Sonoporation is a targeted drug delivery technique that employs cavitation microbubbles to generate transient pores in the cell membrane, allowing foreign substances to enter cells by passing through the pores. Due to the broad size distribution of microbubbles, cavitation events appear to be a random process, making it difficult to achieve controllable and efficient sonoporation. In this work a technique is reported using a microfluidic device that enables in parallel modulation of membrane permeability by an oscillating microbubble array. Multirectangular channels of uniform size are created at the sidewall to generate an array of monodispersed microbubbles, which oscillate with almost the same amplitude and resonant frequency, ensuring homogeneous sonoporation with high efficacy. Stable harmonic and high harmonic signals emitted by individual oscillating microbubbles are detected by a laser Doppler vibrometer, which indicates stable cavitation occurred. Under the influence of the acoustic radiation forces induced by the oscillating microbubble, single cells can be trapped at an oscillating microbubble surface. The sonoporation of single cells is directly influenced by the individual oscillating microbubble. The parallel sonoporation of multiple cells is achieved with an efficiency of 96.6 ± 1.74% at an acoustic pressure as low as 41.7 kPa.

Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning. We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96. The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.

This paper presents an energy ratio-based method and a wavelet-based cascaded adaptive filter (CAF) for detecting and removing baseline drift from pulse waveforms. Experiments on 50 simulated and five hundred real pulse signals demonstrate that this CAF outperforms traditional filters both in removing baseline drift and in preserving the diagnostic information of pulse waveforms.

A wireless electrocardiogram (ECG) monitoring system is developed which integrates Bluetooth Low Energy (BLE) technology. This BLE-based system is comprised of a single-chip ECG signal acquisition module, a Bluetooth module and a smart-phone. Apple's iPhone 4S is selected as the mobile device platform, which embedded with Bluetooth v4.0, Wi-Fi and iOS. In this paper, the monitoring system is able to acquire ECG signals through 2-lead electrocardiogram (ECG) sensor, transmit the ECG data via the Bluetooth wireless link, process and display the ECG waveform in a smart-phone. The results show that implementation of Bluetooth Low Energy (BLE) technology in the existing ECG monitoring system not only eliminates the physical constraints imposed by hard-wired link but also highly reduces the power consumption of the long-term monitoring system.