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ASCE Open: Multidisciplinary Journal of Civil engineering 2024年第1期2卷

作者： Lambert, James H. Dorn, R. Ranger Ayyub, Bilal M. Barletta, William A. Organek, John F. Piras, Marco Gunn, Megan E. Zichichi, S. Fabrizio Center for Risk Management of Engineering Systems Dept. of Systems and Information Engineering Univ. of Virginia CharlottesvilleVA22904 United States Electric Infrastructure Security Council Oak ViewCA93022 United States Center for Technology and Systems Management Dept. of Civil and Environmental Engineering Univ. of Maryland College ParkMD20742 United States Dept. of Commerce Applied Economics Office National Institute of Standards and Technology GaithersburgMD20899 United States International Joint Research Center for Resilient Infrastructure Tongji Univ. Shanghai China Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Program Planning & Operational Architecture Electric Infrastructure Security Council WashingtonDC20002 United States Dept. of Environment Land and Infrastructure Engineering Politecnico di Torino Turin10129 Italy Dept. of Systems and Information Engineering Univ. of Virginia CharlottesvilleVA22904 United States Ettore Majorana Foundation Centre for Scientific Culture Sicily91016 Italy

Recognizing wildfires as a multiscale planetary emergency, this paper describes a systems analysis of technology countermeasures, with special attention to Sicily, California, and several other regions. The paper describes a framework for systems integration of firefighting, satellites, fixed imaging, and aerial vehicles, with mathematical simulation and machine learning, to support both policy mitigations and responses to incidents. The approach includes (1) the characterization of the planetary emergency of wildfires, (2) early identification of dangerous wildfires and means for their control, and (3) reduction of risks to population centers in both rural and urban areas. Societal metrics of success include human health and safety, environment and ecology, critical food and industrial supply chains, geopolitical security, and others. Several technology and policy countermeasures of particular interest are regional fuel management, distributed sensors, computational intelligence, advanced firefighting materials, protocols, and automatic and semiautonomous responses, benefiting from the latest advancements in information technologies including artificial intelligence, machine learning, and others. © 2024 This work is made available under the terms of the Creative Commons Attribution 4.0 International license.

关键词： Risk assessment

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Comparative studies on characterizations and cytotoxicity of oil extracted from Lingzhi (Ganoderma lucidum) G2 spore using Soxhlet extraction and microwave-assisted extraction

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applied Food Research 2024年第2期4卷

作者： Senphan, Theeraphol Benjakul, Soottawat Sukketsiri, Wanida Chotphruethipong, Lalita Sriket, Chodsana Program in Food Science and Technology Faculty of Engineering and Agro-Industry Maejo University Chiangmai Sansai 50290 Thailand International Center of Excellence in Seafood Science and Innovation Faculty of Agro-Industry Prince of Songkla University Songkhla Hat Yai 90110 Thailand Division of Health and Applied Sciences Faculty of Science Prince of Songkla University Songkhla Hat Yai 90110 Thailand Department of Food Science Faculty of Science Burapha University Chonburi Mueang Chonburi 20131 Thailand Food Innovation and Management Program Department of General Science and Liberal Arts King Mongkut's Institute of Technology Ladkrabang Prince of Chumphon Campus Chumphon Pathiu 86160 Thailand

This study aimed to compare the characteristics, antioxidant activity, and cytotoxicity of Ganoderma lucidum G2 spore (GLS) oil extracted using two different extraction methods, including Soxhlet extraction (SLE) and microwave-assisted extraction (MAE). The SLE method yielded higher oil content than the MAE method. MAE-extracted oil showed higher lightness (L*) and yellowness (b*) values but lower redness (a*) values. Both methods provided similar fatty acid compositions in GLS oils, including palmitic acid, oleic acid, and linoleic acid as major components. Higher saturated fatty acid content was found in SLE-extracted oil, while MAE-extracted oil had increased levels of mono- and poly-unsaturated fatty acids. The oil obtained from SLE showed slightly higher di- and tri-glycerides than that obtained from MAE (P < 0.05). Furthermore, MAE-extracted oil had high oxidative stability as indicated by lower levels of conjugated diene (CD), peroxide value (PV), thiobarbituric acid-reactive substances (TBARS), and free fatty acids (FFA) as compared to SLE (P < 0.05). MAE-extracted oil had a higher total triterpenoid content than SLE-extracted oil (P < 0.05). Also, MAE-extracted oil showed lower DPPH radical scavenging activity (DPPH-RSA) than SLE-extracted oil (P < 0.05). FTIR spectra of both samples exhibited similar functional groups. Additionally, oils obtained from both processes (at 0.1–10 µg/mL) showed low toxicity on Caco-2 cells (P > 0.05). Therefore, GLS oil with high antioxidant activity and oxidative stability, especially oil extracted by MAE, could be a food supplement for health. Also, the MAE used was a potential implement for extracting oil from GLS. © 2024

