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Curing CLL: Long-Term Outcomes of Chronic Lymphocytic Leukemia Patients with at Least One Year of Response to CART-19 Therapy

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Blood 2024年 144卷 588-588页

作者： Benjamin F. Frost Noelle Frey Elizabeth O. Hexner Stephen J. Schuster Sunita D. Nasta Alison W Loren Jakub Svoboda Daniel J. Landsburg Bruce Levine Joseph A. Fraietta J. Joseph Melenhorst Elizabeth Veloso Wei-Ting Hwang Carl H. June David L. Porter Saar Gill Perelman School of Medicine University of Pennsylvania Philadelphia PA Department of Medicine Brigham and Women's Hospital Boston MA Center for Cellular Immunotherapies University of Pennsylvania Philadelphia PA University of Pennsylvania Philadelphia PA Abramson Cancer Center Department of Medicine Division of Hematology-Oncology University of Pennsylvania Philadelphia PA Lymphoma Program Abramson Cancer Center University of Pennsylvania Philadelphia PA Center for Cellular Immunotherapies Perelman School of Medicine University of Pennsylvania Philadelphia PA Center for Immunotherapy and Precision Immuno-Oncology Cell Therapy & Immuno-Engineering Program Lerner Research Institute Cleveland Clinic Cleveland OH

Background : While outcomes have improved, relapsed or refractory chronic lymphocytic leukemia (R/R CLL) remains incurable with standard therapies and has a poor prognosis. We and others have reported remarkably durable responses to chimeric antigen receptor T cell (CART) therapy, with several patients remaining disease free without additional treatment for over 10 years (Nature 2022 Feb: 503-9). However, long-term outcomes after CART cells are available for a limited number of patients. Here, with a median follow-up of over 6.5 years, we report long-term outcomes of a large cohort of R/R CLL patients treated in two clinical trials of CART therapy. Methods : Treatment protocols were previously published (J Clin Oncol. 202 Sep:2862-71; Blood Adv. 2022 Nov:5774-85). Briefly, patients on NCT01747486 (n=38) had R/R CLL with at least two prior treatments and received murine anti-human CD19 directed CART monotherapy. Patients on NCT02640209 (n = 19) had R/R CLL with at least one prior treatment and at least six months of ibrutinib, which was continued during and after human anti-human CD19 directed CART infusion. We conducted a landmark analysis of the 31 patients from these trials (14 from NCT01747486, 17 from NCT02640209) who had achieved at least partial response without progression at one year. Endpoints included overall survival (OS) and progression free survival (PFS), and we evaluated the association between these endpoints and complete response (CR; iwCLL) achieved within the first year of treatment. Results : The median age at infusion was 62 (range 42 -78) years. Patients had received a median of 3 (range 1-17) prior lines of therapy, and 28 (90.3 %) patients had an adverse cytologic or molecular marker. The median bone marrow CLL burden was 30% (range 1-90%) prior to starting CART-19. The median follow-up after infusion was 9.1 (range 1.2-13.4) years for patients treated with CART-19 alone, 6.1 (range 1.4-7.3) years for patients treated with concurrent ibrutini

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Large language models for diabetes training:a prospective study

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Science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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Molecular-driven Foundation Model for Oncologic Pathology

arXiv

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arXiv 2025年

作者： Vaidya, Anurag Zhang, Andrew Jaume, Guillaume Song, Andrew H. Ding, Tong Wagner, Sophia J. Lu, Ming Y. Doucet, Paul Robertson, Harry Almagro-Pérez, Cristina Chen, Richard J. ElHarouni, Dina Ayoub, Georges Bossi, Connor Ligon, Keith L. Gerber, Georg Le, Long Phi Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich – German Research Center for Environment and Health Munich Germany School of Computation Information and Technology TUM Munich Germany CambridgeMA United States Sydney Precision Data Science Center The University of Sydney CamperdownNSW Australia Department of Oncologic Pathology Dana-Farber Cancer Institute BostonMA02215 United States Department of Pathology Boston Children’s Hospital BostonMA02115 United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce THREADS, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. THREADS was pretrained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles—the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables THREADS to capture the tissue’s underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction and survival prediction THREADS outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well-suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community. © 2025, CC BY-NC-ND.

