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Tumor burden quantified by soluble B-cell maturation antigen and metabolic tumor volume determines myeloma CAR-T outcomes

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BLOOD 2025年第15期145卷 1645-1657页

作者： Freeman, Ciara L. Noble, Jerald Menges, Meghan Villanueva, Ricardo Nakashima, Justyn Y. Figura, Nicholas B. Tonseth, Rolf Petter Idiaquez, Dietrich Werner Skelson, Lawrence Smith, Eric Abraham-Miranda, Julieta Corallo, Salvatore De Avila, Gabriel Puglianini, Omar A. Castaneda Liu, Hien Alsina, Melissa Nishihori, Taiga Shain, Kenneth H. Baz, Rachid Blue, Brandon Grajales-Cruz, Ariel Koomen, John M. Atkins, Reginald M. Hansen, Doris K. Silva, Ariosto S. Kim, Jongphil Balagurunathan, Yoganand Locke, Frederick L. H Lee Moffitt Canc Ctr & Res Inst Dept Blood & Marrow Transplantat & Cellular Immuno 12902 USF Magnolia Dr Tampa FL 33612 USA H Lee Moffitt Canc Ctr & Res Inst Dept Clin Sci Tampa FL USA Univ S Florida Morsani Coll Med Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Dept Radiat Oncol Tampa FL USA BC Canc Mol Imaging & Therapy Program Vancouver BC Canada H Lee Moffitt Canc Ctr & Res Inst Dept Hematopathol Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Dept Malignant Hematol Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Dept Mol Oncol Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Dept Metab & Physiol Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Dept Biostat & Bioinformat Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Dept Machine Learning Tampa FL USA

Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a breakthrough treatment for relapsed and refractory multiple myeloma (RRMM). However, these products are complex to deliver, and alternative options are now available. Identifying biomarkers that can predict therapeutic outcomes is crucial for optimizing patient selection. There is a paucity of data evaluating the utility of both serum soluble B-cell maturation antigen (sBCMA) levels and metabolic tumor volume (MTV) at baseline in patients with RRMM undergoing CAR-T therapy. We identified a cohort of 183 patients with available serum to measure sBCMA and/or pretreatment MTV, derived from positron emission tomography-computed tomography scans obtained per standard of care. Expectedly, high pretreatment levels of sBCMA correlated with other established markers of tumor burden (eg, bone marrow plasma cells and beta 2 microglobulin) and inflammation and were highly prognostic for CAR-T-related toxicities and inferior progression-free survival (PFS). High MTV values were also associated with shorter PFS and inferior overall survival. The poor correlation observed between these 2 measures prompted evaluation of those with discordant results, identifying that those with low sBCMA and high MTV frequently had low/absent BCMA expression on plasma cells and suboptimal response. Our findings highlight the potential utility of sBCMA and MTV to facilitate more personalized treatment strategies in the management of RRMM eligible for BCMA-directed CAR-T.

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Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期160卷 106998-106998页

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Delfan, Niloufar Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Alinejad-Rokny, Hamid Acharya, U. Rajendra UNSW Sydney Grad Sch Biomed Engn Internship Biomed Machine Learning Lab Sydney NSW 2052 Australia Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Australia Univ Technol Sydney UTS Fac Engn & IT Sydney Australia KN Toosi Univ Technol Fac Comp Engn Dept Artificial Intelligence Engn Tehran Iran Univ Leicester Sch Comp & Math Sci Leicester England Univ Cambridge Dept Psychiat Cambridge England UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney NSW 2052 Australia Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ Ctr Appl Artificial Intelligence Hlth Data Analyt Program Sydney 2109 Australia Univ Southern Queensland Sch Math Phys & Comp Springfield Australia Asia Univ Dept Biomed Informat & Med Engn Taichung Taiwan

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies per-formed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.

关键词： Cardiovascular disease Diagnosis CMR Deep learning Classification Segmentation

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Cosmology with Galaxy Photometry Alone

arXiv

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arXiv 2023年

作者： Hahn, ChangHoon Villaescusa-Navarro, Francisco Melchior, Peter Teyssier, Romain Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Center for Statistics & Machine Learning Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. (2022b) recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g. optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of ∼20,000 NASA-Sloan Atlas galaxies, we constrain (formula presented) and (formula presented). © 2023, CC BY.

