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This study explores the application of the RIP3-caspase3-assay in heterogeneous spheroid cultures to analyze cell death pathways, emphasizing the nuanced roles of apoptosis and necroptosis. By employing directly conjugated monoclonal antibodies, we provide detailed insights into the complex mechanisms of cell death. Our findings demonstrate the assay's capability to differentiate between RIP1-independent apoptosis, necroptosis, and RIP1-dependent apoptosis, marking a significant advancement in organoid research. Additionally, we investigate the effects of TNFα on isolated intestinal epithelial cells, revealing a concentration-dependent response and an adaptive or threshold reaction to TNFα-induced stress. The results indicate a preference for RIP1-independent cell death pathways upon TNFα stimulation, with a notable increase in apoptosis and a secondary role of necroptosis. Our research underscores the importance of the RIP3-caspase3-assay in understanding cell death mechanisms in organoid cultures, offering valuable insights for disease modeling and the development of targeted therapies. The assay's adaptability and robustness in spheroid cultures enhances its potential as a tool in personalized medicine and translational research. [ABSTRACT FROM AUTHOR]

Objectives: To determine the diagnostic accuracy of ultra-high-resolution photon-counting detector CT angiography (UHR PCD-CTA) for evaluating coronary stent patency compared to invasive coronary angiography (ICA). Methods: Consecutive, clinically referred patients with prior coronary stent implantation were prospectively enrolled between August 2022 and March 2023 and underwent UHR PCD-CTA (collimation, 120 × 0.2 mm). Two radiologists independently analyzed image quality of the in-stent lumen using a 5-point Likert scale, ranging from 1 ("excellent") to 5 ("non-diagnostic"), and assessed all coronary stents for the presence of in-stent stenosis (≥ 50% lumen narrowing). The diagnostic accuracy of UHR PCD-CTA was determined, with ICA serving as the standard of reference. Results: A total of 44 coronary stents in 18 participants (mean age, 83 years ± 6 [standard deviation]; 12 women) were included in the analysis. In 3/44 stents, both readers described image quality as non-diagnostic, whereas reader 2 noted a fourth stent to have non-diagnostic image quality. In comparison to ICA, UHR PCD-CTA demonstrated a sensitivity, specificity, and accuracy of 100% (95% CI [confidence interval] 47.8, 100), 92.3% (95% CI 79.1, 98.4), and 93.2% (95% CI 81.3, 98.6) for reader 1 and 100% (95% CI 47.8, 100), 87.2% (95% CI 72.6, 95.7), and 88.6% (95% CI 75.4, 96.2) for reader 2, respectively. Both readers observed a 100% negative predictive value (36/36 stents and 34/34 stents). Stent patency inter-reader agreement was 90.1%, corresponding to a substantial Cohen's kappa value of 0.72. Conclusions: UHR PCD-CTA enables non-invasive assessment of coronary stent patency with high image quality and diagnostic accuracy. Clinical relevance statement: Ultra-high-resolution photon-counting detector CT angiography represents a reliable and non-invasive method for assessing coronary stent patency. Its high negative predictive value makes it a promising alternative over invasive coronary angiography for the rule-out of in-stent stenosis. Key Points: • CT-based evaluation of coronary stent patency is limited by stent-induced artifacts and spatial resolution. • Ultra-high-resolution photon-counting detector CT accurately evaluates coronary stent patency compared to invasive coronary angiography. • Photon-counting detector CT represents a promising method for the non-invasive rule-out of in-stent stenosis. [ABSTRACT FROM AUTHOR]

OBJECTIVES To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone. METHODS We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans. A deep learning model performed aortic landmark detection and aorta segmentation to derive 8 vessel segments. Associated calcifications were detected and their volumes measured using a mean-based density thresholding. Algorithm parameters (calcium cluster size threshold, aortic mask dilatation) were varied to determine optimal performance for the upper ascending aorta that encompasses the aortic clamping zone. A binary visual rating served as a reference. Standard estimates of diagnostic accuracy and inter-rater agreement using Cohen's Kappa were calculated. RESULTS Thoracic aortic calcifications were observed in 74% of patients with a prevalence of 27–70% by aorta segment. Using different parameter combinations, the algorithm provided binary ratings for all scans and segments. The best performing parameter combination for the presence of calcifications in the aortic clamping zone yielded a sensitivity of 93% and a specificity of 82%, with an area under the receiver operating characteristic curve of 0.874. Using these parameters, the inter-rater agreement ranged from κ 0.66 to 0.92 per segment. CONCLUSIONS Fully automated segmental detection of thoracic aortic calcifications in chest CT performs with high accuracy. This includes the critical preoperative assessment of the aortic clamping zone. [ABSTRACT FROM AUTHOR]

