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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

IEEE Access 2025年

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Large Language Models in Healthcare

arXiv

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arXiv 2025年

作者： Al-Garadi, Mohammed Mungle, Tushar Ahmed, Abdulaziz Sarker, Abeed Miao, Zhuqi Matheny, Michael E. Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biomedical Data Science Stanford University StanfordCA United States Department of Health Services Administration UAB BirminghamAL United States Department of Biomedical Informatics and Data Science UAB BirminghamAL United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States School of Business The State University of New York at New Paltz New PaltzNY United States Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Geriatric Research Education and Clinical Care Service Tennessee Valley Healthcare System VA NashvilleTN United States

Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinical decision-making. However, their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs. In this Review, we highlight recent advancements, explore emerging opportunities for LLM-driven innovation, and propose a framework for their responsible implementation in healthcare settings. We examine strategies for adapting LLMs to domain-specific healthcare tasks, such as fine-tuning, prompt engineering, and multimodal integration with electronic health records. We also summarize various evaluation metrics tailored to healthcare, addressing clinical accuracy, fairness, robustness, and patient outcomes. Furthermore, we discuss the challenges associated with deploying LLMs in healthcare—including data privacy, bias mitigation, regulatory compliance, and computational sustainability—and underscore the need for interdisciplinary collaboration. Finally, these challenges present promising future research directions for advancing LLM implementation in clinical settings and healthcare. © 2025, CC BY-NC-ND.

关键词： Electronic health record

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Devider: Long-Read Reconstruction of Many Diverse Haplotypes 29th

Devider: Long-Read Reconstruction of Many Diverse Haplotype...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Shaw, Jim Boucher, Christina Yu, Yun William Noyes, Noelle Li, Heng Department of Data Science Dana-Farber Cancer Institute BostonMA02215 United States Department of Biomedical Informatics Harvard Medical School BostonMA02215 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States Department of Veterinary Population Medicine University of Minnesota St. PaulMN55421 United States

ISBN: (纸本)9783031902512

Reconstructing haplotypes through sequencing of a mixture of similar sequences is a fundamental problem. Long-read sequencing technologies can connect distant alleles to disentangle similar haplotypes, but handling elevated sequencing error rates requires specialized techniques. We present devider, an algorithm for haplotyping small sequences—such as viruses or genes—from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. Benchmarking on synthetic mixtures of antimicrobial resistance (AMR) genes showed that devider recovered 83% of haplotypes, 23% points higher than the next best method. On real PacBio and Nanopore datasets, devider recapitulates previously known results in seconds, disentangling a bacterial community with >10 strains and an HIV-1 co-infection dataset. We used devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering a history of recombination for diverse AMR gene haplotypes and showcasing devider ’s ability to unveil ecological signals for heterogeneous mixtures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammals

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Deciphering the toxic effects of polystyrene nanoparticles on erythropoiesis at single-cell resolution

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Zoological Research 2025年第1期46卷 165-176页

作者： Eun Jung Kwon Hyeon Mi Sung Hansong Lee Soyul Ahn Yejin Kim Chae Rin Lee Kihun Kim Kyungjae Myung Won Kyu Kim Dokyoung Kim Sanghwa Jeong Chang-Kyu Oh Yun Hak Kim Medical Research Institute Pusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Institute for Future Earth Pusan National UniversityBusan 46241Republic of Korea Department of Convergence Medical Sciences School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Department of Biomedical Informatics School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Department of Anatomy School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Center for Genomic Integrity Institute for Basic ScienceUlsan 689-798Republic of Korea Department of Biomedical Engineering College of Information-Bio Convergence EngineeringUlsan National Institute of Science and TechnologyUlsan 44919Republic of Korea Natural Product Research Center Korea Institute of Science and Technology(KIST)GangneungGangwon-do 25451Republic of Korea Department of Convergence Medicine Yonsei University Wonju College of MedicineWonjuGangwon-do 26426Republic of Korea Natural Product Applied Science KIST SchoolUniversity of Science and Technology(UST)GangneungGangwon-do 25451Republic of Korea Department of Biomedical Science Graduate SchoolKyung Hee UniversitySeoul 02447Republic of Korea Department of Anatomy and Neurobiology College of MedicineKyung Hee UniversitySeoul 02447Republic of Korea Department of Precision Medicine Graduate SchoolKyung Hee UniversitySeoul 02447Republic of Korea Center for Converging Humanities Kyung Hee UniversitySeoul 02447Republic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoul 02447Republic of Korea UC San Diego Materials Research Science and Engineering Center 9500 Gilman DriveLa JollaCalifornia 92093USA Medical Research Center for Bioreaction to Reactive Oxygen Species and Biomedical Science Institute Core Research Institute(CRI)Kyung Hee UniversitySeoul 02447Republic of Korea Center for Brain Technology Brain Science Institute

