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A digital microfluidic device integrated with electrochemical sensor and 3D matrix for detecting soluble PD-L1

Biosensors and Bioelectronics: X 2024年 19卷 100490页

作者： Zhang, Yuqian Liu, Jing Lo, Ting-Wen Kim, Yohan Lucien, Fabrice Dong, Haidong Liu, Yuguang Department of Physiology & Biomedical Engineering Mayo Clinic RochesterMN55905 United States Microbiomics Program Center for Individualized Medicine Mayo Clinic RochesterMN55905 United States Department of Urology Mayo Clinic College of Medicine and Science RochesterMN55905 United States Department of Immunology Mayo Clinic RochesterMN55905 United States Department of Surgery Mayo Clinic RochesterMN55905 United States

PD1/PD-L1 checkpoint inhibitors are at the forefront of cancer immunotherapies. However, the overall response rate remains only 10–30%. Even among initial responders, drug resistance often occurs, which can lead to prolonged use of a futile therapy in the race with the fatal disease. It would be ideal to closely monitor key indicators of patients’ immune responsiveness, such as circulating PD-L1 levels. Traditional PD-L1 detection methods, such as ELISA, are limited in sensitivity and rely on core lab facilities, preventing their use for the regular monitoring. Electrochemical sensors exist as an attractive candidate for point-of-care tool, yet, streamlining multiple processes in a single platform remains a challenge. To overcome this challenge, this work integrated electrochemical sensor arrays into a digital microfluidic device to combine their distinct merits, so that soluble PD-L1 (sPD-L1) molecules can be rapidly detected in a programmed and automated manner. This new platform featured microscale electrochemical sensor arrays modified with electrically conductive 3D matrix, and can detect as low as 1 pg/mL sPD-L1 with high specificity. The sensors also have desired repeatability and can obtain reproducible results on different days. To demonstrate the functionality of the device to process more complex biofluids, we used the device to detect sPD-L1 molecules secreted by human breast cancer cell line in culture media directly and observed 2X increase in signal compared with control experiment. This novel platform holds promise for the close monitoring of sPD-L1 level in human physiological fluids to evaluate the efficacy of PD-1/PD-L1 immunotherapy. © 2024 The Author(s)

关键词： Digital microfluidics

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Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches

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Computers in Biology and Medicine 2025年 190卷 110044-110044页

作者： Saif, Ahmed Islam, Md. Tarikul Raihan, Md. Obayed Yousefi, Niloofar Rahman, Md. Ajijur Faridi, Hafeez Hasan, Al Riyad Hossain, Mirza Mahfuj Saleem, Rasha Mohammed Albadrani, Ghadeer M. Al-Ghadi, Muath Q. Ahasan Setu, Md. Ali Kamel, Mohamed Abdel-Daim, Mohamed M. Aktaruzzaman, Md. Department of Pharmacy Faculty of Science University of Rajshahi Rajshahi6205 Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Jashore7408 Bangladesh Department of Pharmaceutical Sciences College of Health Sciences and Pharmacy Chicago State University ChicagoIL United States Department of Industrial Engineering and Management Systems University of Central Florida USA OrlandoFL United States Department of Pharmacy Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Laboratory Medicine Faculty of Applied Medical Sciences Al-Baha University Al-Baha65431 Saudi Arabia Department of Biology College of Science Princess Nourah bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Microbiology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Medicine and Infectious Diseases Faculty of Veterinary Medicine Cairo University Giza12211 Egypt Department of Pharmaceutical Sciences Pharmacy Program Batterjee Medical College P.O. Box 6231 Jeddah21442 Saudi Arabia Pharmacology Department Faculty of Veterinary Medicine Suez Canal University Ismailia41522 Egypt

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysis of CDC7 across multiple cancers is lacking, and existing therapeutic options have come with limited clinical success. The aim of this is to integrate a comprehensive pan-cancer analysis of CDC7 with the identification of novel marine-derived inhibitors, bridging the understanding of CDC7's role as a prognostic biomarker and therapeutic target across diverse cancer types. In this study, we conducted a pan-cancer analysis of CDC7 across 33 tumor types using publicly available datasets to evaluate its expression, genetic alterations, immune interactions, survival, and prognostic significance. Additionally, a marine-derived compound library of 31,492 molecules was screened to identify potential CDC7 inhibitors using chemoinformatics and machine learning. The top candidates underwent rigorous evaluations, including molecular docking, pharmacokinetics, toxicity, Density Functional Theory (DFT) calculations, and Molecular Dynamics (MD) simulations. The findings revealed that CDC7 is overexpressed in several cancers and is associated with poor survival outcomes and unfavorable prognosis. Enrichment analysis linked CDC7 to critical DNA replication pathways, while its role in modulating tumor-immune interactions highlighted its potential as a target for immunotherapy. Among all tested compounds, Tetrahydroaltersolanol D (CMNPD21999) exhibited the strongest binding affinity and stability, along with better drug-likeness and zero toxicity. These attributes highlight its potential as a promising drug candidate for CDC7 inhibition and future cancer treatment development. Furthermore, additional in vitro and in vivo studies are required to confirm the effectiveness of this drug candidate against the CDC7 protein. © 2025

关键词： Gene expression

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Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Computational and Systems Oncology

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Computational and Systems Oncology 2023年第3期3卷

作者： Ai, Xusheng Smith, Melissa C Feltus, Frank Alex Electrical and Computer Engineering Department Clemson University Clemson SC United States Department of Genetics and Biochemistry Biomedical Data Science & Informatics Program Clemson Center for Human Genetics Clemson University Clemson SC United States

The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA-seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA-seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio-informatics. Finally, we propose potential directions for future research. © 2023 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.

关键词： biomarkers data augmentation generative adversarial network RNA sequencing

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Quantification of transient evoked otoacoustic emissions by sample entropy to assess cochlear symptoms (Hearing and Tinnitus) in Meniere's disease

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Applied Acoustics 2023年第1期212卷

作者： Fang, Jui Liu, Yi-Wen Chen, Yi-Wen Shih, Tzu-Ching Yao, Chun-Hsu Tsai, Chon-Haw Tyler, Richard S. Hsu, Alan Wang, Tang-Chuan x-Dimension Center for Medical Research and Translation China Medical University Hospital Taichung City Taiwan Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung City Taiwan Department of Biomedical Imaging and Radiological Science China Medical University Taichung City Taiwan School of Medicine College of Medicine China Medical University Taichung City Taiwan Department of Otolaryngology University of Iowa Iowa CityIA United States Department of Otorhinolaryngology - Head and Neck Surgery China Medical University Hsinchu Hospital Zhubei City Taiwan Master Program for Biomedical Engineering College of Biomedical Engineering China Medical University Taichung City Taiwan

The deteriorations of Meniere's disease (MD) cochlear symptoms (i.e., hearing loss and tinnitus) are progressive. We proposed sample entropy (SampEn) of transiently evoked otoacoustic emission (TEOAE) for quantitative assessment of MD cochlear symptoms without subject-dependent variations and system-dependent differences to prevent impacts of MD cochlear symptoms on life qualities of patients. This cross-sectional study based in a single center investigated 1) the preferred parameters for estimation of TEOAE SampEn and 2) resulting performances in diagnosing of MD cochlear symptoms. Nineteen normal (38 ears) and 29 (29 ears) unilateral definitive MD were recruited between June 2021 and March 2022. Hearing loss and tinnitus of MD were measured by pure-tone audiometry and described by chief complaint, respectively. TEOAE SampEn were estimated using different parameters of data size N = 772, template length m from 1 to 9 and similarity tolerance r from 0.1 to 1. Areas under receiver operating characteristic curves (AUROCs) were used to evaluate the performances of TEOAE SampEn in diagnosing MD cochlear symptoms. In terms of result, TEOAE SampEn based on the parameters of (N = 772, m = 2, r = 0.2) resulted in the maximum AUROCs = 0.7732 and 0.8421 in diagnosing MD hearing loss and tinnitus, respectively. In conclusion, TEOAE SampEn provides good performances and demonstrates great potential of assessment for MD cochlear symptoms. In future, recruitment of more participants, especially MD with tinnitus only without hearing loss, and other OHC-related disease, is necessary to further support the findings of this study. © 2023 The Author(s)

关键词： Audition

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Preparation and Characterization of Nanopatterned Polycaprolactone/Cellulose Nanocrystal Composite Membranes for Cardiovascular Tissue engineering 15

Preparation and Characterization of Nanopatterned Polycaprol...

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15th IEEE International Conference on Nano/Molecular Medicine and engineering, NANOMED 2021

作者： Batta-Mpouma, Joseph Kandhola, Gurshagan Kuczwara, Patrick Chivers, Cody Kim, Woochan Jensen, Hanna Kim, Jangho Jensen, Morten Kim, Jin-Woo Materials Science & Engineering Program Institute For Nanoscience & Engineering University of Arkansas Department of Biological & Agricultural Engineering FayettevilleAR United States Engineering And Institute For Nanoscience & Engineering University of Arkansas Department of Biological & Agricultural FayettevilleAR United States Institute For Nanoscience & Engineering University of Arkansas FayettevilleAR United States Chonnam National University Department of Rural & Biosystems Engineering Gwangju Korea Republic of University of Arkansas Department of Biomedical Engineering FayettevilleAR United States

