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Cold-hot nature theory is the core basic theory of traditional Chinese medicine (TCM). "Treating the hot syndrome with cold nature medicine and treating cold syndrome with hot nature medicine" indicates that correct classification of medical properties (cold or hot nature) of Chinese herbal medicines (CHMs) is an important basis for TCM treatment. In this study, we propose a novel multisolvent similarity measure retrieval scheme (MSSMRS) for discriminating CHMs as cold or hot. We explore a multisolvent distance metric learning algorithm to calculate similarity measure of CHM ingredients, and a retrieval scheme for nature identification. First, four solvents (chloroform, distilled water, absolute ethanol, and petroleum ether) are applied to extract ultraviolet (UV) spectrum data of CHM ingredients. Second, we study quantifying the similarity of CHM ingredients to fingerprint similarity. We explore a multisolvent distance metric learning (MSDML) algorithm to measure the similarity of CHM ingredients. MSDML can discover complementary characteristics of different solvent data sets through an optimization algorithm. Finally, a retrieval scheme is designed to analyze the relationship between the CHM ingredients and cold-hot nature. Extensive experimental results demonstrate that CHMs with similar compositions of substances have similar medicinal natures. Experimental evaluations based on the proposed retrieval scheme suggest the effectiveness of MSDML in the identification of the nature of CHMs.

Background and objectives Bone age assessment (BAA) is widely used in determination of discrepancy between skeletal age and chronological age. Manual approaches are complicated which require experienced experts, while existing automatic approaches are perplexed with small and imbalanced samples which is a big challenge in deep learning. Methods In this study, we proposed a new deep learning based method to improve the BAA training in both pre-training and training architecture. In pre-training, we proposed a framework using a new distance metric of cosine distance in the framework of optimal transport for data augmentation (CNN-GAN-OTD). In the training architecture, we explored the order of gender label and bone age information, supervised and semi-supervised training. Results We found that the training architecture with the CNN-GAN-OTD based data augmentation and supervised gender-last classification with supervised Inception v3 network yielded the best assessment (mean average error of 4.23 months). Conclusions The proposed data augmentation framework could be a potential built-in component of general deep learning networks and the training strategy with different label order could inspire more and deeper consideration of label priority in multi-label tasks.

Background The nature of Chinese herbal medicines (CHMs) is a bridge between traditional Chinese medicine and clinical application. Accurate nature identification of CHMs is essential for guiding the clinical application of CHMs. Objective To develop a new method for nature identification of CHMs according to compounds in CHMs. Methods The nature of a CHM is a comprehensive manifestation of the properties of various compounds in the CHM. In this study, 2012 CHM compounds were extracted to construct a compound data set. Molecular descriptors were utilized to build an identification model for classification of the cold-hot-neutral nature of CHM compounds. Results The predictive accuracy and confusion matrix were validated using the assembled data set. The best model produced accuracies of 96.5 ± 0.5% and 86.5 ± 1.5% on training set and test set, respectively. Furthermore, the identification model is robust in predicting the cold-hot-neutral nature of CHM compounds. Conclusion This work shows how a classification model for medical nature identification can be developed. The derived model can be utilized for the application of CHMs. Highlights To construct a nature identification model for analysis of the cold-hot-neutral nature of CHMs according to the compounds in CHMs.