Reduced iodine loads for computed tomography (CT)-based vascular assessment prior to transcatheter aortic valve implantation (TAVI) may be feasible in conjunction with a spectral detector CT scanner. This prospective single-center study considered 100 consecutive patients clinically referred for pre-TAVI CT. They were examined on a dual-layer detector CT scanner to obtain an ECG-gated cardiac scan and a non-ECG-gated aortoiliofemoral scan. Either a standard contrast media (SCM) protocol using 80 mL Iohexol 350 mgI/mL (iodine load: 28 gI) or a body-mass-index adjusted reduced contrast media (RCM) protocol using 40–70 mL Iohexol 350 mgI/mL (iodine load: 14–24.5 gI) were employed. Conventional images and virtual monoenergetic images at 40–80 keV were reconstructed. A threshold of 250 HU was set for sufficient attenuation along the arterial access pathway. A qualitative assessment used a five-point Likert scale. Sufficient attenuation in the thoracic aorta was observed for all patients in both groups using conventional images. In the abdominal, iliac, and femoral segments, sufficient attenuation was observed for the majority of patients when using virtual monoenergetic images (SCM: 96–100% of patients, RCM: 88–94%) without statistical difference between both groups. Segments with attenuation measurements below the threshold remained qualitatively assessable as well. Likert scores were 'excellent' for virtual monoenergetic images 50 keV and 55 keV in both groups (RCM: 1.2–1.4, SCM: 1.2–1.3). With diagnostic image quality maintained, it can be concluded that reduced iodine loads of 14–24.5 gI are feasible for pre-TAVI vascular assessment on a spectral detector CT scanner. [ABSTRACT FROM AUTHOR]

Background: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. Results: We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. Conclusions: NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/. The implementation detailed in this paper corresponds to the version 0.2.1 release of the software. [ABSTRACT FROM AUTHOR]

To compare the diagnostic value of ultrahigh-resolution CT-angiography (UHR-CTA) compared with high-pitch spiral CTA (HPS-CTA) using a first-generation, dual-source photon-counting CT (PCD-CT) scanner for preprocedural planning of transcatheter aortic valve replacement (TAVR). Clinically referred patients with severe aortic valve stenosis underwent both, retrospective ECG-gated cardiac UHR-CTA (collimation: 120 × 0.2 mm) and prospective ECG-triggered aortoiliac HPS-CTA (collimation: 144 × 0.4 mm, full spectral capabilities) for TAVR planning from August 2022 to March 2023. Radiation dose was extracted from the CT reports, and the effective dose was calculated. Two radiologists analyzed UHR-CTA and HPS-CTA datasets, assessing the image quality of the aortic annulus, with regard to the lumen visibility and margin delineation using a 4-point visual-grading scale (ranges: 4 = "excellent" to 1 = "poor"). Aortic annulus area (AAA) measurements were taken for valve prosthesis sizing, with retrospective UHR-CTA serving as reference standard. A total of 64 patients were included (mean age, 81 years ± 7 SD; 28 women) in this retrospective study. HPS-CTA showed a lower radiation dose, 4.1 mSv vs. 12.6 mSv (p < 0.001). UHR-CTA demonstrated higher image quality to HPS-CTA (median score, 4 [IQR, 3–4] vs. 3 [IQR, 2–3]; p < 0.001). Quantitative assessments of AAA from both CTA datasets were strongly positively correlated (mean 477.4 ± 91.1 mm2 on UHR-CTA and mean 476.5 ± 90.4 mm2 on HPS-CTA, Pearson r2 = 0.857, p < 0.001) with a mean error of 22.3 ± 24.6 mm2 and resulted in identical valve prosthesis sizing in the majority of patients (91%). Patients with lower image quality on HPS-CTA (score value 1 or 2, n = 28) were more likely to receive different sizing recommendations (82%). Both UHR-CTA and HPS-CTA acquisitions using photon-counting CT technology provided reliable aortic annular assessments for TAVR planning. While UHR-CTA offers superior image quality, HPS-CTA is associated with lower radiation exposure. However, severely impaired image quality on HPS-CTA may impact on prosthesis sizing, suggesting that immediate post-scan image evaluations may require complementary UHR-CTA scanning. [ABSTRACT FROM AUTHOR]