关键词： Antioxidant activity Cytotoxicity G. lucidum spore Microwave-assisted extraction Oil Soxhlet extraction

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Accelerated and interpretable prediction of local properties in composites

APL Machine Learning

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APL Machine Learning 2023年第3期1卷

作者： Shengtong Zhang Satyajit Mojumder Wing Kam Liu Wei Chen Daniel W. Apley Department of Industrial Engineering and Management Sciences Northwestern University Evanston Illinois 60208 USA Theoretical and Applied Mechanics Program Northwestern University Evanston Illinois 60208 USA Department of Mechanical Engineering Northwestern University Evanston Illinois 60208 USA

The localized stress and strain field simulation results are critical for understanding the mechanical properties of materials, such as strength and toughness. However, applying off-the-shelf machine learning or deep learning methods to a digitized microstructure restricts the image samples to be of a fixed size and also lacks interpretability. Additionally, existing methods that utilize deep learning models to solve boundary value problems require retraining the model for each set of boundary conditions. To address these limitations, we propose a customized Pixel-Wise Convolutional Neural Network (PWCNN) to make fast predictions of stress and strain fields pixel-by-pixel under different loading conditions and for a wide range of composite microstructures of any size (e.g., much larger or smaller than the sample on which the PWCNN is trained). Through numerical experiments, we show that our PWCNN model serves as an alternative approach to numerical solution methods, such as finite element analysis, but is computationally more efficient, and the prediction errors on the test microstructure are around 5%. Moreover, we also propose an interpretable machine learning framework to facilitate the scientific discovery of why certain microstructures have better or worse performance than others, which has important implications in the design of composite microstructures in advanced manufacturing.

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Using Cluster Analysis and Dynamic programming for Demand Response applied to Electricity Load in Residential Homes

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Journal of engineering for Sustainable Buildings and Cities 2020年第1期1卷 011006页

作者： Chanpiwat, Pattanun Gabriel, Steven A. Moglen, Rachel L. Siemann, Michael J. Department of Mechanical Engineering University of Maryland College Park 20742 MD United States Department of Mechanical Engineering and the Applied Mathematics Statistics and Scientific Computation Program University of Maryland College Park 20742 MD United States Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim NO-7491 Norway Operations Research and Industrial Engineering The University of Texas at Austin Austin 78712 TX United States Resideo Technologies Austin 78702 TX United States

This paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool. Copyright © 2020 by ASME.

关键词： cluster analysis customer classification demand response dynamic programming energy efficiency integrated systems occupant behavior optimization residential household

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A Survey on the Impact of Data Analytics and Machine Learning Techniques in E-commerce

A Survey on the Impact of Data Analytics and Machine Learnin...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Dheva Rajan Sivajee Vavilapalli Shahriar Hasan Ritesh Kumar Nafisa Rafa Iskandar Muda Information Technology University of Technology and Applied Sciences Almusannah Sultanate of Oman Department of Management Studies Vignan’s Institute of information technology (A) Duvvada Visakhapatnam Department of Finance University of Dhaka Bangladesh Maharishi School of Engineering and Technology Lucknow Uttar Pradesh India BBA in Marketing Institute of Business Administration University of Dhaka Dhaka Bangladesh Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara Medan Indonesia

Current article has been developed for understanding the impact of data analytics and machine learning technique in e-commerce. If the observation has been developed for the current market of this technology it can be detected that real-life industry increases their investment which showed the interest in getting advantages. Identification of the problem of costing and different algorithm suitability objectives has been set for understanding the real impact of e-commerce. Secondary data collection has been used for understanding the different usability of machine learning and data analytics for e-commerce. It has been detected that using machine learning brings the benefits of automation and improves continuous growth. Data analytics provide the scope of accuracy build-up. Apart from this different area of application and the different techniques has been analyzed. Understanding become easier and the scope of future benefits can be identified.

