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3,139 results on '"SEBASTIAN, R."'

1. Extracellular vesicles from pancreatic cancer and its tumour microenvironment promote increased Schwann cell migration: Cellular and Molecular Biology

Author

Wong, Fang Cheng, Merker, Sebastian R., Bauer, Lisa, Han, Yi, Le, Van Manh Hung, Wenzel, Carina, Böthig, Lukas, Heiduk, Max, Drobisch, Pascal, Rao, Venkatesh Sadananda, Malekian, Farzaneh, Mansourkiaei, Ana, Sperling, Christian, Polster, Heike, Pecqueux, Mathieu, Istvanffy, Rouzanna, Ye, Linhan, Kong, Bo, Aust, Daniela E., Baretton, Gustavo, Seifert, Lena, Seifert, Adrian M., Weitz, Jürgen, Demir, Ihsan Ekin, and Kahlert, Christoph

Published
2025
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5. Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning

Author

Peach, Robert, Friedrich, Maximilian, Fronemann, Lara, Muthuraman, Muthuraman, Schreglmann, Sebastian R., Zeller, Daniel, Schrader, Christoph, Krauss, Joachim K., Schnitzler, Alfons, Wittstock, Matthias, Helmers, Ann-Kristin, Paschen, Steffen, Kühn, Andrea, Skogseid, Inger Marie, Eisner, Wilhelm, Mueller, Joerg, Matthies, Cordula, Reich, Martin, Volkmann, Jens, and Ip, Chi Wang

Published
2024
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8. ACE2-independent sarbecovirus cell entry can be supported by TMPRSS2-related enzymes and can reduce sensitivity to antibody-mediated neutralization.

Author

Lu Zhang, Hsiu-Hsin Cheng, Nadine Krüger, Bojan Hörnich, Luise Graichen, Alexander S Hahn, Sebastian R Schulz, Hans-Martin Jäck, Metodi V Stankov, Georg M N Behrens, Marcel A Müller, Christian Drosten, Onnen Mörer, Martin Sebastian Winkler, ZhaoHui Qian, Stefan Pöhlmann, and Markus Hoffmann

Subjects
Immunologic diseases. Allergy, RC581-607, Biology (General), QH301-705.5
Abstract

The COVID-19 pandemic, caused by SARS-CoV-2, demonstrated that zoonotic transmission of animal sarbecoviruses threatens human health but the determinants of transmission are incompletely understood. Here, we show that most spike (S) proteins of horseshoe bat and Malayan pangolin sarbecoviruses employ ACE2 for entry, with human and raccoon dog ACE2 exhibiting broad receptor activity. The insertion of a multibasic cleavage site into the S proteins increased entry into human lung cells driven by most S proteins tested, suggesting that acquisition of a multibasic cleavage site might increase infectivity of diverse animal sarbecoviruses for the human respiratory tract. In contrast, two bat sarbecovirus S proteins drove cell entry in an ACE2-independent, trypsin-dependent fashion and several ACE2-dependent S proteins could switch to the ACE2-independent entry pathway when exposed to trypsin. Several TMPRSS2-related cellular proteases but not the insertion of a multibasic cleavage site into the S protein allowed for ACE2-independent entry in the absence of trypsin and may support viral spread in the respiratory tract. Finally, the pan-sarbecovirus antibody S2H97 enhanced cell entry driven by two S proteins and this effect was reversed by trypsin while trypsin protected entry driven by a third S protein from neutralization by S2H97. Similarly, plasma from quadruple vaccinated individuals neutralized entry driven by all S proteins studied, and availability of the ACE2-independent, trypsin-dependent pathway reduced neutralization sensitivity. In sum, our study reports a pathway for entry into human cells that is ACE2-independent, can be supported by TMPRSS2-related proteases and may be associated with antibody evasion.

Published
2024
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9. Guess the cheese flavour by the size of its holes: A cosmological test using the abundance of Popcorn voids

Author

Paz, Dante J., Correa, Carlos M., Gualpa, Sebastián R., Ruiz, Andres N., Bederián, Carlos S., Graña, R. Dario, and Padilla, Nelson D.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a new definition of cosmic void and a publicly available code with the algorithm that implements it. Underdense regions are defined as free-form objects, called popcorn voids, made from the union of spheres of maximum volume with a given joint integrated underdensity contrast.The method is inspired by the excursion-set theory and consequently no rescaling processing is needed, the removal of overlapping voids and objects with sizes below the shot noise threshold is inherent in the algorithm. The abundance of popcorn voids in the matter field can be fitted using the excursion-set theory provided the relationship between the linear density contrast of the barrier and the threshold used in void identification is modified relative to the spherical evolution model. We also analysed the abundance of voids in biased tracer samples in redshift space. We show how the void abundance can be used to measure the geometric distortions due to the assumed fiducial cosmology, in a test similar to an Alcock-Paczy\'nski test. Using the formalism derived from previous works, we show how to correct the abundance of popcorn voids for redshift-space distortion effects. Using this treatment, in combination with the excursion-set theory, we demonstrate the feasibility of void abundance measurements as cosmological probes. We obtain unbiased estimates of the target parameters, albeit with large degeneracies in the parameter space. Therefore, we conclude that the proposed test in combination with other cosmological probes has potential to improve current cosmological parameter constraints., Comment: Updated manuscript sent to the MNRAS after referee report: 16 pages, 8 figures. Corrections were made to Fig. 4, some related conclusions were modified. The main conclusions remain unchanged

Published
2022
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10. Machine Learning for Increased Profits in the Cryptocurrency Market Through Pattern Recognition with Artificial Neural Networks

Author

Lazo Lazo, Juan G., Ruiz Cárdenas, Diego A., Esquives Bravo, Sebastián R., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Mishra, Durgesh, editor, and Joshi, Amit, editor

Published
2024
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11. Inhibition of ATM or ATR in combination with hypo-fractionated radiotherapy leads to a different immunophenotype on transcript and protein level in HNSCC

Author

Julia Meidenbauer, Matthias Wachter, Sebastian R. Schulz, Nada Mostafa, Lilli Zülch, Benjamin Frey, Rainer Fietkau, Udo S. Gaipl, and Tina Jost

Subjects
HNSCC, DNA damage repair, kinase inhibitors, immunomodulation, ATM inhibition, ATR inhibition, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

BackgroundThe treatment of head and neck tumors remains a challenge due to their reduced radiosensitivity. Small molecule kinase inhibitors (smKI) that inhibit the DNA damage response, may increase the radiosensitivity of tumor cells. However, little is known about how the immunophenotype of the tumor cells is modulated thereby. Therefore, we investigated whether the combination of ATM or ATR inhibitors with hypo-fractionated radiotherapy (RT) has a different impact on the expression of immune checkpoint markers (extrinsic), the release of cytokines or the transcriptome (intrinsic) of head and neck squamous cell carcinoma (HNSCC) cells.MethodsThe toxic and immunogenic effects of the smKI AZD0156 (ATMi) and VE-822 (ATRi) in combination with a hypo-fractionated scheme of 2x5Gy RT on HPV-negative (HSC4, Cal-33) and HPV-positive (UM-SCC-47, UD-SCC-2) HNSCC cell lines were analyzed as follows: cell death (necrosis, apoptosis; detected by AnxV/PI), expression of immunostimulatory (ICOS-L, OX40-L, TNFSFR9, CD70) and immunosuppressive (PD-L1, PD-L2, HVEM) checkpoint marker using flow cytometry; the release of cytokines using multiplex ELISA and the gene expression of Cal-33 on mRNA level 48 h post-RT.ResultsCell death was mainly induced by the combination of RT with both inhibitors, but stronger with ATRi. Further, the immune phenotype of cancer cells, not dying from combination therapy itself, is altered predominantly by RT+ATRi in an immune-stimulatory manner by the up-regulation of ICOS-L. However, the analysis of secreted cytokines after treatment of HNSCC cell lines revealed an ambivalent influence of both inhibitors, as we observed the intensified secretion of IL-6 and IL-8 after RT+ATRi. These findings were confirmed by RNAseq analysis and further the stronger immune-suppressive character of RT+ATMi was enlightened. We detected the down-regulation of a central protein of cytoplasmatic sensing pathways of nucleic acids, RIG-1, and found one immune-suppressive target, EDIL3, strongly up-regulated by RT+ATMi.ConclusionIndependent of a restrictive toxicity, the combination of RT + either ATMi or ATRi leads to comprehensive and immune-modulating alterations in HNSCC. This includes pro-inflammatory signaling induced by RT + ATRi but also anti-inflammatory signals. These findings were confirmed by RNAseq analysis, which further highlighted the immune-suppressive nature of RT + ATMi.

