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Molecular Mechanisms Promoting Long-Term Cytopenia after BCMA CAR-T Therapy in Multiple Myeloma

BLOOD 2024年 144卷 4798-4799页

作者： [Anonymous] Hematology and Cell Therapy Department. Clinica Universidad de Navarra. IdiSNA. Pamplona Spain Hemato-Oncology Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Computational Biology Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Immunology and Immunotherapy Department. Clinica Universidad de Navarra. Pamplona Spain Centro de Investigación Biomedica en Red de Cancer (CIBERONC). Madrid Spain Cancer Center Clinica Universidad de Navarra (CCUN). Pamplona Spain Cancer Center Clinica Universidad de Navarra (CCUN) Pamplona Spain Immunology and Immunotherapy Program. Cima Universidad de Navarra. IdiSNA. Pamplona Spain Center for Data Science and Artificial Intelligence Institute (DATAI) Universidad de Navarra Pamplona Pamplona Spain Hematology and Cell Therapy Department Clinica Universidad de Navarra IdiSNA Pamplona Spain Centro de Investigación Biomedica en Red de Cancer (CIBERONC) Madrid Spain

Hematologic toxicity, specifically prolonged cytopenia, is a common side effect associated with CAR-T therapies, being particularly severe in patients with relapsed/refractory Multiple Myeloma (MM) (1). However, to date, its etiology is poorly understood. Thus, the main objective of this study was to identify and understand the underlying mechanisms of prolonged cytopenias associated with CAR-T therapy targeting BCMA. In our work, we combined the retrospective analysis of a cohort of 48 patients treated with BCMA CAR-T cells at Clinica Universidad de Navarra, with ex vivo transcriptomic analysis using single-cell RNA sequencing, to characterize the kinetics of cytopenia, identify predictive factors and determine potential mechanism underlying these toxicities. The overall incidence of cytopenia was 95.7%, and grade>3 thrombocytopenia and neutropenia one month after infusion, was observed in 57% and 53% of the patients, being still present after one year in 4 and 3 patients respectively. Presence of cytopenia at baseline and high peak inflammatory markers highly correlated with cytopenia persisting up to three months. To determine potential mechanisms underpinning cytopenias, we evaluated the paracrine effect of BCMA CAR-T cells on HSPCs differentiation, using an ex vivo myeloid differentiation model (2). Thus, supernatants from CAR-T cells or control T lymphocytes, co-cultured with MM tumor cell line U266, were added to the differentiation process. Phenotypic analysis by flow cytometry showed that supernatants from activated CAR-T cells (spCAR) halted HSPCs differentiation, promoting more immature phenotypes, with reduced expression of granulocytic, monocytic, and erythroid maturation markers, suggesting a paracrine effect related to CAR-T cells activation. Interestingly, this immature phenotype could be prevented by a combination of IFNγ, TNFα/β, TGFβ, IL-6, and IL-17 inhibitors, indicating the essential role of these cytokines and providing potential targets to pr

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Future Glycemic Events Prediction Model Based On Artificial Neural Network

Future Glycemic Events Prediction Model Based On Artificial ...

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2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Syafrudin, Muhammad Alfian, Ganjar Fitriyani, Norma Latif Hadibarata, Tony Rhee, Jongtae Anshari, Muhammad Sejong University Department of Artificial Intelligence Seoul Korea Republic of Universitas Gadjah Mada Department of Electrical Engineering and Informatics Vocational College Yogyakarta Indonesia Sejong University Department of Data Science Seoul Korea Republic of Curtin University Environmental Engineering Program Miri Malaysia Dongguk University Department of Industrial and Systems Engineering Seoul Korea Republic of Universiti Brunei Darussalam School of Business and Economics Brunei

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Predicting future glycemic events such as hypoglycemia, hyperglycemia, and normal for type 1 diabetes (T1D) remains a significant and challenging issue. In this study, an artificial neural network (ANN)-based model is proposed to predict the future glycemic events of T1D patients. We utilized five T1D patient datasets to build the models and predict future glycemic events with a prediction horizon (PH) of 30 and 60 minutes ahead of time. We applied the data preprocessing method based on the sliding window approach by sliding the blood glucose time-series data from the past 60 minutes (the last 12 data points) as input and using the next 30 and 60 minutes (the next 6 and 12-th data points) as output. All the numeric blood glucose output data are then transformed into a multi-class classification label, such as hypoglycemia, hyperglycemia, and normal. Our proposed model is then used to learn and create the prediction model from the preprocessed blood glucose dataset. Four performance metrics such as accuracy, precision, recall, and f-1 score were utilized to measure the performance of the classification models used in this study, such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). The results showed that our proposed ANN-based model performed better at predicting future glycemic events than other models, with an average accuracy, precision, recall, and f-1 score of 88.649%, 76.661%, 71.731%, 72.609%, and 83.364%, 60.437%, 61.345%, 60.62% for the PH of 30 and 60 minutes, respectively. As a result, knowing this future glycemic event sooner can help patients avoid potentially dangerous conditions and can eventually be used to improve diabetes management. © 2022 IEEE.

