing the overall percentage of the segmented human body on a separate cohort of 150 whole-body CT scans. RESULTS: The results showed that the BCA outperformed the previous publication, achieving a higher Sørensen-Dice score for the previously existing classes, including subcutaneous tissue (0.971 vs 0.962), muscle (0.959 vs 0.933), abdominal cavity (0.983 vs 0.973), thoracic cavity (0.982 vs 0.965), bone (0.961 vs 0.942), and an overall good segmentation efficiency for newly introduced classes: brain (0.985), breast implant (0.943), glands (0.766), mediastinum (0.880), pericardium (0.964), and spinal cord (0.896). All in all, it achieved a 0.935 average Sørensen-Dice score, which is comparable to the one of the TotalSegmentator (0.94). The TotalSegmentator had a mean voxel body coverage of 31% ± 6%, whereas BCA had a coverage of 75% ± 6% and BOA achieved 93% ± 2%. CONCLUSIONS: The open-source BOA merges different segmentation algorithms with a focus on workflow integration through DICOM node integration, offering a comprehensive body segmentation in CT images with a high coverage of the body volume.

Post-Stroke Detection of Subclinical Paroxysmal Atrial Fibrillation in Patients With Embolic Stroke of Undetermined Source in the Real World Practice: The Empoli ESUS Atrial Fibrillation (E 2 AF) Study.

BACKGROUND: Subclinical paroxysmal atrial fibrillation (AF) is one of the main occult causative mechanisms of embolic stroke of undetermined source (ESUS). Aim of this study was to identify AF predictors, and to develop a score to predict the probability of AF detection in ESUS. METHODS: We retrospectively analyzed ESUS patients undergoing 2-week external electrocardiographic monitoring. Patients with and without AF detection were compared. On the basis of multivariate analysis, predictors of AF were identified and used to develop a predictive score, which was then compared with other existing literature scores. RESULTS: Eighty-two patients, 48 females, mean age±SD 72±10 years, were included. In 36 patients (43.9%) AF was detected. The frequency of age 75 years or above and arterial hypertension, and the median CHA 2 DS 2 -VASc score were significantly higher in patients with AF compared with those without. National Institutes of Health Stroke Scale (NIHSS) score ≥8 was the only independent variable associated with AF detection. We derived the Empoli ESUS-AF (E 2 AF) score (NIHSS ≥8 5 points, arterial hypertension 3 points, age 75 years or above 2 points, age 65 to 74 years 1 point, history of coronary/peripheral artery disease 1 point, left atrial enlargement 1 point, posterior lesion 1 point, cortical or cortical-subcortical lesion 1 point), whose predictive power in detecting AF was good (area under the curve: 0.746, 95% confidence interval: 0.638-0.836) and higher than that of CHA 2 DS 2 -VASc and other scores. CONCLUSIONS: In our study NIHSS score ≥8 was the only independent predictor of post-ESUS-AF detection. The E 2 AF score appears to have a good predictive power for detecting AF. External validations are required.

Activating transcription factor 6 limits intracellular accumulation of mutant α(1)-antitrypsin Z and mitochondrial damage in hepatoma cells.

α(1)-Antitrypsin is a serine protease inhibitor secreted by hepatocytes. A variant of α(1)-antitrypsin with an E342K (Z) mutation (ATZ) has propensity to form polymers, is retained in the endoplasmic reticulum (ER), is degraded by both ER-associated degradation and autophagy, and causes hepatocyte loss. Constant features in hepatocytes of PiZZ individuals and in PiZ transgenic mice expressing ATZ are the formation of membrane-limited globular inclusions containing ATZ and mitochondrial damage. Expression of ATZ in the liver does not induce the unfolded protein response (UPR), a protective mechanism aimed to maintain ER homeostasis in the face of an increased load of proteins. Here we found that in hepatoma cells the ER E3 ligase HRD1 functioned to degrade most of the ATZ before globular inclusions are formed. Activation of the activating transcription factor 6 (ATF6) branch of the UPR by expression of spliced ATF6(1-373) decreased intracellular accumulation of ATZ and the formation of globular inclusions by a pathway that required HRD1 and the proteasome. Expression of ATF6(1-373) in ATZ-expressing hepatoma cells did not induce autophagy and increased the level of the proapoptotic factor CCAAT/enhancer-binding protein (C/EBP) homologous protein (CHOP) but did not lead to apoptotic DNA fragmentation. Expression of ATF6(1-373) did not cause inhibition of protein synthesis and prevented mitochondrial damage induced by ATZ expression. It was concluded that activation of the ATF6 pathway of the UPR limits ATZ-dependent cell toxicity by selectively promoting ER-associated degradation of ATZ and is thereby a potential target to prevent hepatocyte loss in addition to autophagy-enhancing drugs.

CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients.

Patients with neuroendocrine tumors of gastro-entero-pancreatic origin (GEP-NET) experience changes in fat and muscle composition. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are currently used to analyze body composition. Changes thereof could indicate cancer progression or response to treatment. This study examines the correlation between CT-based (computed tomography) body composition analysis (BCA) and DXA or BIA measurement. 74 GEP-NET-patients received whole-body [68Ga]-DOTATOC-PET/CT, BIA, and DXA-scans. BCA was performed based on the non-contrast-enhanced, 5 mm, whole-body-CT images. BCA from CT shows a strong correlation between body fat ratio with DXA (r = 0.95, ρC = 0.83) and BIA (r = 0.92, ρC = 0.76) and between skeletal muscle ratio with BIA: r = 0.81, ρC = 0.49. The deep learning-network achieves highly accurate results (mean Sørensen-Dice-score 0.93). Using BCA on routine Positron emission tomography/CT-scans to monitor patients' body composition in the diagnostic workflow can reduce additional exams whilst substantially amplifying measurement in slower progressing cancers such as GEP-NET.

Elevation of the unfolded protein response increases RANKL expression.

Increased production of the osteoclastogenic cytokine RANKL is a common feature of pathologic bone loss, but the underlying cause of this increase is poorly understood. The unfolded protein response (UPR) is activated in response to accumulation of misfolded proteins in the endoplasmic reticulum (ER). Failure to resolve misfolding results in excess UPR signaling that stimulates cytokine production and cell death. We therefore investigated whether RANKL is one of the cytokines stimulated in response to elevated UPR in bone cells. Pharmacologic induction of UPR with tunicamycin (Tm)-stimulated RANKL expression in cultures of primary osteoblastic cells and in osteoblast and osteocyte cell lines. Pharmacologic inhibition of the UPR blunted Tm-induced RANKL production. Silencing Edem1 or Sel1l, proteins that aid in degradation of misfolded proteins, also induced UPR and increased RANKL mRNA. Moreover, Tm or hypoxia increased RANKL and bone resorption in cultures of neonatal murine calvaria. Administration of Tm to adult mice caused dilation of ER in osteoblasts and osteocytes, elevated the UPR, and increased RANKL expression and osteoclast number. These findings support the hypothesis that excessive UPR signaling stimulates the expression of RANKL by osteoblasts and osteocytes, and thereby facilitates excessive bone resorption and bone loss in pathologic conditions.