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Brief Neurocognitive Testing Predicts Early and Delayed Neurotoxicity Following CAR T-Cell Therapy for Hematologic Malignancies

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Transplantation and Cellular Therapy 2025年第2期31卷 S232-S233页

作者： Suresh, Arvind, MD Wishart, Heather A, PhD Arslan, Maeen N, BA Lizcano, Raphael A, MD Shah, Parth, MD PonnamReddy, Swaroopa, MD Hayes, Christi Ann, MD Martinez-Camblor, Pablo, PhD Chan, Amy M, MD Meehan, Kenneth R, MD Hill, John M, MD Department of Medicine University of California San Francisco San Francisco CA Department of Psychiatry Dartmouth-Hitchcock Medical Center Lebanon NH Dartmouth College Hanover NH Transplant and Cellular Therapy Program Dartmouth-Hitchcock Medical Center Lebanon NH Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH

IntroductionImmune effector cell-associated neurotoxicity syndrome (ICANS) can be a severe, life-threatening toxicity following CAR T-cell therapy and is characterized by a wide range of clinical and neuropsychiatric signs and symptoms. While these are currently evaluated by the immune effector cell-associated encephalopathy (ICE) score, not all patients who develop ICANS necessarily have changes in their ICE score and not all signs and symptoms of neurotoxicity are captured. MethodsWe conducted a prospective, single center cohort study to evaluate a novel rapid neurocognitive assessment tool (CART-NS) in detecting early, subtle neurotoxicity prior to the onset of ICANS and any deterioration in the ICE score. CART-NS includes eight abbreviated forms of formal neurocognitive tests as well as patient and caregiver symptom questionnaires. Following baseline measurements, the resulting 8-minute battery of tests was administered at eight-hour intervals during the first 30 days after CAR T-cell infusion, in parallel with administration of the ICE assessment. We also evaluated if changes in serum biomarkers are associated with the development of ICANS. ResultsTwelve patients underwent CART-NS testing during the first 30 days after CAR T-cell infusion for a total of 862 test administrations. Eight patients received CD19-directed CAR T-cells and four received BCMA-directed CAR T-cells. Performance on Oral Symbol Digit (OSD), Trails, Stroop, Paced Visual Serial Addition (PVSAT), Connecting Dots, Intersecting Figures, and Verbal Fluency tests was significantly lower as patients developed Grade 1 or 2 ICANS (p < 0.05). Performance on OSD, Stroop, and PVSAT was initially lower after CAR T-cell infusion in patients who later developed ICANS and persisted even after clinical resolution (Figure 1). Early changes in the Stroop test (AUC = 0.857, 95% CI 0.628-1.000) were more predictive of the onset of ICANS when measured during the first 36 hours following CAR T-cell therapy, as com

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Classification ensembles for multivariate functional data with application to mouse movements in web surveys

arXiv

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arXiv 2022年

作者： Fernández-Fontelo, Amanda Henninger, Felix Kieslich, Pascal J. Kreuter, Frauke Greven, Sonja Statistics School of Business and Economics Humboldt-Universität zu Berlin Berlin Germany Departament de Matemàtiques Universitat Autònoma de Barcelona Barcelona Spain Statistics and Data Science in Social Sciences and the Humanities Ludwig-Maximilians-Universität München Munich Germany Mannheim Centre for European Social Research University of Mannheim Mannheim Germany Joint Program in Survey Methodology University of Maryland College ParkMD United States

We propose new ensemble models for multivariate functional data classification as combinations of semi-metric-based weak learners. Our models extend current semi-metric-type methods from the univariate to the multivariate case, propose new semi-metrics to compute distances between functions, and consider more flexible options for combining weak learners using stacked generalisation methods. We apply these ensemble models to identify respondents' difficulty with survey questions, with the aim to improve survey data quality. As predictors of difficulty, we use mouse movement trajectories from the respondents' interaction with a web survey, in which several questions were manipulated to create two scenarios with different levels of difficulty. © 2022, CC BY.

关键词： Surveys

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Digitalisierung von Therapieprotokollen

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Forum 2020年第2期35卷 129-136页

作者： Link, Hartmut Wickenkamp, Axel Dresel, Annika van Lengen, Rolf H. Hämatologie Internistische Onkologie Privatärztliche Praxis Kaiserslautern Deutschland Onkodin GmbH Kaiserslautern Kaiserslautern Deutschland Data Science Department Fraunhofer IESE Kaiserslautern Deutschland Research Program Digital Healthcare Fraunhofer IESE Kaiserslautern Deutschland