关键词： Galaxies

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Protocol to explain support vector machine predictions via exact Shapley value computation

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STAR Protocols 2024年第2期5卷 103010页

作者： Mastropietro, Andrea Bajorath, Jürgen Deparment of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany Lamarr Institute for Machine Learning and Artificial Intelligence Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany

Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shapley value computation. We detail the application of these algorithms and provide ready-to-use Python scripts and custom code. The final output of the protocol includes quantitative feature analysis and mapping of important features for visualization. For complete details on the use and execution of this protocol, please refer to Feldmann and Bajorath¹ and Mastropietro et al.² © 2024 The Author(s)

关键词： Bioinformatics Chemistry Computer sciences

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Subcellular spatially resolved gene neighborhood networks in single cells

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CELL REPORTS METHODS 2023年第5期3卷 100476页

作者： Fang, Zhou Ford, Adam J. Hu, Thomas Zhang, Nicholas Mantalaris, Athanasios Coskun, Ahmet F. Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA 30332 USA Georgia Inst Technol Machine Learning Grad Program Atlanta GA USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA USA Georgia Inst Technol Interdisciplinary Bioengn Grad Program Atlanta GA 30332 USA Georgia Inst Technol Parker H Petit Inst Bioengn & Biosci Atlanta GA 30332 USA

Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript fea-tures. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.

关键词： spatial omics subcellular transcriptomics gene neighborhood networks RNA proximity RNA-RNA interactions cell-type classification CP: Systems biology CP: Stem cell

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uTUG: An unsupervised Timed Up and Go test for Parkinson's disease

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第0期81卷 000-000页

作者： Tavares, Joao Elison da Rosa Ullrich, Martin Roth, Nils Kluge, Felix Eskofier, Bjoern M. Gassner, Heiko Klucken, Jochen Gladow, Till Marxreiter, Franz da Costa, Cristiano Andre Righi, Rodrigo da Rosa Barbosa, Jorge Luis Victoria Univ Vale Rio Sinos UNISINOS Appl Comp Grad Program Sao Leopoldo Brazil Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Artificial Intelligence Biomed Engn AIBE Machine Learning & Data Analyt Lab MaDLab Erlangen Germany Med Valley Digital Hlth Applicat Ctr Bamberg Germany Friedrich Alexander Univ Dept Mol Neurol Univ Klinikum Erlangen Erlangen Nurnberg FAU Erlangen Germany Fraunhofer Inst Integrated Circuits IIS Erlangen Germany Univ Luxembourg Digital Med Esch Sur Alzette Luxembourg Luxembourg Inst Hlth Strassen Luxembourg

Inertial measurement units (IMU) are used diagnostically in the movement analysis of Parkinson's disease (PD) patients, allowing an objective way to assess biomechanical motion and gait parameters. The Timed Up and Go (TUG) is a standardized clinical gait test widely used in the monitoring of patient fall risk and disease progression. Gait tests performed at home have been applied as part of movement monitoring protocols, enabling a link to clinical supervised reference assessments. However, unsupervised gait tests in a real-world data context present challenges, mainly regarding the interaction between participants and the recording system. Therefore, we developed and evaluated a novel algorithmic pipeline called unsupervised TUG (uTUG). Our contribution is the automatic detection and decomposition of TUG tests into their subphases, performed at home with no clinician supervision. In contrast to related studies, we used only foot-mounted IMU with no additional markers or manual annotations, allowing the detection of TUG test frames for subsequent classification by machine learning Support Vector machine (SVM), Random Forest (RF) and Naive Bayes Classifier (NBC) algorithms. The evaluation comprised 96 daily recordings of real-world gait data and 81 clinical visits accumulating 300 real TUG test samples processed from 32 PD patients. A prefiltering sensitivity of 98.6%, followed by the precision of 90.6%, recall of 88.5%, and Fl-score of 89.6% for TUG test detection were achieved using RF for the automatic classification in continuous real-world gait data. Thus, uTUG simplifies the test for patients and avoids manual annotations for clinicians, automatically detecting TUG tests.

