Publications by authors named "Jianbiao Xiao"

Speech recognition in noisy environments has long posed a challenge. Air conduction microphone (ACM), the devices typically used, are susceptible to environmental noise. In this work, a customized bone conduction microphone (BCM) system based on a piezoelectric micromachined ultrasonic transducer is developed to capture speech through real-time bone conduction (BC), while a commercial ACM is integrated for simultaneous capture of speech through air conduction (AC).

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The prevalence and mortality rates of colorectal cancer (CRC) are increasing worldwide. Radiation resistance hinders radiotherapy, a standard treatment for advanced CRC, leading to local recurrence and metastasis. Elucidating the molecular mechanisms underlying radioresistance in CRC is critical to enhance therapeutic efficacy and patient outcomes.

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Background: Dysfunction of CD8 T cells in the tumor microenvironment (TME) contributes to tumor immune escape and immunotherapy tolerance. The effects of hormones such as leptin, steroid hormones, and glucocorticoids on T cell function have been reported previously. However, the mechanism underlying thyroid-stimulating hormone (TSH)/thyroid-stimulating hormone receptor (TSHR) signaling in CD8 T cell exhaustion and tumor immune evasion remain poorly understood.

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Background: Colorectal cancer (CRC) lacks established biomarkers or molecular targets for predicting or enhancing radiation response. Phosphatidylinositol-3,4,5-triphosphate-dependent Rac exchange factor 2 (PREX2) exhibits intricate implications in tumorigenesis and progression. Nevertheless, the precise role and underlying mechanisms of PREX2 in CRC radioresistance remain unclear.

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In recent years, environmental sound classification (ESC) has prevailed in many artificial intelligence Internet of Things (AIoT) applications, as environmental sound contains a wealth of information that can be used to detect particular events. However, existing ESC methods have high computational complexity and are not suitable for deployment on AIoT devices with constrained computing resources. Therefore, it is of great importance to propose a model with both high classification accuracy and low computational complexity.

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Wearable intelligent health monitoring devices with on-device biomedical AI processor can be used to detect the abnormity in users' biomedical signals (e.g., ECG arrythmia classification, EEG-based seizure detection).

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The respiratory rate is widely used for evaluating a person's health condition. Compared to other invasive and expensive methods, the ECG-derived respiration estimation is a more comfortable and affordable method to obtain the respiration rate. However, the existing ECG-derived respiration estimation methods suffer from low accuracy or high computational complexity.

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The ECG classification processor is a key component in wearable intelligent ECG monitoring devices which monitor the ECG signals in real time and detect the abnormality automatically. The state-of-the-art ECG classification processors for wearable intelligent ECG monitoring devices are faced with two challenges, including ultra-low energy consumption demand and high classification accuracy demand against patient-to-patient variability. To address the above two challenges, in this work, an ultra-energy-efficient ECG classification processor with high classification accuracy is proposed.

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Background: Intracranial hemangiopericytoma is a rare disease and surgery is the mainstay treatment. Although postoperative adjuvant radiotherapy is often used, there are no reports comparing different radiotherapy techniques. The purpose of this study is to analyze the impact of post-operative radiotherapy and different radiotherapy technique on the results in patients with intracranial hemangiopericytoma (HPC).

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Article Synopsis
  • - ECG classification is crucial for monitoring heart health, transitioning from traditional machine learning methods like SVM and KNN to advanced end-to-end neural networks, which offer better accuracy but are computationally heavy.
  • - The new study introduces an ultra-lightweight ECG classification neural network with significantly reduced computational complexity, making it suitable for low-cost microcontrollers while maintaining high accuracy (99.1%) in classification.
  • - Implemented on the MSP432 microcontroller, this innovative design consumes minimal power (0.4 mJ for normal and 3.1 mJ for abnormal heartbeats), enabling efficient real-time ECG monitoring without the drawbacks of existing hardware solutions.
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In colorectal cancer (CRC), overt metastases often appear after years of latency. But the signals that cause micro-metastatic cells to remain indolent, thereby enabling them to survive for extended periods of time, are unclear. Immunofluorescence and co-immunoprecipitation assays were used to explore the co-localization of CCL7 and CCR2.

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Background: CMTM6 is a novel key regulator of PD-L1. High expression of both CMTM6 and PD-L1 may predict the benefit of PD-1 axis blockade in lung cancer. We aimed to investigate the expression pattern of CMTM6 between mismatch repair-defective (dMMR) and mismatch repair-proficient (pMMR) colorectal cancer (CRC) tissues and assess its correlation with the response to PD-1/PD-L1 pathway blockade.

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High-mobility group box 1 protein (HMGB1) is an evolutionarily ancient and critical regulator of cell death and survival. HMGB1 is a chromatin-associated nuclear protein molecule that triggers extracellular damage. The expression of HMGB1 has been reported in many types of cancers, but the role of HMGB1 in hepato cellular carcinoma (HCC) is unknown.

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