Smartphone-Based Biosensors and Diagnostics

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 7091

Special Issue Editors


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Guest Editor
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA
Interests: imaging and sensing devices; point-of-care diagnostics; lab on a chip; nanoscience and nanoengineering; global health
Special Issues, Collections and Topics in MDPI journals
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA
Interests: microfluidics; point-of care diagnostics; biosensing and imaging; automotive diagnostic devices; flow cytometry; time-resolved detection

Special Issue Information

Dear Colleagues,

Over the past few decades, smartphone-based imaging and sensing platforms have emerged as promising alternatives for health monitoring and medical diagnostics, offering practical features such as portability and cost-effectiveness, particularly in regions with limited access to healthcare facilities. Many of these applications leverage the ever-increasing penetration of smartphones in our life and have significantly improved built-in modules of smartphones, such as the processor chip, complementary metal-oxide-semiconductor (CMOS) image sensor, and wireless connectivity. With the help of these embedded functions, smartphones have been coupled with various add-ons such as optical lenses and filters to bring a wide range of detection modalities, such as optical, electrochemical, and magnetic sensing, to the field. Images and videos captured by smartphone microscopy devices can be readily processed by custom developed smartphone applications for on-site bioanalysis.

In this Special Issue, we invite you to contribute original research articles and reviews on any aspects related to smartphone-based point-of-care diagnostics, including but not limited to device and assay development, time-dependent monitoring of health matrices, diagnosis of human diseases such as infectious diseases and cancer, as well as the detection of health-related threats in food or environmental samples.

Dr. Qingshan Wei
Dr. Yan Wang
Guest Editors

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Keywords

  • smartphone
  • health monitoring
  • point-of-care diagnostics
  • imaging
  • sensing
  • telemedicine
  • global health

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Published Papers (1 paper)

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Research

9 pages, 508 KiB  
Communication
Superior Adaptations in Adolescent Runners Using Heart Rate Variability (HRV)-Guided Training at Altitude
by Petr Bahenský and Gregory J. Grosicki
Biosensors 2021, 11(3), 77; https://doi.org/10.3390/bios11030077 - 11 Mar 2021
Cited by 5 | Viewed by 6300
Abstract
We evaluated the efficacy of heart rate variability (HRV)-guided training in adolescent athletes during a 2-week, high altitude (≈1900 m) training camp. Sixteen middle- and long-distance runners (4 female/12 male, 16.9 ± 1.0 years, 65.44 ± 4.03 mL·kg−1·min−1) were [...] Read more.
We evaluated the efficacy of heart rate variability (HRV)-guided training in adolescent athletes during a 2-week, high altitude (≈1900 m) training camp. Sixteen middle- and long-distance runners (4 female/12 male, 16.9 ± 1.0 years, 65.44 ± 4.03 mL·kg−1·min−1) were divided into 2 matched groups, both of which received the same training plan, but one of which acquired postwaking HRV values that were used to tailor the training prescription. During the camp, seven athletes in the HRV-guided group combined for a total of 32 training adjustments, whereas there were only 3 runners combined for 14 total training adjustments in the control group. A significant group by time interaction (p < 0.001) for VO2max was driven by VO2max improvements in the HRV group (+2.8 mL·kg−1·min−1, +4.27%; pBonf = 0.002) that were not observed in the control condition (+0.8 mL·kg−1·min−1, +1.26%; pBonf = 0.643). After returning from the camp, all athletes in the HRV group set a personal best, and six out of eight achieved their best positions in the National Championship, whereas only 75% of athletes in the control group set a personal best and five out of eight achieved their best positions in the National Championship. These data provide evidence in support of HRV-guided training as a way to optimize training prescriptions in adolescent athletes. Full article
(This article belongs to the Special Issue Smartphone-Based Biosensors and Diagnostics)
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