Polystyrene nanoparticles pose significant toxicological risks to aquatic ecosystems,yet their impact on zebrafish(Danio rerio)embryonic development,particularly erythropoiesis,remains *** study used single-cell RNA sequencing to comprehensively evaluate the effects of polystyrene nanoparticle exposure on erythropoiesis in zebrafish *** vivo validation experiments corroborated the transcriptomic findings,revealing that polystyrene nanoparticle exposure disrupted erythrocyte differentiation,as evidenced by the decrease in mature erythrocytes and concomitant increase in immature ***,impaired heme synthesis further contributed to the diminished erythrocyte *** findings underscore the toxic effects of polystyrene nanoparticles on hematopoietic processes,highlighting their potential to compromise organismal health in aquatic environments.

关键词： Polystyrene nanoparticles Zebrafish embryos Single-cell RNA sequencing Erythropoiesis

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Development of a Machine Learning-Based Robotic System for Medical-Targeted Drug Delivery System 8th

Development of a Machine Learning-Based Robotic System for M...

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8th National Conference on New Trends in Information and Communications Technology Applications, NTICT 2024

作者： Simango, Doubt Manyanye, Trish Aldhaibani, Jaafar A. Yahya, Abid Mushiri, Tawanda Ndaimani, Augustine Department of Mechatronic Engineering School of Engineering Sciences and Technology Private Bag Off-Chirundu Road Chinhoyi7724 Zimbabwe College of Engineering University of Information Technology and Communication Baghdad Iraq Botswana International University of Science and Technology Private Bag 16 Palapye Botswana Department of Industrial and Mechatronics Engineering Faculty of Engineering and the Built Environment University of Zimbabwe 630 Churchill Avenue Mt Pleasant Harare Zimbabwe Department of Biomedical Informatics and Biomedical Engineering University of Zimbabwe 630 Churchill Avenue Mt Pleasant Harare Zimbabwe

ISBN: (纸本)9783031870750

Medical robotics is a field that combines engineering and artificial intelligence to create robotic systems used in healthcare. This study aims to delve into the exciting field of developing medical-targeted drug delivery systems, which hold immense potential for revolutionizing how we administer therapeutic substances. By precisely delivering drugs to specific sites within the body, these innovative systems offer improved treatment outcomes, reduced side effects, and enhanced patient compliance. Therefore, an image processing algorithm for visible target detection was designed, a system to determine the coordinates of the exact location of identified regions of the target was developed, and a trajectory plan for the robotic arm end effector for precise drug delivery was also designed. The integration of robotic systems into MRI scanners, the development of image processing algorithms, and also real-time feedback control systems are mechatronics-based processes. Medical image navigation technology has many uses in minimally invasive robots, mainly image acquisition techniques such as Magnetic Resonance Imaging (MRI) to acquire equivalent image data;however, medical imaging techniques have inefficient visibility in robotic-assisted surgeries of small and confined incisions. Therefore, image processing methods are applied to precisely regulate the operation’s location and visibility and improve the procedure’s efficacy, accuracy, and safety. To develop an advanced medical-targeted drug delivery system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： End effectors

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A prospective cohort observational study to validate a simplified postoperative nausea and vomiting severity scale and its effects on sleep and vitality

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BMC Anesthesiology 2025年第1期25卷 1-10页

作者： Weng, Shih-Feng Wu, Yu-Hwa Kang, Tin-Wei Tseng, Chia-Chih Alex Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Biomedical Engineering National Cheng Kung University Tainan Taiwan Department of Anaesthesiology College of Medicine National Cheng Kung University No.138 Sheng Li Road Tainan 704 Taiwan Department of Anaesthesiology Kaohsiung Show Chwan Memorial Hospital Kaohsiung Taiwan

