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Comparisons of efficacy and safety of immunotherapies for Alzheimer's disease treatment: a network meta-analysis of randomized controlled trials

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Clinical medicine (London, England) 2025年 100336页

作者： Ching-Hui Su Ying-Tzu Chang Huan-Shu Tseng Chan-Yen Kuo Jin-Hua Chen Po- Yu Chien Yao-Jen Chang Chin-Chuan Hung Department of Pharmacy College of Pharmacy China Medical University Taichung 406040 Taiwan. Institute of Oral Medicine and Materials College of Medicine Tzu Chi University Hualien 97004 Taiwan. Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan Health Data Analytics and Statistics Centre Office of Data Science Taipei Medical University Taipei Taiwan. Department of Pharmacy College of Pharmacy China Medical University Taichung 406040 Taiwan Department of Pharmacy China Medical University Hospital Taichung 404327 Taiwan. Department of Surgery Taipei Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation New Taipei City 23142 Taiwan Department of Surgery School of Medicine Buddhist Tzu Chi University Hualien 97004 Taiwan. Department of Pharmacy China Medical University Hospital Taichung 404327 Taiwan Department of Healthcare Administration Asia University Taichung 41354 Taiwan. Electronic address: cchung@mail.cmu.edu.tw.

BACKGROUND:Alzheimer's disease (AD) remains a major challenge due to limited effective therapies. Moreover, direct comparisons between newly developed and symptomatic drugs are lacking. This network meta-analysis aimed to compare the efficacy and safety of immunotherapies for AD. METHODS:A systematic search of PubMed, Embase, the Cochrane Library, and *** was conducted for randomised controlled trials (RCTs) up to 29 June 2024. Eligible studies included adults with AD receiving immunotherapy versus placebo or symptomatic treatment. RESULTS:Fifty-nine RCTs were included. Donanemab and lecanemab ranked among the most effective treatments for improving cognitive function (Clinical Dementia Rating Scale-Sum of Boxes P-scores: 0.88 and 0.77) and daily activities (Alzheimer's Disease Cooperative Study-Activities of Daily Living P-scores: 0.85 and 0.90), based on network meta-analysis findings. CONCLUSIONS:Anti-Aβ mAbs, particularly donanemab and lecanemab, demonstrated superior efficacy over other immunotherapies in slowing cognitive deterioration, supporting their role in AD management. PROSPERO REGISTRATION NUMBER:CRD42023461680.

关键词： Alzheimer’s disease Anti-amyloid beta Anti-tau proteins Immunotherapy Network meta-analysis

On the Jacobian of hyperelliptic curves y2 = x5 + m2

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arXiv 2021年

作者： Jeong, Keunyoung Park, Junyeong Yhee, Donggeon Department of Mathematical Sciences Ulsan National Institute of Science and Technology UNIST-gil 50 Ulsan44919 Korea Republic of San 31 Hyoja-Dong Nam-Gu Gyeongsangbuk-do Pohang-si790-784 Korea Republic of Industrial and Mathematical Data Analytics Research Center Seoul National University Gwanak-Ro 1 Gwanak-Gu Seoul Korea Republic of

MSC Codes 11G30, 11G10, *** this paper, we study the algebraic rank and the analytic rank of the Jacobian of hyperelliptic curves y2 = x5 + m2 for integers m. Namely, we first provide a condition on m that gives a bound of the size of Selmer group and then we provide a condition on m that makes L-functions non-vanishing. As a consequence, we construct a Jacobian that satisfies the rank part of the Birch–Swinnerton-Dyer conjecture. © 2021, CC BY-NC-ND.