Published
2024
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12. Prenatal tetrahydrocannabinol and cannabidiol exposure produce sex-specific pathophysiological phenotypes in the adolescent prefrontal cortex and hippocampus

Author

Marieka V. DeVuono, Mina G. Nashed, Mohammed H. Sarikahya, Andrea Kocsis, Kendrick Lee, Sebastian R. Vanin, Roger Hudson, Eryn P. Lonnee, Walter J. Rushlow, Daniel B. Hardy, and Steven R. Laviolette

Subjects
Cannabis, ∆9-tetrahydrocannabinol (THC), Cannabidiol (CBD), Pregnancy, Adolescence, Development, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Clinical and preclinical evidence has demonstrated an increased risk for neuropsychiatric disorders following prenatal cannabinoid exposure. However, given the phytochemical complexity of cannabis, there is a need to understand how specific components of cannabis may contribute to these neurodevelopmental risks later in life. To investigate this, a rat model of prenatal cannabinoid exposure was utilized to examine the impacts of specific cannabis constituents (Δ9-tetrahydrocannabinol [THC]; cannabidiol [CBD]) alone and in combination on future neuropsychiatric liability in male and female offspring. Prenatal THC and CBD exposure were associated with low birth weight. At adolescence, offspring displayed sex-specific behavioural changes in anxiety, temporal order and social cognition, and sensorimotor gating. These phenotypes were associated with sex and treatment-specific neuronal and gene transcriptional alterations in the prefrontal cortex, and ventral hippocampus, regions where the endocannabinoid system is implicated in affective and cognitive development. Electrophysiology and RT-qPCR analysis in these regions implicated dysregulation of the endocannabinoid system and balance of excitatory and inhibitory signalling in the developmental consequences of prenatal cannabinoids. These findings reveal critical insights into how specific cannabinoids can differentially impact the developing fetal brains of males and females to enhance subsequent neuropsychiatric risk.

Published
2024
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13. Computer-aided diagnosis and prediction in brain disorders

Author

Venkatraghavan, Vikram, van der Voort, Sebastian R., Bos, Daniel, Smits, Marion, Barkhof, Frederik, Niessen, Wiro J., Klein, Stefan, and Bron, Esther E.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumours, and decision making in stroke. Regarding prediction, i.e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer. Furthermore, based on these use cases, we will assess the current state-of-the-art methodology and highlight current efforts on benchmarking of these methods and the importance of open science therein. Finally, we assess the current clinical impact of computer-aided methods and discuss the required next steps to increase clinical impact.

Published
2022

17. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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18. Using segment-based features of jaw movements to recognize foraging activities in grazing cattle

Author

Chelotti, José O., Vanrell, Sebastián R., Martinez-Rau, Luciano S., Galli, Julio R., Utsumi, Santiago A., Planisich, Alejandra M., Almirón, Suyai A., Milone, Diego H., Giovanini, Leonardo L., and Rufiner, H. Leonardo

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Precision livestock farming optimizes livestock production through the use of sensor information and communication technologies to support decision making, proactively and near real-time. Among available technologies to monitor foraging behavior, the acoustic method has been highly reliable and repeatable, but can be subject to further computational improvements to increase precision and specificity of recognition of foraging activities. In this study, an algorithm called Jaw Movement segment-based Foraging Activity Recognizer (JMFAR) is proposed. The method is based on the computation and analysis of temporal, statistical and spectral features of jaw movement sounds for detection of rumination and grazing bouts. They are called JM-segment features because they are extracted from a sound segment and expect to capture JM information of the whole segment rather than individual JMs. Two variants of the method are proposed and tested: (i) the temporal and statistical features only JMFAR-ns; and (ii) a feature selection process (JMFAR-sel). The JMFAR was tested on signals registered in a free grazing environment, achieving an average weighted F1-score of 93%. Then, it was compared with a state-of-the-art algorithm, showing improved performance for estimation of grazing bouts (+19%). The JMFAR-ns variant reduced the computational cost by 25.4%, but achieved a slightly lower performance than the JMFAR. The good performance and low computational cost of JMFAR-ns supports the feasibility of using this algorithm variant for real-time implementation in low-cost embedded systems. The method presented within this publication is protected by a pending patent application: AR P20220100910., Comment: Preprint submitted to journal

Published
2022
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19. Static force characteristic of annular gaps -- Experimental and simulation results

Author

Kuhr, Maximilian M. G., Lang, Sebastian R., and Pelz, Peter F.

Subjects
Physics - Fluid Dynamics
Abstract

We discuss the static force characteristic of annular gaps resulting from an axial flow component. So far there is a severe lack of understanding of the flow inside the annulus. First, the state-of-the-art modelling approaches to describe the flow inside the annulus are recapped and discussed. The discussion focuses in particular on the modelling of inertia effects. Second, a new calculation method, the Clearance-Averaged Pressure Model (CAPM) is presented. The CAPM uses an integro-differential approach in combination with power law ansatz functions for the velocity profiles and a Hirs' model to calculate the resulting pressure field. Third, for experimental validation, a setup is presented using magnetic bearings to inherently measure the position as well as the force on the rotor induced by the flow field inside the gap. The experiments focus on the characteristic load behaviour, attitude angle and pressure difference across the annulus. Fourth, the experimental results are compared to the calculation results., Comment: Pre-validation chapter was added. Order of magnitude analysis for the hydrodynamic entrance length was added in chapter II. Layout of the pre-print was changed

Published
2021
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24. Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies

Author

Tsvetan R. Yordanov, Anita C. J. Ravelli, Saba Amiri, Marije Vis, Saskia Houterman, Sebastian R. Van der Voort, and Ameen Abu-Hanna

Subjects
federated learning, multicenter, prediction models, TAVI, distributed machine learning, privacy-preserving algorithms, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

BackgroundFederated learning (FL) is a technique for learning prediction models without sharing records between hospitals. Compared to centralized training approaches, the adoption of FL could negatively impact model performance.AimThis study aimed to evaluate four types of multicenter model development strategies for predicting 30-day mortality for patients undergoing transcatheter aortic valve implantation (TAVI): (1) central, learning one model from a centralized dataset of all hospitals; (2) local, learning one model per hospital; (3) federated averaging (FedAvg), averaging of local model coefficients; and (4) ensemble, aggregating local model predictions.MethodsData from all 16 Dutch TAVI hospitals from 2013 to 2021 in the Netherlands Heart Registration (NHR) were used. All approaches were internally validated. For the central and federated approaches, external geographic validation was also performed. Predictive performance in terms of discrimination [the area under the ROC curve (AUC-ROC, hereafter referred to as AUC)] and calibration (intercept and slope, and calibration graph) was measured.ResultsThe dataset comprised 16,661 TAVI records with a 30-day mortality rate of 3.4%. In internal validation the AUCs of central, local, FedAvg, and ensemble models were 0.68, 0.65, 0.67, and 0.67, respectively. The central and local models were miscalibrated by slope, while the FedAvg and ensemble models were miscalibrated by intercept. During external geographic validation, central, FedAvg, and ensemble all achieved a mean AUC of 0.68. Miscalibration was observed for the central, FedAvg, and ensemble models in 44%, 44%, and 38% of the hospitals, respectively.ConclusionCompared to centralized training approaches, FL techniques such as FedAvg and ensemble demonstrated comparable AUC and calibration. The use of FL techniques should be considered a viable option for clinical prediction model development.

Published
2024
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25. Maternal posture-physiology interactions in human pregnancy: a narrative review

Author

Allan J. Kember, Jennifer L. Anderson, Natalyn E. Gorazd, Sarah C. House, Katherine E. Kerr, Paula A. Torres Loza, David G. Reuter, Sebastian R. Hobson, and Craig J. Goergen

Subjects
gravity, posture, maternal, pregnancy, obstetrics, physiology, Physiology, QP1-981
Abstract

There are several well-known medical conditions in which posture and gravity interact with natural history, including pregnancy. In this review, we provide a comprehensive overview of interactions between maternal posture and maternal physiology and pathophysiology at rest during pregnancy. We conducted a systematic literature search of the MEDLINE database and identified 644 studies from 1991 through 2021, inclusive, that met our inclusion criteria. We present a narrative review of the resulting literature and highlight discrepancies, research gaps, and potential clinical implications. We organize the results by organ system and, commencing with the neurological system, proceed in our synthesis generally in the craniocaudal direction, concluding with the skin. The circulatory system warranted our greatest and closest consideration–literature concerning the dynamic interplay between physiology (heart rate, stroke volume, cardiac output, blood pressure, and systemic vascular resistance), pathophysiology (e.g., hypertension in pregnancy), and postural changes provide an intricate and fascinating example of the importance of the subject of this review. Other organ systems discussed include respiratory, renal, genitourinary, gastrointestinal, abdominal, and endocrine. In addition to summarizing the existing literature on maternal posture-physiology interactions, we also point out gaps and opportunities for further research and clinical developments in this area. Overall, our review provides both insight into and relevance of maternal posture-physiology interactions vis à vis healthcare’s mission to improve health and wellness during pregnancy and beyond.