Induced pluripotent stem cells (iPSCs) and their derivatives have been described to display epigenetic memory of their founder cells, as well as de novo reprogramming-associated alterations. In order to selectively explore changes due to the reprogramming process and not to heterologous somatic memory, we devised a circular reprogramming approach where somatic stem cells are used to generate iPSCs, which are subsequently re-differentiated into their original fate. As somatic founder cells, we employed human embryonic stem cell-derived neural stem cells (NSCs) and compared them to iPSC-derived NSCs derived thereof. Global transcription profiling of this isogenic circular system revealed remarkably similar transcriptomes of both NSC populations, with the exception of 36 transcripts. Amongst these we detected a disproportionately large fraction of X chromosomal genes, all of which were upregulated in iPSC-NSCs. Concurrently, we detected differential methylation of X chromosomal sites spatially coinciding with regions harboring differentially expressed genes. While our data point to a pronounced overall reinstallation of autosomal transcriptomic and methylation signatures when a defined somatic lineage is propagated through pluripotency, they also indicate that X chromosomal genes may partially escape this reinstallation process. Considering the broad application of iPSCs in disease modeling and regenerative approaches, such reprogramming-associated alterations in X chromosomal gene expression and DNA methylation deserve particular attention. [ABSTRACT FROM AUTHOR]

Background: Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites. Methods: Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models. Results: In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque. Conclusions: Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD. [ABSTRACT FROM AUTHOR]

The Anthropocene signifies the start of a no-analogue trajectory of the Earth system that is fundamentally different from the Holocene. This new trajectory is characterized by rising risks of triggering irreversible and unmanageable shifts in Earth system functioning. We urgently need a new global approach to safeguard critical Earth system regulating functions more effectively and comprehensively. The global commons framework is the closest example of an existing approach with the aim of governing biophysical systems on Earth upon which the world collectively depends. Derived during stable Holocene conditions, the global commons framework must now evolve in the light of new Anthropocene dynamics. This requires a fundamental shift from a focus only on governing shared resources beyond national jurisdiction, to one that secures critical functions of the Earth system irrespective of national boundaries. We propose a new framework--the planetary commons--which differs from the global commons framework by including not only globally shared geographic regions but also critical biophysical systems that regulate the resilience and state, and therefore livability, on Earth. The new planetary commons should articulate and create comprehensive stewardship obligations through Earth system governance aimed at restoring and strengthening planetary resilience and justice. [ABSTRACT FROM AUTHOR]

Key Clinical Message: This case report aims to raise awareness of differential diagnoses of hypercalcemia and primary hyperparathyroidism, including parathyroid carcinoma and atypical adenoma, and to highlight the diagnostic challenges. Parathyroid carcinoma is a rare and often fatal cause of primary hyperparathyroidism and hypercalcemia. To date, there is still no clear‐cut diagnostic pathway for parathyroid carcinoma established, which results in major diagnostic ambiguity and complexity. Clinical differentiation between benign parathyroid adenoma and carcinoma is challenging and ultimately the diagnosis remains histopathological. We present a case of a 58‐year‐old female patient with parathyroid tumor recurrence after parathyroidectomy because of primary hyperparathyroidism. The first tumor was histologically classified as an atypical parathyroid adenoma by a specialized endocrine pathologist. Eleven years after the primary tumor resection a new tumor recurred. Retrospectively, after the tumor recurrence, the primary diagnosis of the atypical adenoma was questioned, and the tumor was temporarily classified to rather be a parathyroid carcinoma. This case aims to raise awareness for the diagnostic challenge of parathyroid carcinomas as a rare cause of primary hyperparathyroidism and therewith to improve treatment and prognosis. [ABSTRACT FROM AUTHOR]

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73–100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, ‘structured noise maximum’ was the strongest predictor for the technologists’ choice, followed by ‘N/2 ghosting average’. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists’ choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers. [ABSTRACT FROM AUTHOR]

Aims To determine the conditions under which early hypoattenuated leaflet thickening (HALT) after transcatheter aortic valve implantation (TAVI) becomes haemodynamically relevant. Methods and results The study included 100 patients (age: 81.5 ± 5.5 years; female 63%), thereof 50 patients with HALT. After anonymization and randomization, blinded readers measured maximum thrombus thickness per prosthesis (MT_pr) and movement restriction (MR_pr) on electrocardiogram (ECG)-gated whole heart cycle computed tomography angiography. These measurements were compared with echocardiographic mean pressure gradient (mPG), its increase from baseline (ΔmPG), and Doppler velocity index (DVI). Haemodynamic valve deterioration (HVD) was defined as mPG > 20 mmHg. Age, body mass index, valve type, valve size, left ventricular ejection fraction, and atrial fibrillation were considered as influencing factors. Multiple regression analysis revealed that only valve size (P = 0.001) and MT_pr (P = 0.02) had a significant influence on mPG. In an interaction model, valve size moderated the effect of MT_pr on mPG significantly (P = 0.004). Sub-group analysis stratified by valve sizes showed a strong correlation between MT_pr and echocardiographic parameters for 23 mm valves (mPG: r = 0.57, ΔmPG: r = 0.68, DVI: r = 0.55, each with P < 0.001), but neither for 26 nor 29 mm valves (r < 0.2, P > 0.2 for all correlations). Six of seven prostheses with HVD had a 23 mm valve diameter, while one had 29 mm (P = 0.02). Conclusion Early HALT rarely causes significant mPG increase. Our study shows that valve size is a key factor influencing the haemodynamic impact of HALT. In small valve sizes, mPG is more likely to increase. Our study is the first to offer in vivo evidence supporting previous in vitro findings on this topic. [ABSTRACT FROM AUTHOR]

Background: Performance anxiety is the most frequently reported anxiety disorder among professional musicians. Typical symptoms are - on a physical level - the consequences of an increase in sympathetic tone with cardiac stress, such as acceleration of heartbeat, increase in blood pressure, increased respiratory rate and tremor up to nausea or flush reactions. These symptoms can cause emotional distress, a reduced musical and artistical performance up to an impaired functioning. While anxiety disorders are preferably treated using cognitive-behavioral therapy with exposure, this approach is rather difficult for treating music performance anxiety since the presence of a public or professional jury is required and not easily available. The use of virtual reality (VR) could therefore display an alternative. So far, no therapy studies on music performance anxiety applying virtual reality exposure therapy have investigated the therapy outcome including cardiovascular changes as outcome parameters. Methods: This mono-center, prospective, randomized and controlled clinical trial has a pre-post design with a follow-up period of 6 months. 46 professional and semi-professional musicians will be recruited and allocated randomly to an VR exposure group or a control group receiving progressive muscle relaxation training. Both groups will be treated over 4 single sessions. Music performance anxiety will be diagnosed based on a clinical interview using ICD-10 and DSM-5 criteria for specific phobia or social anxiety. A behavioral assessment test is conducted three times (pre, post, follow-up) in VR through an audition in a concert hall. Primary outcomes are the changes in music performance anxiety measured by the German Bühnenangstfragebogen and the cardiovascular reactivity reflected by heart rate variability (HRV). Secondary outcomes are changes in blood pressure, stress parameters such as cortisol in the blood and saliva, neuropeptides, and DNA-methylation. Discussion: The trial investigates the effect of VR exposure in musicians with performance anxiety compared to a relaxation technique on anxiety symptoms and corresponding cardiovascular parameters. We expect a reduction of anxiety but also a consecutive improvement of HRV with cardiovascular protective effects. Trial registration: : This study was registered on clinicaltrials.gov. (ClinicalTrials.gov Number: NCT05735860) [ABSTRACT FROM AUTHOR]

Exposure to plant toxins or microbiota that are able to digest common food ingredients to toxic structures might be responsible for biliary atresia (BA). An isoflavonoid, biliatresone is known to effectively alter the extrahepatic bile duct (EHBD) development in BALB/c mice. Biliatresone causes a reduction of Glutathione (GSH) levels, SOX17 downregulation and is effectively countered with N-Acetyl-L-cysteine treatment in vitro. Therefore, reversing GSH-loss appears to be a promising treatment target for a translational approach. Since BALB/c mice have been described as sensitive in various models, we evaluated the toxic effect of biliatresone in robust C57BL/6J mice and confirmed its toxicity. Comparison between BALB/c and C57BL/6J mice revealed similarity in the toxic model. Affected neonates exhibited clinical symptoms of BA, such as jaundice, ascites, clay-colored stools, yellow urine and impaired weight gain. The gallbladders of jaundiced neonates were hydropic and EHBD were twisted and enlarged. Serum and histological analysis proved cholestasis. No anomalies were seen in the liver and EHBD of control animals. With our study we join a chain of evidence confirming that biliatresone is an effective agent for cross-lineage targeted alteration of the EHBD system. [ABSTRACT FROM AUTHOR]