Ethnopharmacological relevance : Traditional Chinese Medicine (TCM) has been widely used as an approach worldwide. Chinese Medicines (CMs) had been used to treat and prevent viral infection pneumonia diseases for thousands of years and had accumulated a large number of clinical experiences and effective prescriptions. Aim of the study : This research aimed to systematically excavate the classical prescriptions of Chinese Medicine (CM), which have been used to prevent and treat Pestilence (Wenbing, Wenyi, Shiyi or Yibing) for long history in China, to obtain the potential prescriptions and ingredients to alternatively treat COVID-19. Materials and methods We developed the screening system based on data mining, molecular docking and network pharmacology. Data mining and association network were used to mine the high-frequency herbs and formulas from ancient prescriptions. Virtual screening for the effective components of high frequency CMs and compatibility Chinese Medicine was explored by a molecular docking approach. Furthermore, network pharmacology method was used to preliminarily uncover the molecule mechanism. Results 574 prescriptions were obtained from 96,606 classical prescriptions with the key words to treat “Warm diseases (Wenbing)”, “Pestilence (Wenyi or Yibing)” or “Epidemic diseases (Shiyi)”. Meanwhile, 40 kinds of CMs, 36 CMs-pairs, 6 triple-CMs-groups existed with high frequency among the 574 prescriptions. Additionally, the key targets of SARS-COV-2, namely 3CL hydrolase (Mpro) and angiotensin-converting enzyme 2(ACE2), were used to dock the main ingredients from the 40 kinds by the LigandFitDock method. A total of 66 compounds components with higher frequency were docked with the COVID-19 targets, which were distributed in 26 kinds of CMs, among which Gancao (Glycyrrhizae Radix Et Rhizoma), HuangQin (Scutellariae Radix), Dahuang (Rhei Radix Et Rhizome) and Chaihu (Bupleuri Radix) contain more potential compounds. Network pharmacology results showed that Gancao (Glycyrrhizae Radix Et Rhizoma) and HuangQin (Scutellariae Radix) CMs-pairs could also interact with the targets involving in immune and inflammation diseases. Conclusions These results we obtained probably provided potential candidate CMs formulas or active ingredients to overcome COVID-19. Prospectively, animal experiment and rigorous clinic studies are needed to confirm the potential preventive and treat effect of these CMs and compounds., Graphical abstract Image 1

Ethnopharmacological relevance Danshen, the dried rhizome of Salvia miltiorrhiza Bge., is widely used to treat cardio-cerebrovascular diseases in China. However, its role in nourishing blood, which has been detailed in historical literature for thousands of years, is perpetually disputed in the academic field. Moreover, there is no systematic research on Danshen in treating anemia. This research aimed to investigate the effects and mechanisms of Danshen on anemia in a zebrafish model based on the results of a network pharmacology study. Materials and methods The network pharmacology study was based on the screening of chemical components and related targets from TCMSP and SwissADME database. The genes associated with anemia were obtained from DisgeNet database, and the genes with the intersection of Danshen target genes were screened out. The Cytoscape 3.7.2 software package was used to construct the “ingredient–target–pathway” network. The exploration of target interaction by String system and the enrichment analysis by Metascape system, was used to discover the possible anti-anemia action mechanism of Danshen. Then, a zebrafish anemia model was induced by vinorelbine followed by the administration of aqueous/ethanol extract of Danshen in contrast to SiWu Decoction (SWD), which is generally acknowledged as a positive drug for tonifying blood. Afterward, the red blood cell signal, cardiac output, and blood flow velocity were detected to evaluate their blood-enriching effects. Quantitative real-time polymerase chain reaction (qPCR) was used to analyze the mRNA levels of hematopoietic-related factors, which were predicted in network pharmacology. Results Compounds and target screening hinted that 115 chemical targets from Danshen were related to anemia, KEGG pathway enrichment results suggested that the mechanism of Danshen in treating anemia was significantly related to the Jak-STAT signaling pathway. Pharmacodynamic results showed that aqueous extract of Danshen (DSAE) and ethanol extract of Danshen (DSEE) markedly enhanced the number of red blood cells, cardiac output, and blood flow velocity. Compared with DSAE, DSEE exerted anti-anemia effects at a lower dose; however, along with higher toxicity. PCR data demonstrated that DSAE and DSEE treatment both upregulated the mRNA expression of erythroid hematopoiesis-related factors in the Epo-JAK-STAT signaling pathway, such as Gata-1, Epo, EpoR, Jak2, STAT3, and STAT5. In general, DSAE exhibited higher activation of this signaling than DSEE. Conclusions These results indicated that DSAE and DSEE both possess blood-enriching functions related with their ability to promote Jak-STAT signaling. DSAE exerted lower toxicity and attenuated anemia over a wider dose range than DSEE, which suggests that DSAE may be more suitable for the treatment for anemia. These results presented experimental evidence for the clinical use of Danshen as an intervention for anemia, especially in chemotherapy-induced anemia.

