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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Jinjing Han Tawen Ho Justin M. Stine Michael A. Straker Jens Herberholz Reza Ghodssi Department of Electrical and Computer Engineering University of Maryland College Park MD USA Institute for System Research University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA Department of Psychology University of Maryland College Park MD USA Program in Neuroscience and Cognitive Science University of Maryland College Park MD USA Fischell Department of Bioengineering University of Maryland College Park MD USA

This work presents, for the first time, an implantable biosensor for continuous in vivo serotonin (5-HT) monitoring in freely moving crayfish. 5-HT is an important neurotransmitter that regulates animal behaviors. Conventional methods for detecting 5-HT do not provide real-time information. Our approach facilitates continuous electrochemical monitoring of 5-HT levels without impeding animal natural behavior. The sensing system shows a linear range of 0.4 - 1.2 μΜ with a sensitivity of 0.07 μΑ/μΜ for 5-HT detection. With a 3D-printed package, the sensor can be easily attached to crayfish dorsal carapace allowing electrodes implantation into crayfish heart to monitor neurohormonal dynamics of 5-HT. The implantable biosensor system displayed a significant increase in oxidation currents after 5-HT injections. The device successfully demonstrates the application for in vivo detection of 5-HT in the hemolymph (i.e., blood) of freely behaving crayfish underwater.

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Boron carbide decorated tungsten disulfide nanosheet composite for determination of chloramphenicol in food and pharmaceutical samples

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Microchemical Journal 2024年 203卷

作者： Senthilkumar, Dhayanithi Kuo, Chih-Yu Aldossari, Samar A. Govindasamy, Mani Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 10608 Taiwan Department of Chemistry P. O. Box 2455 College of Science King Saud University Riyadh 11451 Saudi Arabia Centre for Applied Research Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamil Nadu Chennai 602105 India International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan Research Center for Intelligence Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan

Making the nanocomposites of metal oxides or metal sulfides with carbon source materials has a huge impact on the electrochemical determination of toxic chemicals. To determine the adverse side effects of the antibiotics, electrochemical sensors have been used as they can peek into the sensitivity of the drug sample. This work has done the sensitive amperometric evaluation of a chloramphenicol antibiotic by modifying the electrode of tungsten disulfide decorated boron carbide. Here, tungsten disulfide (WS₂) nanosheets and boron carbide (B₄C) materials were synthesized by conventional methods which give a good amount of yield. The composite was prepared by the green and cost-effective sonochemical approach with good morphology. The conductivity of the nanocomposite and the response to chloramphenicol were determined by cyclic voltammetry. The sensitivity, limit of detection (LOD), and the linear range were observed by the highly sensitive amperometric technique. From the amperometric analysis, the prepared nanocomposite shows nominal LOD as a 17.4 nM, a broad linear range (0.025–1134.6 µM), and good sensitivity of 1.52 µA µM⁻¹ cm⁻². Practicability of the modified sensor was studied in food and pharmaceutical samples with adequate recovery performances. Moreover, the modified electrode shows good stability, repeatability, reproducibility, and anti-interfering properties against the array of food and biological interferences. © 2024 Elsevier B.V.

关键词： Amperometric technique Antibiotic detection Boron carbide Food quality test Health analysis Tungsten disulfide

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

arXiv

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arXiv 2023年

作者： Song, Andrew H. Williams, Mane Williamson, Drew F.K. Jaume, Guillaume Zhang, Andrew Chen, Bowen Serafin, Robert Liu, Jonathan T.C. Baras, Alex Parwani, Anil V. 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 Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Department of Pathology The Ohio State University ColumbusOH United States

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3–6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7–18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12–14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, M

关键词： Pathology

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Effect of alginate and chitosan addition in the fabrication of polyvinyl alcohol-based matrix for tissue engineering applications

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AIP Conference Proceedings 2024年第1期3215卷

作者： Retno Wahyu Nurhayati Fatimah Azzahra Nuzli Fahdia Mazfufah Siti Fauziyah Rahman Wildan Mubarok Department of Chemical Engineering Faculty of Engineering Universitas Indonesia 16464 Depok Indonesia Research Center for Biomedical Engineering Faculty of Engineering Universitas Indonesia 16464 Depok Indonesia Stem Cells & Tissue Engineering Cluster Indonesian Medical Education and Research Institute Faculty of Medicine Universitas Indonesia Jakarta 10430 Indonesia Doctoral Program in Biomedical Sciences Faculty of Medicine Universitas Indonesia Jakarta 10430 Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Indonesia 16464 Depok Indonesia Division of Chemical Engineering Department of Materials Engineering Science Graduate School of Engineering Science Osaka University 560-0031 Toyonaka Japan