关键词： Decision trees

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A Development of Object Detection System based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

A Development of Object Detection System based on Deep Learn...

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2022 International Electrical Engineering Congress, iEECON 2022

作者： Sonsilphong, Suphachoke Sonsilphong, Amornthep Hormdee, Daranee Khampitak, Kovit Khon Kaen University Data Science Health Informatics Program Academic Affairs Faculty of Medicine Khon Kaen Thailand Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Embedded System RD Group Department of Computer Engineering Faculty of Engineering Khon Kean Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two main phases, the training phase, and the real-time operating phase. In the training phase, a modified YOLOv4 algorithm is proposed to be adopted within the Darknet framework for training the weights of detection models. The best detection model is then selected to be assigned to the object detector in the real-time operating phase. The validation result of the detection model across the experiment datasets is 90% accuracy on average during the training phase. To validate the viability of real-time object detection, the proposed system was implemented in two test-cases of gynecologic surgery with the soft-body cadavers. © 2022 IEEE.

关键词： Laparoscopy

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Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance

arXiv

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arXiv 2022年

作者： McNeely, Trey Khokhlov, Pavel Dalmasso, Niccolò Wood, Kimberly M. Lee, Ann B. Carnegie Mellon University Department of Statistics and Data Science United States J.P. Morgan AI Research Mississippi State University Department of Geosciences United States Microsoft AI Development Acceleration Program

Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model which is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 hours prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 hours by applying a Deep Autoregressive Generative Model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a "nowcasting" convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center's official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. © 2022, CC BY.

关键词： Geostationary satellites

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Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics data

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Procedia Computer science 2021年 179卷 202-210页

作者： Arif Budiarto Bharuno Mahesworo Alam Ahmad Hidayat Ika Nurlaila Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Genomics study, as opposed to socio-anthropology, has been demonstrated as an excellent tool to picture biological relatedness and disease risk factors. To analyze the data obtained from the study, Genome-wide Association Study (GWAS) has been more than decades known as the mainstay approach., is the most popular approach in analysing genomics data. The confounding variables selection, being that ancestry estimation or population stratification, is substantial to maintain the quality of GWAS. Researchers have developed various methods in extracting the population stratification information from high dimensional genomics data, especially Single Nucleotide Polymorphisms (SNPs) data. In the present study, we proposed an implementation of Principal Component Analysis (PCA)-complemented Gaussian Mixture Model (GMM) as an unsupervised model to estimate population stratification from samples. The results derived from this approach was further compared to that resulted from K-means and from the commonly used ancestry estimation software, fast STRUCTURE. We figured out that our recent improved approach outperformed the two later mentioned as shown by the average cluster and population scores. Furthermore, it was able to generate the probability distribution of each sample across all population, despite its limited quality. These intriguing results worth further investigations with much more comprehensive population coverage and more advanced algorithm.

关键词： Population Stratification Ancestry Estimation Gaussian Mixture Model Genomics SNPs

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Performance Prediction in Major League Baseball by Long Short-Term Memory Networks

arXiv

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arXiv 2022年

作者： Sun, Hsuan-Cheng Lin, Tse-Yu Tsai, Yen-Lung Center for Information Systems and Technology Claremont Graduate University Claremont United States Data Science Degree Program National Taiwan University Taipei Taiwan Department of Mathematical Sciences National Chengchi University Taipei Taiwan

Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models' ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users' needs. Copyright © 2022, The Authors. All rights reserved.

关键词： Baseball

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Locla: A Novel Local Optimization Tool for Chromosome-Level Assembly of Multiple Sequencing Technologies

SSRN

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SSRN 2024年

作者： Chuang, Wei-Hsuan Cheng, Hsueh-Chien Chang, Yu-Jung Fu, Pao-Ying Huang, Yi-Chen Hsieh, Ping-Heng Lin, Chung-Yen Ho, Jan-Ming Chen, Shu-Hwa Wellcome Genome Campus United Kingdom Institute of Information Science Academia Sinica Taipei115 Taiwan Wellcome Sanger Institute Hinxton United Kingdom Ocean Data Bank Institute of Oceanography National Taiwan University Taipei10663 Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei10663 Taiwan Institute of Fisheries Science National Taiwan University Taipei10663 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei11031 Taiwan

