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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alfano, Caterina Farina, Lorenzo Petti, Manuela Sapienza University of Rome PhD Program in Network Oncology and Precision Medicine Rome Italy Sapienza University of Rome PhD Program in Network Oncology and Precision Medicine Dept. of Computer Control and Management Engineering Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Colorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the disease. This study focuses on mRNA expression profiles of colon cancer patients to look for molecular differences in the development of colon cancer between men and women. We used paired expression data (i.e., data collected in pairs of normal and cancer cells, by taking samples from the same individual), we identified differentially expressed genes (cancer vs normal) and computed co-expression and differential co-expression gene networks (men vs women). Doing so, we inferred the main changes and alterations happening in cancer tissues, and specifically how these changes were different among men and women. We found that the co-expression networks of women and men affected by colon cancer are quite different and we reported the genes that show the most differences in this comparison, checking if they could also be associated to sexual dimorphism or sexual hormones. Among these genes we found a interesting presence of genes associated to the Wnt signaling pathway which has been found to be regulated by estrogen and whose activation is strongly linked with colon cancer. © 2022 IEEE.

关键词： Diseases

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Atomistic modeling and rational design of optothermal tweezers for targeted applications

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Nano Research 2021年第1期14卷 295-303页

作者： Hongru Ding Pavana Siddhartha Kollipara Linhan Lin Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTX 78712USA State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084China Materials Science&Engineering Program and Texas Materials Institute The University of Texas at AustinAustinTX 78712USA

Optical manipulation of micro/nanoscale objects is of importance in life sciences,colloidal science,and *** tweezers exhibit superior manipulation capability at low optical ***,our implicit understanding of the working mechanism has limited the further applications and innovations of optothermal ***,we present an atomistic view of opto-thermo-electro-mechanic coupling in optothermal tweezers,which enables us to rationally design the tweezers for optimum performance in targeted ***,we have revealed that the non-uniform temperature distribution induces water polarization and charge separation,which creates the thermoelectric field dominating the optothermal *** further design experiments to systematically verify our atomistic *** by our new model,we develop new types of optothermal tweezers of high performance using low-concentrated ***,we demonstrate the use of new tweezers in opto-thermophoretic separation of colloidal particles of the same size based on the difference in their surface charge,which has been challenging for conventional optical *** the atomistic understanding that enables the performance optimization and function expansion,optothermal tweezers will further their impacts.

关键词： optothermal tweezers optical tweezers optical manipulation thermophoresis molecular dynamics simulation

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Ionizable polymeric nanocarriers for the codelivery of bi-adjuvant and neoantigens in combination tumor immunotherapy

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Bioactive Materials 2023年第8期26卷 169-180页

作者： Ting Su Xiang Liu Shuibin Lin Furong Cheng Guizhi Zhu Center for Translational Medicine Precision Medicine InstituteThe First Affiliated HospitalSun Yat-sen UniversityGuangzhou510080China Department of Pharmaceutics and Center for Pharmaceutical Engineering and Sciences Institute for Structural Biology and Drug DiscoverySchool of PharmacyThe Developmental Therapeutics ProgramMassey Cancer CenterVirginia Commonwealth UniversityRichmondVA23298USA

Ionizable lipid nanocarriers have made historical contribution to COVID-19 mRNA ***,we report ionizable polymeric nanoparticles that co-deliver bi-adjuvant and neoantigen peptides for cancer immunotherapy in combination with immune checkpoint blockade(ICB).Current cancer ICB benefits only a small subset of patients,largely due to a lack of pre-existing target cells and checkpoint targets for ICB,tumor antigenic heterogeneity,and tumor *** vaccines hold the potential to enhance ICB therapeutic efficacy by expanding antitumor cell repertoires,upregulating immune checkpoint levels and hence sensitizing ICB,and reducing tumor *** defined peptide vaccines are attractive,but their current therapeutic efficacy has been limited due to 1)poor vaccine delivery to immunomodulatory lymph nodes(LNs)and antigen(Ag)-presenting cells(APCs),2)poor immunostimulant adjuvant efficacy with restricted target cell subsets in humans,3)limited adjuvant/Ag codelivery to enhance Ag immunogenicity,and 4)limited ability to overcome tumor antigenic ***,we developed nanovaccines(NVs)using pH-responsive polymeric micellular nanoparticles(NPs)for the codelivery of bi-adjuvant[Toll-like receptor(TLR)7/8 agonist R848 and TLR9 agonist CpG]and peptide neoantigens(neoAgs)to draining LNs for efficient Ag presentation in a broad range of APC *** NVs potentiated the immunogenicity of peptide Ags and elicits robust antitumor T cell responses with memory,and remodeled the tumor immune milium with reduced tumor *** a result,NVs significantly enhanced ICB therapeutic efficacy for murine colorectal tumors and orthotopic glioblastoma multiforme(GBM).These results suggest marked potential of bi-adjuvant/neoAg-codelivering NVs for combination cancer immunotherapy.