Onkologische Therapieprotokolle entstehen über mehrere komplexe Entwicklungsstufen von der Analyse der Originalpublikation und der Extraktion der wesentlichen Inhalte über die Ergänzung der standardisierten Supportivtherapie bis hin zur Überprüfung der Kompatibilität mit Leitlinien und Qualitätskontrollen. Die Digitalisierung ermöglicht eine Standardisierung, erhebliche Effizienzsteigerung und Optimierung dieser Prozesse. Kern der Datenbank digitalisierter Therapieprotokolle des Onkopti®-Projekts (Onkodin GmbH, Kaiserslautern, Deutschland, ***) ist eine umfassende relationale Datenbank, die kontinuierlich ergänzt wird. Aus den Modulen der Datenbank werden die Protokolle dynamisch generiert und in verschiedene Anwendungen, wie z.B. BD Cato™, das programm J-MED oder eine Microsoft Excel Anwendung, in standardisierten digitalen Formaten einzeln oder komplett exportiert. Protokolle können im standardisierten XML-Format (XML: Extensible Markup Language) oder in anderen Formaten für andere Softwareanwendung exportiert werden. Neu publizierte Therapieprotokolle werden kontinuierlich standardisiert in die Datenbank aufgenommen, sodass neue Therapiemöglichkeiten rasch zur Verfügung stehen, im Februar 2020 sind dies bereits über 1400 Protokolle. Als wesentlicher Schritt folgt die Verordnung und Anwendung der Therapie über elektronische Verordnungssysteme, mit denen hohe Qualitätsstandards erreicht werden. Auch die Abläufe und die Zubereitung in der Apotheke sowie die administrativen Erfordernisse wie Dokumentation im Krankenhaus- oder Praxisinformationssystem, Abrechnung und Rezepterstellung können mit digitalisierten Abläufen beschleunigt, standardisiert und effizient gestaltet werden. Dadurch können personelle Ressourcen geschont und Kosten eingespart werden. Die Fehlerrate bei der Chemotherapie kann mit digitalisierten Therapieprotokollen und einem elektronischen Verordnungssystem minimiert werden und es lässt sich eine hohe Arzneimitteltherapiesicherheit erreic

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Investigation of structural and electronic properties of boron carbide based on SCAN meta-GGA functional

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AIP Conference Proceedings 2023年第1期2604卷

作者： Muhammad R. Ramadhan Imam M. Al-Khafiiz Jihan Anavisha Jofim Y. Napitupulu Wanda R. Tiana Fahmi Astuti M. Zuhnir Piliang Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor 16810 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

Boron Carbide (B4C) is one of the known superhard material that can be utilized in many engineering and scientific applications. Here we investigated B4C system in its rhombohedral structure (a=5.17 Å, α=65.52°) by using the meta-GGA functional, SCAN, within density functional theory framework. We observed that the optimized lattice parameters given by the SCAN functional is more realistic compared to the LDA and GGA calculations. From the optimized structure of B4C, we determined the bulk modulus value of 255 GPa which only differs by ∼3 % from experimental data. Furthermore, the underestimation of band gap in LDA and GGA implementations is also remedied, where the meta-GGA, SCAN, functional estimates 1.98 eV insulating gap from density of states calculation. This value is very close to the known experimental observations (2.09 eV).

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Biases in Inverse Ising Estimates of Near-Critical Behaviour

arXiv

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arXiv 2023年

作者： Kloucek, Maximilian B. Machon, Thomas Kajimura, Shogo Royall, C. Patrick Masuda, Naoki Turci, Francesco H.H. Wills Laboratory School of Physics University of Bristol Bristol United Kingdom Bristol Centre for Functional Nanomaterials University of Bristol Bristol United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL Paris75005 France Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as Pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick (SK) model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer-to-criticality than one would expect from the data. data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging (fMRI) dataset from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Criticality (nuclear fission)

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A decomposition of Fisher’s information to inform sample size for developing fair and precise clinical prediction models - Part 2: time-to-event outcomes

arXiv

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arXiv 2025年

作者： Riley, Richard D. Collins, Gary S. Archer, Lucinda While, Rebecca Legha, Amardeep Kirton, Laura Dhiman, Paula Sadatsafavi, Mohsen Adderley, Nicola J. Alderman, Joseph MarGn, Glen P. Ensor, Joie Department of Applied Health Sciences School of Health Sciences College of Medicine and Health University of Birmingham Birmingham United Kingdom Birmingham Biomedical Research Centre Birmingham United Kingdom Centre for Sta?s?cs in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford OxfordOX3 7LD United Kingdom Cancer Research UK Clinical Trials Unit Ins?tute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom Respiratory Evalua?on Sciences Program Faculty of Pharmaceu?cal Sciences The University of Bri?sh Columbia Vancouver Canada Ins?tute of Inflamma?on and Ageing College of Medicine and Health University of Birmingham Birmingham United Kingdom Queen Elizabeth Hospital University Hospitals Birmingham NHS Founda?on Trust Birmingham United Kingdom Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom

Background When developing a clinical predicGon model using Gme-to-event data (i.e., with censoring and different lengths of follow-up), previous research focuses on the sample size needed to minimise overfifng and precisely esGmaGng the overall risk. However, instability of individual-level risk esGmates may sGll be large. Methods We propose using a decomposiGon of Fisher’s informaGon matrix to help examine and calculate the sample size required for developing a model that aims for precise and fair risk esGmates. We propose a six-step process which can be used before data collecGon or when an exisGng dataset is available. Steps (1) to (5) require researchers to specify the overall risk in the target populaGon at a key Gme-point of interest;an assumed pragmaGc ‘core model’ in the form of an exponenGal regression model;the (anGcipated) joint distribuGon of core predictors included in that model;and the distribuGon of any censoring. The ‘core model’ can be specified directly or based on a specified C-index and relaGve effects of (standardised) predictors. The joint distribuGon of predictors may be available directly in an exisGng dataset, in a pilot study, or in a syntheGc dataset provided by other researchers. Results We derive closed-form soluGons that decompose the variance of an individual’s esGmated event rate into Fisher’s unit informaGon matrix, predictor values and total sample size;this allows researchers to calculate and examine uncertainty distribuGons around individual risk esGmates and misclassificaGon probabiliGes for specified sample sizes. We provide an illustraGve example in breast cancer and emphasise the importance of clinical context, including any risk thresholds for decision making, and examine fairness concerns for pre- and post-menopausal women. Lastly, in two empirical evaluaGons, we provide reassurance that uncertainty interval widths based on our exponenGal approach are close to using more flexible parametric models. Conclusions Our approach

关键词： Fisher information matrix

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Unraveling the genetic mechanisms of UV radiation resistance in Bacillus through biofilm formation, sporulation, and carotenoid production

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Genomics 2025年第4期117卷 111066页

作者： Shrestha, Prasansah Kim, Byeollee Han, So-Ra Lee, Hyun Oh, Tae-Jin Department of Life Science and Biochemical Engineering SunMoon University Asan 31460 South Korea Bio Big Data-based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan 31460 South Korea Genome-based BioIT Convergence Institute Asan 31460 South Korea Division of Computer Science and Engineering SunMoon University Asan 31460 South Korea Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan 31460 South Korea

Bacillus species are Gram-positive bacteria that are rod-shaped, endospore-forming, and aerobic or facultatively anaerobic. With over 300 recognized species, Bacillus subtilis stands out as a well-studied model organism. The genus's various species exhibit a wide range of physiological capabilities, allowing them to thrive in diverse environmental conditions. Each cell produces a single endospore, which is highly resistant to heat, cold, radiation, desiccation, and disinfectants. Among Bacillus strains, those capable of producing spores, biofilms, and carotenoids demonstrate significant resilience to UV light. This review examines the genes involved in spore formation, biofilm development, and carotenoid synthesis, emphasizing their roles in UV radiation survival. We explore the interconnections between these processes and their combined contribution to UV resistance, focusing on the underlying genetic mechanisms. These insights will benefit researchers studying the genetic basis of UV radiation resistance in Bacillus species. Importance: Bacteria employ adaptive strategies in extreme environments through rapid changes in gene expression, altering their phenotype for survival. Bacillus species, for example, defend against UV radiation by making spores, creating biofilms, and producing pigments. During sporulation, sigma factors (σ^F, σ^E, σ^G, and σ^K) regulate gene expression to adapt to environmental shifts. It has been found that the spores of some species may contain pigments that strongly absorb UV radiation, playing a crucial role in spore UV resistance. UV light penetrates biofilm matrices minimally, mainly affecting surface cells, which produce compounds like mycosporine-like amino acids and carotenoids to shield against UV damage. © 2025 The Authors

关键词： Bacillus Biofilm Carotenoid Spore UV-radiation resistance

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Faster indicators of dengue fever case counts using Google and Twitter

arXiv

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arXiv 2021年

作者： Mizzi, Giovanni Preis, Tobias Bastos, Leonardo Soares da Costa Gomes, Marcelo Ferreira Codeço, Claudia Torres Moat, Helen Susannah Centre for Complexity Science University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom Data Science Lab Behavioural Science Warwick Business School University of Warwick Scarman Road CoventryCV4 7AL United Kingdom The Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Scientific Computing Program Oswaldo Cruz Foundation Av. Brazil 4365 - Manguinhos Rio de Janeiro Brazil

Dengue is a major threat to public health in Brazil, the world's sixth biggest country by population, with over 1.5 million cases recorded in 2019 alone. Official data on dengue case counts is delivered incrementally and, for many reasons, often subject to delays of weeks. In contrast, data on dengue-related Google searches and Twitter messages is available in full with no delay. Here, we describe a model which uses online data to deliver improved weekly estimates of dengue incidence in Rio de Janeiro. We address a key shortcoming of previous online data disease surveillance models by explicitly accounting for the incremental delivery of case count data, to ensure that our approach can be used in practice. We also draw on data from Google Trends and Twitter in tandem, and demonstrate that this leads to slightly better estimates than a model using only one of these data streams alone. Our results provide evidence that online data can be used to improve both the accuracy and precision of rapid estimates of disease incidence, even where the underlying case count data is subject to long and varied delays. © 2021, CC BY.

关键词： Social networking (online)

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Graph AI in Medicine

arXiv

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arXiv 2023年

作者： Johnson, Ruth Li, Michelle M. Noori, Ayush Queen, Owen Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Berkowitz Family Living Laboratory Harvard Medical School BostonMA02115 United States Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal to no re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions. © 2023, CC BY.

关键词： Knowledge graph

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A Consensus Privacy Metrics Framework for Synthetic data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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