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Biosensors
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Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human...
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Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 35027

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Jong-Hoon Kim

E-Mail Website
Guest Editor
Engineering and Computer Science (VECS), Washington State University, 14204 NE Salmon Creek Avenue, Vancouver, WA 98686,USA
Interests: biosensors; microfluidics
Dr. Woon-Hong Yeo

E-Mail Website1 Website2
Guest Editor
Georgia Institute of Technology, 791 Atlantic Drive, Atlanta, GA 30332, USA
Interests: nanomanufacturing; biosensors; bioelectronics; soft robotics; human–machine interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research and development related to Smart, Connected, and Portable Biosensors and Bioelectronics have been a game-changer in the fields of diagnostics and health management. Unlike conventional biosensors, these devices allow rapid, accurate, and on-site detection of biomarkers, which helps to prevent disease spread by the source control. This Biosensors Special Issue on “Smart, Connected, and Portable Biosensors and for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics” is dedicated to reporting advances towards addressing current challenges and the future scope of the field of portable biosensors and bioelectronics. Topics include but are not restricted to:

• Applications of portable biosensors and handheld POC devices, ranging from the support of primary healthcare to food and environmental safety screening;
• Advances in the design and optimization of portable biosensor and bioelectronics systems;
• Various technologies for fabricating portable diagnostic devices and biosensors;
• Novel materials for bioelectronics and power sources;
• New biomarkers for portable biosensors and bioelectronics;
• Signal processing and wireless transmission schemes for smart biosensors and bioelectronics;
• New sample preparation methods for portable biosensors;
• Wearable systems for health monitoring and interventionl
• Systems engineering and integration for smart and portable sensors and bioelectronics.

Research papers, short communications, perspective article, and reviews are all welcome. Prior discussion with the Guest Editors would be helpful if the author(s) are interested in submitting a review/perspective article.

Dr. Jong-Hoon Kim
Prof. Dr. W. Hong Yeo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Portable biosensors
  • Wearable and implantable bioelectronics
  • Wireless sensors and systems
  • Smart biosensors and bioelectronics
  • Machine learning-enabled healthcare
  • Smart systems for advanced disease diagnosis
  • Smart systems for advanced therapeutics

Published Papers (10 papers)

