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Intravenous delivery is the fastest conventional method of delivering drugs to their targets in seconds, whereas intramuscular and subcutaneous injections provide a slower continuous delivery of drugs. In recent years, nanoparticle-based drug-delivery systems have gained considerable attention. During the progression of nanoparticles into the blood, the sound waves generated by the particles create acoustic pressure that affects the movement of nanoparticles. To overcome this issue, the impact of sound pressure levels on the development of nanoparticles was studied herein. In addition, a composite nanostructure was developed using different types of nanoscale substances to overcome the effect of sound pressure levels in the drug-delivery process. The results demonstrate the efficacy of the proposed nanostructure based on a group of different nanoparticles. This study suggests five materials, namely, polyimide, acrylic plastic, Aluminum 3003-H18, Magnesium AZ31B, and polysilicon for the design of the proposed structure. The best results were obtained in the case of the movement of these molecules at lower frequencies. The performance of acrylic plastic is better than other materials; the sound pressure levels reached minimum values at frequencies of 1, 10, 20, and 60 nHz. Furthermore, an experimental setup was designed to validate the proposed idea using advanced biomedical imaging technologies. The experimental results demonstrate the possibilities of detecting, tracking, and evaluating the movement behaviors of nanoparticles. The experimental results also demonstrate that the lowest sound pressure levels were observed at lower frequency levels, thus proving the validity of the proposed computational model assumptions. The outcome of this study will pave the way to understand the interaction behaviors of nanoparticles with the surrounding biological environments, including the sound pressure effect, which could lead to the useof such an effect in facilitating directional and tactic movements of the micro- and nano-motors.

High cholesterol could be dangerous, along with the deposits of other substances, such as fat, on the walls of the arteries. These plaques can reduce blood flow through the arteries, which in turn can cause complications such as chest pain, blood clots, and heart attack. Hence, there is a need for an efficient way to measure the concentration of cholesterol in blood vessels with great accuracy to predict its risk on health. The present study aims to measure the concentration of cholesterol and track it accurately in the blood vessels using an ultrasound pressure sensor, which detects the concentration of cholesterol and produces a pressure field around its surface that is directly proportional to the concentration. This field can be tracked by ultrasound. In this study, the experiments conducted involved the insertion of aluminum nanoparticles, which represent a pressure sensor coated with a massless piezoelectric aluminum nitride nanoplate, into simulated blood vessels containing different concentrations that mimic human blood. The sensitivity of the blood vessels was monitored at different time periods. Moreover, an experimental setup was constructed to validate the possibility of using existing ultrasound medical imaging technologies in tracking the proposed nano biosensors. The setup involves prototyping a medical phantom with a specific acoustic characteristic for simulating human tissues. Various clinical scenarios have been imitated and the possibility of tracking these novel micro electromechanical pressure biosensors has been discussed.

Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign -heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as 'normal' or 'abnormal' is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine.