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

Abstract 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. Predictors of IVIG resistance and the optimal cut-off value were determined by multiple logistic regression analyses and receiver operating characteristic (ROC) curve analysis. Results: There were no significant differences in IVIG resistance among the 4 groups (p = 0.335). The sensitivity analysis also confirmed no difference in the IVIG resistance between those who started the initial IVIG ≤ day 7 of illness and those who received IVIG >day 7 of illness (p = 0.761). In addition, patients who received IVIG administration more than 7 days from the onset had a higher proportion of coronary artery abnormalities (p = 0.034) and longer length of hospitalization (p = 0.033) than those who started IVIG administration less than 7 days. The optimal cut-off value of initial IVIG administration time for predicting IVIG resistance was >7 days, with a sensitivity of 75.25% and specificity of 82.41%. Conclusions: IVIG therapy within 7 days of illness is found to be more effective for reducing the risk of coronary artery abnormalities than those who received IVIG >day 7 of illness. IVIG treatment within the 7 days of illness seems to be the optimal therapeutic window of IVIG. However, further prospective studies with long-term follow-up are required.

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. Predictors of IVIG resistance and the optimal cut-off value were determined by multiple logistic regression analyses and receiver operating characteristic (ROC) curve analysis. RESULTS: There were no significant differences in IVIG resistance among the 4 groups (p = 0.335). The sensitivity analysis also confirmed no difference in the IVIG resistance between those who started the initial IVIG ≤ day 7 of illness and those who received IVIG >day 7 of illness (p = 0.761). In addition, patients who received IVIG administration more than 7 days from the onset had a higher proportion of coronary artery abnormalities (p = 0.034) and longer length of hospitalization (p = 0.033) than those who started IVIG administration less than 7 days. The optimal cut-off value of initial IVIG administration time for predicting IVIG resistance was >7 days, with a sensitivity of 75.25% and specificity of 82.41%. CONCLUSIONS: IVIG therapy within 7 days of illness is found to be more effective for reducing the risk of coronary artery abnormalities than those who received IVIG >day 7 of illness. IVIG treatment within the 7 days of illness seems to be the optimal therapeutic window of IVIG. However, further prospective studies with long-term follow-up are required.

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.gov. The number and percentage were used for statistical analysis, and a time trend analysis was conducted by calculating the annual growth rate. We aimed to provide the first overview of the changing landscape and status of global peptide drugs in this prospective field, including exploring drug targets, the cutting-edge oncotherapy of peptide vaccines and peptide-drug conjugates, and unsolved challenges with oral administration.

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. This review examines peptide vaccine clinical trials in gynecology registered on ClinicalTrials.gov through January 1, 2022, analyzes the global progress and current achievements of peptide vaccines in gynecology, and explores the efforts focused on devising new methods to boost immunotherapeutic outcomes, including the use of adjuvants, multi-epitope vaccines, combinations of helper T cell epitopes, personalized peptide vaccines, synthetic long peptides, new peptide delivery, and combination therapy.

[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. Secondly, local outlier subspaces are extracted by PSO (particle swarm optimization) algorithm and identified. Additionally, the effects of sparse coefficient and sparse factor on the number of outliers are discussed by experiments on the sample of SDSS M stellar spectral line index set, and the outliers are compared with spectral type provided by SDSS. In this way, the feasibility and value of this system were validated.

[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. In the end, some experimental results on LAMOST's stellar spectra data show that, with no classification accuracy reduction, the efficiency of auto classification is significantly improved.

[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. Firstly, a new tree structure, i. e., classification pattern tree, is introduced based on the different frequencies of stellar spectral attributes in data base and its different importance used for classification. The related concepts and the construction method of classification pattern tree are also described in this paper. Then, the characteristics of the stellar spectrum are mapped to the classification pattern tree. Two modes of top-to-down and bottom-to-up are used to traverse the classification pattern tree and extract the classification rules. Meanwhile, the concept of pattern capability is introduced to adjust the number of classification rules and improve the construction efficiency of the classification pattern tree. Finally, the SDSS (the Sloan Digital Sky Survey) stellar spectral data provided by the National Astronomical Observatory are used to verify the accuracy of the method. The results show that a higher classification accuracy has been got.

[Rapid identification of variable star spectrum based on information entropy].

Variable star is very important for mankind studying cosmic origin and evolution. For studying variable star, the chief difficulty results from the filtration and identification of variable star, that is how to validly identify variable star spectra from large high-dimensional star spectra data. The traditional outlier definition tries to find the difference between the outlier data and the general model by different ways, and then the result is quantitatively analyzed and filtrated. However, the time complexity of this method is over size and its results are inscrutable and unaccountable. Information entropy is a measure of the uncertainty associated with a random variable. In the present paper, information entropy is imported as the standard of dataset common mode. A novel method is proposed to identify the variable star spectrum rapidly based on information entropy. The time complexity of this method is observably reduced and the man-made impact is effectively overcome. The preliminary experimental results based on Sloan star spectrum data show that the method is workable for rapid identification of variable star spectrum.

[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. On the second stage, the result of the first stage is taken as the initial cluster centers, and PSO mechanism is inducted into fuzzy clustering to solve the locality and the sensitiveness of the initial condition of Fuzzy C-means Clustering. The running results of the system show that it is feasible and valuable to apply this method to mining the clustering in spectrum data.

[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. Its key techniques such as preprocessing of celestial body spectrum data,background knowledge representing, constraint FP-tree constructing, constraint frequent patterns and association rules mining etc are discussed in details. The running results show that the system is feasible and valuable for adopting association rule to describe the above interrelationships. Therefore, the interrelation analysis system of celestial body spectrum data provides an effective means for seeking after the inherent and unknown celestial law.

[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. The preliminary experimental results based on SDSS star spectrum data show that the system is workable for outlier mining of celestial body spectrum data, and a new kind of effective way of finding unknown and peculiar celestial body spectrum data.