Time course analysis of large-scale gene expression in incised muscle using correspondence analysis

Genetic background affects neutrophil activity and determines the severity of autoinflammatory osteomyelitis in mice

The knowledge about the contribution of the innate immune system to health and disease is expanding. However, to obtain reliable results, it is critical to select appropriate mouse models for in vivo studies. Data on genetic and phenotypic changes associated with different mouse strains can assist in this task. Such data can also facilitate our understanding of how specific polymorphisms and genetic alterations affect gene function, phenotypes, and disease outcomes. Extensive information is available on genetic changes in all major mouse strains. However, comparatively little is known about their impact on immune response and, in particular, on innate immunity. Here, we analyzed a mouse model of chronic multifocal osteomyelitis, an autoinflammatory disease driven exclusively by the innate immune system, which is caused by an inactivating mutation in the Pstpip2 gene. We investigated how the genetic background of BALB/c, C57BL/6J, and C57BL/6NCrl strains alters the molecular mechanisms controlling disease progression. While all mice developed the disease, symptoms were significantly milder in BALB/c and partially also in C57BL/6J when compared to C57BL/6NCrl. Disease severity correlated with the number of infiltrating neutrophils and monocytes and with the production of chemokines attracting these cells to the site of inflammation. It also correlated with increased expression of genes associated with autoinflammation, rheumatoid arthritis, neutrophil activation, and degranulation, resulting in altered neutrophil activation in vivo. Together, our data demonstrate striking effects of genetic background on multiple parameters of neutrophil function and activity influencing the onset and course of chronic multifocal osteomyelitis.

Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA

(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts.

Visualizing adverse events in clinical trials using correspondence analysis with R-package visae

Background

Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis.

Methods

We propose the application of stacked CA using contribution biplots as a tool to explore differences in AE data among treatments in clinical trials. We defined five levels of refinement for the analysis based on data derived from the Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms and their combinations. In addition, we developed a Shiny app built in an R-package, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 factorial trial comparing radiation therapy with either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to anastrozole in postmenopausal women with hormone-positive ductal carcinoma in situ.

Results

In the R04 trial (n = 1308), CA biplots displayed the discrepancies between single agent treatments and their combinations with Oxa at all levels of AE classes, such that these discrepancies were responsible for the largest portion of the explained variability among treatments. In addition, an interaction effect when adding Oxa to Cape/5-FU was identified when the distance between Cape+Oxa and 5-FU + Oxa was observed to be larger than the distance between 5-FU and Cape, with Cape+Oxa and 5-FU + Oxa in different quadrants of the CA biplots. In the B35 trial (n = 3009), CA biplots showed different patterns for non-adherent Anastrozole and Tamoxifen compared with their adherent counterparts.

Conclusion

CA with contribution biplot is an effective tool that can be used to summarize AE data in a two-dimensional display while minimizing the loss of information and interpretation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01368-w.