Contribution of neural networks in the diagnosis and treatment of cardiac arrhythmia.

Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very slow. Rapid heartbeats can lead to shortness of breath, chest pain, and sudden weakness, whereas slow heartbeats can lead to dizziness, problems with concentration, and constant stress. Finding an effective treatment for arrhythmia has become a very important endeavor for researchers and clinicians. In this article, we review the latest methodologies used in arrhythmia diagnosis and treatment. They include the application of five different types of artificial neural networks trained by machine learning and powered by artificial intelligence: convolutional, recurrent, feedforward, radial basis function, and modular neural network. Some of these methodologies are merged to enhance accuracy and efficacy. This review suggests that more research needs to be carried out in merging neural network types for their application in electrocardiogram (ECG).

Qualitative theoretical modeling to study the possibility of detecting multi-virus in blood flow using Nano-quartz crystal microbalance.

Methods for testing the presence of a virus in the blood are of interest to researchers and doctors because they determine how rapidly a virus is detected. In general, virus detection is a major scientific problem due to the serious effects of viruses on the human body. At present, only one virus can be detected in a single test. This potentially costs the medical establishment more time and money that could be saved if blood testing was more efficient. This study presents a qualitative method to enable doctors and researchers to detect more than one virus simultaneously. This was performed using quartz nanoparticles. Using polymer thin films of polydimethylsiloxane (PDMS), each chip emits a different frequency for each specific type of virus on the chip. The multiplicity of these chips allows for the detection of a number of viruses with the same number of nanoscale chips simultaneously. Blood flow around quartz nanoparticles was modelled. In this model, several conventional Quartz Crystal Microbalance (QCM) with nanostructures (Nano-QCM) particles are inserted into the three main types of blood vessels. The results showed that the best location for the Nano-QCM is the large artery and that it is possible to test for a number of viruses in all types of blood vessels.

Validation of Nanoparticle Response to the Sound Pressure Effect during the Drug-Delivery Process.

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.