ISBN: (纸本)9781665458566

Cardiac tissue exhibits very low regenerative capacity. Polymeric tissue scaffolds designed for repair of damaged cardiac tissue need to have the right balance of mechanical and biological characteristics that are necessary to withstand inherent stresses generated by cyclic contraction of cardiomyocytes and to promote cell attachment and growth. Polycaprolactone (PCL), a biocompatible polymer with low biodegradability, is a promising material to develop scaffolds for tissue engineering (TE). However, its high stiffness limits its use as a matrix for cardiovascular TE. In this work, we evaluated the physical, mechanical and surface properties of PCL and cellulose nanocrystal (CNC) composite membranes, imprinted with a three-dimensional groove-ridge pattern, and their potential for cardiomyocyte adhesion and growth. This work demonstrates the impact of (a) incorporating CNCs within the PCL matrix and (b) patterning the membrane, on the surface roughness, wettability, and mechanical properties of composites, with an aim to alleviate the limitations of pure PCL scaffolds for cardiac TE applications. © 2021 IEEE.

关键词： Biocompatibility

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Label-Free and Liquid State Sers Detection of Multi-Scaled Bioanalytes Via Light-Induced Pinpoint Colloidal Assembly

SSRN

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SSRN 2024年

作者： Han, Seungyeon Park, Junhee Moon, Sunghyun Eom, Seonghyeon Jin, Chang Min Kim, Seungmin Ryu, Yong-Sang Choi, Yeonho Lee, Jong Bum Choi, Inhee Department of Life Science University of Seoul Seoul02504 Korea Republic of Department of Chemical Engineering University of Seoul Seoul02504 Korea Republic of Department of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of Exopert Corporation Seoul02580 Korea Republic of Department of Applied Chemistry University of Seoul Seoul02504 Korea Republic of

Surface-enhanced Raman scattering (SERS) has been extensively applied to detect complex analytes due to its ability to enhance the fingerprint signals of molecules around nanostructured metallic surfaces. Thus, it is essential to design SERS-active nanostructures with abundant electromagnetic hotspots in a probed volume according to the dimensions of the analytes, as the analytes must be located in their hotspots for maximum signal enhancement. Herein, we demonstrate a simple method for detecting robust SERS signals from multi-scaled bioanalytes, regardless of their dimensions in the liquid state, through a photothermally driven co-assembly with colloidal plasmonic nanoparticles as signal enhancers. Under resonant light illumination, plasmonic nanoparticles and analytes in the solution quickly assemble at the focused surface area by convective movements induced by the photothermal heating of the plasmonic nanoparticles without any surface modification. Such collective assemblies of plasmonic nanoparticles and analytes were optimized by varying the optical density and surface charge of the nanoparticles, the viscosity of the solvent, and the light illumination time to maximize the SERS signals. Using these light-induced co-assemblies, the intrinsic SERS signals of small biomolecules can be detected down to nanomolar concentrations based on their fingerprint spectra. Furthermore, large-sized biomarkers, such as viruses and exosomes, were successfully detected without labels, and the complexity of the collected spectra was statistically analyzed using t-distributed stochastic neighbor embedding combined with support vector machine (t-SNE+SVM). The proposed method is expected to provide a robust and convenient method to sensitively detect biologically and environmentally relevant analytes at multiple scales in liquid samples. © 2024, The Authors. All rights reserved.

关键词： Viruses

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Conformational ensemble-dependent lipid recognition and segregation by prenylated intrinsically disordered regions in small GTPases

Communications Biology

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Communications Biology 2023年第1期6卷 1111页

作者： Araya, Mussie K. Gorfe, Alemayehu A. McGovern Medical School University of Texas Health Science Center at Houston Department of Integrative Biology and Pharmacology 6431 Fannin St. Houston 77030 TX United States Biochemistry and Cell Biology Program & Therapeutics and Pharmacology Program UTHealth MD Anderson Cancer Center Graduate School of Biomedical Sciences 6431 Fannin St. Houston 77030 TX United States

We studied diverse prenylated intrinsically disordered regions (PIDRs) of Ras and Rho family small GTPases using long timescale atomistic molecular dynamics simulations in an asymmetric model membrane of phosphatidylcholine (PC) and phosphatidylserine (PS) lipids. Here we show that conformational plasticity is a key determinant of lipid sorting by polybasic PIDRs and provide evidence for lipid sorting based on both headgroup and acyl chain structures. We further show that conformational ensemble-based lipid recognition is generalizable to all polybasic PIDRs, and that the sequence outside the polybasic domain (PBD) modulates the conformational plasticity, bilayer adsorption, and interactions of PIDRs with membrane lipids. Specifically, we find that palmitoylation, the ratio of basic to acidic residues, and the hydrophobic content of the sequence outside the PBD significantly impact the diversity of conformational substates and hence the extent of conformation-dependent lipid interactions. We thus propose that the PBD is required but not sufficient for the full realization of lipid sorting by prenylated PBD-containing membrane anchors, and that the membrane anchor is not only responsible for high affinity membrane binding but also directs the protein to the right target membrane where it participates in lipid sorting. © 2023, The Author(s).