Background: Validating postoperative nausea and vomiting (PONV) severity based on antiemetics prescription can help actively manage PONV by the Rhodes Index of Nausea, Vomiting, and Retching (RINVR) scale but is relatively complicated. This study aims to validate the RINVR scale on PONV intensity with prescribed antiemetics usage, and further simplify and validate this scale to serve as the simplified PONV severity score (SPONVSS) and its effects on sleep quality and vitality. Methods: This study analyzed data from patients who underwent anesthesia with sleep and vitality reports after surgery. We assessed the PONV severity by RINVR. We also simplified the RINVR score with one element selected from each dimension of PONV. The RINVR score and all 18 simplified combinations were validated with antiemetic usage. These scores were used to study patient-reported sleep quality and vitality. Results: The AUC of the RINVR score and 18 combinations have a similar high impact on antiemetics administration, sleep, and vitality. Nausea frequency, vomiting, and retching times were chosen as the SPONVSS elements because of overall high AUC and convenience in implementation. Multivariate logistic regression analyses show that besides pain, the SPONVSS score provides a significant impact on postoperative sleep and vitality. Conclusions: We developed a simple and practical scale to monitor PONV intensity in a broad clinical surgical setting. A high SPONVSS score (≥ 3) is an independent risk for rescue antiemesis, poor sleep quality, and vitality. This scale will be useful to monitor the postoperative care quality and improve postoperative care. © The Author(s) 2025.

关键词： Antiemesis PONV Severity Sleep SPONVSS score Vitality

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CBCT Reconstruction using Single X-ray Projection with Cycle-domain Geometry-integrated Denoising Diffusion Probabilistic Models

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Pan, Shaoyan Peng, Junbo Gao, Yuan Lo, Shao-Yuan Luan, Tianyu Li, Junyuan Wang, Tonghe Chang, Chih-Wei Tian, Zhen Yang, Xiaofeng Emory University Department of Biomedical Informatics Department of Radiation Oncology Winship Cancer Institute AtlantaGA30322 United States Honda Research Institute USA San JoseCA95134 United States State University of New York at Buffalo AmherstNY14068 United States Johns Hopkins University BaltimoreMD21205 United States Memorial Sloan Kettering Cancer Center Department of Medical Physics New YorkNY10065 United States University of Chicago Department of Radiation & Cellular Oncology ChicagoIL60637 United States Emory University Department of Radiation Oncology Winship Cancer Institute Department of Computer Science and Biomedical Informatics Department of Biomedical Engineering AtlantaGA30322 United States

In the sphere of Cone Beam Computed Tomography (CBCT), acquiring X-ray projections from sufficient angles is indispensable for traditional image reconstruction methods to accurately reconstruct 3D anatomical intricacies. However, this acquisition procedure for the linear accelerator-mounted CBCT systems in radiotherapy takes approximately one minute, impeding its use for ultra-fast intra-fractional motion monitoring during treatment delivery. To address this challenge, we introduce the Patient-specific Cycle-domain Geometric-integrated Denoising Diffusion Probabilistic Model (CG-DDPM). This model aims to leverage patient-specific priors from patient’s CT/4DCT images, which are acquired for treatment planning purposes, to reconstruct 3D CBCT from a single-view 2D CBCT projection of any arbitrary angle during treatment, namely single-view reconstructed CBCT (svCBCT). The CG-DDPM framework encompasses a dual DDPM structure: the Projection-DDPM for synthesizing comprehensive full-view projections and the CBCT-DDPM for creating CBCT images. A key innovation is our Cycle-Domain Geometry-Integrated (CDGI) method, incorporating a Cone Beam X-ray Geometric Transformation Module (GTM) to ensure precise, synergistic operation between the dual DDPMs, thereby enhancing reconstruction accuracy and reducing artifacts. Evaluated in a study involving 37 lung cancer patients, the method demonstrated its ability to reconstruct CBCT not only from simulated X-ray projections but also from real-world data. The CG-DDPM significantly outperforms existing V-shape convolutional neural networks (V-nets), Generative Adversarial Networks (GANs), and DDPM methods in terms of reconstruction fidelity and artifact minimization. This was confirmed through extensive voxel-level, structural, visual, and clinical assessments. The capability of CG-DDPM to generate high-quality reconstructed CBCT from a single-view projection at any arbitrary angle using a single model opens the door for ultra-fast, in-t

