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Fusing Artificial Intelligence with Flexible Sensing to Forge Digital Health Innovations 将人工智能与灵活传感技术相结合,打造数字健康创新技术
IF 5 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-08-08 DOI: 10.34133/bmef.0062
Lingting Huang, Zhen Yang
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引用次数: 0
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling. 利用深度学习和金字塔采样在乳腺癌图像中自动进行 HER2 评分
IF 5 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-23 eCollection Date: 2024-01-01 DOI: 10.34133/bmef.0048
Sahan Yoruc Selcuk, Xilin Yang, Bijie Bai, Yijie Zhang, Yuzhu Li, Musa Aydin, Aras Firat Unal, Aditya Gomatam, Zhen Guo, Darrow Morgan Angus, Goren Kolodney, Karine Atlan, Tal Keidar Haran, Nir Pillar, Aydogan Ozcan

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

目标和影响声明:人表皮生长因子受体 2(HER2)是癌细胞生长过程中的一种关键蛋白,它标志着乳腺癌(BC)的侵袭性,有助于预测其预后。在此,我们介绍一种基于深度学习的方法,该方法利用金字塔采样对免疫组化(IHC)染色的乳腺癌组织图像中的 HER2 状态进行自动分类。简介准确评估 IHC 染色组织切片的 HER2 表达水平对于指导治疗和了解癌症机制至关重要。然而,由获得认证的病理学家进行人工检查的传统工作流程面临着挑战,包括观察者之间和观察者内部的不一致性以及周转时间延长。方法:我们基于深度学习的方法可分析各种空间尺度的形态特征,有效管理计算负荷,促进对细胞和更大规模组织层面细节的详细检查。结果这种方法通过提供一个全面的视角来解决 HER2 表达的组织异质性问题,在一个由 523 张组织芯片核心图像组成的数据集上,盲测分类准确率达到 84.70%。结论该自动化系统作为病理学辅助工具证明是可靠的,有可能提高诊断精确度和评估速度,并对癌症治疗规划产生重大影响。
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引用次数: 0
Brain-Targeted Drug Delivery Platforms for Ischemic Stroke Therapy 用于缺血性中风治疗的脑靶向给药平台
IF 5 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-09 DOI: 10.34133/bmef.0055
Jianxun Ding
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引用次数: 0
Multi-quantifying maxillofacial traits via a demographic parity-based AI model 通过基于人口奇偶性的人工智能模型多量化颌面特征
IF 5 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-09 DOI: 10.34133/bmef.0054
Mengru Shi, Zhuohong Gong, Peisheng Zeng, Dawei Xiang, Gengbin Cai, Hengyi Liu, Shijie Chen, Runheng Liu, Zhuofan Chen, Xinchun Zhang, Zetao Chen
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引用次数: 0
NucleoCraft: The Art of Stimuli-Responsive Precision in DNA and RNA Bioengineering 核工艺:DNA 和 RNA 生物工程中的刺激响应精准艺术
IF 5 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-02 DOI: 10.34133/bmef.0050
Hao Yan, Lu Yu, Liangxiao Chen, Deeksha Satyabola, Abhay Prasad
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引用次数: 0
Engineering Bacteria and Their Derivatives for Cancer Immunotherapy 工程细菌及其衍生物用于癌症免疫疗法
Pub Date : 2024-06-06 DOI: 10.34133/bmef.0047
Yuji Tang, Lang Rao
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引用次数: 0
Baroreceptor-inspired microneedle skin patch for pressure-controlled drug release 用于压力控制药物释放的气压感受器微针皮肤贴片
Pub Date : 2024-04-19 DOI: 10.34133/bmef.0044
Jiahui He, M. Zheng, Tianli Hu, Ya Huang, Jingyou Su, Chunyi Zhi, Xinge Yu, Chenjie Xu
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引用次数: 0
Application of Nanomaterials in Endodontics 纳米材料在牙髓病学中的应用
Pub Date : 2024-03-27 DOI: 10.34133/bmef.0043
Farzaneh Afkhami, Yuan Chen, Laurence J. Walsh, O. Peters, Chun Xu
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引用次数: 0
Controllable Thrombolysis Using a Nanobubble-Imaging-Guided rtPA Targeted Delivery Strategy. 利用纳米气泡成像引导的 rtPA 靶向输送策略实现可控溶栓。
Pub Date : 2024-03-26 eCollection Date: 2024-01-01 DOI: 10.34133/bmef.0040
Jian Tang, Huiting Xu, Mingxi Li, Yang Liu, Fang Yang

