A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System.

Cardiovascular disease (CVD) is one of the leading causes of death globally. Currently, clinical diagnosis of CVD primarily relies on electrocardiograms (ECG), which are relatively easier to identify compared to other diagnostic methods. However, ensuring the accuracy of ECG readings requires specialized training for healthcare professionals. Therefore, developing a CVD diagnostic system based on ECGs can provide preliminary diagnostic results, effectively reducing the workload of healthcare staff and enhancing the accuracy of CVD diagnosis. In this study, a deep neural network with a cross-stage partial network and a cross-attention-based transformer is used to develop an ECG-based CVD decision system. To accurately represent the characteristics of ECG, the cross-stage partial network is employed to extract embedding features. This network can effectively capture and leverage partial information from different stages, enhancing the feature extraction process. To effectively distill the embedding features, a cross-attention-based transformer model, known for its robust scalability that enables it to process data sequences with different lengths and complexities, is employed to extract meaningful embedding features, resulting in more accurate outcomes. The experimental results showed that the challenge scoring metric of the proposed approach is 0.6112, which outperforms others. Therefore, the proposed ECG-based CVD decision system is useful for clinical diagnosis.

Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.

Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain-computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.

Attenuating spinal cord injury by conditioned medium from human umbilical cord blood-derived CD34⁺ cells in rats.

OBJECTIVE: Intravenous or intraspinal transplantation of human umbilical cord blood cells-derived CD34(+) cells (human CD34(+) cells) or mesenchymal stem cells after spinal cord injury (SCI) improved hind limb functional recovery in adult rats. The objective of this study is to ascertain whether SCI in rats can be attenuated by conditioned medium (CM) or secretome obtained from cultured human CD34(+) stem cells. MATERIALS AND METHODS: Sprague-Dawley rats were assigned to one of the following five groups: the sham group, the SCI group treated with vehicle solution (SCI + V), the SCI group treated with CM (SCI + CM), the SCI group treated with 17ß-estradiol E2 (10 µg; SCI + E2), and the SCI group treated with CM plus E2 (SCI + CM + E2). A 0.5-mL volume of CM or vehicle solution was administered intravenously immediately after SCI. RESULTS: Compared with the sham group, the (SCI + V) group had significantly higher scores of neurological motor dysfunction as well as inflammation apoptosis, oxidative stress, and astrogliosis in the injured spinal cord. The neurological deficits, numbers of apoptotic cell, extent of inflammation, oxidative stress, and astrogliosis in the injured spinal cord were significantly attenuated by CM, E2, or CM plus E2, but not by the vehicle solution. In addition, the neuroprotective effect exerted by a combination of CM and E2 is superior to that exerted by CM- or E2-alone therapy. CONCLUSION: The neuroprotective effects of CM from cultured human CD34(+) cells are similar to those of human CD34(+) cells and the CM was found to enhance the neuroprotective effects of E2 in rat SCI.