LOCLA (Local Optimization for Chromosome-Level Assembly) is an innovative tool designed to enhance genome assemblies by addressing challenges in repetitive regions and alignment errors through iterative gap-filling. It improves draft assemblies by utilizing original raw reads and new sequencing data from various technologies, such as PacBio HiFi, Bionano, 10x Genomics Linked-Reads, Oxford Nanopore, and Illumina. LOCLA can optimize genomes using the original reads or by integrating new data from different platforms to achieve superior *** LOCLA algorithm consists of four key modules: (1) local-contig-based (LCB) gap filling, (2) global-contig-based (GCB) gap filling, (3) LCB scaffolding, and (4) GCB scaffolding. LCB gap filling utilizes high-quality NGS reads aligned on gap flanks to create local contigs (L-contigs), while GCB gap filling uses global contigs (G-contigs) from TGS reads or assembled scaffolds. The scaffolding modules link scaffold pairs based on barcode distribution or align G-contigs to scaffold ends for assembly *** experiments, LOCLA significantly improved genome assembly quality across multiple datasets. For example, in the de novo assembly of the LLD0021C human genome, LOCLA added 23.3 million base pairs, enhancing the resolution of noncoding transcripts and pseudogenes. The CHM13 human genome increased the N50 value and added 51.8 million base pairs, improving the resolution of functional genes related to complex diseases. LOCLA also enhanced the RagTag reference-guided assemblies of the HG002 human genome by 63 million bases and filled 41.4% of gaps in the Bruguiera sexangula genome, raising its BUSCO score from 97.90% to 98.10%.Overall, LOCLA consistently improves the quantity and quality of genome assemblies, demonstrating its potential to advance personalized medicine and our understanding of complex biological pathways. © 2024, The Authors. All rights reserved.

关键词： Genome

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Hybrid ACO and PSO to minimize makespan in job shop scheduling problems

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AIP Conference Proceedings 2024年第1期3222卷

作者： Priljen Habeahan Erna Budhiarti Nababan Mahyuddin K. M. Nasution Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The lowest time search in the dataset that E. Taillard utilized employs a heuristic approach based on tabu search techniques to get the predicted solution. Glover’s study gives a broad description of tabu search, which is commonly encountered in Taillard’s job shop scheduling difficulties and Widmer et al.’s flow shop sorting challenges. Although tabu search is relatively simple to use and typically yields excellent results, it takes a long time to complete. In this research a hybrid ACO and PSO was carried out to minimize makespan in the Job Shop Scheduling Problem which was used as sourced from benchmark data which is secondary data obtained from E. Taillard “Benchmarks for basic scheduling problems” which consists of job shop matrix data (job × machine) measuring 4 × 4, 5 × 5, 7 × 7, 10 × 10, 15 × 15, 20 × 20, 30 × 15, 30 × 20, 50 × 15 and 50 × 20. Hybrid is carried out by calculating the Pbest value, namely the process position of each job on the machine to get the best solution using the PSO algorithm. Next, calculate the Gbest (Global best) value for the position of each job on the best machine on the entire machine using the PSO algorithm and initialize the ACO parameters using the PBest and Gbest values. The results of research on datasets with sizes 10×10, 15×15, 20×20, 30×15, 30×20, 50×15 and 50×20 produce smaller makespan compared to the lower bound on the dataset with an average minimum makespan improvement value of 1.184.

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Quality of dental treatments under intravenous sedation among patients with disability: A self-matching study

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Journal of Dental sciences 2023年第2期18卷 761-766页

作者： Chen, Hsiu-Hsien Lin, Po-Yen Lin, Ching-Kai Hsu, Le-Yin Hung, Ming-Hua Lin, Ping-Yi Chi, Lin-Yang Department of Dentistry School of Dentistry National Yang Ming Chiao Tung University Taipei Taiwan Department of Dentistry National Defense Medical Center Taipei Taiwan Department of Dentistry New Taipei Municipal Tucheng Hospital New Taipei City Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Graduate Program of Data Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Dentistry Far Eastern Memorial Hospital New Taipei City Taiwan Department of Stomatology Kaohsiung Veterans General Hospital Kaohsiung Taiwan

Background/purpose: Patients with disabilities usually have difficulties in communication and mobility, and the quality of the dental treatments are often inferior. This study uses the Taiwan National Health Insurance database to analyze the quality of dental treatment for patients with disabilities who receive intravenous sedation (IVS). Materials and methods: This study selected patients with disabilities who received dental treatment under IVS. Their oral cavity was divided into three major sections: anterior teeth, upper posterior teeth, and lower posterior teeth. Self-matching of the same section was conducted to observe whether operative dental treatment (OD) with or without IVS affected the occurrence of dental retreatment. This study observed the occurrence of refilling and the duration from the end of treatment until retreatment to compare treatment differences with or without IVS. Results: After self-matching, this study found 158 patients who received dental treatment within the same section with and without IVS. During a follow-up period of 17 years, 75.18% of the patients who received OD treatment required refilling, 10.87% required endodontic treatment, and 5.67% required tooth extraction. After OD treatment with IVS, the risks of refilling, endodontic treatment, and tooth extraction were 0.71 (95% CI: 0.58–0.87, P < 0.001), 0.77 (95% CI: 0.48–1.23, P = 0.28), and 0.64 (95% CI: 0.32–1.27, P = 0.20), respectively. Conclusion: For patients with disabilities, OD treatment with IVS significantly reduces the risk of refilling compared with OD treatment without IVS. IVS can be ideal for people with disabilities who receive dental treatment. © 2023 Association for Dental sciences of the Republic of China

关键词： Intravenous sedation National health insurance database Patients with disabilities

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Correction: Unbiased and multilevel methods for a class of diffusions partially observed via marked point processes

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Statistics and Computing 2024年第6期34卷 1-1页

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

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