Smartphone-Based pH Sensor for Home Monitoring of Pulmonary Exacerbations in Cystic Fibrosis

Moving toward smart biomedical sensing

The development of novel biomedical sensors as highly promising devices/tools in early diagnosis and therapy monitoring of many diseases and disorders has recently witnessed unprecedented growth; more and faster than ever. Nonetheless, on the eve of Industry 5.0 and by learning from defects of current sensors in smart diagnostics of pandemics, there is still a long way to go to achieve the ideal biomedical sensors capable of meeting the growing needs and expectations for smart biomedical/diagnostic sensing through eHealth systems. Herein, an overview is provided to highlight the importance and necessity of an inevitable transition in the era of digital health/Healthcare 4.0 towards smart biomedical/diagnostic sensing and how to approach it via new digital technologies including Internet of Things (IoT), artificial intelligence, IoT gateways (smartphones, readers), etc. This review will bring together the different types of smartphone/reader-based biomedical sensors, which have been employing for a wide variety of optical/electrical/electrochemical biosensing applications and paving the way for future eHealth diagnostic devices by moving towards smart biomedical sensing. Here, alongside highlighting the characteristics/criteria that should be met by the developed sensors towards smart biomedical sensing, the challenging issues ahead are delineated along with a comprehensive outlook on this extremely necessary field.

Strategies to simplify operation procedures for applying labeled antibody-based immunosensors to point-of-care testing

Point-of-care testing (POCT) is an ideal testing format for the rapid and on-site detection of analytes in patients, and facilitates disease diagnosis and monitoring. Molecular recognition elements are required for the specific detection of analytes, and biosensors that use antibodies as the molecular recognition elements are called immunosensors. Traditional immunosensors such as sandwich enzyme-linked immunosorbent assay (ELISA) require complicated procedures to form immunocomplexes consisting of detection antibodies, analytes, and capture antibodies. They also require long incubation times, washing procedures, and large and expensive specialized equipment that must be operated by laboratory technicians. Immunosensors for POCT should be systems that use relatively small pieces of equipment and do not require special training. In this review, to help in the construction of immunosensors for POCT, we have summarized the recently reported strategies for simplifying the operation, incubation, and washing procedures. We focused on the optical and electrochemical detection principles of immunosensors, compared the strategies for operation, sensitivity, and detection devices and discussed the ideal system. Combining detection devices that can be fabricated inexpensively and strategies that enable simplification of operation procedures and enhance sensitivities will contribute to the development of immunosensors for POCT.

Development of a handheld dual-channel optical fiber fluorescence sensor based on a smartphone

In order to meet the needs of on-site, accurate, fast, and remote detection, we design a smartphone-based handheld dual-channel optical fiber fluorescence sensor (DOFFS), which is composed of a semiconductor laser for exciting fluorescence signals, a smartphone with a dual-bandpass filter for collecting fluorescence signals, a fiber coupler for transmitting light, and batteries for laser power supply. All the components are integrated into a 3D printed shell, on the side of which there are two fiber flanges used for fiber probe connection. The fluorescence signals of green and red quantum dots modified on the fiber probes can be captured by the smartphone camera and calculated by a self-developed Android application. The comparisons of single-channel and dual-channel fluorescence signals with pH show that the performance of the sensor is good. The proposed sensor not only can simultaneously detect dual-channel signals for fast detection needs, but it also is handheld with a small size of 79×57×154mm³ and inner power supply, and the fiber probes can be easily replaced, supporting remote and on-site applications. It is a potential tool for many occasions in many fields.

Noise Source Visualization Using a Digital Voice Recorder and Low-Cost Sensors

Accurate sound visualization of noise sources is required for optimal noise control. Typically, noise measurement systems require microphones, an analog-digital converter, cables, a data acquisition system, etc., which may not be affordable for potential users. Also, many such systems are not highly portable and may not be convenient for travel. Handheld personal electronic devices such as smartphones and digital voice recorders with relatively lower costs and higher performance have become widely available recently. Even though such devices are highly portable, directly implementing them for noise measurement may lead to erroneous results since such equipment was originally designed for voice recording. In this study, external microphones were connected to a digital voice recorder to conduct measurements and the input received was processed for noise visualization. In this way, a low cost, compact sound visualization system was designed and introduced to visualize two actual noise sources for verification with different characteristics: an enclosed loud speaker and a small air compressor. Reasonable accuracy of noise visualization for these two sources was shown over a relatively wide frequency range. This very affordable and compact sound visualization system can be used for many actual noise visualization applications in addition to educational purposes.

Smartphone-based analytical biosensors

With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health.