Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD.

The electrocardiogram (ECG) electrode, as a sensor, is an important part of the wearable ECG monitoring device. Natural leather is rarely used as the electrode substrate. In this paper, wearable flexible silver electrodes based on cowhide were prepared by sputtering and brush-painting. A signal generator, oscilloscope, impedance test instrument, and ECG monitor were used to build the test platform evaluating the performance of electrodes with six subjects. The lossless waveform transmission can be achieved with our electrodes. Therefore, the Pearson’s correlation coefficient calculated with input waveform and output waveform of the electrodes based on the top grain layer (GLE) and the split layer (SLE) of cowhide were 0.997 and 0.998 at 0.1 Hz respectively. The skin electrode impedance (Z) was tested, and the parameters of the equivalent circuit model of the skin electrode interface were calculated by a fitting method, indicating that the Z of the prepared electrodes was comparable with the standard gel electrode when the skin is moist enough. The signal-to-noise ratio of the ECG of the GLE and the SLE were 1.148 and 1.205 times that of the standard electrode in the standing posture, which meant the ECG measured by our electrodes was basically consistent with that measured by the standard electrode.

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

Cell mechanical stretching in vitro is a fundamental technique commonly used in cardiovascular mechanobiology research. Accordingly, it is crucial to measure the accurate strain field of cell substrate under different strains. Digital image correlation (DIC) is a widely used measurement technique, which is able to obtain the accurate displacement and strain distribution. However, the traditional DIC algorithm used in digital image correlation engine (DICe) cannot obtain accurate result when utilized in large strain measurement. In this paper, an improved method aiming to acquire accurate strain distribution of substrate in large deformation was proposed, to evaluate the effect and accuracy, based on numerical experiments. The results showed that this method was effective and highly accurate. Then, we carried out uniaxial substrate stretching experiments and applied our method to measure strain distribution of the substrate. The proposed method could obtain accurate strain distribution of substrate film during large stretching, which would allow researchers to adequately describe the response of cells to different strains of substrate.