Published
2024
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26. Impact of maternal posture on fetal physiology in human pregnancy: a narrative review

Author

Allan J. Kember, Jennifer L. Anderson, Sarah C. House, David G. Reuter, Craig J. Goergen, and Sebastian R. Hobson

Subjects
gravity, posture, maternal, pregnancy, obstetrics, physiology, Physiology, QP1-981
Abstract

In numerous medical conditions, including pregnancy, gravity and posture interact to impact physiology and pathophysiology. Recent investigations, for example, pertaining to maternal sleeping posture during the third trimester and possible impact on fetal growth and stillbirth risk highlight the importance and potential clinical implications of the subject. In this review, we provide an extensive discussion of the impact of maternal posture on fetal physiology from conception to the postpartum period in human pregnancy. We conducted a systematic literature search of the MEDLINE database and identified 242 studies from 1991 through 2021, inclusive, that met our inclusion criteria. Herein, we provide a synthesis of the resulting literature. In the first section of the review, we group the results by the impact of maternal posture at rest on the cervix, uterus, placenta, umbilical cord, amniotic fluid, and fetus. In the second section of the review, we address the impact on fetal-related outcomes of maternal posture during various maternal activities (e.g., sleep, work, exercise), medical procedures (e.g., fertility, imaging, surgery), and labor and birth. We present the published literature, highlight gaps and discrepancies, and suggest future research opportunities and clinical practice changes. In sum, we anticipate that this review will shed light on the impact of maternal posture on fetal physiology in a manner that lends utility to researchers and clinicians who are working to improve maternal, fetal, and child health.

Published
2024
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27. Reproducible radiomics through automated machine learning validated on twelve clinical applications

Author

Starmans, Martijn P. A., van der Voort, Sebastian R., Phil, Thomas, Timbergen, Milea J. M., Vos, Melissa, Padmos, Guillaume A., Kessels, Wouter, Hanff, David, Grunhagen, Dirk J., Verhoef, Cornelis, Sleijfer, Stefan, Bent, Martin J. van den, Smits, Marion, Dwarkasing, Roy S., Els, Christopher J., Fiduzi, Federico, van Leenders, Geert J. L. H., Blazevic, Anela, Hofland, Johannes, Brabander, Tessa, van Gils, Renza A. H., Franssen, Gaston J. H., Feelders, Richard A., de Herder, Wouter W., Buisman, Florian E., Willemssen, Francois E. J. A., Koerkamp, Bas Groot, Angus, Lindsay, van der Veldt, Astrid A. M., Rajicic, Ana, Odink, Arlette E., Deen, Mitchell, T., Jose M. Castillo, Veenland, Jifke, Schoots, Ivo, Renckens, Michel, Doukas, Michail, de Man, Rob A., IJzermans, Jan N. M., Miclea, Razvan L., Vermeulen, Peter B., Bron, Esther E., Thomeer, Maarten G., Visser, Jacob J., Niessen, Wiro J., and Klein, Stefan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, finding the optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-and-error process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms for each component. To optimize the workflow per application, we employ automated machine learning using a random search and ensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1) liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77); 5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis (0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer's disease (0.87); and 12) head and neck cancer (0.84). We show that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performs similar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automatically optimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications. To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework, and the code to reproduce this study., Comment: 33 pages, 4 figures, 4 tables, 2 supplementary figures, 3 supplementary table, submitted to Medical Image Analysis; revision