关键词： Transfer learning

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Diabetic Retinopathy Assessment through Multitask Learning Approach on Heterogeneous Fundus Image Datasets

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Ophthalmology Science 2025年第5期5卷 100755页

作者： Wu, Hongkang Jin, Kai Jing, Yiyang Shen, Wenyue Tham, Yih Chung Pan, Xiangji Koh, Victor Grzybowski, Andrzej Ye, Juan Zhejiang Provincial Key Laboratory of Ophthalmology Zhejiang Provincial Clinical Research Center for Eye Diseases Zhejiang Provincial Engineering Institute on Eye Diseases Eye Center of Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Centre for Innovation and Precision Eye Health Department of Ophthalmology National University of Singapore Singapore Ophthalmology and Visual Science Academic Clinical Program Singapore Eye Research Institute Singapore National Eye Centre Duke-NUS Medical School Singapore Institute for Research in Ophthalmology Foundation for Ophthalmology Development Poznan Poland

Objective: To develop and validate an artificial intelligence (AI)-based system, Diabetic Retinopathy Analysis Model Assistant (DRAMA), for diagnosing diabetic retinopathy (DR) across multisource heterogeneous datasets and aimed at improving the diagnostic accuracy and efficiency. Design: This was a cross-sectional study conducted at Zhejiang University Eye Hospital and approved by the ethics committee. Subjects: The study included 1500 retinal images from 957 participants aged 18 to 83 years. The dataset was divided into 3 subdatasets: color fundus photography, ultra-widefield imaging, and portable fundus camera. Images were annotated by 3 experienced ophthalmologists. Methods: The AI system was built using EfficientNet-B2, pretrained on the ImageNet dataset. It performed 11 multilabel tasks, including image type identification, quality assessment, lesion detection, and diabetic macular edema (DME) detection. The model used LabelSmoothingCrossEntropy and AdamP optimizer to enhance robustness and convergence. The system's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC). External validation was conducted using datasets from different clinical centers. Main Outcome Measures: The primary outcomes measured were the accuracy, sensitivity, specificity, and AUC of the AI system in diagnosing DR. Results: After excluding 218 poor-quality images, DRAMA demonstrated high diagnostic accuracy, with EfficientNet-B2 achieving 87.02% accuracy in quality assessment and 91.60% accuracy in lesion detection. Area under the curves were >0.95 for most tasks, with 0.93 for grading and DME detection. External validation showed slightly lower accuracy in some tasks but outperformed in identifying hemorrhages and DME. Diabetic Retinopathy Analysis Model Assistant diagnosed the entire test set in 86 ms, significantly faster than the 90 to 100 minutes required by humans. Conclusions: Diabetic Retinopathy Analysis Model Assista

关键词： Artificial intelligence Deep learning Diabetic retinopathy Multisource heterogeneous dataset Multitask learning

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Development of microcontroller trainer kit as a teaching aid for electronics course

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AIP Conference Proceedings 2024年第1期3115卷

作者： Adhan Efendi Armansyah Maria Mahardini Sakanti Ary Surjanto C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology No. 57 Section 2 Zhongshan Rd Taiping District Taichung City Taiwan Mechanical Engineering Study Program Faculty of Engineering Universitas Pembangunan Nasional Veteran Jakarta Jalan RS. Fatmawati Raya Pondok Labu Cilandak South Jakarta City Jakarta 12450 Indonesia Department of Refrigeration and Air Conditioning Engineering Politeknik Negeri Bandung Polban Jl. Gegerkalong Hilir Ciwaruga Kec. Parongpong Kabupaten Bandung Barat Jawa Barat 4001 Indonesia Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology No. 57 Section 2 Zhongshan Rd Taiping District Taichung City Taiwan

