Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by major social, communication and behavioural challenges. The cause of ASD is still unclear and it is assumed that environmental, genetic and epigenetic factors influence the risk of ASD occurrence. MicroRNAs (miRNAs) are short 21–25 nucleotide long RNA molecules which post-transcriptionally regulate gene expression. MiRNAs play an important role in central nervous system development; therefore, dysregulation of miRNAs is connected to changes in behaviour and cognition observed in many disorders including ASD. Based on previously published work, on diagnosing ASD using miRNAs, we hypothesized that miRNAs can be used as biomarkers in children with suspected developmental disorders (DD) including ASD within Bosnian-Herzegovinian (B&H) population. 14 selected miRNAs were tested on saliva of children with suspected developmental disorders including ASD. The method of choice was qRT-PCR as a relatively cheap method available in most diagnostic laboratories in low to mid-income countries (LMIC). Out of 14 analysed miRNAs, 6 were differentially expressed between typically developing children and children with some type of developmental disorder including autism spectrum disorder. Using the most optimal logistic regression, we were able to distinguish between ASD and typically developing (TD) children. We have found 5 miRNAs as potential biomarkers. From those, 3 were differentially expressed within the ASD cohort. All 5 miRNAs had shown good chi-square statistics within the logistic regression performed on all 14 analysed miRNAs. The accuracy of 5-miRNAs model training set was 90.2%, while the validation set had a 90% accuracy. This study has shown that miRNAs may be considered as biomarkers for ASD detection and may be used to identify children with ASD along with standard developmental screening tests. By combining these methods we may be able to reach a reliable and accessible diagnostic model for children with ASD in LMIC such as B&H.

Due to the development of information communication technologies (ICT), the number of medical devices (MDs) with telemetric possibilities is rising, so the concept of homecare is gaining importance. Also, new generation medical devices are equipped with artificial intelligence that is able to perform real-time analysis of measurement result and provide diagnosis prediction. This is the Industry 4.0 happening now. However, there is still traditional approach in management of medical devices. As medical devices have been sophisticated, management systems should improve so they can encompass all the important aspects regarding safety of patients and quality of care. This chapter presents how the technology of Industry 4.0 can be used to improve medical device maintenance systems by application of artificial intelligence (AI). Clinical engineering and health technology management departments benefit from such systems in terms of increase of safety and quality of patient diagnosis and treatments, and cost optimization in medical device management.

Abstract The aim of this study is to provide an insight into Balkan populations’ genetic relations utilizing in silico analysis of Y-STR haplotypes and performing haplogroup predictions together with network analysis of the same haplotypes for visualization of the relations between chosen haplotypes and Balkan populations in general. The population dataset used in this study was obtained using 23, 17, 12, 9 and 7 Y-STR loci for 13 populations. The 13 populations include: Bosnia and Herzegovina (B&H), Croatia, Macedonia, Slovenia, Greece, Romany (Hungary), Hungary, Serbia, Montenegro, Albania, Kosovo, Romania and Bulgaria. The overall dataset contains a total of 2179 samples with 1878 different haplotypes. I2a was detected as the major haplogroup in four out of thirteen analysed Balkan populations. The four populations (B&H, Croatia, Montenegro and Serbia) which had I2a as the most prevalent haplogroup were all from the former Yugoslavian republic. The remaining two major populations from former Yugoslavia, Macedonia and Slovenia, had E1b1b and R1a haplogroups as the most prevalent, respectively. The populations with E1b1b haplogroup as the most prevalent one are Macedonian, Romanian, as well as Albanian populations from Kosovo and Albania. The I2a haplogroup cluster is more compact when compared to E1b1b and R1b haplogroup clusters, indicating a larger degree of homogeneity within the haplotypes that belong to the I2a haplogroup. Our study demonstrates that a combination of haplogroup prediction and network analysis represents an effective approach to utilize publicly available Y-STR datasets for population genetics.