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Research

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13 pages, 1908 KiB  
Communication
Wearable Biosensor with Molecularly Imprinted Conductive Polymer Structure to Detect Lentivirus in Aerosol
by Jaskirat Singh Batra, Ting-Yen Chi, Mo-Fan Huang, Dandan Zhu, Zheyuan Chen, Dung-Fang Lee and Jun Kameoka
Biosensors 2023, 13(9), 861; https://doi.org/10.3390/bios13090861 - 31 Aug 2023
Viewed by 1308
Abstract
The coronavirus disease (COVID-19) pandemic has increased pressure to develop low-cost, compact, user-friendly, and ubiquitous virus sensors for monitoring infection outbreaks in communities and preventing economic damage resulting from city lockdowns. As proof of concept, we developed a wearable paper-based virus sensor based [...] Read more.
The coronavirus disease (COVID-19) pandemic has increased pressure to develop low-cost, compact, user-friendly, and ubiquitous virus sensors for monitoring infection outbreaks in communities and preventing economic damage resulting from city lockdowns. As proof of concept, we developed a wearable paper-based virus sensor based on a molecular imprinting technique, using a conductive polyaniline (PANI) polymer to detect the lentivirus as a test sample. This sensor detected the lentivirus with a 4181 TU/mL detection limit in liquid and 0.33% to 2.90% detection efficiency in aerosols at distances ranging from 30 cm to 60 cm. For fabrication, a mixture of a PANI monomer solution and virus were polymerized together to form a conductive PANI sensing element on a polyethylene terephthalate (PET) paper substrate. The sensing element exhibited formation of virus recognition sites after the removal of the virus via ultrasound sonication. A dry measurement technique was established that showed aerosol virus detection by the molecularly imprinted sensors within 1.5 h of virus spraying. This was based on the mechanism via which dispensing virus droplets on the PANI sensing element induced hybridization of the virus and molecularly imprinted virus recognition templates in PANI, influencing the conductivity of the PANI film upon drying. Interestingly, the paper-based virus sensor was easily integrated with a wearable face mask for the detection of viruses in aerosols. Since the paper sensor with molecular imprinting of virus recognition sites showed excellent stability in dry conditions for long periods of time, unlike biological reagents, this wearable biosensor will offer an alternative approach to monitoring virus infections in communities. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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21 pages, 4326 KiB  
Article
A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography
by Jesse Fine, Michael J. McShane, Gerard L. Coté and Christopher G. Scully
Biosensors 2022, 12(8), 598; https://doi.org/10.3390/bios12080598 - 04 Aug 2022
Cited by 3 | Viewed by 1933
Abstract
Cardiovascular disease is the leading cause of death globally. To provide continuous monitoring of blood pressure (BP), a parameter which has shown to improve health outcomes when monitored closely, many groups are trying to measure blood pressure via noninvasive photoplethysmography (PPG). However, the [...] Read more.
Cardiovascular disease is the leading cause of death globally. To provide continuous monitoring of blood pressure (BP), a parameter which has shown to improve health outcomes when monitored closely, many groups are trying to measure blood pressure via noninvasive photoplethysmography (PPG). However, the PPG waveform is subject to variation as a function of patient-specific and device factors and thus a platform to enable the evaluation of these factors on the PPG waveform and subsequent hemodynamic parameter prediction would enable device development. Here, we present a computational workflow that combines Monte Carlo modeling (MC), gaussian combination, and additive noise to create synthetic dataset of volar fingertip PPG waveforms representative of a diverse cohort. First, MC is used to determine PPG amplitude across age, skin tone, and device wavelength. Then, gaussian combination generates accurate PPG waveforms, and signal processing enables data filtration and feature extraction. We improve the limitations of current synthetic PPG frameworks by enabling inclusion of physiological and anatomical effects from body site, skin tone, and age. We then show how the datasets can be used to examine effects of device characteristics such as wavelength, analog to digital converter specifications, filtering method, and feature extraction. Lastly, we demonstrate the use of this framework to show the insensitivity of a support vector machine predictive algorithm compared to a neural network and bagged trees algorithm. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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14 pages, 2374 KiB  
Communication
Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes
by Kangkyu Kwon, Shinjae Kwon and Woon-Hong Yeo
Biosensors 2022, 12(3), 155; https://doi.org/10.3390/bios12030155 - 02 Mar 2022
Cited by 16 | Viewed by 3284
Abstract
Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input [...] Read more.
Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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18 pages, 3004 KiB  
Article
Highly Sensitive Immunoresistive Sensor for Point-Of-Care Screening for COVID-19
by Tianyi Li, Scott D. Soelberg, Zachary Taylor, Vigneshwar Sakthivelpathi, Clement E. Furlong, Jong-Hoon Kim, Sang-gyeun Ahn, Peter D. Han, Lea M. Starita, Jia Zhu and Jae-Hyun Chung
Biosensors 2022, 12(3), 149; https://doi.org/10.3390/bios12030149 - 28 Feb 2022
Cited by 8 | Viewed by 2424
Abstract
Current point-of-care (POC) screening of Coronavirus disease 2019 (COVID-19) requires further improvements to achieve highly sensitive, rapid, and inexpensive detection. Here we describe an immunoresistive sensor on a polyethylene terephthalate (PET) film for simple, inexpensive, and highly sensitive COVID-19 screening. The sensor is [...] Read more.
Current point-of-care (POC) screening of Coronavirus disease 2019 (COVID-19) requires further improvements to achieve highly sensitive, rapid, and inexpensive detection. Here we describe an immunoresistive sensor on a polyethylene terephthalate (PET) film for simple, inexpensive, and highly sensitive COVID-19 screening. The sensor is composed of single-walled carbon nanotubes (SWCNTs) functionalized with monoclonal antibodies that bind to the spike protein of SARS-CoV-2. Silver electrodes are silkscreen-printed on SWCNTs to reduce contact resistance. We determine the SARS-CoV-2 status via the resistance ratio of control- and SARS-CoV-2 sensor electrodes. A combined measurement of two adjacent sensors enhances the sensitivity and specificity of the detection protocol. The lower limit of detection (LLD) of the SWCNT assay is 350 genome equivalents/mL. The developed SWCNT sensor shows 100% sensitivity and 90% specificity in clinical sample testing. Further, our device adds benefits of a small form factor, simple operation, low power requirement, and low assay cost. This highly sensitive film sensor will facilitate rapid COVID-19 screening and expedite the development of POC screening platforms. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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18 pages, 4355 KiB  
Article
A Wearable and Real-Time Pulse Wave Monitoring System Based on a Flexible Compound Sensor
by Xiaoxiao Kang, Jun Zhang, Zheming Shao, Guotai Wang, Xingguang Geng, Yitao Zhang and Haiying Zhang