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. [ABSTRACT FROM AUTHOR]

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Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular. Therefore, traditional machine learning methods like PCA are ill-suited for analysis of EHR-derived patient data. We propose to address these issues with a new methodology based on training a gated recurrent unit (GRU) autoencoder directly on health record data. Our method learns a low-dimensional feature space by training on patient data time series, where the time of each data point is expressed explicitly. We use positional encodings for time, allowing our model to better handle the temporal irregularity of the data. We apply our method to data from the Medical Information Mart for Intensive Care (MIMIC-III). Using our data-derived feature space, we can cluster patients into groups representing major classes of disease patterns. Additionally, we show that our feature space exhibits a rich substructure at multiple scales. [ABSTRACT FROM AUTHOR]

Artificial neural networks are referred to as universal approximators due to their inherent ability to reconstruct complex linear and nonlinear output maps conceived as input-output relationships from data sets. This can be done by reducing large networks via regularization in order to establish compact models containing fewer parameters aimed at describing vital dependencies in data sets. In situations where the data sets contain non-informative input features, devising a continuous, optimal input feature selection technique can lead to improved prediction or classification. We propose a continuous input selection technique through a dimensional reduction mechanism using a 'structured' l2 − norm regularization. The implementation is done by identifying the most informative feature subsets from a given data set via an adaptive training mechanism. The adaptation involves introducing a novel, modified gradient approach during training to deal with the non-differentiability associated with the gradient of the structured norm penalty. When the method is applied to process data sets, results indicate that the most informative inputs of artificial neural networks can be selected using a structured l2 − norm penalization. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support. [ABSTRACT FROM AUTHOR]

Vaccine‐induced immune thrombotic thrombocytopenia (VITT) is a rare clinical condition that has emerged during the mass immunization against SARS‐CoV‐2. Reports indicate that VITT may also be induced by other vaccines, such as the human papillomavirus vaccine, or occur independently of vaccination. Its recognition requires a high index of suspicion, especially in patients presenting with thrombocytopenia and thrombosis several days after vaccination with an adenoviral vector‐based vaccine against SARS‐CoV‐2. Bleeding manifestations do not exclude VITT, as initially assumed. It is of great importance to perform the appropriate diagnostic tests early in the course of the disease, as false‐negative results may occur and many aspects of VITT are not fully understood. These two cases from Germany demonstrate unusual presentations of VITT. [ABSTRACT FROM AUTHOR]

Mechanistic/data-driven hybrid modeling is a key approach when the mechanistic details of the processes at hand are not sufficiently well understood, but also inferring a model purely from data is too complex. By the integration of first principles into a data-driven approach, hybrid modeling promises a feasible data demand alongside extrapolation. In this work, we introduce a learning strategy for tree-structured hybrid models to perform a binary classification task. Given a set of binary labeled data, the challenge is to use them to develop a model that accurately assesses labels of new unlabeled data. Our strategy employs graph-theoretic methods to analyze the data and deduce a function that maps input features to output labels. Our focus here is on data sets represented by binary features in which the label assessment of unlabeled data points is always extrapolation. Our strategy shows the existence of small sets of data points within given binary data for which knowing the labels allows for extrapolation to the entire valid input space. An implementation of our strategy yields a notable reduction of training-data demand in a binary classification task compared with different supervised machine learning algorithms. As an application, we have fitted a tree-structured hybrid model to the vital status of a cohort of COVID-19 patients requiring intensive-care unit treatment and mechanical ventilation. Our learning strategy yields the existence of patient cohorts for whom knowing the vital status enables extrapolation to the entire valid input space of the developed hybrid model. [ABSTRACT FROM AUTHOR]

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A case of refractory primary hypothyroidism is presented. Despite laboratory‐guided hormonal substitution, the patient remained hypothyroid. Multiple diagnostic tests ruled out all known causes of levothyroxine malabsorption. Interestingly, clinical and laboratory responding was promptly achieved, after switching the application format from solid tablets to liquid formula. [ABSTRACT FROM AUTHOR]