Recently, there has been a growing interest in artificial skin on hydrogels as a potential alternative for animal testing to evaluate the efficacy and safety of new drugs or cosmetics. Synthetic polymers have been widely used due to their superior physicochemical properties. However, synthetic polymers such as polyvinyl alcohol (PVA) are limited by their poor biocompatibility and cell adhesion. Combining synthetic and natural polymers can improve these properties. Therefore, in this study, we aimed to investigate the effect of alginate and chitosan addition to PVA-based hydrogels on protein adsorption, cell viability and cell attachment. The addition of chitosan decreased protein adsorption, which was caused by the changes in electrostatic charge by PVA-chitosan interaction and changes in surface roughness. Primary human fibroblast cells cultured on PVA, PVA-alginate and PVA-chitosan had >80% viability which indicates the cytocompatibility of the hydrogels. More importantly, alginate and chitosan also improved the cell attachment on the PVA-based hydrogels. Taken together, the addition of both alginate and chitosan has the potential to improve PVA-based hydrogels for artificial skin application.

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Tumor microenvironment responsive nano-PROTAC for BRD4 degradation enhanced cancer photo-immunotherapy

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Biomaterials 2025年 322卷 123387-123387页

作者： Li, Zheng Ren, Guodong Wang, Xuewei Li, Xiaowan Ding, Lingwen Zhu, Jianwei Zhang, Yajie Zhang, Chengwu Zou, Jianhua Chen, Xiaoyuan Departments of Diagnostic Radiology Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine and College of Design and Engineering National University of Singapore Singapore 119074 Singapore School of Basic Medical Sciences Shanxi Medical University Taiyuan030001 China Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore Nanomedicine Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Yong Loo Lin School of Medicine National University of Singapore 11 Biopolis Way Helios Singapore 138667 Singapore 61 Biopolis Drive Proteos Singapore 138673 Singapore Department of Pharmacy and Pharmaceutical Sciences National University of Singapore Lower Kent Ridge Road 4 Science Drive 2 117544 Singapore

Proteolysis Targeting Chimeras (PROTAC) technology has garnered great attention due to its advantages in targeted protein degradation, promising its potential for treating malignant cancer. Nevertheless, the inherent drawbacks of PROTAC technology hinder its clinical translation. The integration of nanotechnology with PROTAC molecules to create nano-PROTACs for combined therapy offers a promising solution. Among the various cancer treatment methods, phototherapy is considered the optimal choice to integrate with specific PROTACs due to its proven effectiveness and non-invasive nature. Herein, a nano-PROTAC formulation (ARV@PEG-ICG) consisting of a phototherapeutic agent named indocyanine green functionalized polyethylene glycol (PEG-ICG) and a BRD4 degrader (ARV-825) was fabricated for cancer photo-immunotherapy. Activated by acidic tumor microenvironment (TME), ARV@PEG-ICG nanoparticles (NPs) will decompose rapidly for ARV delivery. PEG-ICG generated abundant ROS with laser irradiation, downregulating the expression of Bcl-xL and inducing the cleavage of PARP to stimulate cell apoptosis. Furthermore, the degradation of BRD4, a transcriptional cofactor, inhibited nitric oxide synthase (iNOS) generation to improve phototherapeutic efficacy. In a 4T1 breast tumor model, dying 4T1 cells released tumor associated antigens (TAAs) to serve as the immunogenic cell death (ICD) inducer, facilitating DC maturation and T cell activation and amplifying systemic immune response. The distant tumor growth can also be inhibited due to the activation of long-term immune response. Overall, the current study aims to combine typical PROTAC with functional nanomaterials to form nano-PROTAC with high performance for PROTAC delivery mediated cancer treatment. © 2025 Elsevier Ltd

关键词： Nanocomposites

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Tribological properties of hydroxyapatite-coated nanorods on Ti-6Al-4V surfaces

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Scientific Reports 2025年第1期15卷 1-24页

