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3,172 results on '"SEBASTIAN, R."'

4. 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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7. DSA-based perfusion parameters versus TICI score after mechanical thrombectomy in acute ischaemic stroke patients: a congruence analysis

Author

Sebastian R. Reder, Andrea Kronfeld, Sonja Gröschel, Arda Civelek, Klaus Gröschel, Marc A. Brockmann, Timo Uphaus, Marianne Hahn, Carolin Brockmann, and Ahmed E. Othman

Subjects
Angiography (digital subtraction), Ischemic stroke, Outcome, Perfusion imaging, Thrombectomy, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Background Several factors are frequently considered for outcome prediction rin stroke patients. We assessed the value of digital subtraction angiography (DSA)-based brain perfusion measurements after mechanical thrombectomy (MT) for outcome prediction in acute ischaemic stroke. Methods From DSA image data (n = 90; 38 females; age 73.3 ± 13.1 years [mean ± standard deviation]), time-contrast agent (CA) concentration curves were acquired, and maximum slope (MS), time to peak (TTP), and maximum CA concentration (CAmax) were calculated using an arterial input function. This data was used to predict neurological deficits at 24 h and upon discharge by using multiple regression analysis; the predictive capability was compared with the predictive power of the “Thrombolysis in cerebral infarction” (TICI) score. Intraclass correlation coefficients (ICC) of the NIHSS values were analysed. Results The comparison of means revealed a linear trend after stratification into TICI classes for CAmax (TICI 0: 0.07 ± 0.02 a.u. to TICI 3: 0.22 ± 0.07 a.u.; p

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. Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study

Author

Allan J. Kember, Hafsa Zia, Praniya Elangainesan, Min-En Hsieh, Ramak Adijeh, Ivan Li, Leah Ritchie, Sina Akbarian, Babak Taati, Sebastian R. Hobson, and Elham Dolatabadi

Subjects
Medicine, Science
Abstract

Abstract Sleeping on the back after 28 weeks of pregnancy has recently been associated with giving birth to a small-for-gestational-age infant and late stillbirth, but whether a causal relationship exists is currently unknown and difficult to study prospectively. This study was conducted to build a computer vision model that can automatically detect sleeping position in pregnancy under real-world conditions. Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present.

Published
2024
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11. Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning

Author

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

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.

Published
2024
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12. 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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13. 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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14. 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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15. 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

19. Comparing a common clavicle maturation-based age estimation method to ordinary regression analyses with quadratic and sex-specific interaction terms in adolescents

Author

Sebastian R. Reder, Isabel Fritzen, Marc A. Brockmann, Jochen Hardt, Katrin Elsner, Katja Petrowski, and Monika Bjelopavlovic

Subjects
Medicine, Science
Abstract

Abstract Established methods of age estimation are based on correlating defined maturation stages of bony structures with tables representing the observed range of biological ages in the majority of cases. In this retrospective monocentric study in southwestern Germany, common age estimation methodology was assessed in n = 198 subjects at the age of 25 or younger by analyzing the influence of age, quadratic age, biological sex and age-sex interaction on the ossification stages of the medial epiphysis fugue. Three readers (ICC ≥ 0.81 for left/right side) evaluated routine care computed tomography images of the clavicle with a slice thickness of 1 mm. By using least square regression analyses, to determine the real biological age a quadratic function was determined corrected for the age estimated by established methods and sex (R2 = 0.6 each side), reducing the mean absolute error and root mean squared error in the age estimation of women (2.57 and 3.19) and men (2.57 and 3.47) to 1.54 and 1.82 for women, and 1.54 and 2.25 for men. In women, the medial clavicle epiphysis seem to fuse faster, which was particularly observable from approximately 18 years of age. Before that age, the estimation method was relatively close to the ideal correlation between assessed and real age. To conclude, the presented new method enables more precise age estimation in individuals and facilitates the determination and quantification of additional variables, quantifying their influence on the maturation of the medial clavicle epiphysis based on the established ossification stages.