Hepatic encephalopathy (HE) is a frequent and debilitating complication of liver disease and is oftentimes associated with hyperammonaemia. However, hyperammonaemia may occur in underlying conditions other than acute or chronic liver failure and clinical awareness is needed in order to be recognized and treated properly. A 24-year-old woman presented herself to our emergency department with acute confusion and altered mental state due to severe hyperammonaemia. The patient was diagnosed in the age of 14 with ornithine transcarbamylase (OTC) deficiency and was incompliant regarding her diet and suggested medication to treat this disorder. She was treated with sodium benzoate 250 mg/kg i.v., sodium phenylbutyrate/sodium phenylacetate 250 mg/kg i.v., L-arginine 250 mg/kg i.v., and received continuous hemofiltration. Under simultaneous medical treatment and haemodialysis, ammonia levels dropped to normal within 24 h and symptomatic encephalopathy ceased completely. OTC deficiency is rare in adults, and the majority of patients are diagnosed in childhood. It can lead to death if not diagnosed and treated properly. Our case underlines the importance of considering causes of HE other than liver cirrhosis. [ABSTRACT FROM AUTHOR]

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Copyright of Rechtswissenschaft (Baden-Baden) is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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VIPoma, a neuroendocrine tumour mostly occurring in the human pancreas and producing high levels of vasoactive intestinal peptide, is a rare disease that presents with a wide spectrum of symptoms, including intense diarrhoea, hypokalaemia, and cardiac complications, with life-threatening consequences. In most cases, metastatic lesions are present at VIPoma diagnosis. Treatment options include symptomatic therapy, chemotherapy, radiation and surgery. Due to its low incidence, there are no evidence-based therapy recommendations to date. Here, we present a case of a 39-year-old woman with severe symptoms due to VIPoma of the pancreas with diffuse hepatic metastasis, who underwent simultaneous resection of the primary tumour, extensive liver resection and radiofrequency ablation. The patient was released in good health and was recurrence-free during 12 months surveillance. According to the existing literature and our own experience, surgical procedures appear to be the most promising therapy option for cases with diffuse hepatic metastasis, offering patients relief from their symptoms and (chemo)therapy-free time. [ABSTRACT FROM AUTHOR]

A third SARS-CoV-2 infection wave has affected Germany from March 2021 until April 24th, until the ´Bundesnotbremse´ introduced nationwide shutdown measures. The ´Bundesnotbremse´ is the technical term which was used by the German government to describe nationwide shutdown measures to control the rising infection numbers. These measures included mainly contact restrictions on several level. This study investigates which effects locally dispersed pre- and post-´Bundesnotbremse´ measures had on the infection dynamics. We analyzed the variability and strength of the rates of the changes of weekly case numbers considering different regions, age groups, and contact restrictions. Regionally diverse measures slowed the rate of weekly increase by about 50% and about 75% in regions with stronger contact restrictions. The 'Bundesnotbremse' induced a coherent reduction of infection numbers across all German federal states and age groups throughout May 2021. The coherence of the infection dynamics after the 'Bundesnotbremse' indicates that these stronger measures induced the decrease of infection numbers. The regionally diverse non-pharmaceutical interventions before could only decelerate further spreading, but not prevent it alone. [ABSTRACT FROM AUTHOR]

Myeloproliferative neoplasms (MPN), comprising essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (PMF), are hematological disorders of the myeloid lineage characterized by hyperproliferation of mature blood cells. The prediction of the clinical course and progression remains difficult and new therapeutic modalities are required. We conducted a CD34+ gene expression study to identify signatures and potential biomarkers in the different MPN subtypes with the aim to improve treatment and prevent the transformation from the rather benign chronic state to a more malignant aggressive state. We report here on a systematic gene expression analysis (GEA) of CD34+ peripheral blood or bone marrow cells derived from 30 patients with MPN including all subtypes (ET (n = 6), PV (n = 11), PMF (n = 9), secondary MF (SMF; post-ET-/post-PV-MF; n = 4)) and six healthy donors. GEA revealed a variety of differentially regulated genes in the different MPN subtypes vs. controls, with a higher number in PMF/SMF (200/272 genes) than in ET/PV (132/121). PROGENγ analysis revealed significant induction of TNFα/NF-κB signaling (particularly in SMF) and reduction of estrogen signaling (PMF and SMF). Consistently, inflammatory GO terms were enriched in PMF/SMF, whereas RNA splicing–associated biological processes were downregulated in PMF. Differentially regulated genes that might be utilized as diagnostic/prognostic markers were identified, such as AREG, CYBB, DNTT, TIMD4, VCAM1, and S100 family members (S100A4/8/9/10/12). Additionally, 98 genes (including CLEC1B, CMTM5, CXCL8, DACH1, and RADX) were deregulated solely in SMF and may be used to predict progression from early to late stage MPN. [ABSTRACT FROM AUTHOR]

