Optical
excitation of plasmonic nanoparticles that generate heat
or induce photoacoustic signals is gathering immense attention in
biomedical applications such as imaging, photothermal therapy, and
drug delivery. Generally, these nanoparticles are encompassed by a
silica coating that enhances their overall activity and that imparts
stability. Intuitively, only an extreme high pressure and temperature
can lead to the morphological rupture of these heterogeneous composites;
however, herein, we report a study which shows that these drastic
structural defects can also be mimicked by simple optical pulse irradiation.
This happens because of the heat and pressure waves generated in these
hybrids. The structural differential expansion of constituent materials
induces a thermal stress in the system which causes a structural instability
and ultimately ruptures the coating. To demonstrate this phenomenon,
a comprehensive theoretical and experimental study has been conducted
on silica-coated gold nanoparticles, with diameter ca. 80 nm. The
heat and pressure waves generated because of the irradiation initiate
a crack in the silica coating and rupture the structure eventually.
The mechanism of this phenomenon has also been elucidated in this
paper via theoretical and experimental means.
In order to assist cardiac diagnosis by phonocardiography, the automated identification of fundamental heart sounds for heart beat segmentation in a cardiac cycle plays a significant role in signal processing. Recent advancements in signal processing have also shown the potential of multifractality in biomedical applications. Hence, in this paper, the multifractal property of heart sounds is utilized to identify first and second heart sounds. The root mean square (rms) fluctuation used to obtain multifractal/singularity spectrum is used to decompose the heart sound into its own fractally-important components in time domain along with simultaneous Gaussianity test to filter out fundamental components. The performance is evaluated on an experimental database of 23 different heart sounds and 6 patients' recordings done in a real clinical environment. Simulation results have shown that it is a promising approach in Heart Sound Segmentation (HSS).
Owing to its efficiency and cost effectiveness, auscultation using a stethoscope is the primary diagnosis method preferred by most medical examiners for cardiovascular disease. Phonocardiography (PCG) is the next level of auscultation and an effective analysis technique in cardiology. This paper presents a method to quantify Mitral Regurgitation (MR) using PCG. MR is one of the most common cardiovascular diseases associated with a defect in mitral valve. We analyzed the multifractal nature of heart sounds with murmur followed by classification and clustering using Hurst exponents. Thus, we were able to project the difference between severities of MR patients by correlating the complexity of heart sounds from four major auscultation sites, using a complexity analysis tool called singularity spectra. The simulation results show that the method can quantify the severity of MR using PCG.
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