Developmental disorders (DDs), such as autism spectrum disorder (ASD), incorporate various conditions; once identified, further diagnostics are necessary to specify their type and severity. The aim of this exploratory study was to identify genetic variants that can help differentiate ASD early from other DDs. We selected 36 children (mean age 60.1 months) with DDs using Developmental Behavioral Scales (DBS) through “EDUS-Education for All”, an organization providing services for children with DDs in Bosnia and Herzegovina. We further rated children’s autistic traits with the preschool version of the Childhood Autism Rating Scale, second edition (CARS-II). We defined ASD if scores were >25.5 and other DDs if scores were <25.5. Diagnosis of ASD and DD were independently confirmed by child psychiatrists. Whole exome sequencing (WES) was performed by Veritas Genetics, USA, using Illumina NovaSeq 6000 (Illumina Inc., San Diego, CA, USA) NGS sequencing apparatus. We tested genetic association by applying SKAT-O, which optimally combines the standard Sequence Kernel Association Test (SKAT) and burden tests to identify rare variants associated with complex traits in samples of limited power. The analysis yielded seven genes (DSE, COL10A1, DLK2, CSMD1, FAM47E, PPIA, and PYDC2) to potentially differentiate observed phenotypic characteristics between our cohort participants with ASD and other DDs. Our exploratory study in a small sample of participants with ASD and other DDs contributed to gene discovery in differentiating ASD from DDs. A replication study is needed in a larger sample to confirm our results.

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The possibility of injectable biomaterials being used in the therapy of peripheral artery disease (PAD) is investigated in this article. We conducted a thorough review of the literature on the use and efficacy of biomaterials (BMs) and drug-coated balloons (DCBs). These BMs included hydrogels, collagen scaffolds, and nanoparticles. These BMs could be used alone or in combination with growth factors, stem cells, or gene therapy. The treatment of peripheral artery disease with DCBs is increasingly common in the field of interventional angiology. Studies have been carried out to examine the effectiveness of paclitaxel-coated balloons such as PaccocathTM in lowering the frequency with which further revascularization operations are required. PCB angioplasty and angioplasty without paclitaxel did not significantly vary in terms of mortality, according to the findings of a recent meta-analysis that included the results of four randomized controlled studies. On the other hand, age was found to be a factor that predicted mortality. There was a correlation between the routine utilization of scoring balloon angioplasty along with DCBs and improved clinical outcomes in de novo lesions. In both preclinical and clinical testing, the SelutionTM DCB has demonstrated efficacy and safety, but further research is required to determine whether or not it is effective and safe over the long term. In addition, we reviewed the difficulties involved in bringing injectable BMs-based medicines to clinical trials, including the approval processes required by regulatory bodies. Injectable BMs have a significant amount of therapeutic promise for PAD, which highlights the need for more research and clinical studies to be conducted in this field. In conclusion, this research focuses on the potential of injectable BMs and DCBs in the treatment of PAD as well as the hurdles that must be overcome in order to translate these treatments into clinical trials. In this particular field, there is a demand for further research as well as clinical trials.

BACKGROUND: Premature born infants or infants born sick require immediate medical attention and decreasing the stress imposed onto their body by the environment. Infant incubators provide an enclosed environment that can be controlled to fit the needs of the infant. As such, their performance must be consistent and without significant deviations. The only manner to ensure this is by post-market surveillance (PMS) focused on evaluation of both safety and performance. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of infant incubators for post-market surveillance purposes. METHOD: The method was developed based on guidelines for devices providing measurements laid out by the International Organisation of Legal Metrology (OIML). The methodology was validated during a four year period in healthcare institutions of all levels. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of infant incubators as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of infant incubators during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

Cellular respiration is a pathway that uses energy from food molecules and oxygen for different processes of life such as movement. Oxygen, transported through mass transport from blood vessels to skeletal muscle can bind to myoglobin that stores a small amount of oxygen or can be used in cellular respiration by mitochondria. This paper presents a Simulink model of oxygen distribution in skeletal muscle, based on previously published mathematical models. Different parameters for supply concentration of oxygen at capillary wall and consumption rate of the muscle tissue simulate oxygen distribution when the body is under certain conditions: in rest state, dysoxia and exercise state.

This paper presents the results of development of Artificial Neural Networks (ANNs) for prediction of medical device performance based on conformity assessment data. Conformity assessment data of medical devices was obtained from periodical inspections conducted by ISO 17020 accredited laboratory during 2015–2019 period. For the development of ANNs, 1738 samples of conformity assessment of infusion and perfusor pumps was used. Out of the overall number of samples, 1391 (80%) of them were used during system development and 346 (20%) samples were used for subsequent validation of system performance. During system development, the impact on overall system accuracy of different number of neurons in hidden layer and the activation functions was tested. Also, two neural network architectures were tested: feedforward and feedback. The results show that feedforward neural network architecture with 10 neurons in single hidden layer has the best performance. The overall accuracy of that neural network is 98.06% for performance prediction of perfusor pumps and 98.83% for performance prediction of infusion pumps. The recurrent neural network resulted in accuracy of 98.41% for both infusion pumps and perfusor pumps. The results show that conformity assessment data obtained through yearly inspections of medical devices can successfully be used for prediction of performance of single medical device. This is very important in increasing the safety and accuracy of diagnosis and treatment of patients.

Increasing incidence of cardiovascular disease and their mortality rate render them as second leading cause of death worldwide. Artificial Intelligence (AI) is used in many fields of science and industry, but also has found its use in medicine for diagnosis, treatment and prediction of diseases. This paper presents the review of AI application in cardiology. The review is based on research papers published in Medline database. Findings of the review indicate that, according to accuracy parameter, the overall performance of AI based models for cardiovascular application is above 83%. Based on the results, AI algorithms and deep learning can be rendered as accurate, hence showing possibility to be used as a diagnostic tool now and in the future. New era of modern diagnosing is coming and Artificial Intelligence has the potential to change the way in which medicine is practiced.

Glucose is a main source of energy in human body and its regulation is controlled by a biological mechanism with organ/cell interactions that are related to glucose-insulin dynamics. This paper presents the model of physiological behaviors of glucose-insulin regulatory mechanism. This model allows investigation of blood glucose dynamics dependency on food intake. The model presented in this paper discusses several parameters within this complex system.