Published
2021

28. Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study

Author

Sahner, David, Tierney, John, Vogel, Susan E., Herpin, Betsey R., Smolskis, Mary C., McKay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Raim, Sharon Segal, Hensely, Lisa, Lorenzo, Johsua, Mock, Rebecca, Zuckerman, Judith, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Kang, Nayon, Barrett, Kevin, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Currier, Judy, Hughes, Eric, Harrigan, Rachel H., Amos, Laura, Carlsen, Amy, Carter, Anita, Collins, Gary, Davis, Bionca, Denning, Eileen, DuChene, Alain, Eckroth, Kate, Engen, Nicole, Frase, Alex, Gandits, Greg, Grund, Birgit, Harrison, Merrie, Hurlbut, Nancy, Kaiser, Payton, Koopmeiners, Joseph, Larson, Gregg, Meger, Sue, Mistry, Shweta Sharma, Murray, Thomas, Nelson, Ray, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Siegel, Lianne, Thompson, Greg, Vock, David, Walski, Jamie, Gelijns, Annetine C., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Burris, Sarah, Bedoya, Gabriela, Gupta, Lola, Overbey, Jessica R., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Sandkovsky, Uriel, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samatha, Wang, Samatha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacious, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Smith, Peter K., Barkauskas, Christina E., Vekstein, Andrew M., Ko, Emily R., Dreyer, Grace R., Stafford, Neil, Brooks, Megan, Der, Tatyana, Witte, Marie, Gamarallage, Ruwan, Franzone, John, Ivey, Noel, Lumsden, Rebecca H., Mosaly, Nilima, Mourad, Ahmaad, Holland, Thomas L., Motta, Mary, Lane, Kathleen, McGowan, Lauren M., Stout, Jennifer, Aloor, Heather, Bragg, Kennesha M., Toledo, Barvina, McLendon-Arvik, Beth, Bussadori, Barbara, Hollister, Beth A., Griffin, Michelle, Giangiacomo, Dana M., Rodriguez, Vicente, Bokhart, Gordon, Eichman, Sharon M., Parrino, Patrick E., Spindel, Stephen, Bansal, Aditya, Baumgarten, Katherine, Hand, Johnathan, Vonderhaar, Derek, Nossaman, Bobby, Sylvia Laudun, Ames, DeAnna, Broussard, Shane, Hernandez, Nilmo, Isaac, Geralyn, Dinh, Huan, Zheng, Yiling, Tran, Sonny, McDaniel, Hunter, Crovetto, Nicolle, Perin, Emerson, Costello, Briana, Manian, Prasad, Sohail, M. Rizwan, Postalian, Alexander, Hinsu, Punit, Watson, Carolyn, Chen, James, Fink, Melyssa, Sturgis, Lydia, Walker, Kim, Mahon, Kim, Parenti, Jennifer, Kappenman, Casey, Knight, Aryn, Sturek, Jeffrey M., Barros, Andrew, Enfield, Kyle B., Kadl, Alexandra, Green, China J., Simon, Rachel M., Fox, Ashley, Thornton, Kara, Adams, Amy, Badhwar, Vinay, Sharma, Sunil, Peppers, Briana, McCarthy, Paul, Krupica, Troy, Sarwari, Arif, Reece, Rebecca, Fornaresico, Lisa, Glaze, Chad, Evans, Raquel, Di, Fang, Carlson, Shawn, Aucremanne, Tanja, Tennant, Connie, Sutton, Lisa Giblin, Buterbaugh, Sabrina, Williams, Roger, Bunner, Robin, Traverse, Jay H., Rhame, Frank, Huelster, Joshua, Kethireddy, Rajesh, Davies, Irena, Salamanca, Julianne, Majeski, Christine, Skelton, Paige, Zarambo, Maria, Sarafolean, Andrea, Bowdish, Michael E., Borok, Zea, Wald-Dickler, Noah, Hutcheon, Douglass, Towfighi, Amytis, Lee, Mary, Lewis, Meghan R., Spellberg, Brad, Sher, Linda, Sharma, Aniket, Olds, Anna P., Justino, Chris, Loxano, Edward, Romero, Chris, Leong, Janet, Rodina, Valentina, Quesada, Christine, Hamilton, Luke, Escobar, Jose, Leshnower, Brad, Bender, William, Sharifpour, Milad, Miller, Jeffrey, Farrington, Woodrow, Baio, Kim T., McBride, Mary, Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Maton, Missy, Ponder, Chari, Haley, Elisabeth, Spainhour, Christine, Rogers, Susan, Tyler, Derrick, Madathil, Ronson J., Rabin, Joseph, Levine, Andrea, Saharia, Kapil, Tabatabai, Ali, Lau, Christine, Gammie, James S., Peguero, Maya-Loren, McKernan, Kimberly, Audette, Mathew, Fleischmann, Emily, Akbari, Kreshta, Lee, Myounghee, Chi, Andrew, Salehi, Hanna, Pariser, Alan, Nyguyen, Phuong Tran, Moore, Jessica, Gee, Adrienne, Vincent, Shelika, Zuckerman, Richard A., Iribarne, Alexander, Metzler, Sara, Shipman, Samantha, Johnson, Haley, Newton, Crystallee, Parr, Doug, Miller, Leslie, Schelle, Beth, McLean, Sherry, Rothbaum, Howard R., Alvarez, Michael S., Kalan, Shivam P., Germann, Heather H., Hendershot, Jennifer, Moroney, Karen, Herring, Karen, Cook, Sharri, Paul, Pam, Walker-Ignasiak, Rebecca, North, Crystal, Oldmixon, Cathryn, Ringwood, Nancy, Muzikansky, Ariela, Morse, Richard, Fitzgerald, Laura, Morin, Haley D., Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Steingrub, Jay H., Hou, Peter K., Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, DeSouza, Leslie, Romain, Sarah, Thornton-Thompson, Sherell, Talmor, Daniel, Shapiro, Nathan, Andromidas, Konstantinos, Banner-Goodspeed, Valerie, Bolstad, Michael, Boyle, Katherine L., Cabrera, Payton, deVilla, Arnaldo, Ellis, Joshua C., Grafals, Ana, Hayes, Sharon, Higgins, Conor, Kurt, Lisa, Kurtzman, Nicholas, Redman, Kimberly, Rosseto, Elinita, Scaffidi, Douglas, Filbin, Michael R., Hibbert, Kathryn A., Parry, Blair, Margolin, Justin, Hillis, Brooklynn, Hamer, Rhonda, Brait, Kelsey, Beakes, Caroline, McKaig, Brenna, Kugener, Eleonore, Jones, Alan E., Galbraith, James, Nandi, Utsav, Peacock, Rebekah, Hendey, Gregory, Kangelaris, Kirsten, Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Yee, Kimberley J., Jauregui, Alejandra E., Zhuo, Hanjing, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley A., Hughes, Alyssa R., Garcia, Rebekah L., Lim, George W., Chang, Steven Y., Lin, Michael Y., Vargas, Julia, Sihota, Hena, Beutler, Rebecca, Agarwal, Trisha, Wilson, Jennifer G., Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan H., Roque, Jonasel, Wang, Henry, Huebinger, Ryan M., Patel, Bela, Vidales, Elizabeth, Albertson, Timothy, Hardy, Erin, Harper, Richart, Moss, Marc A., Baduashvili, Amiran, Chauhan, Lakshmi, Douin, David J., Martinez, Flora, Finck, Lani L., Bastman, Jill, Howell, Michelle, Higgins, Carrie, McKeehan, Jeffrey, Finigan, Jay, Stubenrauch, Peter, Janssen, William J., Griesmer, Christine, VerBurg, Olivia, Hyzy, Robert C., Park, Pauline K., Nelson, Kristine, McSparron, Jake I., Co, Ivan N., Wang, Bonnie R., Jimenez, Jose, Olbrich, Norman, McDonough, Kelli, Jia, Shijing, Hanna, Sinan, Gong, Michelle N., Richardson, Lynne D., Nair, Rahul, Lopez, Brenda, Amosu, Omowunmi, Offor, Obiageli, Tzehaie, Hiwet, Nkemdirim, William, Boujid, Sabah, Mosier, Jarrod M., Hypes, Cameron, Campbell, Elizabeth Salvagio, Bixby, Billie, Gilson, Boris, Lopez, Anitza, Bime, Christian, Parthasarathy, Sairam, Cano, Ariana M., Hite, R. Duncan, Terndrup, Thomas E., Wiedemann, Herbert P., Hudock, Kristin, Tanzeem, Hammad, More, Harshada, Martinkovic, Jamie, Sellers, Susan, Houston, Judy, Burns, Mary, Kiran, Simra, Roads, Tammy, Kennedy, Sarah, Duggal, Abhijit, Thiruchelvam, Nirosshan, Ashok, Kiran, King, Alexander H., Mehkri, Omar, Dugar, Siddharth, Sahoo, Debasis, Yealy, Donald M., Angus, Derek C., Weissman, Alexandra J., Vita, Tina M., Berryman, Emily, Hough, Catherine L., Khan, Akram, Krol, Olivia F., Mills, Emmanuel, Kinjal, Mistry, Briceno, Genesis, Reddy, Raju, Hubel, Kinsley, Jouzestani, Milad K., McDougal, Madeline, Deshmukh, Rupali, Johnston, Nicholas J., Robinson, Bryce H., Gundel, Staphanie J., Katsandres, Sarah C., Chen, Peter, Torbati, Sam S., Parimon, Tanyalak, Caudill, Antonina, Mattison, Brittany, Jackman, Susan E., Chen, Po-En, Bayoumi, Emad, Ojukwu, Cristabelle, Fine, Devin, Weissberg, Gwendolyn, Isip, Katherine, Choi-Kuaea, Yunhee, Mehdikhani, Shaunt, Dar, Tahir B., Fleury Augustin, Nsole Biteghe, Tran, Dana, Dukov, Jennifer Emilow, Matusov, Yuri, Choe, June, Hindoyan, Niree A., Wynter, Timothy, Pascual, Ethan, Clapham, Gregg J., Herrera, Lisa, Caudill, Antonia, O’Mahony, D. Shane, Nyatsatsang, Sonam T., Wilson, David M., Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Miller, Chadwick, Files, D. Clark, Gibbs, Kevin W., Flores, Lori S., LaRose, Mary E., Landreth, Leigha