Six solutions for more reliable infant research

Infant research is often underpowered, undermining the robustness and replicability of our findings. Improving the reliability of infant measures offers a solution for increasing statistical power independent of sample size. Here, we discuss two senses of the term reliability in the context of infant research: reliable (large) effects and reliable measures. We examine the circumstances under which effects are strongest and measures are most reliable, and provide simulations to illustrate the relationship between effect size, measurement reliability, and statistical power. We then present six concrete solutions for improving measurement in infant research: (1) routinely estimating and reporting the effect size and measurement reliability of infant tasks, (2) selecting the best measurement tool, (3) developing better infant paradigms, (4) collecting more data points per infant, (5) excluding unreliable data from analysis, and (6) conducting more sophisticated data analyses. Deeper consideration of measurement in infant research will improve our ability to study infant development.

Specific microbiome profile in Takayasu’s arteritis and giant cell arteritis

Recent studies have provided evidence of a close link between specific microbiota and inflammatory disorders. While the vessel wall microbiota has been recently described in large vessel vasculitis (LVV) and controls, the blood microbiome in these diseases has not been previously reported (LVV). We aimed to analyse the blood microbiome profile of LVV patients (Takayasu’s arteritis [TAK], giant cell arteritis [GCA]) and healthy blood donors (HD). We studied the blood samples of 13 patients with TAK (20 samples), 9 patients with GCA (11 samples) and 15 HD patients. We assessed the blood microbiome profile by sequencing the 16S rDNA blood bacterial DNA. We used linear discriminant analysis (LDA) coupled with linear discriminant effect size measurement (LEfSe) to investigate the differences in the blood microbiome profile between TAK and GCA patients. An increase in the levels of Clostridia, Cytophagia and Deltaproteobacteria and a decrease in Bacilli at the class level were found in TAK patients compared with HD patients (LDA > 2, p < 0.05). Active TAK patients had significantly lower levels of Staphylococcus compared with inactive TAK patients. Samples of GCA patients had an increased abundance of Rhodococcus and an unidentified member of the Cytophagaceae family. Microbiota of TAK compared with GCA patients was found to show higher levels of Candidatus Aquiluna and Cloacibacterium (LDA > 2; p < 0.05). Differences highlighted in the blood microbiome were also associated with a shift of bacterial predicted metabolic functions in TAK in comparison with HD. Similar results were also found in patients with active versus inactive TAK. In conclusion, patients with TAK were found to present a specific blood microbiome profile in comparison with healthy donors and GCA subjects. Significant changes in the blood microbiome profiles of TAK patients were associated with specific metabolic functions.

Performance Criteria-Based Effect Size (PCES) Measurement of Single-Case Experimental Designs: A Real-World Data Study

To date, several effect size measurement methods have been proposed to determine the effect sizes of single case experimental designs (SCEDs) based on probability, mean or overlap. All these methods have certain considerable limitations. In this study, a new effect size calculation model for SCEDs, named performance criteria-based effect size (PCES), is proposed considering the limitations of four nonoverlap-based effect size measures, which are widely accepted in the literature and blend well with visual analysis. In the field test of PCES, real data from published studies were utilized and the relationship between PCES, visual analysis and the four nonoverlap-based methods was examined. In determining the data to be used in the field test, 1,012 tiers (AB phases) were identified from the issues of the four journals, which have most frequency SCEDs studies, published in the last five years. The findings revealed a weak or moderate relationship between PCES and nonoverlap-based methods due to its focus on performance criteria. Although PCES has some weaknesses, it was found to be promising in eliminating the cases that may create issues in nonoverlap-based methods, using quantitative data to determine the presence of socially important changes in behavior and complementing the visual analysis.

Specific microbiome profile in Takayasu Arteritis (TAK) and Giant Cell Arteritis (GCA).

Objectives: There is increasing evidence of a close link between microbiota and inflammatory diseases. Microbiota has never been studied in large vessel vasculitis (LVV). We aimed to analyse the blood microbiome profile of patients with LVV [Takayasu arteritis (TAK) or giant cell arteritis (GCA)] and healthy donors (HD). Methods : We studied blood samples of 13 patients with TAK (20 samples), 9 (11 samples) with GCA and 15 HD. The blood microbiome profile was assessed by sequencing of the 16S rDNA blood bacterial DNA. Linear Discriminant Analysis (LDA) coupled with effect size measurement (LEfSe) was used to analyse the differences in the blood microbiome profile between the groups. Results: Samples of TAK patients showed an increase in the levels of Clostridia, Cytophagia and Deltaproteobacteria and a decrease in Bacilli at the class level as compared to HD (LDA>2, p<0.05). Active compared to inactive TAK patients had significantly lower levels of Staphylococcus. Samples of GCA patients showed an increased abundance of Rhodococcus and an unidentified member of the Cytophagaceae family. Microbiota of TAK compared to GCA patients showed higher levels of Candidatus Aquiluna and Cloacibacterium (LDA>2; p<0.05). Differences in blood microbiome were also associated with a shift of bacterial predicted metabolic functions in TAK compared to HD. Similar results were also found in active compared to inactive TAK patients. In conclusion , TAK patients showed a specific blood microbiome profile as compared to healthy controls and GCA patients. Among TAK patients, significant changes of blood microbiome profile were associated with specific metabolic functions.