关键词： Industries Data analysis Machine learning algorithms Decision making Machine learning Electronic commerce Object recognition

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Oxidative Stress and Natural Antioxidants: Back and Forth in the Neurological Mechanisms of Alzheimer’s Disease

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Journal of Alzheimer's Disease 2023年第3期96卷 877-912页

作者： Mani, Shalini Dubey, Rajni Lai, I-Chun Babu, M. Arockia Tyagi, Sakshi Swargiary, Geeta Mody, Deepansh Singh, Manisha Agarwal, Shriya Iqbal, Danish Kumar, Sanjay Hamed, Munerah Sachdeva, Punya Almutary, Abdulmajeed G. Albadrani, Hind Muteb Ojha, Shreesh Singh, Sandeep Kumar Jha, Niraj Kumar Centre for Emerging Diseases Department of Biotechnology Jaypee Institute of Information Technology UP Noida India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Division of Radiation Oncology Department of Oncology Taipei Veterans General Hospital Taipei Taiwan The Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Academia Sinica Taipei Taiwan Institute of Pharmaceutical Research GLA University Mathura India Department of Molecular Sciences Macquarie University Sydney Australia Department of Health Information Management College of Applied Medical Sciences Buraydah Private Colleges Buraydah Saudi Arabia Department of Life Sciences School of Basic Sciences and Research (SBSR) Sharda University Uttar Pradesh Greater Noida India Department of Pathology Faculty of Medicine Umm Al-Qura University Makkah Saudi Arabia GloNeuro Vishwakarma Road Noida India Department of Biomedical Sciences College of Health Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Department of Clinical Laboratory Sciences College of Applied Medical Sciences Imam Abdulrahman Bin Faisal University Eastern Province Dammam Saudi Arabia Department of Pharmacology and Therapeutics College of Medicine and Health Sciences United Arab Emirates University Abu Dhabi United Arab Emirates Indian Scientific Education and Technology Foundation Lucknow India Department of Biotechnology School of Engineering & Technology (SET) Sharda University Uttar Pradesh Greater Noida India School of Bioengineering and Biosciences Lovely Professional University Punjab Phagwara India Department of Biotechnology School of Applied & Life Sciences (SALS) Uttaranchal University Uttarakhand Dehradun India Department of Biotechnology Engineering and Food Technology Chandigarh University Mohali India

Alzheimer’s disease (AD) is characterized by the progressive degeneration of neuronal cells. With the increase in aged population, there is a prevalence of irreversible neurodegenerative changes, causing a significant mental, social, and economic burden globally. The factors contributing to AD are multidimensional, highly complex, and not completely understood. However, it is widely known that aging, neuroinflammation, and excessive production of reactive oxygen species (ROS), along with other free radicals, substantially contribute to oxidative stress and cell death, which are inextricably linked. While oxidative stress is undeniably important in AD, limiting free radicals and ROS levels is an intriguing and potential strategy for deferring the process of neurodegeneration and alleviating associated symptoms. Therapeutic compounds from natural sources have recently become increasingly accepted and have been effectively studied for AD treatment. These phytocompounds are widely available and a multitude of holistic therapeutic efficiencies for treating AD owing to their antioxidant, anti-inflammatory, and biological activities. Some of these compounds also function by stimulating cholinergic neurotransmission, facilitating the suppression of beta-site amyloid precursor protein-cleaving enzyme 1, α-synuclein, and monoamine oxidase proteins, and deterring the occurrence of AD. Additionally, various phenolic, flavonoid, and terpenoid phytocompounds have been extensively described as potential palliative agents for AD progression. Preclinical studies have shown their involvement in modulating the cellular redox balance and minimizing ROS formation, displaying them as antioxidant agents with neuroprotective abilities. This review emphasizes the mechanistic role of natural products in the treatment of AD and discusses the various pathological hypotheses proposed for AD. © 2023 – IOS Press. All rights reserved.