Image segmentation is critical and challenging in computer vision and medical image analysis. Despite decades of research, existing segmentation algorithms are still subject to typical segmentation problems, such as over-segmentation, under-segmentation, and non-closed and spurious edges. In this paper, taking the carpal bones from hand X-ray images as the foreground regions, we propose a segmentation approach to integrate segmentations from region-based and boundary-based methods to tackle these typical segmentation problems. First, adaptive local thresholding and adaptive Canny edge detection are explored to extract foreground regions and the edge map. Second, the integration of the edge map and foreground regions by XORing is proposed, to tackle the over-segmentation by adding a background boundary from the edge map near the carpal bone boundary so as to break the connection between the foreground and the over-segmented background, to handle under-segmentation by adding a foreground boundary from the edge map near the carpal bone boundary so as to enclose the missing foreground due to under-segmentation, and to complement non-closed edge and spurious edge from the edge map through the carpal bone regions from the local adaptive thresholding. Optionally, marker-controlled watershed segmentation or an active contourbased method is employed to refine the integrated segmentation. The segmented foreground regions are identified through exploring a prior model. The proposed approach has been validated on 30 representative X-ray hand images for non-overlapping carpal bones to yield an average Dice coefficient of 0.976 ± 0.006. The proposed method could provide a theory or tool for accurately segmenting images with discernible boundaries and non-uniform grayscale distribution both within the background and foreground.

Objectives: Salvia miltiorrhiza research is one of the hotspots of modern Chinese medicine study in China. Analysing the salvia miltiorrhiza literatures in the database of China National Knowledge Infrastructure (CNKI) will show the research status of slavia miltiorrhiza and provide the researchers a good reference. Methods: In CNKI “Chinese Academic Journal” database, the keyword “salvia miltiorrhiza” is used to do subject searching, and the time range cover all the literatures of CNKI. Python's pandas data analysis package is used to analyze the search results. The analysis of publishing annual trends, institution of research, authors, journals and keywords are going to be done. Results: A total of 41,683 articles were retrieved. The number of published literatures has undergone three stages: slow growth, rapid growth, and steady growth. Nanjing University of Traditional Chinese Medicine (TCM) has accomplished the most paper in all the institutions. The authors who published more than three articles are the core of salvia miltiorrhiza researchers. The journal of “Shaanxi Chinese Medicine” have published the most literatures in all the publishers. The research hotpots is mainly on Salvia miltiorrhiza injection, compound salvia miltiorrhiza dripping pills, tanshinone and the treatment of coronary heart disease and angina pectoris using salvia miltiorrhiza. Conclusions: In China, the universities of TCM are the main force of salvia miltiorrhiza research. The related research of Salvia miltiorrhiza has been in a stable development stage in recent years. Besides Salvia miltiorrhiza injection[1], compound salvia miltiorrhiza dripping pills[2] and tanshinone[3], salvianolate[4] and salvia miltiorrhiza & ligustrazine Injection[5] has also gradually become the focus of researches.

Objectives: To better explore the valuable experience and knowledge in traditional Chinese medicine (TCM) literature and promote the academic heritage and innovation of TCM, this paper tries to provide methods and a system of word segmentation and POS tagging for ancient Chinese medicine literature. Method: Based on HMM and Java language for POS tagging, this paper develops a word segmentation system of Chinese ancient books by constructing a Thesaurus of Chinese medicine terminology and a special POS tagging method for Chinese medicine, and adopting Ansj open source code as the core word segmentation algorithm. Results: Thesaurus of Chinese medicine terminology involving 155,343 Chinese medicine words are constructed. These words were divided into 14 categories and 7 levels of 891 parts of speech (POS). Online website of Chinese word segmentation and POS tagging proprietary system were established (http://www.zhongyifenci.org). And the F measure reaches 90.65%, which is much higher than the system of Ansj. Clinical or Biological Impact: This system, with high precision and recall rate, will be very helpful to knowledge discovery of Chinese medicine and be beneficial to giving full play to the original advantages of Chinese medicine.