This research aims to develop educational and learning support facilities by developing teaching aids for instructors and practical training to enhance the capabilities of laboratories. The developed exercise materials will be used in the electronics laboratory activities to equip vocational students (Diploma 3 and Diploma 4) with practical skills and knowledge of applied electronics. Microcontroller-based teaching materials designed systematically and interactively developed to support the applied electronic system. As a result, the students learn quickly while installing and programming applied electronic hardware applications. There are exercise manuals for up to seven exercise competencies of electronics laboratory activities. Based on the evaluation results from the expert group member and the laboratory chairman, the developed teaching aids are convenient for electronics laboratory classes. Based on student responses using the developed teaching aids, 43% of students mentioned that the microcontroller teaching laboratory kit and hands-on instructions were interactive, easy to use, understandable, and practical, along with the required electronic instructions. They indicated that the material was presented continuously. Meanwhile, 64% of students report that the microcontroller teaching laboratory kit and hands-on instructions motivate, contribute to comprehension, provide convenience, and increase student attention in electronics laboratory activities.

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Surface bio-engineering of melt electrowritten tubular scaffolds via plasma immersion ion implantation (PIII)

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Materials Today Bio 2025年 33卷

作者： Anyu Zhang Anne Metje van Genderen Bingyan Liu Junyi Qian Jirawat Iamsamang Ziyu Wang Miguel Castilho Behnam Akhavan School of Biomedical Engineering University of Sydney Sydney Australia School of Physics University of Sydney Sydney Australia Sydney Nano Institute University of Sydney 2006 NSW Australia School of Engineering University of Newcastle Callaghan 2308 NSW Australia Div. Pharmacology Utrecht Institute for Pharmaceutical Sciences Utrecht University the Netherlands Department of Biomedical Engineering Eindhoven University of Technology Eindhoven the Netherlands Charles Perkins Centre University of Sydney 2006 NSW Australia School of Life and Environmental Sciences University of Sydney 2006 NSW Australia Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven the Netherlands Hunter Medical Research Institute (HMRI) Precision Medicine Program New Lambton Heights 2305 NSW Australia

Melt electrowriting (MEW) enables the fabrication of highly controlled, open-pore tubular constructs for replicating the complex architectures of vascular, renal, and other tissues. However, a key challenge is to functionalize their surfaces so that they not only support but also instruct key biological interactions, particularly in promoting vascularization. Here, we propose plasma immersion ion implantation (PIII) as a biofunctionalization strategy for open-pore tubular constructs fabricated by MEW. Surface chemistry analysis confirmed homogeneous treatment across PIII-treated MEW 3D structures, while uniaxial tensile tests demonstrated no significant changes in mechanical properties following the treatment. Electron paramagnetic resonance (EPR) data provided evidence of the formation of a stable, radical-rich surface, which was further validated by fluorescence imaging with a model molecule, confirming the radicals’ role in enabling uniform covalent biomolecule attachment. The PIII-treated MEW constructs were covalently functionalized with vascular endothelial growth factor (VEGF), thereby modulating the behavior of seeded cells. Endothelialization studies using conditionally immortalized glomerular endothelial cells (ciGEnC) demonstrated that VEGF-immobilized MEW tubes effectively support monolayer formation, achieving outcomes comparable to those observed with VEGF supplementation in culture media. Remarkably, the immobilized VEGF sustained endothelialization with a similar effectiveness to traditional VEGF suspension methods over prolonged culture conditions (21 days), but without the need for continuous VEGF supplementation. These findings establish a novel biofunctionalization strategy for vascularized tissue engineering scaffolds and pave the way for plasma-modified MEW tubes as platforms for preclinical models and regenerative medicine applications.

关键词： Vascular tube Biofunctionalization Plasma surface engineering Covalent immobilization

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基于大语言模型的糖尿病管理:潜力与展望

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Science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Wireless, self‑powered phototrophic algaerobot‑infused nanotherapeutic hydrogels for enhanced diabetic wound healing

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Chemical engineering Journal 2025年 515卷

作者： Huang, Wei-Yung Kang, Jiunn-Horng Chen, Yan-Ting Liang, Jia-Wei Hoai Hoang, Lan Pham Nguyen, Hieu Trung Chuang, Andrew E.-Y. Graduate Institute of Biomedical Materials and Tissue Engineering International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University New Taipei City23561 Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University No.250 Wuxing Street Xinyi District Taipei110 Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei110 Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan Department of Orthopedics and Trauma Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City700000 Viet Nam Cell Physiology and Molecular Image Research Center Taipei Medical University-Wan Fang Hospital Taipei11696 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan

Diabetic wounds present a significant clinical challenge due to impaired immunity, hypoxia, poor neovascularization, and chronic inflammation, often resulting in severe complications such as amputation. Conventional therapies, including hyperbaric oxygen, antioxidants, and electrical stimulation, face limitations in delivering sustained and safe oxygen, hydrogen, and electrical stimulation to the wound site. This study introduces a groundbreaking approach by integrating phototrophic algae robots (AR) into a methylcellulose (MC) hydrogel, combined with platelet-derived extracellular vesicles (pEVs) and conductive glycol chitosan-polypyrrole nanoparticles (GCS-PPy NPs), forming a multifunctional composite system (MC-pEV-AR-GCS-PPy NPs). This advanced composite addresses the limitations of traditional treatments by integrating continuous, self-powered hydrogen and oxygen generation, electrical stimulation, and hyperthermia, all triggered by light-activated, electromagnetically driven mechanisms. Photosynthetic algae robots offer a sustainable solution for the generation of hydrogen and oxygen, effectively targeting the prevalent issues of hypoxia and the deficit frequently encountered in diabetic wounds. This innovative approach improves therapeutic outcomes through the integration of electrochemical processes and nanophototherapeutics, creating a multimodal platform. This platform not only promotes tissue regeneration and angiogenesis but also alleviates hypoxic conditions, modulates immune responses, and addresses key challenges associated with chronic wound healing. Incorporating pEVs amplifies the regenerative potential of the system, accelerating wound healing and promoting tissue regeneration. This self-powered platform not only reduces inflammation and suppresses M1 macrophage bioactivity but also regulates immune responses, creating an optimized healing environment. The MC-pEV-AR-GCS-PPy NPs composite represents a promising translational approach to diabetic wo

关键词： Hyperthermia therapy

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Novel design of NiS 2 and V 3 Ni(SO 4 ) 4 · 24H 2 O composites with controllable synthetic temperature and duration as an efficient active material for battery supercapacitor hybrids

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Journal of Energy Storage 2025年 130卷

作者： Tsung-Rong Kuo Wei-Lun Huang Chutima Kongvarhodom Muhammad Saukani Sibidou Yougbaré Hung-Ming Chen Lu-Yin Lin Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei 11031 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok 10140 Thailand Department of Mechanical Engineering Faculty of Engineering Universitas Islam Kalimantan MAB Jl. Adhyaksa No. 2 Banjarmasin 70124 Indonesia Institut de Recherche en Sciences de la Santé (IRSS-DRCO)/Nanoro 03 B.P 7192 Ouagadougou 03 Burkina Faso Gingen technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei 11054 Taiwan

Nickel–vanadium (Ni V) composites are considered as promising active materials for battery supercapacitor hybrids (BSHs), owing to multiple oxidation states, high theoretical capacitance, and tunable redox activity. Structural optimization and sulfur incorporation are effective strategies to improve conductivity and structural stability. Hydrothermal parameters such as temperature and duration significantly influence the material and electrochemical properties. In this study, novel Ni V composites are synthesized via a hydrothermal process at different temperatures and durations as the active material of BSHs. The Ni V composites primarily consist of NiS 2 and V 3 Ni(SO 4 ) 4 · 24H 2 O. The Ni V composites synthesized using 150 °C and 10 h (Ni-V150@10) shows the largest specific capacitance (C F ) of 1381.3 F/g (621.6 C/g) at 20 mV/s, owing to its favorable twisted layered structure and abundant electroactive NiS 2 . A BSH constructed with Ni-V150@10 and reduced graphene oxide electrodes shows a maximum energy density of 9.6 Wh/kg at the power density of 857 W/kg. The capacitance retention of 96.6% and a Coulombic efficiency of 96.9% are also attained after 10,000 charge/discharge cycles. This study provides a clear approach for designing high-performance active materials with controllable hydrothermal temperature and duration, which can be extended to improve the electrochemical performance of other bimetallic composites in the future.

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Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score

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