关键词： Alzheimer’s disease antioxidants neuroprotection oxidative stress therapeutics vitamins

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Regulatory effects of Ishige okamurae extract and Diphlorethohydroxycarmalol on skin barrier function

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Heliyon 2024年第23期10卷 e40227页

作者： Bak, Seon Gyeong Lim, Hyung Jin Won, Yeong-Seon Park, Sang-Ik Cheong, Sun Hee Lee, Seung Jae Functional Biomaterial Research Center Korea Research Institute of Bioscience and Biotechnology (KRIBB) Jeongeup 56212 South Korea Scripps Korea Antibody Institute Chuncheon South Korea Division of Research Management Department of Bioresource Industrialization Honam National Institute of Biological Resource Mokpo South Korea Department of Veterinary Pathology College of Veterinary Medicine and BK21 FOUR Program Chonnam National University Gwangju 61186 South Korea Department of Marine Bio Food Science Chonnam National University Yeosu 59626 South Korea Applied Biological Engineering KRIBB School of Biotechnology University of Science and Technology Daejeon 34113 South Korea

Ethnopharmacological relevance: The pharmacological potential of marine organisms remains largely unexplored. Ishige Okamurae, commonly known as Pae, is extensively distributed over Asia. Its antioxidant, antibacterial, antiobesity, and anti-inflammatory properties are also being investigated. Aim of the study: In most cases of atopic dermatitis, the stratum corneum, the outermost layer of the epidermis, is damaged, causing symptoms such as dryness and hyperproliferation of the epidermis. In particular, the disruption of cell junctions leads to damage of the skin barrier, exacerbating the disease and becoming a target for therapeutic development. Our study aims to investigate of Ishige okamurae extract (IOE) and a major compound derived from it, called Diphlorethohydroxycarmalol (DPHC), can help strengthen the skin barrier in animals with atopic dermatitis induced by 2,4-dinitrochlorobenzene (DNCB). Materials and methods: In keratinocyte cell lines, HaCaT cells, the cytotoxicity of IOE and DPHC was assessed by MTT analysis. The gene expression of skin barrier factors and tight junctions were determined by real-time PCR in tumor necrosis factor-α/interferon-γ-stimulated HaCaT cells. In addition, JAK/STAT signaling pathway was performed to evaluating the mechanism of drugs by Western blot. Next, we studied the effects of IOE and DPHC on the skin of animals with DNCB-induced atopic dermatitis. We measured the expression of genes related of the skin barrier and tight junctions in their ear tissue. Results: As a result, IOE and DPHC confirmed that the expression of skin barrier proteins (thymic stromal lymphopoietin, filaggrin, loricrin, and involucrin) was improved in the DNCB-induced atopic dermatitis model and HaCaT cells. In addition, the expression of tight junction-related proteins (claudin, occludin, and tight junction protein-1) were improved. Conclusion: IOE and DPHC ameliorated the atopic dermatitis lesions through alleviating the pro-inflammatory responses and

关键词： 2,4-Dinitrochlorobenzene Ceramide Diphlorethohydroxycarmalol Ishige okamurae Skin barrier Tight junction

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Cellulose Microcrystalline: A Promising Ecofriendly Approach to Control Culex Quinquefasciatus Larvae

SSRN

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SSRN 2023年

作者： de Souza, Sindoval Silva Gomes, Alex Rodrigues Batista Guimarães, Abraão Tiago Rodrigues, Aline Sueli de Lima de Matos, Letícia Paiva Mendonça, Juliana dos Santos Luz, Thiarlen Marinho da da Silva Matos, Stênio Gonçalves Safiur, M. Rahman Ragavendran, Chinnasamy Senthil-Nathan, Sengottayan Guru, Ajay Rakib, Md. Refat Jahan Mubarak, Nabisab Mujawar Mostafizur, Md. Rahman Rocha, Thiago Lopes Towfiqul Islam, Abu Reza Md. Malafaia, Guilherme Post-Graduation Program in Biotechnology and Biodiversity Federal University of Goiás GO Goiânia Brazil Laboratory of Toxicology Applied to the Environment Goiano Federal Institute Urutaí Campus. Rodovia Geraldo Silva Nascimento 25 km Zona Rural Urutaí GO Brazil Post-Graduation Program in Ecology Conservation and Biodiversity Federal University of Uberlândia MG Uberlândia Brazil Post-Graduation Program in Conservation of Cerrado Natural Resources Goiano Federal Institute Urutaí GO Brazil Department of Environmental Science and Disaster Management Noakhali Science and Technology University Noakhali3814 Bangladesh Saveetha University Tamil Nadu Chennai India Division of Biopesticides and Environmental Toxicology Sri Paramakalyani Centre for Excellence in Environmental Sciences Manonmaniam Sundaranar University Tirunelveli Tamil Nadu Alwarkurichi627 412 India Department of Cariology Saveetha Dental College and Hospitals SIMATS Tamil Nadu Chennai600 077 India Noakhali Science and Technology University Noakhali Bangladesh Petroleum and Chemical Engineering Faculty of Engineering Universiti Teknologi Brunei Bandar Seri BegawanBE1410 Brunei Jahangirnagar University Dhaka1342 Bangladesh Laboratory of Environmental Biotechnology and Ecotoxicology Institute of Tropical Pathology and Public Health Federal University of Goiás Goiás Goiânia Brazil Department of Disaster Management Begum Rokeya University Rangpur5400 Bangladesh