作者： Osafo, Sarah Akua Asumadu, Tabiri Klenam, Desmond Etinosa, Precious Obayemi, John David Agyei-Tuffour, Benjamin Yaya, Abu Dodoo-Arhin, David Eluu, Stanley Chijioke Soboyejo, Wole Department of Materials Science and Engineering School of Engineering Sciences College of Basic and Applied Sciences University of Ghana Accra Legon Ghana Department of Biomaterial Sciences Dental School University of Ghana Korle Bu Campus Accra Ghana Department of Mechanical Engineering Program in Materials Science and Engineering Worcester Polytechnic Institute 100 Institute Road Worcester 01609 MA United States Department of Materials Engineering Sunyani Technical University Sunyani Ghana Global Center of Advanced Materials and Manufacturing Department of Mechanical Engineering College of Engineering State University of New York (SUNY) Polytechnic Institute 100 Seymour Street Utica 13502 NY United States Next Frontiers in Advanced Material Laboratory School of Chemical and Metallurgical Engineering University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg 2050 South Africa Department of Biomedical Engineering Worcester Polytechnic Institute Gateway Park Life Sciences and Bioengineering Centre 60 Prescott Street Worcester 01609 MA United States Department of Biotechnology Ebonyi State University Abakaliki Nigeria Department of Pharmaceutical Microbiology and Biotechnology Faculty of Pharmaceutical Sciences Nnamdi Azikiwe University Anambra State Awka Nigeria

This paper presents the tribological properties and bioactivity of nanostructured hydroxyapatite (HA) from biowaste sources and coated onto Ti-6Al-4V substrates using a novel pack cementation method. The process introduced HA pillars/nanorods on the surfaces of Ti-6Al-4V to enhance their osseointegration for dental implants. The mechanical and tribological properties were studied with nanoindentation, and pin-on-disk techniques, following the microstructural characterization of the coatings with atomic force microscopy, X-ray diffraction, Raman spectroscopy, scanning electron microscopy, and energy dispersive x-ray spectroscopy. The study also examines the surface bioactivity and elucidates the underlying friction and wear mechanisms of the HA-coated and annealed Ti-6Al-4V surface. The study results show a bone bonding capacity of the biowaste-derived HA-coated substrate with improved hardness and tribological properties. The implications of the study are discussed for the development of nano-structured HA-coated Ti-6Al-4V for dental implants with improved osseointegration for dental and biomedical applications. © The Author(s) 2025.

关键词： Bioactivity Dental implants Osseointegration Surface modification Titanium alloys Wear resistance

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

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Nanoscale 2021年第15期13卷 7285-7293页

作者： Casasanta, Michael A. Jonaid, G.M. Kaylor, Liam Luqiu, William Y. Solares, Maria J. Schroen, Mariah L. Dearnaley, William J. Wilson, Jarad Dukes, Madeline J. Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Materials Research Institute Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States RayBiotech Life Peachtree Corners 30092 GA United States Applications Science Protochips Inc Morrisville 27560 NC United States

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low-molecular weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal "top hat"motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks. This journal is © The Royal Society of Chemistry.

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Correction: H3.3-G34W in giant cell tumor of bone functionally aligns with the exon choice repressor hnRNPA1L2

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Cancer gene therapy 2024年第8期31卷 1281页

作者： Eunbi Lee Yoon Jung Park Anders M Lindroth Graduate School of Cancer Science and Policy Cancer Biomedical Science National Cancer Center Goyang-si Republic of Korea. Department of Nutritional Science and Food Management Ewha Womans University Seoul Republic of Korea. Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Republic of Korea. Graduate School of Cancer Science and Policy Cancer Biomedical Science National Cancer Center Goyang-si Republic of Korea. lindroth@ncc.re.kr.

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SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images

arXiv

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arXiv 2022年

作者： Wang, Yongwei Wang, Yuheng Lee, Tim K. Miao, Chunyan Wang, Z. Jane Joint NTU-WeBank Research Center on Fintech Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Dermatology and Skin Science University of British Columbia VancouverBC Canada Photomedicine Institute Vancouver Coast Health Research Institute VancouverBC Canada Cancer Control Research Program Bc Cancer VancouverBC Canada School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burdenworldwide. Over the last fewyears, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervisedly trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled lightweight model can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performances. To the best of our knowledge, this is the first deep knowledge distillation application for multi-diseases cl

关键词： Dermatology

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