关键词： Gait analysis Gait test IMU machine learning TUG Wearable sensors

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Multimodal Sentiment Analysis using Kernel Based Support Vector machine

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Ashish Khanna Deepak Gupta Madhumala R B Department Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceará Brazil CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Support vector machines Sentiment analysis Analytical models Sensitivity Veins Tagging Probabilistic logic

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Churn Prediction in Financial Risk Management using Deep learning Techniques

Churn Prediction in Financial Risk Management using Deep Lea...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Sowmyashree S Piyush Kumar Pareek Ashish Khanna Victor Hugo Costa de Albuquerque Deepak Gupta Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Computer Science & Engineering East West Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil CSE dept Maharaja Agrasen Institute of Technology GGSIPU Department of Teleinformatics Engineering Federal University of Cearά Brazil

ISBN: (纸本)9781665493970

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered “active,” which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered “agitate,” which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Leadership Roads Companies Predictive models Media Planning Data mining

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A Developer Recommendation Method Based on Code Quality

A Developer Recommendation Method Based on Code Quality

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International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Camilo da Silva André Armstrong Janino Cizotto Emerson Cabrera Paraiso Graduate Program in Informatics Pontificia Universidade Catolica do Parana Curitiba Brazil Machine Learning Committee Siemens Ltd Curitiba Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

During the development cycle of a project, it is common for software requirements and functionality to change and for code errors to occur. To deal with these unforeseen changes, the artifact known as change request, which is a formal proposal to alter a system, is used. Its assignment is an important step in the development process. Projects can receive a very high number of requests daily, which makes the automation of this process compelling. This work proposes a method for assigning unresolved requests, based on developer's profiles. The proposed method consists of three steps. The first step is to extract code quality metrics, commit data and previously resolved requests, in order to model developers through the mining of repositories. The second step concerns with the selection of the profile of potential developers through the application of natural language processing and information retrieval techniques. And finally, in the third step the appropriate developers are selected based on the quality of their code and the impact of their commits. Results from experimental evaluation show that the method is able to recommend more developers with a positive impact on the repository quality if compared to the iMacPro method.

关键词： Tools Data mining Measurement Computer bugs Task analysis Software Maintenance engineering

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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

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NATURE COMMUNICATIONS 2024年第1期15卷 1619页

作者： Martindale, Alexander P. L. Ng, Benjamin Ngai, Victoria Kale, Aditya U. di Ruffano, Lavinia Ferrante Golub, Robert M. Collins, Gary S. Moher, David McCradden, Melissa D. Oakden-Rayner, Lauren Rivera, Samantha Cruz Calvert, Melanie Kelly, Christopher J. Lee, Cecilia S. Yau, Christopher Chan, An-Wen Keane, Pearse A. Beam, Andrew L. Denniston, Alastair K. Liu, Xiaoxuan Brighton & Sussex Med Sch Brighton England Birmingham & Midland Eye Ctr Sandwell & West Birmingham NHS Trust Birmingham England Univ Oxford Christ Church Oxford England UCL Med Sch London England Univ Birmingham Inst Inflammat & Ageing Birmingham England Univ Hosp Birmingham NHS Fdn Trust Birmingham England Univ Birmingham Natl Inst Hlth & Care Res NIHR Birmingham Biomed Res Ctr Birmingham England Univ York York Hlth Econ Consortium York England Northwestern Univ Feinberg Sch Med Chicago IL USA Univ Oxford Ctr Stat Med UK EQUATOR Ctr Nuffield Dept Orthopaed Rheumatol & Musculoskelet Oxford England Ottawa Hosp Res Inst Ctr Journalol Clin Epidemiol Program Ottawa ON Canada Hosp Sick Children Dept Bioeth Toronto ON Canada Peter Gilgan Ctr Res & Learning Genet & Genome Biol Res Program Toronto ON Canada Dalla Lana Sch Publ Hlth Div Clin & Publ Hlth Toronto ON Canada Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ Birmingham Birmingham Hlth Partners Ctr Regulatory Sci & Inn Birmingham England Univ Birmingham Inst Appl Hlth Res Coll Med & Dent Sci Ctr Patient Reported Outcomes Res CPROR Birmingham England Univ Birmingham NIHR Appl Res Collaborat ARC West Midlands Birmingham England Univ Birmingham NIHR Blood & Transplant Res Unit BTRU Precis Tran Birmingham England Google Hlth London England Univ Washington Seattle WA USA Univ Oxford Nuffield Dept Womens & Reprod Hlth Oxford England Hlth Data Res UK London England Univ Toronto Womens Coll Hosp Dept Med Toronto ON Canada Moorfields EyeHospital NHS Fdn Trust NIHR Biomed Res Ctr Moorfields London England UCL Inst Ophthalmol London England Harvard TH Chan Sch Publ Hlth Dept Epidemiol Boston MA USA Harvard Med Sch Dept Biomed Informat Boston MA USA

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines. The CONSORT-AI extension was developed to provide specific guidance for randomised controlled trials involving Artificial Intelligence (AI) interventions. Here, the authors show that since publication of CONSORT-AI, several AI-specific considerations remain systematically underreported.

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