Dicarboxylic Acid Dietary Supplementation Protects against AKI.

SIGNIFICANCE STATEMENT: In this study, we demonstrate that a common, low-cost compound known as octanedioic acid (DC 8 ) can protect mice from kidney damage typically caused by ischemia-reperfusion injury or the chemotherapy drug cisplatin. This compound seems to enhance peroxisomal activity, which is responsible for breaking down fats, without adversely affecting mitochondrial function. DC 8 is not only affordable and easy to administer but also effective. These encouraging findings suggest that DC 8 could potentially be used to assist patients who are at risk of experiencing this type of kidney damage. BACKGROUND: Proximal tubules are rich in peroxisomes, which are damaged during AKI. Previous studies demonstrated that increasing peroxisomal fatty acid oxidation (FAO) is renoprotective, but no therapy has emerged to leverage this mechanism. METHODS: Mice were fed with either a control diet or a diet enriched with dicarboxylic acids, which are peroxisome-specific FAO substrates, then subjected to either ischemia-reperfusion injury-AKI or cisplatin-AKI models. Biochemical, histologic, genetic, and proteomic analyses were performed. RESULTS: Both octanedioic acid (DC 8 ) and dodecanedioic acid (DC 12 ) prevented the rise of AKI markers in mice that were exposed to renal injury. Proteomics analysis demonstrated that DC 8 preserved the peroxisomal and mitochondrial proteomes while inducing extensive remodeling of the lysine succinylome. This latter finding indicates that DC 8 is chain shortened to the anaplerotic substrate succinate and that peroxisomal FAO was increased by DC 8 . CONCLUSIONS: DC 8 supplementation protects kidney mitochondria and peroxisomes and increases peroxisomal FAO, thereby protecting against AKI.

Computational Traditional Chinese Medicine diagnosis: A literature survey.

BACKGROUND AND OBJECTIVE: Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. Inspection, 2. Auscultation and olfaction, 3. Inquiry, and 4. Palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly. METHODS: In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail. RESULTS: This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study. CONCLUSIONS: Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area. INDEX TERMS: Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.

Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis.

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i.e., pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers.

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

Significant Geometry Features in Tongue Image Analysis.

The shape of a human tongue and its relation to a patients' state, either healthy or diseased (and if diseased which disease), is quantitatively analyzed using geometry features by means of computerized methods in this paper. Thirteen geometry features based on measurements, distances, areas, and their ratios are extracted from tongue images captured by a specially designed device with color correction. Using the features, 5 tongue shapes (rectangle, acute and obtuse triangles, square, and circle) are defined based on traditional Chinese medicine (TCM). Classification of the shapes is subsequently carried out with a decision tree. A large dataset consisting of 672 images comprising of 130 healthy and 542 disease examples (labeled according to Western medical practices) are tested. Experimental results show that the extracted geometry features are effective at tongue shape classification (coarse level). Even if more than one disease class belongs to the same shape, the disease classes can still be discriminated via fine level classification using a combination of the geometry features, with an average accuracy of 76.24% for all shapes.

Tongue color analysis for medical application.

An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Disease samples can be split into three clusters, and within each cluster most if not all the illnesses are distinguished from one another. In total 11 illnesses have a classification rate greater than 70%. This demonstrates a relationship between the state of the human body and its tongue color.