Evidential regression-based blood glucose detection using waveform features

<div>As one of the necessary diabetes control and treatment methods, the photoacoustic blood glucose detection technology has great potential due to its deep detection depth and low interference from stray light. Previous research mainly focused on improving the detection capabilities of hardware systems and ignored the exploration of the physical meaning of the signal itself. We analyzed the characteristics of the signal amplitude decay in the photoacoustic signal and employed the forced damping vibration equation to model the signal waveform. A new waveform feature was constructed to describe the amplitude attenuation rate. Moreover, facing low accuracy of blood glucose prediction in the case of small data, we proposed a stable and effective blood glucose detection combining time-frequency feature and waveform features with evidential regression. Finally, in human tissue and glucose solution experiments, the minimum error is achieved 1.02±0.71 mg/dL and 13.28±10.33 mg/dL, respectively.</div><div><br></div>

Evidential regression-based blood glucose detection using waveform features

<div>As one of the necessary diabetes control and treatment methods, the photoacoustic blood glucose detection technology has great potential due to its deep detection depth and low interference from stray light. Previous research mainly focused on improving the detection capabilities of hardware systems and ignored the exploration of the physical meaning of the signal itself. We analyzed the characteristics of the signal amplitude decay in the photoacoustic signal and employed the forced damping vibration equation to model the signal waveform. A new waveform feature was constructed to describe the amplitude attenuation rate. Moreover, facing low accuracy of blood glucose prediction in the case of small data, we proposed a stable and effective blood glucose detection combining time-frequency feature and waveform features with evidential regression. Finally, in human tissue and glucose solution experiments, the minimum error is achieved 1.02±0.71 mg/dL and 13.28±10.33 mg/dL, respectively.</div><div><br></div>

Feasibility of Quantitative Tissue Characterization Using Novel Parameters Extracted From Photoacoustic Power Spectrum Considering Multiple Absorbers

Frequency domain analysis of radio frequency signal is performed to differentiate between different tissue categories in terms of spectral parameters. However, due to complex relationship between the absorber size and spectral parameters, they cannot be used for quantitative tissue characterization. In an earlier study, we showed that using linear relationship between absorber size and two new spectral parameters namely number of lobes and average lobe width, absorber size can be successfully recovered from photoacoustic signal generated by single absorber. As actual biological tissue contains multiple absorbers, in this study we extended the application of these two new spectral parameters for computing absorber size from signals generated by multiple PA absorbers. We revisited our analytical model to establish two new linear relationships between the absorber radius and number of lobes as well as average lobe width considering multiple absorbers with bandlimited acquisition. A simulation study was performed to validate these linear relationships. A retrospective ex vivo study, in which the spectral parameters were computed using multiwavelength photoacoustic signals, was performed with freshly exercised thyroid specimens from 38 actual human patients undergoing thyroidectomy after having a diagnosis of suspected thyroid lesions. From statistical analysis it is shown that both the parameters were significantly different between malignant and non-malignant thyroid and malignant and normal thyroid tissue. Performance of the supervised classification with the computed spectral parameters showed that the extracted parameters could be successfully used to differentiate malignant thyroid tissue from normal thyroid tissue with reasonable degree of accuracy.

Influence of Data Scaling and Normalization on Overall Neural Network Performances in Photoacoustics

In this paper, the influence of the input and output data scaling and normalization on the neural network overall performances is investigated aimed at inverse problem-solving in photoacoustics of semiconductors. The logarithmic scaling of the photoacoustic signal amplitudes as input data and numerical scaling of the sample thermal parameters as output data are presented as useful tools trying to reach maximal network precision. Max and min-max normalizations to the input data are presented to change their numerical values in the dataset to common scales, without distorting differences. It was demonstrated in theory that the largest network prediction error of all targeted parameters is obtained by a network with non-scaled output data. Also, it was found out that the best network prediction was achieved with min-max normalization of the input data and network predicted output data scale within the range of [110]. Network training and prediction performances analyzed with experimental input data show that the benefits and improvements of input and output scaling and normalization are not guaranteed but are strongly dependent on a specific problem to be solved.