Biosensors 2022, 12(2), 133; https://doi.org/10.3390/bios12020133 - 20 Feb 2022
Cited by 15 | Viewed by 3589
Abstract
Continuous monitoring of pulse waves plays a significant role in reflecting physical conditions and disease diagnosis. However, the current collection equipment cannot simultaneously achieve wearable and continuous monitoring under varying pressure and provide personalized pulse wave monitoring targeted different human bodies. To solve [...] Read more.
Continuous monitoring of pulse waves plays a significant role in reflecting physical conditions and disease diagnosis. However, the current collection equipment cannot simultaneously achieve wearable and continuous monitoring under varying pressure and provide personalized pulse wave monitoring targeted different human bodies. To solve the above problems, this paper proposed a novel wearable and real-time pulse wave monitoring system based on a novel flexible compound sensor. Firstly, a custom-packaged pressure sensor, a signal stabilization structure, and a micro pressurization system make up the flexible compound sensor to complete the stable acquisition of pulse wave signals under continuously varying pressure. Secondly, a real-time algorithm completes the analysis of the trend of the pulse wave peak, which can quickly and accurately locate the best pulse wave for different individuals. Finally, the experimental results show that the wearable system can both realize continuous monitoring and reflecting trend differences and quickly locate the best pulse wave for different individuals with the 95% accuracy. The weight of the whole system is only 52.775 g, the working current is 46 mA, and the power consumption is 160 mW. Its small size and low power consumption meet wearable and portable scenarios, which has significant research value and commercialization prospects. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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11 pages, 12983 KiB  
Communication
The Introduction of a New Diagnostic Tool in Forensic Pathology: LiDAR Sensor for 3D Autopsy Documentation
by Aniello Maiese, Alice Chiara Manetti, Costantino Ciallella and Vittorio Fineschi
Biosensors 2022, 12(2), 132; https://doi.org/10.3390/bios12020132 - 19 Feb 2022
Cited by 10 | Viewed by 4886
Abstract
Autopsy is a complex and unrepeatable procedure. It is essential to have the possibility of reviewing the autoptic findings, especially when it is done for medico-legal purposes. Traditional photography is not always adequate to record forensic practice since two-dimensional images could lead to [...] Read more.
Autopsy is a complex and unrepeatable procedure. It is essential to have the possibility of reviewing the autoptic findings, especially when it is done for medico-legal purposes. Traditional photography is not always adequate to record forensic practice since two-dimensional images could lead to distortion and misinterpretation. Three-dimensional (3D) reconstructions of autoptic findings could be a new way to document the autopsy. Besides, nowadays, smartphones and tablets equipped with a LiDAR sensor make it extremely easy to elaborate a 3D model directly in the autopsy room. Herein, a quality and trustworthiness evaluation of 3D models obtained during ten autopsies is made comparing 3D models and conventional autopsy photographic records. Three-dimensional models were realistic and accurate and allowed precise measurements. The review of the autoptic report was facilitated by the 3D model. Conclusions: The LiDAR sensor and 3D models have been demonstrated to be a valid tool to introduce some kind of reproducibility into the autoptic practice. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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8 pages, 2528 KiB  
Communication
Numerical Study of Graphene/Au/SiC Waveguide-Based Surface Plasmon Resonance Sensor
by Wei Du, Lucas Miller and Feng Zhao
Biosensors 2021, 11(11), 455; https://doi.org/10.3390/bios11110455 - 15 Nov 2021
Cited by 7 | Viewed by 2116
Abstract
A new waveguide-based surface plasmon resonance (SPR) sensor was proposed and investigated by numerical simulation. The sensor consists of a graphene cover layer, a gold (Au) thin film, and a silicon carbide (SiC) waveguide layer on a silicon dioxide/silicon (SiO2/Si) substrate. [...] Read more.
A new waveguide-based surface plasmon resonance (SPR) sensor was proposed and investigated by numerical simulation. The sensor consists of a graphene cover layer, a gold (Au) thin film, and a silicon carbide (SiC) waveguide layer on a silicon dioxide/silicon (SiO2/Si) substrate. The large bandgap energy of SiC allows the sensor to operate in the visible and near-infrared wavelength ranges, which effectively reduces the light absorption in water to improve the sensitivity. The sensor was characterized by comparing the shift of the resonance wavelength peak with change of the refractive index (RI), which mimics the change of analyte concentration in the sensing medium. The study showed that in the RI range of 1.33~1.36, the sensitivity was improved when the graphene layers were increased. With 10 graphene layers, a sensitivity of 2810 nm/RIU (refractive index unit) was achieved, corresponding to a 39.1% improvement in sensitivity compared to the Au/SiC sensor without graphene. These results demonstrate that the graphene/Au/SiC waveguide SPR sensor has a promising use in portable biosensors for chemical and biological sensing applications, such as detection of water contaminations (RI = 1.33~1.34), hepatitis B virus (HBV), and glucose (RI = 1.34~1.35), and plasma and white blood cells (RI = 1.35~1.36) for human health and disease diagnosis. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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11 pages, 2292 KiB  
Article
A Portable and Flexible Self-Powered Multifunctional Sensor for Real-Time Monitoring in Swimming
by Yupeng Mao, Yongsheng Zhu, Tianming Zhao, Changjun Jia, Meiyue Bian, Xinxing Li, Yuanguo Liu and Baodan Liu
Biosensors 2021, 11(5), 147; https://doi.org/10.3390/bios11050147 - 08 May 2021
Cited by 23 | Viewed by 3586
Abstract
A portable and flexible self-powered biosensor based on ZnO nanowire arrays (ZnO NWs) and flexible PET substrate has been designed and fabricated for real-time monitoring in swimming. Based on the piezoelectric effect of polar ZnO NWs, the fabricated biosensor can work in both [...] Read more.
A portable and flexible self-powered biosensor based on ZnO nanowire arrays (ZnO NWs) and flexible PET substrate has been designed and fabricated for real-time monitoring in swimming. Based on the piezoelectric effect of polar ZnO NWs, the fabricated biosensor can work in both air and water without any external power supply. In addition, the biosensor can be easily attached to the surface of the skin to precisely monitor the motion state such as joint moving angle and frequency during swimming. The constant output piezoelectric signal in different relative humidity levels enables actual application in different sports, including swimming. Therefore, the biosensor can be utilized to monitor swimming strokes by attaching it on the surface of the skin. Finally, a wireless transmitting application is demonstrated by implanting the biosensor in vivo to detect angiogenesis. This portable and flexible self-powered biosensor system exhibits broad application prospects in sport monitoring, human–computer interaction and wireless sport big data. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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Review