A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protein aggregation rate, a feedforward fully connected neural network (FCN) with one hidden layer is trained on a dataset composed of 21 different kinds of amyloid proteins and tested on 4 rest proteins. FCN shows a much better performance than traditional algorithms, such as multivariable linear regression and support vector regression, with an average accuracy higher than 90%. Furthermore, by the correlation analysis and the principal component analysis, seven key features, folding energy, HP patterns for helix, sheet and helices cross membrane, pH, ionic strength, and protein concentration, are shown to constitute a minimum feature set for characterizing the amyloid aggregation kinetics.

Obesity and hyperlipidemia are the most prevalent independent risk factors of chronic kidney disease (CKD), suggesting that lipid accumulation in the renal parenchyma is detrimental to renal function. Non-esterified fatty acids (also known as free fatty acids, FFA) are especially harmful to the kidneys. A concerted, increased FFA uptake due to high fat diets, overexpression of fatty acid uptake systems such as the CD36 scavenger receptor and the fatty acid transport proteins, and a reduced β-oxidation rate underlie the intracellular lipid accumulation in non-adipose tissues. FFAs in excess can damage podocytes, proximal tubular epithelial cells and the tubulointerstitial tissue through various mechanisms, in particular by boosting the production of reactive oxygen species (ROS) and lipid peroxidation, promoting mitochondrial damage and tissue inflammation, which result in glomerular and tubular lesions. Not all lipids are bad for the kidneys: polyunsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) seem to help lag the progression of chronic kidney disease (CKD). Lifestyle interventions, especially dietary adjustments, and lipid-lowering drugs can contribute to improve the clinical outcome of patients with CKD.

In this study, we selected the dermatology data, constructed a dictionary of dermatology semantic classification, and established a corpus of dermatology literature. Based on the context calculation model we took the case of erythema skin disease as the example, identified the semantic meaning of the dermatology data, and formed the concept attribute of dermatology knowledge, constructed the dermatology ontology. We established a priori database and verified each other with the running data to correct the data error and improve the data definition. In this study, we used the natural language to describe the characteristic information of skin diseases, and carried out the semantic searching. Finally, we constructed the mathematical model of the diagnosis and treatment of dermatology based on the corpus, the ontology and the priori database. The purpose is to supply the gap of unbalance of doctors' personal level of the dermatosis diagnosis and treatment, improve the level and accuracy of clinical diagnosis and treatment of dermatosis, and to achieve effective drive of medical literature data for clinical diagnosis and treatment.

