Ustekinumab treats psoriasis by suppressing RORC and T-box but its suppression of GATA restrains its efficacy

Psoriasis is a T-cell mediated disease that involves IL-23/Th17 and IL-12/Th1 axes. Ustekinumab, a fully human monoclonal antibody targeting the p40 subunit of both IL-12 and IL-23, has proven to be efficient and safe for treating patients with psoriasis. Yet, there have been no reports with human skin/blood samples that would elucidate the molecular mechanisms by which ustekinumab calms psoriasis skin lesions. To investigate the efficacy and molecular pathway (RORC, t-BOX and GATA) of ustekinumab in treating patients with psoriasis skin lesions. A total of 30 patients with psoriasis were randomized into placebo group and treatment group. PASI of each patient was calculated at 0, 12 and 24 weeks post-treatment. The mRNA levels of RORC, t-BOX and GATA in peripheral blood mononuclear cells separated from patients’ whole blood were analyzed using qPCR. Decreased mRNA of RORC, t-BOX and GATA were observed after continuous injections, indicating that ustekinumab exerts its effect by interacting with these molecules; while no significant difference in foxp3 mRNA levels were found between placebo group and treatment group

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients

Background: Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. Objectives: This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. Methods: This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital’s Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., “patients weaned within 24 h” (n = 1042) and “patients not weaned within 24 h” (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. Results: The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. Conclusions: The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment

Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray, Artificial Neural Network, Classification, and Regression Trees

Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC

Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray, Artificial Neural Network, Classification, and Regression Trees

Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC

Obesity disproportionately impacts lung volumes, airflow and exhaled nitric oxide in children

<div><p>Background</p><p>The current literature focusing on the effect of obesity and overweight on lung function and fraction of exhaled nitric oxide (FeNO) in children, particularly among healthy children of non-European descent, remains controversial. Furthermore, whether the relationship of obesity and overweight with lung function and FeNO in children is modified by atopy is unclear. The objective of this study was to examine the effect of excess weight on lung function parameters and FeNO among Asian children, with a particular focus on exploring the potential effect modification by atopy.</p><p>Methods</p><p>We investigated the effect of excess weight on lung function and FeNO in a population sample of 1,717 children aged 5 to 18 years and explored the potential modifying effect of atopy.</p><p>Results</p><p>There were positive associations of body mass index (BMI) z-score with forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), peak expiratory flow (PEF), and forced expiratory flow at 25–75% (FEF_25-75) (all <i>P</i><0.001), after controlling for confounders. The beta coefficient for FEV₁ (0.084) was smaller than that for FVC (0.111). In contrast, a negative association was found between BMI z-score and FEV₁/FVC ratio (<i>P</i><0.001) and FeNO (<i>P</i> = 0.03). A consistent pattern of association for lung function variables was observed when stratifying by atopy. There was a negative association of BMI z-score with FeNO in atopic subjects (<i>P</i> = 0.006), but not in non-atopic subjects (<i>P</i> = 0.46).</p><p>Conclusions</p><p>Excess weight disproportionately impacts lung volumes and airflow in children from the general population, independent of atopic status. Excess weight inversely affects FeNO in atopic but not in non-atopic children.</p></div

Multivariable analysis of associations of BMI z-scores with lung function variables and FeNO^*.

<p>Multivariable analysis of associations of BMI z-scores with lung function variables and FeNO^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174691#t004fn002" target="_blank">*</a>}.</p