关键词： Polymeric nanoparticles Combination adjuvants Cancer neoantigen Nanovaccine codelivery Cancer immunotherapy

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Histogram-based Res-UNet model for optical sectioning HiLo endo-microscopy

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Optics Express 2025年第6期33卷 12253-12268页

作者： Chung, Meng-Chen Chia, Yu-Hsin Vyas, Sunil Luo, Yuan Department of Biomedical Engineering National Taiwan University Taipei10051 Taiwan Institute of Medical Device and Imaging National Taiwan University Taipei10051 Taiwan YongLin Institute of Health National Taiwan University Taipei10087 Taiwan Molecular Imaging Center National Taiwan University Taipei10672 Taiwan Program for Precision Health and Intelligent Medicine National Taiwan University Taipei106319 Taiwan

Optical sectioning endo-microscopy has become a crucial tool for deep brain imaging, but conventional methods face challenges such as time-consuming scanning processes and the need for expensive light sources. HiLo imaging addresses these issues by providing faster acquisition and high-quality images. In this study, we introduce a histogram matching-based Res-UNet model for optical sectioning HiLo endo-microscopy. By applying our model, we achieve substantial improvements in image reconstruction quality compared to the conventional ResNet model. Our evaluation demonstrates significant enhancements in the reconstructed images’ structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics. The PSNR values exceeding 30 dB and SSIM values surpassing 0.8 at various depths indicate that our method achieves image quality comparable to the HiLo system. Importantly, while our approach demonstrates high-quality, real-time reconstruction capabilities using ex-vivo samples, we are actively planning to extend our research to in-vivo imaging applications, which will further enhance the practical implications of our work. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Brain imaging Imaging systems In vivo imaging Light sources Structured light Tissue imaging

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Monitoring landfill volatile organic compounds emissions by an uncrewed aerial vehicle platform with infrared and visible-light cameras, remote monitoring, and sampling systems

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Journal of Environmental Management 2024年 365卷 121575页

作者： Liang, Chen-Wei Zheng, Zhong-Chun Chen, Ting-Nong Master Program in UAV Application and Precision Agriculture National Ilan University Yilan Taiwan Department of Biomechatronic Engineering National Ilan University Yilan Taiwan

An uncrewed aerial vehicle (UAV) platform equipped with dual imaging cameras, a gas sampling system, and a remote synchronous monitoring system was developed to sample and analyze volatile organic compounds (VOCs) emitted from landfills. The remote synchronous monitoring system provided real-time video to administrators with specific permissions to assist in identifying sampling sites within extensive landfill areas. The sampling system included four kits capable of collecting samples from different locations during a single flight mission. Each kit comprised a 1 L Tedlar bag for measuring landfill VOC concentrations according to the TO-15 method prescribed by the US Environmental Protection Agency. The air sample was introduced into a Tedlar bag via pumping. A known volume of the sample was subsequently concentrated using a solid multisorbent concentrator. Following this, the sample underwent cold trap concentration and thermal desorption. The concentrated sample was then transferred to a chromatography-mass spectrometry system for separation and analysis. Since the anaerobic catabolism of organic waste is exothermic and emits VOCs, this study employed UAV thermal imaging to locate principal emission sources for sampling. Visible-light imaging helped identify newer or older landfill sections, aiding in the selection of appropriate sampling sites, particularly when surfaces were thermally disturbed by solar radiation. Field measurements were conducted under three meteorological conditions: sunny morning, cirrus morning, and thin cloud evening (2 h after sunset), identifying 119, 122, and 111 chemical species respectively. The sequence of total VOC concentrations measured correlated with the meteorological conditions as follows: cirrus morning > thin cloud evening > sunny morning. The results indicated that ambient temperature and global solar radiation significantly influenced daytime gas emissions from landfills. Evening thermal images, unaffected by solar heating,

关键词： Volatile organic compounds

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Design and Experiment of a Water Hydraulic Artificial Muscle Actuated Diving Auxiliary Operating System 9

Design and Experiment of a Water Hydraulic Artificial Muscle...

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9th International Conference on Fluid Power and Mechatronics, FPM 2023

作者： Che, Jin-Kai Zhang, Zeng-Meng Yang, Yong Wang, Ji-Xiang Chen, Yin-Long Gong, Yong-Jun Yang, Rui College of Naval Architecture and Ocean Engineering Dalian Maritime University Dalian China Dalian University of Technology Open Research Program of Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education of China Dalian China

ISBN: (数字)9798350304947

ISBN: (纸本)9798350304947

A diving auxiliary operating system based on water hydraulic artificial muscle (WHAM) was developed to enhance the efficiency of underwater work for divers. The water filling pressure of the WHAM was calculated by the interaction torque between the operating system and the wearer. Power assisted experiments were conducted to imitation the underwater rigging connections, desilting, and obstacle clearance for rescue operations. The power assistance effect was evaluated through a comparison of the surface electromyography (EMG) intensity between the wearer with and without the auxiliary operating system. The experiments results indicate that the auxiliary system can enhance the diver's operational capacity 43%. © 2023 IEEE.