To study the influence of Radix aconiti lateralis preparata and Rhizoma zingiberis, two species of Chinese medicinal herbs with hot property, on energy metabolism and gene expression spectrum, and to analyze the possible mechanism of their effects.Forty-eight specific pathogen free Wistar rats were randomly divided into a Radix aconiti lateralis preparata group, a Rhizoma zingiberis group, and a control group. They were intragastrically treated with concentrated decoction of Radix aconiti lateralis preparata, Rhizoma zingiberis and normal saline respectively for 20 days. Toe temperature (TT), energy intake (EI), digestible energy (DE), and metabolizable energy (ME) were measured. The content of adenosine triphosphate (ATP) and energy charge (EC) in hepatic tissue were measured with high performance liquid chromatography (HPLC). The activity of ATPase and succinate dehydrogenase (SDH) in the liver were detected with chemical colorimetry. The gene expression in the liver was detected with Illumina's rat Ref-12 gene array. The differential expression genes were selected, annotated and classified based on Gene Ontology (GO). Real-time quantitative reverse-transcriptase PCR (Q-RT-PCR) was used to test the accuracy of results.Compared with the control group, the TT on the 10(th) day after the beginning of administration and ATP in the Radix aconiti lateralis preparata and Rhizoma zingiberis groups increased significantly (P0.05). EI/body mass (BM), DE/BM, ME/BM, the hepatic EC and the activity of Na(+)-K(+)-ATPase, Ca(2+)-Mg(2+)-ATPase and SDH of liver increased significantly only in the Radix aconiti lateralis preparata group (P0.05). There were 592 differential expression genes in the Radix aconiti lateralis preparata group and 1 159 in the Rhizoma zingiberis group compared with the control group. Among the differential expression genes, genes related to metabolic processes were the most significant based on GO analysis. There were 337 strips of gene differential expression in common in both Radix aconiti lateralis preparata and Rhizoma zingiberis groups compared with the control group.Herbs with hot property such as Radix aconiti lateralis preparata and Rhizoma zingiberis could improve the energy metabolism in rats, through influencing the metabolic process of sugar, lipid, and amino acid. It could also promote the production, storage, and utilization of energy by regulating the gene expression related to metabolism, which may be the main molecular mechanism of warming yang and dispelling cold for the treatment of the cold syndrome according to Chinese medicine theory.

To explore appropriate methods for the research of the theory of Chinese medicine nature property and find the relationship between Nature-Family-Component of Chinese herbs.From perspective of systems biology, we used Associate Network to identify useful relationships among "Nature-Family-Component" of Herbs. In this work, Associate Network combines association rules mining method and network construction method to evaluate the complicate relationship among "Nature-Family-Component" of herbs screened.The results of association rules mining showed that the families had a close relationship with nature properties of herbs. For example, the families of Magnoliaceae, Araceae had a close relationship with hot nature with confidence of 100%, the families of Cucurbitaceae has a close relationship to cold nature with confidence of 90.91%. Moreover, the results of constructed Associate Network implied that herbs belonging to the same families generally had the same natures. In addition, some herbs belonging to different families may also have same natures when they contain the same main components.These results implied that the main components of herbs might affect their natures; the relationships between families and natures were based on the main compounds of herbs.

To provide the distribution pattern and compatibility laws of the constituent herbs in prescriptions, for doctor’s convenience to make decision in choosing correct herbs and prescriptions for treating respiratory disease. Classical prescriptions treating respiratory disease were selected from authoritative prescription books. Data mining methods (frequent itemsets and association rules) were used to analyze the regular patterns and compatibility laws of the constituent herbs in the selected prescriptions. A total of 562 prescriptions were selected to be studied. The result exhibited that, Radix glycyrrhizae was the most frequently used in 47.2% prescriptions, other frequently used were Semen armeniacae amarum, Fructus schisandrae Chinese, Herba ephedrae, and Radix ginseng. Herbal ephedrae was always coupled with Semen armeniacae amarum with the confidence of 73.3%, and many herbs were always accompanied by Radix glycyrrhizae with high confidence. More over, Fructus schisandrae Chinese, Herba ephedrae and Rhizoma pinelliae was most commonly used to treat cough, dyspnoea and associated sputum respectively besides Radix glycyrrhizae and Semen armeniacae amarum. The prescriptions treating dyspnoea often used double herb group of Herba ephedrae & Radix glycyrrhizae, while prescriptions treating sputum often used double herb group of Rhizoma pinelliae & Radix glycyrrhizae and Rhizoma pinelliae & Semen armeniacae amarum, triple herb groups of Rhizoma pinelliae & Semen armeniacae amarum & Radix glycyrrhizae and Pericarpium citri reticulatae & Rhizoma pinelliae & Radix glycyrrhizae. The prescriptions treating respiratory disease showed common compatibility laws in using herbs and special compatibility laws for treating different respiratory symptoms. These principle patterns and special compatibility laws reported here could be useful for doctors to choose correct herbs and prescriptions in treating respiratory disease.