Published
2024
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21. 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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22. 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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23. 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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29. 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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30. 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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31. 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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32. 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

33. Probing the glioma microvasculature: a case series of the comparison between perfusion MRI and intraoperative high-frame-rate ultrafast Doppler ultrasound

Author

Ahmad Alafandi, Sadaf Soloukey Tbalvandany, Fatemeh Arzanforoosh, Sebastian R. van Der Voort, Fatih Incekara, Luuk Verhoef, Esther A. H. Warnert, Pieter Kruizinga, and Marion Smits

Subjects
Cerebral blood volume, Glioma, Magnetic resonance imaging, Perfusion, Ultrasonography (Doppler), Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Background We aimed to describe the microvascular features of three types of adult-type diffuse glioma by comparing dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) with intraoperative high-frame-rate ultrafast Doppler ultrasound. Methods Case series of seven patients with primary brain tumours underwent both DSC perfusion MRI and intra-operative high-frame-rate ultrafast Doppler ultrasound. From the ultrasound images, three-dimensional vessel segmentation was obtained of the tumour vascular bed. Relative cerebral blood volume (rCBV) maps were generated with leakage correction and normalised to the contralateral normal-appearing white matter. From tumour histograms, median, mean, and maximum rCBV ratios were extracted. Results Low-grade gliomas (LGGs) showed lower perfusion than high-grade gliomas (HGGs), as expected. Within the LGG subgroup, oligodendroglioma showed higher perfusion than astrocytoma. In HGG, the median rCBV ratio for glioblastoma was 3.1 while astrocytoma grade 4 showed low perfusion with a median rCBV of 1.2. On the high-frame-rate ultrafast Doppler ultrasound images, all tumours showed a range of rich and organised vascular networks with visually apparent abnormal vessels, even in LGG. Conclusions This unique case series revealed in vivo insights about the microvascular architecture in both LGGs and HGGs. Ultrafast Doppler ultrasound revealed rich vascularisation, also in tumours with low perfusion at DSC MRI. These findings warrant further investigations using advanced MRI postprocessing, in particular for characterising adult-type diffuse glioma. Relevance statement Our findings challenge the current assumption behind the estimation of relative cerebral blood volume that the distribution of blood vessels in a voxel is random. Key points • Ultrafast Doppler ultrasound revealed rich vascularity irrespective of perfusion dynamic susceptibility contrast MRI state. • Rich and organised vascularisation was also observed even in low-grade glioma. • These findings challenge the assumptions for cerebral blood volume estimation with MRI. Graphical Abstract

Published
2024
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34. 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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35. 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

36. 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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40. 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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41. 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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43. 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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44. GLUT1-mediated glucose import in B cells is critical for anaplerotic balance and humoral immunity

Author

Theresa E.H. Bierling, Amelie Gumann, Shannon R. Ottmann, Sebastian R. Schulz, Leonie Weckwerth, Jana Thomas, Arne Gessner, Magdalena Wichert, Frederic Kuwert, Franziska Rost, Manuela Hauke, Tatjana Freudenreich, Dirk Mielenz, Hans-Martin Jäck, and Katharina Pracht

Subjects
CP: Immunology, CP: Metabolism, Biology (General), QH301-705.5
Abstract

Summary: Glucose uptake increases during B cell activation and antibody-secreting cell (ASC) differentiation, but conflicting findings prevent a clear metabolic profile at different stages of B cell activation. Deletion of the glucose transporter type 1 (GLUT1) gene in mature B cells (GLUT1-cKO) results in normal B cell development, but it reduces germinal center B cells and ASCs. GLUT1-cKO mice show decreased antigen-specific antibody titers after vaccination. In vitro, GLUT1-deficient B cells show impaired activation, whereas established plasmablasts abolish glycolysis, relying on mitochondrial activity and fatty acids. Transcriptomics and metabolomics reveal an altered anaplerotic balance in GLUT1-deficient ASCs. Despite attempts to compensate for glucose deprivation by increasing mitochondrial mass and gene expression associated with glycolysis, the tricarboxylic acid cycle, and hexosamine synthesis, GLUT1-deficient ASCs lack the metabolites for energy production and mitochondrial respiration, limiting protein synthesis. We identify GLUT1 as a critical metabolic player defining the germinal center response and humoral immunity.

Published
2024
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46. 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

49. 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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50. 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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