Mitochondria are double-membraned cellular organelles that provide the required energy and metabolic intermediates to cardiomyocytes. Mitochondrial respiratory chain defects, structure abnormalities, and DNA mutations can affect the normal function of cardiomyocytes, causing an imbalance in intracellular calcium ion homeostasis, production of reactive oxygen species, and apoptosis. Mitochondrial quality control (MQC) is an important process that maintains mitochondrial homeostasis in cardiomyocytes and involves multi-level regulatory mechanisms, such as mitophagy, mitochondrial fission and fusion, mitochondrial energy metabolism, mitochondrial antioxidant system, and mitochondrial respiratory chain. Furthermore, MQC plays a role in the pathological mechanisms of various cardiovascular diseases (CVDs). In recent years, the regulatory effects of natural plants, drugs, and active ingredients on MQC in the context of CVDs have received significant attention. Effective active ingredients in natural drugs can influence the production of energy-supplying substances in the mitochondria, interfere with the expression of genes associated with mitochondrial energy requirements, and regulate various mechanisms of MQC modulation. Thus, these ingredients have therapeutic effects against CVDs. This review provides useful information about novel treatment options for CVDs and development of novel drugs targeting MQC.

To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor–patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.

Despite having a generally conserved structure, chloroplast genome data have been helpful for plant population genetics and evolution research. To mine Pueraria montana chloroplast genome variation architecture and phylogeny, we investigated the chloroplast variation architecture of 104 P. montana accessions from across China. P. montana’s chloroplast genome showed high diversity levels, with 1674 variations, including 1118 single nucleotide polymorphisms and 556 indels. The intergenic spacers, psbZ-trnS and ccsA-ndhD, are the two mutation hotspot regions in the P. montana chloroplast genome. Phylogenetic analysis based on the chloroplast genome dataset supported four P. montana clades. P. montana variations were conserved among and within clades, which showed high gene flow levels. Most P. montana clades were estimated to have diverged at 3.82–5.17 million years ago. Moreover, the East Asian summer monsoon and South Asian summer monsoon may have accelerated population divergence. Our results show that chloroplast genome sequences were highly variable and can be used as molecular markers to assess genetic variation and relationships in P. montana.