Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder - a systematic methodological exploration of technical and demographic confounders in the search for biomarkers.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a worldwide prevalence of 1-2%. In low-resource environments, in particular, early identification and diagnosis is a significant challenge. Therefore, there is a great demand for 'language-free, culturally fair' low-cost screening tools for ASD that do not require highly trained professionals. Electroencephalography (EEG) has seen growing interest as an investigational tool for biomarker development in ASD and neurodevelopmental disorders. One of the key challenges is the identification of appropriate multivariate, next-generation analytical methodologies that can characterise the complex, nonlinear dynamics of neural networks in the brain, mindful of technical and demographic confounders that may influence biomarker findings. The aim of this study was to evaluate the robustness of recurrence quantification analysis (RQA) as a potential biomarker for ASD using a systematic methodological exploration of a range of potential technical and demographic confounders. METHODS: RQA feature extraction was performed on continuous 5-second segments of resting state EEG (rsEEG) data and linear and nonlinear classifiers were tested. Data analysis progressed from a full sample of 16 ASD and 46 typically developing (TD) individuals (age 0-18 years, 4802 EEG segments), to a subsample of 16 ASD and 19 TD children (age 0-6 years, 1874 segments), to an age-matched sample of 7 ASD and 7 TD children (age 2-6 years, 666 segments) to prevent sample bias and to avoid misinterpretation of the classification results attributable to technical and demographic confounders. A clinical scenario of diagnosing an unseen subject was simulated using a leave-one-subject-out classification approach. RESULTS: In the age-matched sample, leave-one-subject-out classification with a nonlinear support vector machine classifier showed 92.9% accuracy, 100% sensitivity and 85.7% specificity in differentiating ASD from TD. Age, sex, intellectual ability and the number of training and test segments per group were identified as possible demographic and technical confounders. Consistent repeatability, i.e. the correct identification of all segments per subject, was found to be a challenge. CONCLUSIONS: RQA of rsEEG was an accurate classifier of ASD in an age-matched sample, suggesting the potential of this approach for global screening in ASD. However, this study also showed experimentally how a range of technical challenges and demographic confounders can skew results, and highlights the importance of probing for these in future studies. We recommend validation of this methodology in a large and well-matched sample of infants and children, preferably in a low- and middle-income setting.

Nanofibers offer alternative ways to the treatment of skin infections.

Injury to the skin causes a breach in the protective layer surrounding the body. Many pathogens are resistant to antibiotics, rendering conventional treatment less effective. This led to the use of alternative antimicrobial compounds, such as silver ions, in skin treatment. In this review nanofibers, and the incorporation of natural antimicrobial compounds in these scaffolds, are discussed as an alternative way to control skin infections. Electrospinning as a technique to prepare nanofibers is discussed. The possibility of using these structures as drug delivery systems is investigated.

Encapsulation of Lactobacillus plantarum 423 and its Bacteriocin in Nanofibers.

Plantaricin 423, produced by Lactobacillus plantarum 423, was encapsulated in nanofibers that were produced by the electrospinning of 18% (w/v) polyethylene oxide (200 000 Da). The average diameter of the nanofibers was 288 nm. Plantaricin 423 activity decreased from 51 200 AU/ml to 25 600 AU/ml and from 204 800 AU/ml to 51 200 AU/ml after electrospinning, as determined against Lactobacillus sakei DSM 20017 and Enterococcus faecium HKLHS, respectively. Cells of L. plantarum 423 encapsulated in nanofibers decreased from 2.3 × 10(10) cfu/ml before electrospinning to 4.7 × 10(8) cfu/ml thereafter. Cells entrapped in the nanofibers continued to produce plantaricin 423. This is the first report on the encapsulation of a bacteriocin and cells of L. plantarum in nanofibers. The method may be used to design a drug delivery system for bacteriocins and the encapsulation of probiotic lactic acid bacteria. The technology is currently being optimized.