D., Palacios, D. Rafael, Parks, Lisa, Hicks, Madeline, Goodwin, Andrew J., Kilb, Edward F., Lematty, Caitlan T., Patti, Kerilyn, Grady, Abigail, Rasberry, April, Morris, Peter E., Sturgill, Jamie L., Cassity, Evan P., Dhar, Sanjay, Montgomery-Yates, Ashley A., Pasha, Sarah N., Mayer, Kirby P., Pharm.D., Brittany Bissel, Trott, Terren, Rehman, Shahnaz, de Wit, Marjolein, Mason, Jessica, Bledsoe, Joseph, Knowlton, Kirk U., Brown, Samuel, Lanspa, Michael, Leither, Lindsey, Pelton, Ithan, Armbruster, Brent P., Montgomery, Quinn, Kumar, Naresh, Fergus, Melissa, Imel, Karah, Palmer, Ghazal, Webb, Brandon, Klippel, Carolyn, Jensen, Hannah, Duckworth, Sarah, Gray, Andrew, Burke, Tyler, Knox, Dan, Lumpkin, Jenna, Aston, Valerie T., Applegate, Darrin, Serezlic, Erna, Brown, Katie, Merril, Mardee, Harris, Estelle S., Middleton, Elizabeth A., Barrios, Macy A.G., Greer, Jorden, Schmidt, Amber D., Webb, Melissa K., Paine, Roert, Callahan, Sean J., Waddoups, Lindsey J., Yamane, Misty B., Self, Wesley H., Rice, Todd W., Casey, Jonathan D., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Menon, Vidya, Kasubhai, Moiz, Pillai, Anjana, Daniel, Jean, Sittler, Daniel, Kanna, Balavenkatesh, Jilani, Nargis, Amaro, Francisco, Santana, Jessica, Lyakovestsky, Aleksandr, Madhoun, Issa, Desroches, Louis Marie, Amadon, Nicole, Bahr, Alaa, Ezzat, Imaan, Guerrero, Maryanne, Padilla, Joane, Fullmer, Jessie, Singh, Inderpreet, Ali Shah, Syed Hamad, Narang, Rajeev, Mock, Polly, Shadle, Melissa, Hernandez, Brenda, Welch, Kevin, Payne, Andrea, Ertl, Gabriela, Canario, Daniel, Barrientos, Isabel, Goss, Danielle, DeVries, Mattie, Folowosele, Ibidolapo, Garner, Dorothy, Gomez, Mariana, Price, Justin, Bansal, Ekta, Wong, Jim, Faulhaber, Jason, Fazili, Tasaduq, Yeary, Brian, Ndolo, Ruth, Bryant, Christina, Smigeil, Bridgette, Robinson, Philip, Najjar, Rana, Jones, Patrice, Nguyen, Julie, Chin, Christina, Taha, Hassan, Najm, Salah, Smith, Christopher, Moore, Jason, Nassar, Talal, Gallinger, Nick, Christian, Amy, Mauer, D’Amber, Phipps, Ashley, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Landazuri, Rosalynn, Pineda, Jacob, Uribe, Nicole, Garcia, Jose Ruiz, Barbabosa, Cecilia, Sandler, Kaitlyn, Overcash, J. Scott, Marquez, Adrienna, Chu, Hanh, Lee, Kia, Quillin, Kimberly, Garcia, Andrea, Lew, Pauline, Rogers, Ralph, Shehadeh, Fadi, Mylona, Evangelia K., Kaczynski, Matthew, Tran, Quynh-Lam, Benitez, Gregorio, Mishra, Biswajit, Felix, Lewis Oscar, Vafea, Maria Tsikala, Atalla, Eleftheria, Davies, Robin, Hedili, Salma, Monkeberg, Maria Andrea, Tabler, Sandra, Harrington, Britt, Meegada, Sreenath, Koripalli, Venkata Sandeep, Muddana, Prithvi, Jain, Lakshay, Undavalli, Chaitanya, Kavya, Parasa, Ibiwoye, Mofoluwaso, Akilo, Hameed, Lovette, Bryce D., Wylie, Jamie-Crystal, Smith, Diana M., Poon, Kenneth, Eckardt, Paula, Heysu, Rubio-Gomez, Sundararaman, Nithya, Alaby, Doris, Sareli, Candice, Sánchez, Adriana, Popielski, Laura, Kambo, Amy, Viens, Kimberley, Turner, Melissa, Vjecha, Michael J., Weintrob, Amy, Brar, Indira, Markowitz, Norman, Pastor, Erika, Corpuz, Roweena, Alangaden, George, McKinnon, John, Ramesh, Mayur, Herc, Erica, Yared, Nicholas, Lanfranco, Odaliz Abreu, Rivers, Emanuel, Swiderek, Jennifer, Gupta, Ariella Hodari, Pabla, Pardeep, Eliya, Sonia, Jazrawi, Jehan, Delor, Jeremy, Desai, Mona, Cook, Aaron, Jaehne, Anja Kathrina, Gill, Jasreen Kaur, Renaud, Sheri, Sarveswaran, Siva, Gardner, Edward, Scott, James, Bianchini, Monica, Melvin, Casey, Kim, Gina, Wyles, David, Kamis, Kevin, Miller, Rachel, Douglas, Ivor, Haukoos, Jason, Hicks, Carrie, Lazarte, Susana, Marines-Price, Rubria, Osuji, Alice, Agbor Agbor, Barbine Tchamba, Petersen, Tianna, Kamel, Dena, Hansen, Laura, Garcia, Angie, Cha, Christine, Mozaffari, Azadeh, Hernandez, Rosa, Cutrell, James, Kim, Mina, DellaValle, Natalie, Gonzales, Sonia, Somboonwit, Charurut, Oxner, Asa, Guerra, Lucy, Hayes, Michael, Nguyen, Thi, Tran, Thanh, Pinto, Avenette, Hatlen, Timothy, Anderson, Betty, Zepeda-Gutierrez, Ana, Martin, Dannae, Temblador, Cindi, Cuenca, Avon, Tanoviceanu, Roxanne, Prieto, Martha, Guerrero, Mario, Daar, Eric, Correa, Ramiro, Hartnell, Gabe, Wortmann, Glenn, Doshi, Saumil, Moriarty, Theresa, Gonzales, Melissa, Garman, Kristin, Baker, Jason V., Frosch, Anne, Goldsmith, Rachael, Driver, Brian, Frank, Christine, Leviton, Tzivia, Prekker, Matthew, Jibrell, Hodan, Lo, Melanie, Klaphake, Jonathan, Mackedanz, Shari, Ngo, Linh, Garcia-Myers, Kelly, Kunisaki, Ken M., Wendt, Chris, Melzer, Anne, Wetherbee, Erin, Drekonja, Dimitri, Pragman, Alexa, Hamel, Aimee, Thielen, Abbie, Hassler, Miranda, Walquist, Mary, Augenbraun, Michael, George, Jensen, Demeo, Lynette, Mishko, Motria, Thomas, Lorraine, Tatem, Luis, Dehovitz, Jack, Abassi, Mahsa, Leuck, Anne-Marie, Rao, Via, Pullen, Matthew, Luke, Darlette, LaBar, Derek, Christiansen, Theresa, Howard, Diondra, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Davis-Karim, Anne, Pittman, David, Vasudeva, Shikha S., Johnstone, Jaylynn R., Agnetti, Kate, Davis, Ruby, Trautner, Barbara, Hines-Munson, Casey, Van, John, Dillon, Laura, Wang, Yiqun, Nagy-Agren, Stephanie, Vasudeva, Shikha, Ochalek, Tracy, Caldwell, Erin, Humerickhouse, Edward, Boone, David, McGraw, William, Looney, David J., Mehta, Sanjay R., Johns, Scott Thompson, St. John, Melissa, Raceles, Jacqueline, Sear, Emily, Funk, Stephen, Cesarini, Rosa, Fang, Michelle, Nicalo, Keith, Drake, Wonder, Jones, Beatrice, Holtman, Teresa, Nguyen, Hien H., Maniar, Archana, Johnson, Eric A., Nguyen, Lam, Tran, Michelle T., Barrett, Thomas W., Johnston, Tera, Huggins, John T., Beiko, Tatsiana Y., Hughes, Heather Y., McManigle, William C., Tanner, Nichole T., Washburn, Ronald G., Ardelt, Magdalena, Tuohy, Patricia A., Mixson, Jennifer L., Hinton, Charles G., Thornley, Nicola, Allen, Heather, Elam, Shannon, Boatman, Barry, Baber, Brittany J., Ryant, Rudell, Roller, Brentin, Nguyen, Chinh, Mikail, Amani Morgan, Research, Marivic Hansen, Lichtenberger, Paola, Baracco, Gio, Ramos, Carol, Bjork, Lauren, Sueiro, Melyssa, Tien, Phyllis, Freasier, Heather, Buck, Theresa, Nekach, Hafida, Holodniy, Mark, Chary, Aarthi, Lu, Kan, Peters, Theresa, Lopez, Jessica, Tan, Susanna Yu, Lee, Robert H., Asghar, Aliya, Karyn Isip, Tasadduq Karim, Le, Katherine, Nguyen, Thao, Wong, Shinn, Raben, Dorthe, Murray, Daniel D., Jensen, Tomas O., Peters, Lars, Aagaard, Bitten, Nielsen, Charlotte B., Krapp, Katharina, Nykjær, Bente Rosdahl, Olsson, Christina, Kanne, Katja Lisa, Grevsen, Anne Louise, Joensen, Zillah Maria, Bruun, Tina, Bojesen, Ane, Woldbye, Frederik, Normand, Nick E., Esman, Frederik V.L., Benfield, Thomas, Clausen, Clara Lundetoft, Hovmand, Nichlas, Israelsen, Simone Bastrup, Iversen, Katrine, Leding, Caecilie, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Tingsgard, Sandra, Krohn-Dehli, Louise, Pedersen, Dorthe, Villadsen, Signe, Staehr Jensen, Jens-Ulrik, Overgaard, Rikke, Rastoder, Ema, Heerfordt, Christian, Hedsund, Caroline, Ronn, Christian Phillip, Kamstrup, Peter Thobias, Hogsberg, Dorthe Sandbaek, Bergsoe, Christina, Søborg, Christian, Hissabu, Nuria M.S., Arp, Bodil C., Ostergaard, Lars, Staerke, Nina Breinholt, Yehdego, Yordanos, Sondergaard, Ane, Johansen, Isik S., Pedersen, Andreas Arnholdt, Knudtzen, Fredrikke C., Larsen, Lykke, Hertz, Mathias A., Fabricius, Thilde, Holden, Inge K., Lindvig, Susan O., Helleberg, Marie, Gerstoft, Jan, Kirk, Ole, Jensen, Tomas Ostergaard, Madsen, Birgitte Lindegaard, Pedersen, Thomas Ingemann, Harboe, Zitta Barrella, Roge, Birgit Thorup, Hansen, Thomas Michael, Glesner, Matilde Kanstrup, Lofberg, Sandra Valborg, Nielsen, Ariella Denize, Leicht von Huth, Sebastian, Nielsen, Henrik, Thisted, Rikke Krog, Petersen, Kristine Toft, Juhl, Maria Ruwald, Podlekareva, Daria, Johnsen, Stine, Andreassen, Helle Frost, Pedersen, Lars, Clara Ellinor Lindnér, Cecilia Ebba, Wiese, Lothar, Knudsen, Lene Surland, Skrøder Nytofte, Nikolaj Julian, Havmøller, Signe