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The Design of a Drone-Assisted System for Large Wildlife Vaccination Shooting Based on AI Image Identification Technology

The Design of a Drone-Assisted System for Large Wildlife Vac...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Yao-Yu Tsai Yao-Chuan Tsai Chang-Hsien Tai Chen-Chih Chen Uzu-Kuei Hsu Department of Bio-Industrial Mechatronics Engineering Sustainable Circular Economy R&D Center National Chung Hsing University/National Pingtung University of Science and Technology Pingtung/Taichung Taiwan Department of Bio-Industrial Mechatronics Engineering National Chung Hsing University Taichung Taiwan College of Biomedical Engineering and Technology Tzu Chi University Hualien Taiwan Institute of Wildlife Conservation National Pingtung University of Science and Technology Pingtung Taiwan Program of Artificial Intelligence and Mechatronics National Pingtung University of Science and Technology Pingtung Taiwan

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

This study designs a UAV -assisted system integrated with AI image recognition technology to alleviate the difficulty for operators in stabilizing the UAV and accurately administering vaccines. The system uses thermal imaging modules and AI recognition systems to lock onto and track target animals in real-time. Utilizing MAVLink, the UAV adjusts its flight path based on the dynamic behavior of the animals, ensuring that the target remains within shooting range for precise vaccine administration. This technology not only improves the efficiency and accuracy of wildlife vaccination but also significantly reduces the workload on operators, providing a forward-looking solution for wildlife management.

关键词： Accuracy Image recognition Target tracking Target recognition Wildlife Autonomous aerial vehicles Real-time systems Vaccines Artificial intelligence Monitoring

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SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data

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Computers in Biology and Medicine 2025年 192卷 110232-110232页

作者： Falach, R. Belonosov, G. Schmidig, J.F. Aderka, M. Zhelezniakov, V. Shani-Hershkovich, R. Bar, E. Nir, Y. Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel Department of Physiology and Pharmacology Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel The Sieratzki-Sagol Center for Sleep Medicine Tel Aviv Sourasky Medical Center Tel Aviv Israel Department of Brain Sciences Weizmann Institute of Science Rehovot Israel Department of Biomedical Engineering Faculty of Engineering Tel Aviv University Tel Aviv Israel Sagol Brain Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel

Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is growing interest in advanced EEG analysis that requires extensive preprocessing to improve the signal-to-noise ratio and specialized analysis algorithms. While many EEG software packages exist, sleep research has unique needs (e.g., specific artifacts, event detection). Currently, sleep investigators use different libraries for specific tasks in a ‘fragmented’ configuration that is inefficient, prone to errors, and requires the learning of multiple software environments. This complexity creates a barrier for beginners. Here, we present SleepEEGpy, an open-source Python package that simplifies sleep EEG preprocessing and analysis. SleepEEGpy builds on MNE-Python, PyPREP, YASA, and SpecParam to offer an all-in-one, beginner-friendly package for comprehensive sleep EEG research, including (i) cleaning, (ii) independent component analysis, (iii) sleep event detection, (iv) spectral feature analysis, and visualization tools. A dedicated dashboard provides an overview to evaluate data and preprocessing, serving as an initial step prior to detailed analysis. We demonstrate SleepEEGpy's functionalities using overnight high-density EEG data from healthy participants, revealing characteristic activity signatures typical of each vigilance state: alpha oscillations in wakefulness, spindles and slow waves in NREM sleep, and theta activity in REM sleep. We hope that this software will be adopted and further developed by the sleep research community, and constitute a useful entry point tool for beginners in sleep EEG research. © 2025 The Authors

关键词： Electroencephalography

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Empowering biomedical Discovery with AI Agents

arXiv

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arXiv 2024年

作者： Gao, Shanghua Fang, Ada Huang, Yepeng Giunchiglia, Valentina Noori, Ayush Schwarz, Jonathan Richard Ektefaie, Yasha Kondic, Jovana Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Department of Brain Sciences Imperial College London London United Kingdom Harvard College CambridgeMA United States Program in Biomedical Informatics Harvard Medical School BostonMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI’s ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies. © 2024, CC BY.

关键词： Adversarial machine learning

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