关键词： Geometry

Determinant regional tax revenue in fear of COVID-19 in East Java: Spatial Durbin Model (SDM) spillover approach

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AIP Conference Proceedings 2022年第1期2641卷

作者： N. Atikah B. Widodo S. Rahardjo Mardlijah N. Kholifia D. L. Afifah 1Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Surabaya 60111 Indonesia 2Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Negeri Malang (UM) Malang 65145 Indonesia

The COVID-19 pandemic has caused economic, social and political crises in the infected countries. These countries implemented a lockdown policy to anticipate the spread, resulting in decreased financial revenues, including tax revenues. It is not easy to achieve tax revenue during a pandemic because almost all sectors have declined. Spatial interactions influence tax revenue, so a spatial model must be applied in its analysis. One of the spatial models is Spatial Durbin Model (SDM) is the development of the spatial autoregressive model (SAR). In the SDM model, the dependent variable and the independent variable both contain spatial effects. Spillover is the tendency of a changes in one area are following changes in another areas. This study's main objective is to estimate spillover's effect on districts/cities tax revenue and its spread in East Java. Researchers found a positive spatial spillover of GRDP, inflation, number of industries, and total population on tax revenue in East Java.

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SSMD: Semi-supervised medical image detection with adaptive consistency and heterogeneous perturbation

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arXiv 2021年

作者： Zhou, Hong-Yu Wang, Chengdi Lic, Haofeng Wang, Gang Zhang, Shu Lia, Weimin Yu, Yizhou Department of Respiratory and Critical Care Medicine National Clinical Research Center for Geriatrics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu610041 China Department of Computer Science University of Hong Kong Pokfulam Hong Kong Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518000 Hong Kong AI Lab Deepwise Healthcare Beijing100080 China

Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies. © 2021, CC BY.

关键词： Supervised learning

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

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arXiv 2022年

作者： Gu, Ran Wang, Guotai Lu, Jiangshan Zhang, Jingyang Lei, Wenhui Chen, Yinan Liao, Wenjun Zhang, Shichuan Li, Kang Metaxas, Dimitris N. Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering ShanghaiTech University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Sensetime Research Shanghai China Department of Radiation Oncology Sichuan Cancer Hospital and Institute University of Electronic Science and Technology of China Chengdu China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University Chengdu China Department of Computer Science Rutgers University PiscatawayNJ08854 United States Shanghai AI Lab Shanghai China

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement to get high generalizability. To deal with this problem, we propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Thirdly, to further improve generalizability, we propose a style augmentation method based on the disentanglement representation to synthesize images in various unseen styles with shared anatomical structures. Our method was validated on a public multi-site fundus image dataset for optic cup and disc segmentation and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset for nasopharynx Gross Tumor Volume (GTVnx) segmentation. Experimental results showed that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in domain-generalizable segmentation. Code is available at https://***/HiLab-git/DAG4MIA. © 2022, CC BY.

关键词： Magnetic resonance imaging

Meta-analysis of computational methods for breast cancer classification

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International Journal of Intelligent Information and database Systems 2020年第1期13卷 89-111页

作者： Pham, Tri-Cong Luong, Chi-Mai Doucet, Antoine Hoang, Van-Dung Tran, Diem-Phuc Le, Duc-Hau School of Computer Science and Engineering Thu-yloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Informatics and Communication Technology University of Science and Technology of Hanoi Hanoi Viet Nam Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam Laboratory L3i University of La Rochelle France QuangBinh University Dong Hoi Quang Binh Viet Nam DuyTan University Da Nang Viet Nam Department of Computational Biomedicine Vingroup Big Data Institute No 7 Bang Lang 1 Street Viet Hung Ward Hanoi Viet Nam

Millions of women are suffering from breast cancer pressing burden on their shoulders and the global economy. Meanwhile, general treatment methods are applied without considering personalised health and genetic features. Artificial intelligence appears to be a robust method for breast cancer sub-typing. Most of researches have been implemented on binary classification with limited number of data samples. Multi-classification is much more difficult especially on large number of samples. The study aims to use machine learning to find better ways to subtype breast cancer as well as find new disease causative genes which help facilitate more personalised treatment with limited side effect in the future. This study compares the accuracy of three classification methods in combination with eight feature selection methods on a dataset of 2,682 samples. The study shows that the highest accuracy was 83.96% with the SVM-C005 classifier and percentile feature selection (800 genes). Additionally, our method can predict causative disease genes of breast cancer with four of them known to be associated with breast cancer and 29 promising ones with supporting evidence from the literature. This shows the effectiveness of our research. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Gene expression

Locate, Size and Count: Accurately resolving people in dense crowds via detection