Ravn, Expósito, Maria, Badillo, José, Martínez, Ana, Abad, Elena, Chamorro, Ana, Figuerola, Ariadna, Mateu, Lourdes, España, Sergio, Lucero, Maria Constanza, Santos, José Ramón, Lladós, Gemma, Lopez, Cristina, Carabias, Lydia, Molina-Morant, Daniel, Loste, Cora, Bracke, Carmen, Siles, Adrian, Fernández-Cruz, Eduardo, Di Natale, Marisa, Padure, Sergiu, Gomez, Jimena, Ausin, Cristina, Cervilla, Eva, Balastegui, Héctor, Sainz, Carmen Rodríguez, Lopez, Paco, Carbone, Javier, Escobar, Mariam, Balerdi, Leire, Legarda, Almudena, Roldan, Montserrat, Letona, Laura, Muñoz, José, Camprubí, Daniel, Arribas, Jose R., Sánchez, Rocio Montejano, Díaz-Pollán, Beatriz, Stewart, Stefan Mark, Garcia, Irene, Borobia, Alberto, Mora-Rillo, Marta, Estrada, Vicente, Cabello, Noemi, Nuñez-Orantos, M.J., Sagastagoitia, I., Homen, J.R., Orviz, E., Montalvá, Adrián Sánchez, Espinosa-Pereiro, Juan, Bosch-Nicolau, Pau, Salvador, Fernando, Burgos, Joaquin, Morales-Rull, Jose Luis, Moreno Pena, Anna Maria, Acosta, Cristina, Solé-Felip, Cristina, Horcajada, Juan P., Sendra, Elena, Castañeda, Silvia, López-Montesinos, Inmaculada, Gómez-Junyent, Joan, Gonzáles, Carlota Gudiol, Cuervo, Guilermo, Pujol, Miquel, Carratalà, Jordi, Videla, Sebastià, Günthard, Huldrych, Braun, Dominique L., West, Emily, M’Rabeth-Bensalah, Khadija, Eichinger, Mareile L., Grüttner-Durmaz, Manuela, Grube, Christina, Zink, Veronika, pharmacist, Goes pharmacist, Josefine, Fätkenheuer, Gerd, Malin, Jakob J., Tsertsvadze, Tengiz, Abutidze, Akaki, Chkhartishvili, Nikoloz, Metchurtchlishvili, Revaz, Endeladze, Marina, Paciorek, Marcin, Bursa, Dominik, Krogulec, Dominika, Pulik, Piotr, Ignatowska, Anna, Horban, Andrzej, Bakowska, Elzbieta, Kowaska, Justyna, Bednarska, Agnieszka, Jurek, Natalia, Skrzat-Klapaczynska, Agata, Bienkowski, Carlo, Hackiewicz, Malgorzata, Makowiecki, Michal, Platowski, Antoni, Fishchuk, Roman, Kobrynska, Olena, Levandovska, Khrystyna, Kirieieva, Ivanna, Kuziuk, Mykhailo, Naucler, Pontus, Perlhamre, Emma, Mazouch, Lotta, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina C., Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Young, Barnaby E., Chia, Po Ying, Lee, Tau Hong, Lin, Ray J., Lye, David C., Ong, Sean W.X., Puah, Ser Hon, Yeo, Tsin Wen, Diong, Shiau Hui, Ongko, Juwinda, Yeo, He Ping, Eriobu, Nnakelu, Kwaghe, Vivian, Zaiyad, Habib, Idoko, Godwin, Uche, Blessing, Selvamuthu, Poongulali, Kumarasamy, Nagalingeswaran, Beulah, Faith Ester, Govindarajan, Narayan, Mariyappan, Kowsalya, Losso, Marcelo H., Abela, Cecilia, Moretto, Renzo, Belloc, Carlos G., Ludueña, Jael, Amar, Josefina, Toibaro, Javier, Macias, Laura Moreno, Fernandez, Lucia, Frare, Pablo S., Chaio, Sebastian R., Pachioli, Valeria, Timpano, Stella M., Sanchez, Marisa del Lujan, de Paz Sierra, Mariana, Stanek, Vanina, Belloso, Waldo, Cilenti, Flavia L., Valentini, Ricardo N., Stryjewski, Martin E., Locatelli, Nicolas, Soler Riera, Maria C., Salgado, Clara, Baeck, Ines M., Di Castelnuovo, Valentina, Zarza, Stella M., Hudson, Fleur, Parmar, Mahesh K.B., Goodman, Anna L., Dphil, Badrock, Jonathan, Gregory, Adam, Goodall, Katharine, Harris, Nicola, Wyncoll, James, Bhagani, S., Rodger, A., Luntiel, A., Patterson, C., Morales, J., Witele, E., Preston, A.-M., Nandani, A., Price, D.A., Hanrath, Aiden, Nell, Jeremy, Patel, Bijal, Hays, Carole, Jones, Geraldine, Davidson, Jade, Bawa, T., Mathews, M., Mazzella, A., Bisnauthsing, K., Aguilar-Jimenez, L., Borchini, F., Hammett, S., Touloumi, Giota, Pantazis, Nikos, Gioukari, Vicky, Souliou, Tania, Antoniadou, A., Protopapas, K., Kavatha, D., Grigoropoulou, S., Oikonomopoulo, C., Moschopoulos, C., Koulouris, N.G., Tzimopoulos, K., Koromilias, A., Argyraki, K., Lourida, P., Bakakos, P., Kalomenidis, I., Vlachakos, V., Barmparessou, Z., Balis, E., Zakynthinos, S., Sigala, I., Gianniou, N., Dima, E., Magkouta, S., Synolaki, E., Konstanta, S., Vlachou, M., Stathopoulou, P., Panagopoulos, P., Petrakis, V., Papazoglou, D., Tompaidou, E., Isaakidou, E., Poulakou, G., Rapti, V., Leontis, K., Nitsotolis, T., Athanasiou, K., Syrigos, K., Kyriakoulis, K., Trontzas, I., Arfara-Melanini, M., Kolonis, V., Kityo, Cissy, Mugerwa, Henry, Kiweewa, Francis, Kimuli, Ivan, Lukaakome, Joseph, Nsereko, Christoher, Lubega, Gloria, Kibirige, Moses, Nakahima, William, Wangi, Deus, Aguti, Evelyne, Generous, Lilian, Massa, Rosemary, Nalaki, Margaret, Magala, Felix, Nabaggala, Phiona Kaweesi, Kidega, Robert, Faith, Oryem Daizy, Florence, Apio, Emmanuel, Ocung, Beacham, Mugoonyi Paul, Geoffrey, Amone, Nakiboneka, Dridah, Apiyo, Paska, Kirenga, Bruce, Atukunda, Angella, Muttamba, Winters, Remmy, Kyeyume, Segawa, Ivan, Pheona, Nsubuga, Kigere, David, Mbabazi, Queen Lailah, Boersalino, Ledra, Nyakoolo, Grace, Fred, Aniongo, Alupo, Alice, Ebong, Doryn, Monday, Edson, Nalubwama, Ritah Norah, Kainja, Milton, Ambrose, Munu, Kwehayo, Vanon, Nalubega, Mary Grace, Ongoli, Augustine, Obbo, Stephen, Sebudde, Nicholus, Alaba, Jeniffer, Magombe, Geoffrey, Tino, Harriet, Obonya, Emmanuel, Lutaakome, Joseph, Kitonsa, Jonathan, Onyango, Martin, Naboth, Tukamwesiga, Naluyinda, Hadijah, Nanyunja, Regina, Irene, Muttiibwa, Jane, Biira, Wimfred, Kyobejja, Leonard, Ssemazzi, Deus, Tkiinomuhisha, Babra, Namasaba, Taire, Paul, Nabankema, Evelyn, Ogavu, Joseph, Mugerwa, Oscar, Okoth, Ivan, Mwebaze, Raymond, Mugabi, Timothy, Makhoba, Anthony, Arikiriza, Phiona, Theresa, Nabuuma, Nakayima, Hope, Frank, Kisuule, Ramgi, Patrícia, Pereira, Kássia, Osinusi, Anu, Cao, Huyen, Klekotka, Paul, Price, Karen, Nirula, Ajay, Osei, Suzette, Tipple, Craig, Wills, Angela, Peppercorn, Amanda, Watson, Helen, Gupta, Rajesh, Alexander, Elizabeth, Mogalian, Erik, Lin, Leo, Ding, Xiao, Margolis, David, Yan, Li, Girardet, Jean-Luc, Ma, Ji, Hong, Zhi, Zhu, Quing, Seegobin, Seth, Gibbs, Michael, Latchman, Mickel, Hasior, Katarzyna, Bouquet, Jerome, Wei, Jianxin, Streicher, Katie, Schmelzer, Albert, Brooks, Dennis, Butcher, Jonny, Tonev, Dimitar, Arbetter, Douglas, Damstetter, Philippe, Legenne, Philippe, Stumpp, Michael, Goncalves, Susana, Ramanathan, Krishnan, Chandra, Richa, Baseler, Beth, Teitelbaum, Marc, Schechner, Adam, Holley, H. Preston, Jankelevich, Shirley, Becker, Nancy, Dolney, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Asomah, Mabel, Jato, Yvonne, Stottlemyer, April, Tang, Olivia, Vanderpuye, Sharon, Yeon, Lindsey, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonald, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Schaffhauser, Marylu, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, C.K., Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Kopka, Stacey, Shelley, Denise M., Mendez, Gregg, Brown, Shawn, Albert, Sara, Balde, Abby, Baracz, Michelle, Bielica, Mona, Billouin-Frazier, Shere, Choudary, Jay, Dixon, Mary, Eyler, Carolyn, Frye, Leanne, Gertz, Jensen, Giebeig, Lisa, Gulati, Neelam, Hankinson, Liz, Hogarty, Debi, Huber, Lynda, Krauss, Gary, Lake, Eileen, Manandhar, Meryan, Rudzinski, Erin, Sandrus, Jen, Suders, Connie, Natarajan, Ven, Rupert, Adam W., Baseler, Michael, Lynam, Danielle, Imamichi, Tom, Laverdure, Sylvain, McCormack, Ashley, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Khan, Ayub, Hazen, Allison, Badralmaa, Yunden, Smith, Kenneth, Patel, Bhakti, Kubernac, Amanda, Kubernac, Robert, Hoover, Marie L., Solomon, Courtney, Rashid, Marium, Murphy, Joseph, Brown, Craig, DuChateau, Nadine, Ellis, Sadie, Flosi, Adam, Fox, Lisa, Johnson, Les, Nelson, Rich, Stojanovic, Jelena, Treagus, Amy, Wenner, Christine, Williams, Richard, Jensen, Tomas O, Murray, Thomas A, Grandits, Greg A, Jain, Mamta K, Shaw-Saliba, Kathryn, Matthay, Michael A, Baker, Jason V, Dewar, Robin L, Goodman, Anna L, Hatlen, Timothy J, Highbarger, Helene C, Lallemand, Perrine, Leshnower, Bradley G, Looney, David, Moschopoulos, Charalampos D, Murray, Daniel D, Mylonakis, Eleftherios, Rehman, M Tauseef, Rupert, Adam, Stevens, Randy, Turville, Stuart, Wick, Katherine, Lundgren, Jens, and Ko, Emily R

