Objective:To screen the causative genes of five families with branchio-oto-renal syndrome (BORS) or branchio-oto syndrome(BOS) and to analyze the phenotypic characteristics and clinical management strategies of patients.Methods:Five families with BORS/BOR from December 2018 to September 2021 were recruited, information of patients, including family history and medical history, was collected, and genealogies were drawn. The examinations concerning audiology, nephrology, and radiology were performed on the affected individuals. Peripheral blood was obtained for DNA extraction, then next-generation sequencing technology was used to screen candidate variants associated with BORS/BOS. Based on patient′s clinical results, the appropriate interventions were recommended and implemented.Results:Eight individuals were diagnosed with BOS or BORS. Of the eight patients, all had hearing loss, preauricular pits and ear malformations, and only four presented with branchial cleft fistulae or cysts. Except for two patients(5-I-2, 5-II-2) who did not undergo renal examination, the remaining six lacked renal abnormalities. Genetic analysis identified four likely pathogenic or pathogenic EYA1 variants (c.1715G>T, c.1140+1G>A, c.639G>C, c.1475+1G>C; NM_000503.6), and c.1715G>T was first reported in this study. Middle ear ossicular reconstruction was performed in 1-II-2,2-I-2 and 3-II-2, but did not yield the expected results; then hearing aids and cochlear implantation were recommended and achieved satisfactory results. Conclusions:Next-generation sequencing technology facilitates the diagnosis and genetic counseling of BORS/BOS. Hearing loss, preauricular pits, ear malformations and branchial cleft fistulae or cysts are the most common manifestations of patients in this study. Middle ear surgeries for improving hearing loss may have some limitations in BORS/BOS patients, and hearing aids and cochlear implantation can contribute to hearing gains.

Endothelial cell injury is a major contributor to cardiovascular diseases.The 2,3,5,4’-Tetrahydroxystilbene-2-O-β-D-Glucoside (TSG) contributes to alleviate human umbilical vein endothelial cells (HUVECs) injury through mechanisms still know a little. This study aims to clarify the TSG effects on gene expression (mRNA and microRNA) related to oxidative stress and endoplasmic reticulum stress induced by H2O2 in HUVECs. We found that TSG significantly reduced the death rate of cells and increased intracellular superoxide dismutase activity. At qRT-PCR, experimental data showed that TSG significantly counteracted the expressions of miR-9-5p, miR-16, miR-21, miR-29b, miR-145-5p, and miR-204-5p. Besides, TSG prevented the expression of ATF6 and CHOP increasing. In contrast, TSG promoted the expression of E2F1. In conclusion, our results point to the obvious protective effect of TSG on HUVECs injury induced by H2O2, and the mechanism may through miR16/ATF6/ E2F1 signaling pathway.

Endothelial cell injury is a major contributor to cardiovascular diseases.The 2,3,5,4’-Tetrahydroxystilbene-2-O-β-D-Glucoside (TSG) contributes to alleviate human umbilical vein endothelial cells (HUVECs) injury through mechanisms still know a little. This study aims to clarify the TSG effects on gene expression (mRNA and microRNA) related to oxidative stress and endoplasmic reticulum stress induced by H2O2 in HUVECs. We found that TSG significantly reduced the death rate of cells and increased intracellular superoxide dismutase activity. At qRT-PCR, experimental data showed that TSG significantly counteracted the expressions of miR-9-5p, miR-16, miR-21, miR-29b, miR-145-5p, and miR-204-5p. Besides, TSG prevented the expression of ATF6 and CHOP increasing. In contrast, TSG promoted the expression of E2F1. In conclusion, our results point to the obvious protective effect of TSG on HUVECs injury induced by H2O2, and the mechanism may through miR16/ATF6/ E2F1 signaling pathway.

Idiopathic thrombocytopenic purpura (ITP) is a common bloody disease with a high incidence in children, but its diagnostic method is exclusive diagnosis, and the existing detection techniques are mostly invasive, which may cause secondary injury to patients and also may increase the risk of disease. In order to make up for the lack of the detection method, this study made a preliminary exploration on the diagnosis of children's ITP from the perspective of infrared thermography. In this study, a total of 11 healthy children and 22 ITP children's frontal infrared thermal images were collected, and the pattern characteristic (PFD), average temperature (Troi) and maximum temperature (MAX) characteristics of 7 target areas were extracted. The weighted PFD parameters were correlated with the platelet count commonly used in clinical diagnosis, and the sensitivity and specificity of the weighted PFD parameters for children's ITP were calculated through the receiver operating characteristic curve (ROC). The final results showed that the difference of the weighted PFD parameters between healthy children and ITP children was statistically significant, and the parameters negatively correlated with platelet count. Under the ROC curve, the area under the curve (AUC) of this parameter is as high as 92.1%. Based on the research results of this paper, infrared thermography can clearly show the difference between ITP children and healthy children. It is hoped that the methods proposed in this paper can non-invasively and objectively describe the characteristics of ITP infrared thermal imaging of children, and provide a new ideas for ITP diagnosis.
Area Under Curve ; Child ; Humans ; Platelet Count ; Purpura, Thrombocytopenic, Idiopathic ; Thermography

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.
Algorithms ; MicroRNAs ; chemistry ; Support Vector Machine

This study was aimed to discover the knowledge of urination formula-syndrome in theTreatise on Exogenous Febrile Diseasebased on the Sunshine diagram of multi-layer complex concept network express. A total of 39 items about urination formula-syndrome in theTreatise on Exogenous Febrile Diseasewere collected, and then regulated into standard expression. The database was established and the multi-layer complex concept network express was constructed. The Sunshine diagram was drawn and the connotation rules on urination formula-syndrome in theTreatise on Exogenous Febrile Diseasewere summarized through mode development of the diagram. The results showed that the Sunshine diagram collected 44 objects (i.e., formulas) and 191 properties (i.e. syndromes), which expressed the urination formula-syndrome visually. It was concluded that the application of Sunshine diagram in the formula-syndrome knowledge based on multi-layer complex concept network express provided certain references on the inheritance and development of classics in traditional Chinese medicine (TCM).

Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.
Algorithms ; Electroencephalography ; Entropy ; Epilepsy ; diagnosis ; Humans ; Multivariate Analysis ; Nonlinear Dynamics

This study was aimed to analyze the regulation of syndrome-treatment pattern of classical Chinese medici-nal formulae for emotional diseases based on formal concept analysis. First, we dealt with the decision formal context of 51 prescriptions about emotional symptom in the Treatise on Febrile and Miscellaneous Diseases and the Es-sentials from the Golden Cabinet based on the principle of optimization. Then, we generated a new partial-order at-tribute diagram in order to present the specific character. Finally, we explained properties of partial-order structure graph from traditional Chinese medicine (TCM) experts' point of view based on knowledge discovery. The results indi-cated the relationship between prescription and syndrome of emotional diseases. It was concluded that method pro-posed in this paper worked well in treatment of description of syndrome differentiation and discovery of new knowl-edge from the known data in the clinical diagnosis.

Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCI I, BCI II_IV and USPS. The classification rate were 97%, 82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0. 2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; classification ; Humans

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.
Algorithms ; Artificial Intelligence ; Breast Neoplasms ; classification ; diagnosis ; Discriminant Analysis ; Female ; Humans