Nightmare frequency, nightmare distress, and psychopathology in female victims of childhood maltreatment.

This study investigated the relationships between a history of childhood maltreatment, the frequency of disturbing dreams, their associated distress, and the presence of psychopathology in 352 female undergraduate volunteers. Participants completed questionnaires assessing dream recall, bad dream and nightmare frequency, nightmare distress, psychological well-being, and history of childhood trauma. Four groups were investigated based on the type and severity of childhood maltreatments experienced. Women reporting more severe forms of maltreatment reported higher frequencies of disturbing dreams, higher levels of nightmare distress, and greater psychopathology. Results showed that nightmare distress explains frequency of disturbed dreaming beyond the effect of psychopathology and childhood trauma. The results highlight the importance of assessing waking distress associated with disturbing dreams independently from their actual incidence.

A Dangerous Curve: Impact of the COVID-19 Pandemic on Brace Treatment in Adolescent Idiopathic Scoliosis.

STUDY DESIGN: Observational Cohort study. OBJECTIVES: We aim to document the abandon and irregular compliance rate towards brace treatment during the COVID-19 pandemic and its impact on AIS progression. METHODS: We reviewed a database of AIS patients recruited between March and September 2020. We included AIS patients under brace treatment according to SRS criteria. The patients were divided in 2 cohorts: those with self-reported Good-Compliance (GC) to treatment and those who had a Bad-Compliance (BC). Data analysis included biometric and radiographic data at first visit and last follow-up and percentage of progression. Unpaired student-t tests and Chi2 were used for comparison. RESULTS: 152 patients met inclusion criteria. 89 patients (age:12.1y.o.±1.4) reported good adherence to treatment, while 63 patients (age:12.7y.o.±1.8) were not compliant. Within the BC group, 18 patients reported irregular brace wear, while 45 had completely abandoned treatment (abandon rate of 29%). The GC cohort started treatment with a mean main thoracic (MT) curve of 26° and finished with 27°. The mean difference between measurements was +.65°±7.5; mean progression rate was -4.6%. However, the BC cohort started with a mean MT curve of 27° and finished with 32°, with a mean increase of +5°±8 and a mean progression rate of -13%. The differences between the 2 cohorts were statistically significant (P = .0002). Six patients from the BC group progressed and were offered surgery. CONCLUSION: The abandon rate of brace treatment in AIS significantly increased during the first wave of COVID-19 pandemic. Patients who voluntarily discontinued treatment had significant increases in curve progression and surgical indication rates. LEVEL OF EVIDENCE: III.

Analysis of a simulated microarray dataset: comparison of methods for data normalisation and detection of differential expression (open access publication).

Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.

Analysis of the real EADGENE data set: multivariate approaches and post analysis (open access publication).

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

Analysis of the real EADGENE data set: comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (open access publication).

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.