Background: The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients.Methods: We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step.Results: We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group.Conclusions: The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies. [ABSTRACT FROM AUTHOR]

To evaluate the relationship of cardiac function, including time-volume-curves, with lung volumes derived from pulmonary function tests (PFT) and MRI in subjects without cardiovascular diseases. 216 subjects underwent whole-body MRI and spirometry as part of the KORA-FF4 cohort study. Lung volumes derived semi-automatically using an in-house algorithm. Forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and residual volume were measured. Cardiac parameters derived from Cine-SSFP-sequence using cvi42, while left ventricle (LV) time-volume-curves were evaluated using pyHeart. Linear regression analyses assessed the relationships of cardiac parameters with PFT and MRI-based lung volumes. Mean age was 56.3 ± 9.2 years (57% males). LV and right ventricular (RV) end-diastolic-, end-systolic-, stroke volume, LV peak ejection- and early/late diastolic filling rate were associated with FEV1, FVC, and residual volume (excluding late diastolic filling rate with FEV1, LV end-systolic/stroke volume and RV end-diastolic/end-systolic volumes with residual volume). In contrast, LV end-diastolic volume (ß = − 0.14, p = 0.01), early diastolic filling rate (ß = − 0.11, p = 0.04), and LV/RV stroke volume (ß = − 0.14, p = 0.01; ß = − 0.11, p = 0.01) were inversely associated with MRI-based lung volume. Subclinical cardiac impairment was associated with reduced FEV1, FVC, and residual volume. Cardiac parameters decreased with increasing MRI-based lung volume contrasting the results of PFT. [ABSTRACT FROM AUTHOR]

Background: COVID-19 frequently necessitates in-patient treatment and in-patient mortality is high. Less is known about the long-term outcomes in terms of mortality and readmissions following in-patient treatment. Aim: The aim of this paper is to provide a detailed account of hospitalized COVID-19 patients up to 180 days after their initial hospital admission. Methods: An observational study with claims data from the German Local Health Care Funds of adult patients hospitalized in Germany between February 1 and April 30, 2020, with PCR-confirmed COVID-19 and a related principal diagnosis, for whom 6-month all-cause mortality and readmission rates for 180 days after admission or until death were available. A multivariable logistic regression model identified independent risk factors for 180-day all-cause mortality in this cohort. Results: Of the 8,679 patients with a median age of 72 years, 2,161 (24.9%) died during the index hospitalization. The 30-day all-cause mortality rate was 23.9% (2,073/8,679), the 90-day rate was 27.9% (2,425/8,679), and the 180-day rate, 29.6% (2,566/8,679). The latter was 52.3% (1,472/2,817) for patients aged ≥80 years 23.6% (1,621/6,865) if not ventilated during index hospitalization, but 53.0% in case of those ventilated invasively (853/1,608). Risk factors for the 180-day all-cause mortality included coagulopathy, BMI ≥ 40, and age, while the female sex was a protective factor beyond a fewer prevalence of comorbidities. Of the 6,235 patients discharged alive, 1,668 were readmitted a total of 2,551 times within 180 days, resulting in an overall readmission rate of 26.8%. Conclusions: The 180-day follow-up data of hospitalized COVID-19 patients in a nationwide cohort representing almost one-third of the German population show significant long-term, all-cause mortality and readmission rates, especially among patients with coagulopathy, whereas women have a profoundly better and long-lasting clinical outcome compared to men. [ABSTRACT FROM AUTHOR]

Abdominal tuberculosis is a rare clinical condition in nonendemic countries and should be included as differential diagnosis by unspecific abdominal complaints, especially in patients with immigration background from high‐prevalence regions. [ABSTRACT FROM AUTHOR]

The article focuses on the term resilience is often used in conjunction with other terms such as vulnerability, adaptation, adaptive capacity, transformability, and robustness. It mentions that the term vulnerability which is simply the opposite of resilience and adaptation have been used to refer to individuals or households which are taken to be the minimal decision-making unit.

