Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.
Imaging modalities combined with a multimodal nanocomposite contrast agent hold great potential for significant contributions in the biomedical field. Among modern imaging techniques, photoacoustic (PA) and fluorescence (FL) imaging gained much attention due to their non-invasive feature and the mutually supportive characteristic in terms of spatial resolution, penetration depth, imaging sensitivity, and speed. In this present study, we synthesized IR783 conjugated chitosan–polypyrrole nanocomposites (IR-CS–PPy NCs) as a theragnostic agent used for FL/PA dual-modal imaging. A customized FL and photoacoustic imaging system was constructed to perform required imaging experiments and create high-contrast images. The proposed nanocomposites were confirmed to have great biosafety, essentially a near-infrared (NIR) absorbance property with enhanced photostability. The in vitro photothermal results indicate the high-efficiency MDA-MB-231 breast cancer cell ablation ability of IR-CS–PPy NCs under 808 nm NIR laser irradiation. The in vivo PTT study revealed the complete destruction of the tumor tissues with IR-CS–PPy NCs without further recurrence. The in vitro and in vivo results suggest that the demonstrated nanocomposites, together with the proposed imaging systems could be an effective theragnostic agent for imaging-guided cancer treatment.
Low-level laser therapy (LLLT), also known as photobiomodulation, is a safe and noninvasive method for various dermatological applications. However, most LLLT devices have limitations, such as low flexibility, high energy consumption, and huge equipment size, limiting their usage in daily life and clinical treatment. This study presents a flexible and wireless light emitting diode (LED) patch with an internet of thing (IoT) healthcare platform for wound healing applications. The flexible LED patch was designed with a high-efficiency performance of thermal stability, device uniformity, and mechanical durability for skin-attachable phototherapies application and clinical use. The application of a smartphone app with an IoT-connected healthcare platform for the flexible LED patch opens tremendous opportunities for the development of a remote healthcare system with cost-effectiveness in the future. In wound healing test on normal human fibroblasts, the LED light was proven to have no cytotoxic effect with high fibroblast proliferation and fibroblast migration (over 16% compared to control) under various light irradiations. Furthermore, a high association between wavelengths and exposure duration with biologic responses and migration effects was indicated in the study. The cell proliferation and migration experiments show the necessity of optimizing LED wavelength, radiation doses for better clinical assessment. Based on the results, the flexible LED patch is expected to be a suitable photomedical device for various types of dermatology applications.
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