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28 pages, 5676 KiB  
Review
Adaptive Triboelectric Nanogenerators for Long-Term Self-Treatment: A Review
by Zequan Zhao, Yin Lu, Yajun Mi, Jiajing Meng, Xueqing Wang, Xia Cao and Ning Wang
Biosensors 2022, 12(12), 1127; https://doi.org/10.3390/bios12121127 - 05 Dec 2022
Cited by 10 | Viewed by 1928
Abstract
Triboelectric nanogenerators (TENGs) were initially invented as an innovative energy−harvesting technology for scavenging mechanical energy from our bodies or the ambient environment. Through adaptive customization design, TENGs have also become a promising player in the self-powered wearable medical market for improving physical fitness [...] Read more.
Triboelectric nanogenerators (TENGs) were initially invented as an innovative energy−harvesting technology for scavenging mechanical energy from our bodies or the ambient environment. Through adaptive customization design, TENGs have also become a promising player in the self-powered wearable medical market for improving physical fitness and sustaining a healthy lifestyle. In addition to simultaneously harvesting our body’s mechanical energy and actively detecting our physiological parameters and metabolic status, TENGs can also provide personalized medical treatment solutions in a self-powered modality. This review aims to cover the recent advances in TENG-based electronics in clinical applications, beginning from the basic working principles of TENGs and their general operation modes, continuing to the harvesting of bioenergy from the human body, and arriving at their adaptive design toward applications in chronic disease diagnosis and long-term clinical treatment. Considering the highly personalized usage scenarios, special attention is paid to customized modules that are based on TENGs and support complex medical treatments, where sustainability, biodegradability, compliance, and bio-friendliness may be critical for the operation of clinical systems. While this review provides a comprehensive understanding of TENG-based clinical devices that aims to reach a high level of technological readiness, the challenges and shortcomings of TENG-based clinical devices are also highlighted, with the expectation of providing a useful reference for the further development of such customized healthcare systems and the transfer of their technical capabilities into real-life patient care. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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23 pages, 3997 KiB  
Review
Recent Advances in Portable Biosensors for Biomarker Detection in Body Fluids
by Brian Senf, Woon-Hong Yeo and Jong-Hoon Kim
Biosensors 2020, 10(9), 127; https://doi.org/10.3390/bios10090127 - 18 Sep 2020
Cited by 63 | Viewed by 7971
Abstract
A recent development in portable biosensors allows rapid, accurate, and on-site detection of biomarkers, which helps to prevent disease spread by the control of sources. Less invasive sample collection is necessary to use portable biosensors in remote environments for accurate on-site diagnostics and [...] Read more.
A recent development in portable biosensors allows rapid, accurate, and on-site detection of biomarkers, which helps to prevent disease spread by the control of sources. Less invasive sample collection is necessary to use portable biosensors in remote environments for accurate on-site diagnostics and testing. For non- or minimally invasive sampling, easily accessible body fluids, such as saliva, sweat, blood, or urine, have been utilized. It is also imperative to find accurate biomarkers to provide better clinical intervention and treatment at the onset of disease. At the same time, these reliable biomarkers can be utilized to monitor the progress of the disease. In this review, we summarize the most recent development of portable biosensors to detect various biomarkers accurately. In addition, we discuss ongoing issues and limitations of the existing systems and methods. Lastly, we present the key requirements of portable biosensors and discuss ideas for functional enhancements. Full article
(This article belongs to the Special Issue Smart, Connected, and Portable Biosensors and Bioelectronics for Advancing Human Healthcare, Disease Diagnosis, and Therapeutics)
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