Published
2024
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29. Evaluating glioma growth predictions as a forward ranking problem

Author

van Garderen, Karin A., van der Voort, Sebastian R., Wijnenga, Maarten M. J., Incekara, Fatih, Kapsas, Georgios, Gahrmann, Renske, Alafandi, Ahmad, Smits, Marion, and Klein, Stefan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework for the evaluation of growth predictions that focuses on the spatial infiltration patterns, and specifically evaluating a prediction of future growth. We propose to frame the problem as a ranking problem rather than a segmentation problem. Using the average precision as a metric, we can evaluate the results with segmentations while using the full spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit from future predictive performance, we show that in some cases, a better fit of model parameters does not guarantee a better the predictive power.

Published
2021

30. Comparative Analysis of Host Cell Entry Efficiency and Neutralization Sensitivity of Emerging SARS-CoV-2 Lineages KP.2, KP.2.3, KP.3, and LB.1

Author

Nianzhen Chen, Katharina Emma Decker, Sebastian R. Schulz, Amy Kempf, Inga Nehlmeier, Anna-Sophie Moldenhauer, Alexandra Dopfer-Jablonka, Georg M. N. Behrens, Metodi V. Stankov, Luis Manthey, Hans-Martin Jäck, Markus Hoffmann, Stefan Pöhlmann, and Prerna Arora

Subjects
SARS-CoV-2 lineages, ACE2 receptor interactions, antibody evasion, Medicine
Abstract

New SARS-CoV-2 lineages continue to evolve and may exhibit new characteristics regarding host cell entry efficiency and potential for antibody evasion. Here, employing pseudotyped particles, we compared the host cell entry efficiency, ACE2 receptor usage, and sensitivity to antibody-mediated neutralization of four emerging SARS-CoV-2 lineages, KP.2, KP.2.3, KP.3, and LB.1. The XBB.1.5 and JN.1 lineages served as controls. Our findings reveal that KP.2, KP.2.3, KP.3, and LB.1 lineages enter host cells efficiently and in an ACE2-dependent manner, and that KP.3 is more adept at entering Calu-3 lung cells than JN.1. However, the variants differed in their capacity to employ ACE2 orthologues from animal species for entry, suggesting differences in ACE2 interactions. Moreover, we demonstrate that only two out of seven therapeutic monoclonal antibody (mAbs) in preclinical development retain robust neutralizing activity against the emerging JN.1 sublineages tested, while three mAbs displayed strongly reduced neutralizing activity and two mAbs lacked neutralizing activity against any of the lineages tested. Furthermore, our results show that KP.2, KP.2.3, KP.3, and LB.1 lineages evade neutralization by antibodies induced by infection or vaccination with greater efficiency than JN.1, particularly in individuals without hybrid immunity. This study indicates that KP.2, KP.2.3, KP.3, and LB.1 differ in ACE2 interactions and the efficiency of lung cell entry and suggest that evasion of neutralizing antibodies drove the emergence of these variants.

Published
2024
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33. Development and evaluation of a point-of-care ocular ultrasound curriculum for medical students - a proof-of-concept study

Author

Johannes Matthias Weimer, Maximilian Rink, Thomas Vieth, Jonas Lauff, Andreas Weimer, Lukas Müller, Marie Stäuber, Sebastian R. Reder, Holger Buggenhagen, Henrik Bellhäuser, Roman Kloeckner, Julian Künzel, Esther M. Hoffmann, and Anna Würde

Subjects
Point-of-care-sonography, POCUS, Ocular ultrasound, POCOUS, Ultrasound training, Curriculum development, Special aspects of education, LC8-6691, Medicine
Abstract

Abstract Background Point-of-care Ocular Ultrasound (POCOUS) has gained importance in emergency medicine and intensive care in recent years. This work aimed to establish and evaluate a dedicated ultrasound education program for learning POCOUS-specific skills during medical studies at a university hospital. Methods The blended learning-based program (6 teaching units) based on recent scientific publications and recommendations was developed for students in the clinical part of their medical studies. Experts and trainers consisted of physicians from the Ear-Nose-Throat, radiology, ophthalmology and neurology specialties as well as university educational specialists. Lecture notes containing digital video links for preparation was produced as teaching material. In total, 33 students participated in the study. The education program, including the teaching materials, motivation and subjective gain in competency, was evaluated with the aid of a questionnaire (7-point Likert response format). Objective learning success was assessed on the basis of pre- and post-tests. These covered the skill areas: “anatomical basics”, “ultrasound basics”, “understanding of cross-sectional images”, “normal findings” and “pathology recognition”. Results In the objective assessment of image interpretation, the participants improved significantly (p

Published
2023
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34. Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study

Author

Dylan Young, Bita Houshmand, Chunyi Christie Tan, Abirami Kirubarajan, Ashna Parbhakar, Jazleen Dada, Wendy Whittle, Mara L. Sobel, Luis M. Gomez, Mario Rüdiger, Ulrich Pecks, Peter Oppelt, Joel G. Ray, Sebastian R. Hobson, John W. Snelgrove, Rohan D’Souza, Rasha Kashef, and Dafna Sussman

Subjects
Machine learning, Prognostication, Pregnancy, SARS-CoV-2, COVID-19, Gynecology and obstetrics, RG1-991
Abstract

Abstract Background Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. Methods An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. Results The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. Conclusions We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.

