Prediction of coronary artery lesions based on C-reactive protein levels in children with Kawasaki Disease: a retrospective cohort study.

Objective: Since coronary artery lesions (CALs) are the most severe complication of Kawasaki disease (KD), clinically speaking, early prediction of CALs is crucial. The authors aimed to investigate the predictive value of C-reactive protein (CRP) in predicting CALs in KD patients.

Methods: KD patients were divided into the CALs group and the non-CALs group.

Therapeutic Window of Intravenous Immunoglobulin (IVIG) and its correlation with IVIG-resistant in Kawasaki Disease: a retrospective study.

Objective: To investigate the optimal timing of initial intravenous immunoglobulin (IVIG) treatment in Kawasaki disease (KD) patients.

Methods: KD patients were classified as the early group (day 1-4), conventional group (day 5-7), conventional group (day 8-10), and late group (after day 10). Differences among the groups were analyzed by ANOVA and Chi-square analysis.

Landscape and progress of global peptide drugs in obstetrics and gynaecology.

Peptides have gained popularity in the global market during recent years and have been placed between small molecule drugs and biologics. However, little is known about the comprehensive landscape of peptide drugs in obstetrics and gynaecology. Herein, we analysed new peptide drug-related clinical trials in obstetrics and gynaecology registered on ClinicalTrials.

The progress of peptide vaccine clinical trials in gynecologic oncology.

Peptide vaccine are a type of immunotherapy that are synthesized according to the amino acid sequence of known or predicted tumor antigen epitopes. They are safe and well tolerated and have shown exciting results in gynecologic oncology. However, no peptide vaccine has yet been licensed in this field.

[A late-type star spectra outlier data mining system].

In M star population, some special objects, which may be of magnetic activity, may be giant stars, or may be of other rare properties, are very important for the follow-up observation and the scientific research on galactic structure and evolution. For local bias of M-type star spectral characteristic lines contained in subspace, a late-type star spectra outlier data mining system is given in the present paper. Firstly, for the sample of M stellar spectral characteristic lines indices, its distribution characteristics in attribute spaces are measured by using the sparse factor and sparsity coefficient, and then this sample is discretized and dimension-reduced to the spectral subspace.

[Automatic classification method of star spectrum data based on classification pattern tree].

Frequent pattern, frequently appearing in the data set, plays an important role in data mining. For the stellar spectrum classification tasks, a classification rule mining method based on classification pattern tree is presented on the basis of frequent pattern. The procedures can be shown as follows.

[A post-processing method of classification rule on stellar spectra].

Automatic classification and analysis of observational data is of great significance along with the gradual implementation of LAMOST Survey, which will obtain a large number of spectra data. In classification rules extracted, there is often a great deal of redundancy which will reduce the classification efficiency and quality seriously. In the present paper, a post-processing method of star spectra classification rule based on predicate logic is presented by using predication to describe the classification rules and logical reasoning to eliminate redundant rules.

[Research on two-stage fuzzy clustering method for spectrum data based on PSO].

A novel high-dimensional clustering algorithm is proposed. On the basis of this, a two-stage fuzzy clustering approach, named TSPFCM, is presented. On the first stage, data is clustered by a new clustering method.

[Research on the interrelation analysis system of celestial spectrum data based on constraint FP tree].

It is an effective method of the mankind seeking after the celestial law that the inherent and unknown interrelationships between characteristics of celestial spectrum data and its physical and chemical properties are mined from the mass celestial body spectrum data. In the present paper, the interrelation analysis system of celestial body spectrum data based on constraint FP tree is designed and implemented by using the association rule based constraint FP tree as the way of analyzing celestial spectrum data, and adopting VC++ and Oracle9i as the development tools. At the same time, its software architecture and function modules are outlined.

[A study on the outlier mining system for LAMOST spectra].

To find unknown celestial bodies is one of main goals in mankind's universe exploration, and outlier mining is a kind of effective way of finding unknown celestial bodies from mass spectrum data. In the present work, using VC++ and Oracle9i as development tools, an outlier mining system for star spectra is designed and realized, and its software architecture and function modules are outlined. At the same time, the system's key components such as star spectrum data preprocessing based on median filters, clustering of star spectrum data based on distance, outlier mining of star spectrum data based on distance support and three-dimensional visualization of star spectrum outlier based on PCA, are elaborated.