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AbstractCoronary heart disease (CHD) is a significant global health concern, necessitating continuous advancements in treatment modalities to improve patient outcomes. Traditional Chinese medicine (TCM) offers alternative therapeutic approaches, but integration with modern biomedical technologies remains relatively unexplored. This study aimed to assess the efficacy of a combined treatment approach for CHD, integrating traditional Chinese medicinal interventions with modern biomedical sensors and stellate ganglion modulation. The objective was to evaluate the impact of this combined treatment on symptom relief, clinical outcomes, hemorheological indicators, and inflammatory biomarkers. A randomized controlled trial was conducted on 117 CHD patients with phlegm-turbidity congestion and excessiveness type. Patients were divided into a combined treatment group (CTG) and a traditional Chinese medicinal group (CMG). The CTG group received a combination of herbal decoctions, thread-embedding therapy, and stellate ganglion modulation, while the CMG group only received traditional herbal decoctions. The CTG demonstrated superior outcomes compared to the CMG across multiple parameters. Significant reductions in TCM symptom scores, improved clinical effects, reduced angina manifestation, favorable changes in hemorheological indicators, and decreased serum inflammatory biomarkers were observed in the CTG post-intervention. The combination of traditional Chinese medicinal interventions with modern biomedical sensors and stellate ganglion modulation has shown promising results in improving symptoms, clinical outcomes, and inflammatory markers in CHD patients. This holistic approach enhances treatment efficacy and patient outcomes. Further research and advancements in sensor technology are needed to optimize this approach. [ABSTRACT FROM AUTHOR]

Barrier islands are vital dynamic landforms that not only host ecological resources but often protect coastal ecosystems from storm damage. The Waisanding Barrier (WSDB) in Taiwan has suffered from continuous beach erosion in recent decades. In this study, we developed a SiamUnet network compared to three basic DeepUnet networks with different image sizes to effectively detect barrier waterlines from 207 high-resolution satellite images. The evolution of the barrier waterline shape is obtained to present two special morphologic changes at the southern end and the evolution of the entire waterline. The time periods of separation of the southern end from the main WSDB are determined and discussed. We also show that the southern L-shaped end has occurred recently from the end of 2017 until 2021. The length of the L-shaped end gradually decreases during the summer, but gradually increases during the winter. The L-shaped end obviously has a seasonal and jagged change. The attenuation rate of the land area is analyzed as −0.344 km2/year. We also explore two factors that affect the analysis results, which are the number of valid images selected and the deviation threshold from the mean sea level. [ABSTRACT FROM AUTHOR]

Barrier islands are vital dynamic landforms that not only host ecological resources but often protect coastal ecosystems from storm damage. The Waisanding Barrier (WSDB) in Taiwan has suffered from continuous beach erosion in recent decades. In this study, we developed a SiamUnet network compared to three basic DeepUnet networks with different image sizes to effectively detect barrier waterlines from 207 high-resolution satellite images. The evolution of the barrier waterline shape is obtained to present two special morphologic changes at the southern end and the evolution of the entire waterline. The time periods of separation of the southern end from the main WSDB are determined and discussed. We also show that the southern L-shaped end has occurred recently from the end of 2017 until 2021. The length of the L-shaped end gradually decreases during the summer, but gradually increases during the winter. The L-shaped end obviously has a seasonal and jagged change. The attenuation rate of the land area is analyzed as −0.344 km2/year. We also explore two factors that affect the analysis results, which are the number of valid images selected and the deviation threshold from the mean sea level.

Indirect immunofluorescence of Human Epithelial-2 (HEp-2) cells is a commonly used method for the diagnosis of autoimmune diseases. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labour intensive. In this paper, we proposed a deep-learning network, namely HEp-Net, to automatically classify HEp-2 cell images. The proposed HEp-Net uses multi-scale convolutional component to extract features from Hep-2 cell images and fuses the features extracted by shallow and deep layers for performance improvement. The proposed model is evaluated on publicly available I3A (Indirect Immunofluorescence Image Analysis) and MIVIA data-sets. Experimental result demonstrates that, compared to the state-of-the-art approaches, our proposed HEp-Net yields better performance with smaller network size. [ABSTRACT FROM AUTHOR]

Significance: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound healing process as their thickness is a vital indicator to judge whether the re-epithelialization process is normal or not. Since optical coherence tomography (OCT) is a real-time and non-invasive imaging technique that can perform a cross-sectional evaluation of tissue microstructure, it is an ideal imaging modality to monitor the thickness change of epidermal and scab tissues during wound healing processes in micron-level resolution. Traditional segmentation on epidermal and scab regions was performed manually, which is time-consuming and impractical in real time. Aim: We aim to develop a deep-learning-based skin layer segmentation method for automated quantitative assessment of the thickness of in vivo epidermis and scab tissues during a time course of healing within a rodent model. Approach: Five convolution neural networks were trained using manually labeled epidermis and scab regions segmentation from 1000 OCT B-scan images (assisted by its corresponding angiographic information). The segmentation performance of five segmentation architectures was compared qualitatively and quantitatively for validation set. Results: Our results show higher accuracy and higher speed of the calculated thickness compared with human experts. The U-Net architecture represents a better performance than other deep neural network architectures with 0.894 at F1-score, 0.875 at mean intersection over union, 0.933 at Dice similarity coefficient, and 18.28 μm at an average symmetric surface distance. Furthermore, our algorithm is able to provide abundant quantitative parameters of the wound based on its corresponding thickness maps in different healing phases. Among them, normalized epidermal thickness is recommended as an essential hallmark to describe the re-epithelialization process of the rodent model. Conclusions: The automatic segmentation and thickness measurements within different phases of wound healing data demonstrates that our pipeline provides a robust, quantitative, and accurate method for serving as a standard model for further research into effect of external pharmacological and physical factors. [ABSTRACT FROM AUTHOR]