Published
2023
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36. Sex-specific Impact of the first COVID-19 Lockdown on Age Structure and Case Acuity at Admission in a Patient Population in southwestern Germany: a retrospective comparative Study in Neuroradiology

Author

Ahmed E Othman, Carolin Brockmann, Marc A Brockmann, Sebastian R Reder, Natalie Herrlich, Nils F Grauhan, and Matthias Müller-Eschner

Subjects
Medicine
Abstract

Objectives A hard lockdown was presumed to lead to delayed diagnosis and treatment of serious diseases, resulting in higher acuity at admission. This should be elaborated based on the estimated acuity of the cases, changes in findings during hospitalisation, age structure and biological sex.Design Retrospective monocentric cross-sectional study.Setting German Neuroradiology Department at a .Participants In 2019, n=1158 patients were admitted in contrast to n=884 during the first hard lockdown in 2020 (11th–13th week).Main outcome measures Three radiologists evaluated the initial case acuity, classified them into three groups (not acute, subacute and acute), and evaluated if there was a relevant clinical deterioration. The data analysis was conducted using non-parametric methods and multivariate regression analysis.Results A 24% decrease in the number of examinations from 2019 to 2020 (p=0.025) was revealed. In women, the case acuity increased by 21% during the lockdown period (p=0.002). A 30% decrease in acute cases in men was observable (in women 5% decrease). Not acute cases decreased in both women and men (47%; 24%), while the subacute cases remained stable in men (0%) and decreased in women (28%). Regression analysis revealed the higher the age, the higher the acuity (p

Published
2024
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38. WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning

Author

van der Voort, Sebastian R., Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Tewarie, Rishi Nandoe, Lycklama, Geert J., Hamer, Philip C. De Witt, Eijgelaar, Roelant S., French, Pim J., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., Niessen, Wiro J., Bent, Martin J. van den, Smits, Marion, and Klein, Stefan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean whole tumor DICE score of 0.84. Thus, our method non-invasively predicts multiple, clinically relevant parameters and generalizes well to the broader clinical population.

Published
2020

39. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
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40. Cellular Automata for Fast Simulations of Arrhythmogenic Atrial Substrate

Author

Romitti, G. S., Liberos, A., Romero, P., Serra, D., García, I., Lozano, M., Sebastian, R., Rodrigo, M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bernard, Olivier, editor, Clarysse, Patrick, editor, Duchateau, Nicolas, editor, Ohayon, Jacques, editor, and Viallon, Magalie, editor

Published
2023
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43. Suppression of tumor-associated neutrophils by lorlatinib attenuates pancreatic cancer growth and improves treatment with immune checkpoint blockade

Author

Nielsen, Sebastian R, Strøbech, Jan E, Horton, Edward R, Jackstadt, Rene, Laitala, Anu, Bravo, Marina C, Maltese, Giorgia, Jensen, Adina RD, Reuten, Raphael, Rafaeva, Maria, Karim, Saadia A, Hwang, Chang-Il, Arnes, Luis, Tuveson, David A, Sansom, Owen J, Morton, Jennifer P, and Erler, Janine T

Subjects
Cancer, Rare Diseases, Digestive Diseases, Pancreatic Cancer, 5.1 Pharmaceuticals, Aetiology, 2.1 Biological and endogenous factors, Development of treatments and therapeutic interventions, Aminopyridines, Animals, Antineoplastic Combined Chemotherapy Protocols, CD8-Positive T-Lymphocytes, Carcinoma, Pancreatic Ductal, Cell Line, Tumor, Disease Models, Animal, Drug Synergism, Female, Humans, Immune Checkpoint Inhibitors, Lactams, Lactams, Macrocyclic, Lymphocyte Activation, Lymphocytes, Tumor-Infiltrating, Male, Mice, Mice, Transgenic, Neutrophils, Pancreatic Neoplasms, Programmed Cell Death 1 Receptor, Pyrazoles, Tumor Microenvironment
Abstract

Pancreatic ductal adenocarcinoma (PDAC) patients have a 5-year survival rate of only 8% largely due to late diagnosis and insufficient therapeutic options. Neutrophils are among the most abundant immune cell type within the PDAC tumor microenvironment (TME), and are associated with a poor clinical prognosis. However, despite recent advances in understanding neutrophil biology in cancer, therapies targeting tumor-associated neutrophils are lacking. Here, we demonstrate, using pre-clinical mouse models of PDAC, that lorlatinib attenuates PDAC progression by suppressing neutrophil development and mobilization, and by modulating tumor-promoting neutrophil functions within the TME. When combined, lorlatinib also improves the response to anti-PD-1 blockade resulting in more activated CD8 + T cells in PDAC tumors. In summary, this study identifies an effect of lorlatinib in modulating tumor-associated neutrophils, and demonstrates the potential of lorlatinib to treat PDAC.

Published
2021

46. Virological Traits of the SARS-CoV-2 BA.2.87.1 Lineage

Author

Lu Zhang, Alexandra Dopfer-Jablonka, Inga Nehlmeier, Amy Kempf, Luise Graichen, Noemí Calderón Hampel, Anne Cossmann, Metodi V. Stankov, Gema Morillas Ramos, Sebastian R. Schulz, Hans-Martin Jäck, Georg M. N. Behrens, Stefan Pöhlmann, and Markus Hoffmann

Subjects
SARS-CoV-2, BA.2.87.1, spike protein, host cell entry, ACE2 binding, antibody evasion, Medicine
Abstract

Transmissibility and immune evasion of the recently emerged, highly mutated SARS-CoV-2 BA.2.87.1 are unknown. Here, we report that BA.2.87.1 efficiently enters human cells but is more sensitive to antibody-mediated neutralization than the currently dominating JN.1 variant. Acquisition of adaptive mutations might thus be needed for efficient spread in the population.

Published
2024
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49. On speeding up factoring with quantum SAT solvers

Author

Mosca, Michele, Basso, João Marcos Vensi, and Verschoor, Sebastian R.

Subjects
Computer Science - Cryptography and Security, Quantum Physics
Abstract

There have been several efforts to apply quantum SAT solving methods to factor large integers. While these methods may provide insight into quantum SAT solving, to date they have not led to a convincing path to integer factorization that is competitive with the best known classical method, the Number Field Sieve. Many of the techniques tried involved directly encoding multiplication to SAT or an equivalent NP-hard problem and looking for satisfying assignments of the variables representing the prime factors. The main challenge in these cases is that, to compete with the Number Field Sieve, the quantum SAT solver would need to be superpolynomially faster than classical SAT solvers. In this paper the use of SAT solvers is restricted to a smaller task related to factoring: finding smooth numbers, which is an essential step of the Number Field Sieve. We present a SAT circuit that can be given to quantum SAT solvers such as annealers in order to perform this step of factoring. If quantum SAT solvers achieve any speedup over classical brute-force search, then our factoring algorithm is faster than the classical NFS.

Published
2019

50. The Semantic Asset Administration Shell

Author

Bader, Sebastian R. and Maleshkova, Maria

Subjects
Computer Science - Artificial Intelligence
Abstract

The disruptive potential of the upcoming digital transformations for the industrial manufacturing domain have led to several reference frameworks and numerous standardization approaches. On the other hand, the Semantic Web community has made significant contributions in the field, for instance on data and service description, integration of heterogeneous sources and devices, and AI techniques in distributed systems. These two streams of work are, however, mostly unrelated and only briefly regard each others requirements, practices and terminology. We contribute to closing this gap by providing the Semantic Asset Administration Shell, an RDF-based representation of the Industrie 4.0 Component. We provide an ontology for the latest data model specification, created a RML mapping, supply resources to validate the RDF entities and introduce basic reasoning on the Asset Administration Shell data model. Furthermore, we discuss the different assumptions and presentation patterns, and analyze the implications of a semantic representation on the original data. We evaluate the thereby created overheads, and conclude that the semantic lifting is manageable, also for restricted or embedded devices, and therefore meets the needs of Industrie 4.0 scenarios., Comment: 15 pages, pre-print of Semantics 2019

Published
2019

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