关键词： Muscle

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Self-supervised neural network for phase retrieval in QDPC microscopy

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Optics Express 2023年第12期31卷 19897-19908页

作者： Chen, Ying-Ju Vyas, Sunil Huang, Hsuan-Ming Luo, Yuan Institute of Medical Device and Imaging National Taiwan University Taipei10051 Taiwan Department of Biomedical Engineering National Taiwan University Taipei10051 Taiwan YongLin Institute of Health National Taiwan University Taipei10051 Taiwan Program for Precision Health and Intelligent Medicine Graduate School of Advanced Technology National Taiwan University Taipei10051 Taiwan

Quantitative differential phase contrast (QDPC) microscope plays an important role in biomedical research since it can provide high-resolution images and quantitative phase information for thin transparent objects without staining. With weak phase assumption, the retrieval of phase information in QDPC can be treated as a linearly inverse problem which can be solved by Tikhonov regularization. However, the weak phase assumption is limited to thin objects, and tuning the regularization parameter manually is inconvenient. A self-supervised learning method based on deep image prior (DIP) is proposed to retrieve phase information from intensity measurements. The DIP model that takes intensity measurements as input is trained to output phase image. To achieve this goal, a physical layer that synthesizes the intensity measurements from the predicted phase is used. By minimizing the difference between the measured and predicted intensities, the trained DIP model is expected to reconstruct the phase image from its intensity measurements. To evaluate the performance of the proposed method, we conducted two phantom studies and reconstructed the micro-lens array and standard phase targets with different phase values. In the experimental results, the deviation of the reconstructed phase values obtained from the proposed method was less than 10% of the theoretical values. Our results show the feasibility of the proposed methods to predict quantitative phase with high accuracy, and no use of ground truth phase. © 2023 Optica Publishing Group.

关键词： Inverse problems

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Detection of Alzheimer's Disease and Frontotemporal Dementia: An Explainable Machine Learning Approach Using EEG Signals

Detection of Alzheimer's Disease and Frontotemporal Dementia...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Hwa-Yeon Lee Min-Kyung Jung Choel-Hui Lee Hakseung Kim Dong-Joo Kim Department of Brain and Cognitive Engineering Interdisciplinary Program in Precision Public Health Korea University Seoul South Korea Department of Brain and Cognitive Engineering Korea University Seoul South Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Alzheimer's disease and frontotemporal dementia are among the most prevalent neurodegenerative diseases worldwide. With the absence of definitive treatments, early detection and precise diagnosis remain crucial for alleviating patient suffering and reducing healthcare costs. This study aimed to differentiate Alzheimer's disease, frontotemporal dementia and normal cognition using electroencephalography biomarkers. Significant power spectral density and functional connectivity features were identified via one-way analysis of variance and Scheffe's post-hoc test. A three-class classification was performed by a support vector machine classifier and explainable artificial intelligence technique was employed to investigate feature importance. This classifier achieved an average accuracy of 84.09%, and linear shapley additive explanation model identified T5-Fpl connectivity in alpha band as the most important feature, consistent with findings from previous studies. Further clinical research and brain network analysis using advanced deep learning models could enhance our understanding of the cognitive functions and neural mechanisms underlying these dementias.

关键词： Support vector machines Deep learning Costs Network analyzers Medical services Brain modeling Electroencephalography Cognition Brain-computer interfaces Alzheimer's disease

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MIRROR: MiRNA regulation-level differential network to study sex and ethnic disparities in cancer 46

MIRROR: MiRNA regulation-level differential network to study...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alfano, Caterina Filetti, Marco Farina, Lorenzo Petti, Manuela Sapienza University of Rome Dept. of Experimental Medicine Rome Italy Fondazione Policlinico Universitario A. Gemelli Irccs Phase 1 Unit Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350371499

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients' cohorts, to study the role they play at the target genes' level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge. © 2024 IEEE.

关键词： differential network modeling disparities in cancer graph theory miRNA-target association transcriptomics

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Stratification of metastatic melanoma patients based on mutational signatures

Stratification of metastatic melanoma patients based on muta...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Caterina Alfano Lorenzo Farina Manuela Petti PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Genetic instability is one of the hallmarks of cancer, however mutations can occur for different causes and induce different effects. Mutational signatures are characteristic patterns of somatic mutations in cancer genomes, reflecting the underlying mutational processes. A mutational signature can be determined by studying the kind of mutations a patient has acquired during their life: patient stratification based on mutational signatures has become more and more useful in genomic studies, given its possible clinical implications. In this work we focused on Single Base Substitution (SBS) signatures to study a cohort of 115 metastatic melanoma patients. We inferred and identified two mutational signatures characterizing the patients. Based on these signatures we divided patients into two group: the bigger group was characterized by a signature associated with exposure to ultraviolet light, while the smaller group resulted to be mostly composed of patients which did not respond to immunotherapy (anti-PD1) and that presented a low mutational count. More importantly this second group showed a significantly worse survival outcome. The use of mutational signatures is clearly a powerful tool to identify disease sub-types that have a clinical relevance, however we believe that this topic needs further investigation focused on the characterization of patient subtypes with a multi-omics based approach.

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