Online application for the diagnosis of atherosclerosis by six genes.

BACKGROUND: Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS. METHODS: We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction. RESULTS: Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples. CONCLUSIONS: A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

Emerging concerns of atypical hand foot and mouth disease caused by recombinant Coxsackievirus A6 variants in Henan, China.

An increasing number of studies have reported that atypical hand, foot, and mouth disease (HFMD) is becoming a new concern for children's health. At present, there is no official definition for atypical HFMD, but some studies have defined that it occurs at anatomic sites not listed in the definition of HFMD issued by the World Health Organization. Several pathogens have been reported to cause atypical HFMD, such as Coxsackievirus (CV)A6. As one of the most prevalent enteroviruses in the world, CVA6 seems to affect a wider range of children and causes more severe and prolonged illness than other enteroviruses. The early lesions of atypical HFMD are very similar to the clinical presentations of other diseases, such as eczema, which poses a challenge for clinicians aiming to identify and diagnose HFMD in a timely manner. Here, we report on six atypical HFMD patients caused by recombinant CVA6 variants, and the atypical manifestations include eczema coxsackium, large herpes, rice-like red papules and herpes, purpuric rash, and onychomadesis, as well as and large red herpes on scalp, perianal, testicles, shoulders and neck, and other atypical eruption sites, hoping to draw the attention of other pediatricians. This study will provide scientific guidance for timely diagnosis of HFMD to prevent serious complications.

Value of NSE and S100 Protein of Kawasaki Disease with Aseptic Meningitis in Infant.

The cerebrospinal fluid content was examined for concentrations of S100 protein and neuron-specific enolase (NSE) in two diseases, Kawasaki disease (KD) with aseptic meningitis (1-3 months) and purulent meningitis (PM), to determine whether or not these measuremets could be used in early diagnosis. The content of cerebrospinal fluid S100 protein of KD with aseptic meningitis and PM were significantly higher than those in the control group. There was also a difference between KD and purulent meningitis (PM). The concentration of NSE was highest in the encephalitis group, which was statistically different from control group. However, there was no difference between the KD and control groups. The levels of S100 protein and NSE of KD with aseptic meningitis were lower than those in PM, indicating that the extent of neuronal damage is significantly lower than of the enchephalitis group. The area under the curve (AUCs) of the receiver operating characteristic (ROC) curve for S100 and NSE were both 0.972. The S100 threshold was 0.4315, the sensitivity was 92.1%, and the specificity was 100%, while the NSE threshold was 9.325, sensitivity 92.1%, and specificity 90%. The combined detection of NSE and S100 levels in the cerebrospinal fluid can be used for the differential diagnosis of KD with aseptic meningitis and purulent meningitis.