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Register a new userA Novel Magnetic Respiratory Sensor for Human Healthcare
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Breathing is vital to life. Therefore, the real-time monitoring of breathing pattern of a patient is crucial to respiratory rehabilitation therapies such as magnetic resonance exams for respiratory-triggered imaging, chronic pulmonary disease treatment, and synchronized functional electrical stimulation. While numerous respiratory devices have been developed, they are often in direct contact with a patient, which can yield inaccurate or limited data. In this study, we developed a novel, non-invasive, and contactless magnetic sensing platform that can precisely monitor breathing, movement, or sleep patterns of a patient, thus providing efficient monitoring at a clinic or home. A magneto-LC resonance (MLCR) sensor converts the magnetic oscillations generated by breathing of the patient into an impedance spectrum, which allows for a deep analysis of breath variation to identify respiratory-related diseases like COVID-19. Owing to its ultrahigh sensitivity, the MLCR sensor yields a distinct breathing pattern for each patient tested. The sensor also provides an accurate measure of the strength of breath at multiple stages as well as anomalous variations in respiratory rate and amplitude. This suggests that the MLCR sensor can detect symptoms of COVID-19 in a patient due to shortness of breath or difficulty breathing as well as track the progress of the disease in real time.
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Fatigue, sleepiness and disturbed sleep are important factors in health and safety in modern society and there is considerable interest in developing technologies for routine monitoring of associated physiological indicators. Electrophysiology, the measurement of the electrical activity of biological origin, is a key technique for the measurement of physiological parameters in several applications, but it has been traditionally difficult to develop sensors for measurements outside the laboratory or clinic with the required quality and robustness. In this paper we report the results from first human experiments using a new electrophysiology sensor called ENOBIO, using carbon nanotube arrays for penetration of the outer layers of the skin and improved electrical contact. These tests, which have included traditional protocols for the analysis of the electrical activity of the brain--spontaneous EEG and ERP--indicate performance on a par with state of the art research-oriented wet electrodes, suggesting that the envisioned mechanism--skin penetration--is responsible. No ill side-effects have been observed six months after the tests, and the subject did not report any pain or special sensations on application of the electrode.
Mucus is a viscoelastic gel secreted by the pulmonary epithelium in the tracheobronchial region of the lungs. The coordinated beating of cilia moves mucus upwards towards pharynx, removing inhaled pathogens and particles from the airways. The efficacy of this clearance mechanism depends primarily on the rheological properties of mucus. Here we use magnetic wire based microrheology to study the viscoelastic properties of human mucus collected from human bronchus tubes. The response of wires between 5 and 80 microns in length to a rotating magnetic field is monitored by optical time-lapse microscopy and analyzed using constitutive equations of rheology, including those of Maxwell and Kelvin-Voigt. The static shear viscosity and elastic modulus can be inferred from low frequency (from 0.003 to 30 rad s-1) measurements, leading to the evaluation of the mucin network relaxation time. This relaxation time is found to be widely distributed, from one to several hundred seconds. Mucus is identified as a viscoelastic liquid with an elastic modulus of 2.5 +/- 0.5 Pa and a static viscosity of 100 +/- 40 Pa s. Our work shows that beyond the established spatial variations in rheological properties due to microcavities, mucus exhibits secondary inhomogeneities associated with the relaxation time of the mucin network that may be important for its flow properties.
The current COVID-19 pandemic has highlighted the need for cheap reusable personal protective equipment. The disinfection properties of Ultraviolet (UV) radiation in the 200-300 nm have been long known and documented. Many solutions using UV radiation, such as cavity disinfection and whole room decontamination between uses, are in use in various industries, including healthcare. Here we propose a portable wearable device which can safely, efficiently and economically, continuously disinfect inhaled/exhaled air using UV radiation with possible 99.99% virus elimination. We utilize UV radiation in the 260 nm range where no ozone is produced, and because of the self-contained UV chamber, there would be no UV exposure to the user. We have optimized the cavity design such that an amplification of 10-50 times the irradiated UV power may be obtained. This is crucial in ensuring enough UV dosage is delivered to the air flow during breathing. Further, due to the turbulent nature of airflow, a series of cavities is proposed to ensure efficient actual disinfection. The Personal Ultraviolet Respiratory Germ Eliminating Machine (PUR$diamond$GEM) can be worn by people or attached to devices such as ventilator exhausts/intakes, or be used free-standing as a portable local air disinfection unit, offering modularity with multiple avenues of usage. Patent pending.
Patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principle component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method, and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting respiratory signal. We also identified the applicability of each existing method.
Proton beam therapy can potentially offer improved treatment for cancers of the head and neck and in paediatric patients. There has been a sharp uptake of proton beam therapy in recent years as improved delivery techniques and patient benefits are observed. However, treatments are currently planned using conventional x-ray CT images due to the absence of devices able to perform high quality proton computed tomography (pCT) under realistic clinical conditions. A new plastic-scintillator-based range telescope concept, named ASTRA, is proposed here as the energy tagging detector of a pCT system. Simulations conducted using Geant4 yield an expected energy resolution of 0.7% and have demonstrated the ability of ASTRA to track multiple protons simultaneously. If calorimetric information is used the energy resolution could be further improved to about 0.5%. Assuming clinical beam parameters the system is expected to be able to efficiently reconstruct at least, 10$^8$ protons/s. The performance of ASTRA has been tested by imaging phantoms to evaluate the image contrast and relative stopping power reconstruction.
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