The growing use of synthetic chemical compounds/substances in vector control of mosquitoes, associated with their adverse effects on the environment and non-target organisms, has demanded the development of eco-friendly alternatives. In this context, this study aimed to evaluate the larvicidal action of different cellulose microcrystalline (CMs) concentrations and investigate their toxicity mechanisms in Culex quinquefasciatus fourth instar larvae as a model species. Probit analysis revealed that the median lethal concentrations (LC50) for 24 h and 36 h exposure were 100 and 58.29 mg/L, respectively. We also showed that such concentrations induced a redox imbalance in the larvae, marked by an increase in the production of reactive oxygen species (ROS) and thiobarbituric acid reactive substances (TBARS), as well as a reduction in the activity of superoxide dismutase (SOD) and catalase (CAT). Furthermore, different alterations in the external morphology of the larvae were associated with the ingestion of CMs. On the other hand, exposure of adult zebrafish (Danio rerio) to LC5024h and LC5036h for seven days did not induce any behavioral changes or alterations mutagenic, genotoxic, biochemical, or in the pro- and anti-inflammatory response. Thus, taken together, our study demonstrates for the first time that the use of CMs can constitute a promising strategy in the control of C. quinquefasciatus larvae, combining insecticidal efficiency with an "eco-friendly" approach in the fight against an important mosquito vector of several human diseases. © 2023, The Authors. All rights reserved.

关键词： Cellulose

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Microfluidic nanodevices for drug sensing and screening applications

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Biosensors and Bioelectronics 2023年 219卷 114783页

作者： Pal, Arnab Kaswan, Kuldeep Barman, Snigdha Roy Lin, Yu-Zih Chung, Jun-Hsuan Sharma, Manish Kumar Liu, Kuei-Lin Chen, Bo-Huan Wu, Chih-Cheng Lee, Sangmin Choi, Dongwhi Lin, Zong-Hong Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan International Intercollegiate PhD Program National Tsing Hua University Hsinchu30013 Taiwan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Chemistry National Tsing Hua University Hsinchu30013 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan Department of Gastroenterology and Hepatology Chang Gung Memorial Hospital Linkou Medical Center Taoyuan333 Taiwan Center of Quality Management National Taiwan University Hospital Hsinchu Branch Hsinchu30059 Taiwan College of Medicine National Taiwan University Taipei10051 Taiwan Institute of Cellular and System Medicine National Health Research Institute Zhunan 35053 Taiwan School of Mechanical Engineering Chung-Ang University Seoul06974 Korea Republic of Kyung Hee University Gyeonggi 17104 Korea Republic of

The outbreak of pandemics (e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 in 2019), influenza A viruses (H1N1 in 2009), etc.), and worldwide spike in the aging population have created unprecedented urgency for developing new drugs to improve disease treatment. As a result, extensive efforts have been made to design novel techniques for efficient drug monitoring and screening, which form the backbone of drug development. Compared to traditional techniques, microfluidics-based platforms have emerged as promising alternatives for high-throughput drug screening due to their inherent miniaturization characteristics, low sample consumption, integration, and compatibility with diverse analytical strategies. Moreover, the microfluidic-based models utilizing human cells to produce in-vitro biomimetics of the human body pave new ways to predict more accurate drug effects in humans. This review provides a comprehensive summary of different microfluidics-based drug sensing and screening strategies and briefly discusses their advantages. Most importantly, an in-depth outlook of the commonly used detection techniques integrated with microfluidic chips for highly sensitive drug screening is provided. Then, the influence of critical parameters such as sensing materials and microfluidic platform geometries on screening performance is summarized. This review also outlines the recent applications of microfluidic approaches for screening therapeutic and illicit drugs. Moreover, the current challenges and the future perspective of this research field is elaborately highlighted, which we believe will contribute immensely towards significant achievements in all aspects of drug development. © 2022 Elsevier B.V.

关键词： Microfluidics

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