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arXiv 2019年

作者： Sam, Deepak Babu Peri, Skand Vishwanath Mukuntha, N.S. Kamath, Amogh Babu, R. Venkatesh Video Analytics Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India

We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm. Typical counting models predict crowd density for an image as opposed to detecting every person. These regression methods, in general, fail to localize persons accurate enough for most applications other than counting. Hence, we adopt an architecture that locates every person in the crowd, sizes the spotted heads with bounding box and then counts them. Compared to normal object or face detectors, there exist certain unique challenges in designing such a detection system. Some of them are direct consequences of the huge diversity in dense crowds along with the need to predict boxes contiguously. We solve these issues and develop our LSC-CNN model, which can reliably detect heads of people across sparse to dense crowds. LSC-CNN employs a multi-column architecture with top-down feedback processing to better resolve persons and produce refined predictions at multiple resolutions. Interestingly, the proposed training regime requires only point head annotation, but can estimate approximate size information of heads. We show that LSC-CNN not only has superior localization than existing density regressors, but outperforms in counting as well. The code for our approach is available at http://***/val-iisc/lsc-cnn. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

wChain: A fast fault-tolerant blockchain protocol for multihop wireless networks

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arXiv 2021年

作者： Xu, Minghui Liu, Chunchi Zou, Yifei Zhao, Feng Yu, Jiguo Cheng, Xiuzhen Department of Computer Science George Washington University WashingtonDC20052 United States School of Computer Science and Technology Shandong University Qingdao266510 China Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China Jinan Shandong250353 China Jinan Shandong250014 China Shandong Laboratory of Computer Networks Jinan250014 China

This paper presents wChain, a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within O(log N log Γ) slots where N is the network size and Γ refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides, wChain employs data aggregation and reaggregation, and node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of wChain is upper bounded by O(f log N log Γ), where f = bN2 c is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies, and the results only demonstrate the nice properties of wChain, but also point to a vast new space for the exploration of blockchain protocols in wireless networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal interference

MACHINE LEARNING-ASSISTED RECURRENCE PREDICTION FOR EARLY-STAGE NON-SMALL-CELL LUNG CANCER PATIENTS

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arXiv 2022年

作者： Janik, Adrianna Torrente, Maria Costabello, Luca Calvo, Virginia Walsh, Brian Camps, Carlos Mohamed, Sameh K. Ortega, Ana L. Nováček, Vít Massutí, Bartomeu Minervini, Pasquale Campelo, M. Rosario Garcia del Barco, Edel Bosch-Barrera, Joaquim Menasalvas, Ernestina Timilsina, Mohan Provencio, Mariano Accenture Labs Dublin Ireland Medical Oncology Department Hospital Universitario Puerta de Hierro Majadahonda Madrid Spain Data Science Institute University of Galway Galway Ireland Insight Centre for Data Analytics University of Galway Galway Ireland Hospital General de Valencia Valencia Spain Hospital Universitario de Jaén Jaen Spain Hospital General Universitario de Alicante Alicante Spain University College London London United Kingdom Faculty of Informatics Masaryk University Brno Czech Republic Masaryk Memorial Cancer Institute Brno Czech Republic Complejo Hospitalario Universitario A Coruña A Coruna Spain Hospital Universitario de Salamanca Salamanca Spain Institut Català d’Oncologia Hospital Universitari Dr. Josep Trueta Girona Spain Polytechnic University of Madrid Madrid Spain

Background: Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to utilize machine learning to estimate probability of relapse in early-stage non-small-cell lung cancer patients? Methods: For predicting relapse in 1,387 early-stage (I-II), non-small-cell lung cancer (NSCLC) patients from the Spanish Lung Cancer Group data (65.7 average age, 24.8% females, 75.2% males) we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHAP local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. Results: Machine learning models trained on tabular data exhibit a 76% accuracy for the Random Forest model at predicting relapse evaluated with a 10-fold cross-validation (model was trained 10 times with different independent sets of patients in test, train and validation sets, the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a 200-patient, held-out test set, calibrated on a held-out set of 100 patients. Conclusions: Our results show that machine learning models trained on tabular and graph data can enable objective, personalised and reproducible prediction of relapse and therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer. © 2022, CC BY-NC-SA.

关键词： Forecasting