Photoacoustic Properties of Polypyrrole Nanoparticles

Photoacoustic imaging, an emerging modality, provides supplemental information to ultrasound imaging. We investigated the properties of polypyrrole nanoparticles, which considerably enhance contrast in photoacoustic images, in relation to the synthesis procedure and to their size. We prepared polypyrrole nanoparticles by water-based redox precipitation polymerization in the presence of ammonium persulphate (ratio nPy:nOxi 1:0.5, 1:1, 1:2, 1:3, 1:5) or iron(III) chloride (nPy:nOxi 1:2.3) acting as an oxidant. To stabilize growing nanoparticles, non-ionic polyvinylpyrrolidone was used. The nanoparticles were characterized and tested as a photoacoustic contrast agent in vitro on an imaging platform combining ultrasound and photoacoustic imaging. High photoacoustic signals were obtained with lower ratios of the oxidant (nPy:nAPS ≥ 1:2), which corresponded to higher number of conjugated bonds in the polymer. The increasing portion of oxidized structures probably shifted the absorption spectra towards shorter wavelengths. A strong photoacoustic signal dependence on the nanoparticle size was revealed; the signal linearly increased with particle surface. Coated nanoparticles were also tested in vivo on a mouse model. To conclude, polypyrrole nanoparticles represent a promising contrast agent for photoacoustic imaging. Variations in the preparation result in varying photoacoustic properties related to their structure and allow to optimize the nanoparticles for in vivo imaging.

Application of ultrasound molecular imaging based on compressed sensing reconstruction algorithm to phase change drug-loaded PLGA nanoparticles targeting breast cancer MCF-7 Cells

Objectives: To study the ability of aptamer-modified nano-gold rods and liquid carbon-targeted PLGA nanoparticles to target in vitro using compressed sensing reconstruction algorithm, and observe the phenomenon of mediating ultrasound / photoacoustic imaging. Methods: PLGA nanoparticles were prepared by a double emulsification method, and the MUC1 aptamer was connected to the PLGA nanoparticles by the carbodiimide method to obtain an “aptamer-PLGA nanoparticle” targeted phase change contrast agent. Fluorescence microscopy was used to detect the in vitro targeting of breast cancer MCF-7 cells specifically identified by it, and three control groups were set up: the ordinary nanoparticle group, the aptamer interference group, and the HELA cell group. A photoacoustic instrument was used to observe the phenomenon of enhanced ultrasound / photoacoustic signal mediated in vitro. Results: Many targeted nanoparticles were clustered around MCF-7 cells and bound firmly, but no specific binding was observed in the non-targeted nanoparticles group, the aptamer interference group and the HELA cell group. After the targeted nanoparticle was excited by the photoacoustic instrument, the ultrasonic signal and the photoacoustic signal were significantly enhanced compared with before the excitation. Conclusion: The successfully prepared targeting nanoparticles have good targeting and specificity for breast cancer MCF-7 cells, and it has obvious effects on ultrasound / photoacoustic imaging, and has the potential to become a dual-mode ultrasound / photoacoustic targeted contrast agent. The various characteristics provide experimental basis for subsequent in vivo targeting experiments and are expected to become good target diagnostic molecular probes. doi: https://doi.org/10.12669/pjms.37.6-WIT.4852 How to cite this:You Y, Cheng W, Chen H. Application of ultrasound molecular imaging based on compressed sensing reconstruction algorithm to phase change drug-loaded PLGA nanoparticles targeting breast cancer MCF-7 Cells. Pak J Med Sci. 2021;37(6):1610-1614. doi: https://doi.org/10.12669/pjms.37.6-WIT.4852 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Plasmonic Gold Nanomaterials as Photoacoustic Signal Resonant Enhancers for Cysteine Detection

The development of photoacoustic systems is important for the real-time detection of cysteine (Cys), a biothiol in biological systems that serves as a significant biomarker for human health. Advanced photoacoustic (PA) signals with colloidal plasmonic Au nanomaterials rely on the efficient conversion of light to energy waves under moderately pulsed laser irradiation. In this study, we synthesized Cys-capped Au nanorods (Au@Cys NRs) and Cys-capped Au nanoparticles (Au@Cys NPs) through a conjugate of three Cys concentrations (10, 100, and 1000 μM). These plasmonic Au nanomaterials can be used as a PA resonance reagent due to their maximum localized surface plasmon resonance (LSPR) absorption bands at 650 nm and 520 nm in Au NRs and Au NPs, respectively. Subsequently, the PA signals were noticeably increased proportionally to the concentrations in the Au@Cys NRs and Au@Cys NPs under 658 nm and 520 nm laser irradiation, respectively, according to our portable photoacoustic system. Furthermore, PA signal amplitudes in Cys detection are boosted by ~233.01% with Au@Cys NRs and ~102.84% with Au@Cys NPs enhancement, compared to free Cys, according to ultrasound transducers at frequencies of 3 MHz.