Objective: The objective of this work is to design and fabricate a novel multifunctional nanocarrier combining thrombus-targeted imaging and ultrasound-mediated drug delivery for the theranostics of thrombotic diseases. Impact Statement: This study develops a new technology that can accurately visualize the thrombus and deliver drugs with controllable properties to diagnose and treat thrombotic diseases. Introduction: Thrombotic diseases are a serious threat to human life and health. The diagnosis and treatment of thrombotic diseases have always been a challenge. In recent years, nanomedicine has brought new ideas and new methods for the theranostics of thrombotic diseases. However, there are also many problems need to be solved, such as biosafety and stability of nanocarriers, early diagnosis, and timely treatment of thrombotic diseases, difficulty in clinical translation. Methods: The S1P@CD-PLGA-rtPA nanobubbles (NBs) were prepared by integrating sulfur hexafluoride (SF6)-loaded poly (D, L-lactide-co-glycolide) (PLGA) NBs, cyclodextrin (CD), sphingosine-1-phosphate (S1P), and recombinant tissue plasminogen activator (rtPA). Results: S1P@CD-PLGA-rtPA NBs had rapid and excellent thrombosis targeting imaging performance based on the specific interaction of S1P-S1PR1 (sphingosine-1-phosphate receptor 1). Furthermore, S1P@CD-PLGA-rtPA NBs that specifically targeting to the thrombosis regions could also respond to external ultrasound to achieve accurate and efficient delivery of rtPA to enhance the thrombolysis effectiveness and efficiency. Conclusion: This study proposes a new idea and strategy of targeting thrombus in rats via the specific interaction of S1P-S1PR1. On this basis, the acoustic response properties of bubble carriers could be fully utilized by combining thrombus-specific targeted imaging and ultrasound-mediated drug delivery for effective thrombolysis, which is expected to be applied in targeted diagnosis and treatment of thrombotic diseases in the future.

目的:这项工作的目的是设计和制造一种新型多功能纳米载体,将血栓靶向成像和超声介导给药结合起来,用于血栓性疾病的治疗。影响说明:这项研究开发了一种新技术,能够准确地观察血栓,并以可控的特性递送药物,用于诊断和治疗血栓性疾病。引言:血栓性疾病严重威胁人类的生命和健康。血栓性疾病的诊断和治疗一直是一个难题。近年来,纳米医学为血栓性疾病的治疗带来了新思路和新方法。然而,也有许多问题亟待解决,如纳米载体的生物安全性和稳定性、血栓性疾病的早期诊断和及时治疗、临床转化困难等。研究方法将六氟化硫(SF6)负载的聚(D,L-乳酸-共聚乙二醇)(PLGA)纳米气泡、环糊精(CD)、1-磷酸鞘磷脂(S1P)和重组组织纤溶酶原激活剂(rtPA)整合在一起制备了S1P@CD-PLGA-rtPA纳米气泡(NBs)。结果基于S1P-S1PR1(鞘氨醇-1-磷酸受体1)的特异性相互作用,S1P@CD-PLGA-rtPA NBs具有快速、卓越的血栓靶向成像性能。此外,特异性靶向血栓形成区域的 S1P@CD-PLGA-rtPA NBs 还能响应外部超声,实现 rtPA 的准确高效输送,从而提高溶栓效果和效率。结论本研究提出了通过 S1P-S1PR1 的特异性相互作用靶向大鼠血栓的新思路和新策略。在此基础上,可充分利用气泡载体的声学响应特性,将血栓特异性靶向成像和超声介导给药相结合,实现有效溶栓,有望在未来应用于血栓性疾病的靶向诊断和治疗。
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引用次数: 0
Design and Evaluation of Synthetic Delivery Formulations for Peptide-Based Cancer Vaccines. 设计和评估基于肽的癌症疫苗的合成给药配方。
Pub Date : 2024-03-21 eCollection Date: 2024-01-01 DOI: 10.34133/bmef.0038
Kefan Song, Suzie H Pun

With the recent advances in neoantigen identification, peptide-based cancer vaccines offer substantial potential in the field of immunotherapy. However, rapid clearance, low immunogenicity, and insufficient antigen-presenting cell (APC) uptake limit the efficacy of peptide-based cancer vaccines. This review explores the barriers hindering vaccine efficiency, highlights recent advancements in synthetic delivery systems, and features strategies for the key delivery steps of lymph node (LN) drainage, APC delivery, cross-presentation strategies, and adjuvant incorporation. This paper also discusses the design of preclinical studies evaluating vaccine efficiency, including vaccine administration routes and murine tumor models.

随着新抗原鉴定的最新进展,基于多肽的癌症疫苗在免疫疗法领域具有巨大的潜力。然而,快速清除、低免疫原性和抗原呈递细胞(APC)摄取不足限制了多肽类癌症疫苗的疗效。本综述探讨了阻碍疫苗效率的障碍,重点介绍了合成递送系统的最新进展,并介绍了淋巴结引流、抗原呈递细胞递送、交叉呈递策略和佐剂结合等关键递送步骤的策略。本文还讨论了评估疫苗效率的临床前研究设计,包括疫苗给药途径和小鼠肿瘤模型。
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引用次数: 0
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