Taking the question of phenomenality as its guiding thread, this paper attempts to shed light on the relationship between Heidegger's turn and Derrida's 1964/65 seminar on Heidegger. I argue that deconstruction can be understood as a performative attempt to take into account Heidegger's thinking of originary semblance or errancy, which already announces itself in Sein und Zeit and is a central figure of what the later Heidegger calls 'the turn'. Instead of trying to grasp this errancy or this différance phenomenologically, Derrida takes up the necessity to read and repeat what is already given, what already appears, and thereby undermines, through his reading of Heidegger's Crossing Out of the Word 'Sein', the phenomeno-fundamental ontological approach of Sein und Zeit, or rather: shows that it is always already undermined. [ABSTRACT FROM AUTHOR]

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians' standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients. [ABSTRACT FROM AUTHOR]

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VIPoma, a neuroendocrine tumour mostly occurring in the human pancreas and producing high levels of vasoactive intestinal peptide, is a rare disease that presents with a wide spectrum of symptoms, including intense diarrhoea, hypokalaemia, and cardiac complications, with life-threatening consequences. In most cases, metastatic lesions are present at VIPoma diagnosis. Treatment options include symptomatic therapy, chemotherapy, radiation and surgery. Due to its low incidence, there are no evidence-based therapy recommendations to date. Here, we present a case of a 39-year-old woman with severe symptoms due to VIPoma of the pancreas with diffuse hepatic metastasis, who underwent simultaneous resection of the primary tumour, extensive liver resection and radiofrequency ablation. The patient was released in good health and was recurrence-free during 12 months surveillance. According to the existing literature and our own experience, surgical procedures appear to be the most promising therapy option for cases with diffuse hepatic metastasis, offering patients relief from their symptoms and (chemo)therapy-free time. [ABSTRACT FROM AUTHOR]

Drug sensitivity prediction models for human cancer cell lines constitute important tools in identifying potential computational biomarkers for responsiveness in a pre-clinical setting. Integrating information derived from a range of heterogeneous data is crucial, but remains non-trivial, as differences in data structures may hinder fitting algorithms from assigning adequate weights to complementary information that is contained in distinct omics data. In order to counteract this effect that tends to lead to just one data type dominating supposedly multi-omics models, we developed a novel tool that enables users to train single-omics models separately in a first step and to integrate them into a multi-omics model in a second step. Extensive ablation studies are performed in order to facilitate an in-depth evaluation of the respective contributions of singular data types and of combinations thereof, effectively identifying redundancies and interdependencies between them. Moreover, the integration of the single-omics models is realized by a range of distinct classification algorithms, thus allowing for a performance comparison. Sets of molecular events and tissue types found to be related to significant shifts in drug sensitivity are returned to facilitate a comprehensive and straightforward analysis of potential computational biomarkers for drug responsiveness. Our two-step approach yields sets of actual multi-omics pan-cancer classification models that are highly predictive for a majority of drugs in the GDSC data base. In the context of targeted drugs with particular modes of action, its predictive performances compare favourably to those of classification models that incorporate multi-omics data in a simple one-step approach. Additionally, case studies demonstrate that it succeeds both in correctly identifying known key biomarkers for sensitivity towards specific drug compounds as well as in providing sets of potential candidates for additional computational biomarkers. [ABSTRACT FROM AUTHOR]

Introduction: Immune checkpoint inhibitors are becoming increasingly important in oncology. Immune-related adverse events, including autoimmune hypophysitis, have been reported before. Case report: We present a case series of three males and one female, suffering from either malignant melanoma or renal cell carcinoma, who developed hypophysitis under Nivolumab and/or Ipilimumab. A wide range of clinical manifestations from asymptomatic hypophysitis, headache, general weakness, loss of appetite, visual field impairment, and confusion to acute life-threatening Addison crisis was observed. Management and outcome: All patients received corticosteroids. Immune checkpoint inhibitors were discontinued in three cases until resolution of symptoms. Discussion: The objective of our report is to raise the awareness of physicians, regarding this rare clinical entity, which may become life-threatening, if not promptly recognized and properly treated. [ABSTRACT FROM AUTHOR]