关键词： Radiotherapy

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： G.Anne Nisha Sreetha P Seetha A K. Manickam Sharath Kumar Jagannathan Sekar S D Department of Informatics Kyoto Computer Gakuin Kyoto Japan Department of Biomedical Engineering KPR Institute of Engineering and Technology Tamil Nadu India Department of Information Technology S. A. Engineering College Thiruverkadu Chennai India Department of Mathematics Sona College of Technology Tamil Nadu India Data Science Institute Saint Peter's University New Jersery Department of Mechanical Engineering R.M.K.Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages.

关键词： Accuracy Costs Transportation Boosting Real-time systems Computational efficiency Electronic commerce Random forests Optimization Logistics

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Mass cytometric analysis of circulating monocyte subsets in a murine model of diabetic gastroparesis

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American Journal of Physiology - Gastrointestinal and Liver Physiology 2025年第4期328卷 G323-G341页

作者： AlAsfoor, Shefaa Jessen, Erik Pullapantula, Suraj R. Voisin, Jennifer R. Hsi, Linda C. Pavelko, Kevin D. Farwana, Samera Patraw, Jack A. Chai, Xin-Yi Ji, Sihan Strausbauch, Michael A. Cipriani, Gianluca Wei, Lai Linden, David R. Hou, Ruixue Myers, Richard Bhattarai, Yogesh Wykosky, Jill Burns, Alan J. Dasari, Surendra Farrugia, Gianrico Grover, Madhusudan Enteric Neuroscience Program Mayo Clinic Rochester MN United States Department of Physiology and Biomedical Engineering Mayo Clinic Rochester MN United States Department of Quantitative Health Sciences Mayo Clinic Rochester MN United States Immune Monitoring Core Office of Core Shared Services Mayo Clinic Rochester MN United States Department of Immunology Mayo Clinic Rochester MN United States Department of Neuroendocrine Pharmacology School of Pharmacy China Medical University Shenyang China Gastrointestinal Drug Discovery Unit Takeda Development Center Americas Inc. San Diego CA United States Gastrointestinal Drug Discovery Unit Takeda Development Center Americas Inc. Cambridge MA United States Department of Biomedical Statistics and Informatics Mayo Clinic Rochester MN United States Division of Gastroenterology and Hepatology Mayo Clinic Rochester MN United States

Circulating monocytes (Mo) are precursors to a subset of gastric resident muscularis macrophages. Changes in muscularis macrophages (MMs) result in delayed gastric emptying (DGE) in diabetic gastroparesis. However, the dynamics of Mo in the development of DGE in an animal model are unknown. Using cytometry by time-of-flight and computational approaches, we show a high heterogeneity within the Mo population. In DGE mice, via unbiased clustering, we identified two reduced Mo clusters that exhibit migratory phenotype (Ly6C^hiCCR2^hi-intCD62L^hiLy6G^hiCD45R^hiMERTK^hiintLGALS3^intCD14^intCX3CR1^lowSiglecH^int-low) resembling classical Mo (CMo-like). All markers enriched in these clusters are known to regulate cell differentiation, proliferation, adhesion, and migration. Trajectory inference analysis predicted these Mo as precursors to subsequent Mo lineages. In gastric muscle tissue, we demonstrated an increase in the gene expression levels of chemokine receptor C-C chemokine receptor type 2 (Ccr2) and its C-C motif ligand 2 (Ccl2), suggesting increased trafficking of classical-Mo. These findings establish a link between two CMo-like clusters and the development of the DGE phenotype and contribute to a better understanding of the heterogenicity of the Mo population. Copyright © 2025 The Authors.

关键词： circulating monocytes delayed gastric emptying immune cells surface proteins

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LIDDIA: Language-based Intelligent Drug Discovery Agent

arXiv

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arXiv 2025年

作者： Averly, Reza Baker, Frazier N. Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States College of Pharmacy The Ohio State University United States

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks;however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDIA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDIA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDIA, demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it can identify promising novel drug candidates on EGFR, a critical target for cancers. © 2025, CC BY-NC-SA.

关键词： Drug discovery

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