Background: The aim of our study was to perform a systematic review of the literature and meta-analysis in order to investigate relationship between drug use and oral health. Methods: We searched for studies in English published before July 1, 2019 on PsycINFO, PubMed, SciELO, Scopus, and Web of Science. We assessed the relationship between drug use (methamphetamines, heroin; opiates; crack, cocaine and cannabis as dependent variables) and reported tooth loss, periodontal disease, or decayed, missing, and filled teeth index as an independent variable. The data were analyzed using Stata 12.0 software. Results: We initially identified 1836 potential articles (with 1100 duplicates) and screened the remaining 736 titles and abstracts, comprising 54 studies. In the next step, we evaluated the full-texts; 44 studies were excluded, accordingly. In total, we included 10 publications in the meta-analysis. Drug type was associated with periodontal disease (OR 1.44; 95% CI 0.8–2.6) and pooled estimates showed that type of drug used increased the odds of the number of decayed, missed and filled teeth (DMFT) (OR 4.11; 95% CI 2.07–8.15) respectively. Conclusions: The analytical challenges of segregating the impact of individual drug types on oral health diseases mean that investigations on the direct relationship between oral health status and drug use are limited. Developing programs to improve potential confounding with various substances and addressing the dental health needs of people who use drugs is vital if we are to improve their overall quality of life.

Background: Health-related quality of life (HQoL) indicators are considered valid measures of patient assessment in physical, mental and oral healthcare. This study aimed to examine the evidence on the relationship of oral health status, demographic and socioeconomic characteristics with oral health-related quality of life (OHRQoL) in children.Methods: Studies in English published up to December 2019 were searched on PsycINFO, PubMed, SciELO, Scopus, and Web of Science databases. Epidemiological studies simultaneously assessing sociodemographic factors related to oral health (age, income, gender, maternal education), oral health measures (orthodontic treatment needs, dental caries and periodontal disease) and OHRQoL in children aged 3–12 years were included. Methodological quality was assessed using a Critical Appraisal Checklist. Meta-analysis was used to estimate pooled measures between sociodemographic factors and oral health measures with OHRQoL.Results: Eleven articles were included. Lower children’s age (3–5 years vs > 5), gender (girls vs boys), lower income (< 70$ vs ≥ $70), low maternal education (≤ 6 vs > 6 years) were associated with poor OHRQoL among children. Orthodontic treatment needs, dental caries and periodontal diseases were also associated with poor children’s OHRQoL. Meta-regression showed that Human Development Index, sample size, year of publication and participant’s age were relevant aspects that influenced the above mentioned relationships.Conclusions: Our findings suggest that oral health promotion strategies to improve children’s OHRQoL should consider the social and environmental where they live as well their oral health status. Further longitudinal studies are needed to explore the determinants of OHQoL in children.

Cytogenetic evaluation of products of conception (POC) for chromosomal abnormalities is central to determining the cause of pregnancy loss. We compared the test success rates in various specimen types and the frequencies of chromosomal abnormalities detected by G-banding analysis with those found by Oligo-SNP chromosomal microarray analysis (CMA). We evaluated the benefit of CMA testing in cases of failed culture growth.

Background: Telehealth technology is an excellent solution to resolve the problems of health care delivery. However, this technology may fail during large-scale implementation. As a result, business models can be used to facilitate commercialization of telehealth products and services.Objective: The purpose of this study was to review different types of business models or frameworks and their components used in the telehealth industry.Methods: This was a systematic review conducted in 2020. The databases used for searching related articles included Ovid, PubMed, Scopus, Web of Science, Emerald, and ProQuest. Google Scholar was also searched. These databases and Google Scholar were searched until the end of January 2020 and duplicate references were removed. Finally, articles meeting the inclusion criteria were selected and the Critical Appraisal Skills Programme (CASP) checklist was used for appraising the strengths and limitations of each study. Data were extracted using a data extraction form, and the results were synthesized narratively.Results: Initially, 4998 articles were found and after screening, 23 were selected to be included in the study. The results showed that new telehealth business models were presented in 13 studies, and the applications of the existing business models were reported in 10 studies. These studies were related to different types of services, namely, telemonitoring (4 studies), telemedicine (3 studies), mobile health (3 studies), telerehabilitation (3 studies), telehealth (2 studies), assisted living technologies (2 studies), sensor-based systems (2 studies), and mobile teledermoscopy, teleradiology, telecardiology, and teletreatment (1 study related to each area). In most of the business models, value proposition, financial variables, and revenue streams were the main components.Conclusions: Applying business models in the commercialization of telehealth services will be useful to gain a better understanding of the required components, market challenges, and possible future changes. The results showed that different business models can be used for different telehealth technologies in various health systems and cultures. However, it is necessary to evaluate the effectiveness of these models in practice. Moreover, comparing the usefulness of these models in different domains of telehealth services will help identify the strengths and weaknesses of these models for future optimization.

MicroRNAs (miRNAs) are key regulators of gene expression, playing important roles in development, homeostasis, and disease. Recent experimental evidence indicates that mutation or deregulation of the MIR17HG gene (miR-17 ~ 92 cluster) contributes to the pathogenesis of a variety of human diseases, including cancer and congenital developmental defects. We report on a 9-year-old boy who presented with developmental delay, autism spectrum disorder, short stature, mild macrocephaly, lower facial weakness, hypertelorism, downward slanting palpebral fissures, brachydactyly, and clinodactyly. SNP-microarray analysis revealed 516 kb microduplication at 13q31.3 involving the entire MIR17HG gene encoding the miR-17 ~ 92 polycistronic miRNA cluster, and the first five exons of the GPC5 gene. Family study confirmed that the microduplication was maternally inherited by the proband and one of his five half-brothers; digit and other skeletal anomalies were exclusive to the family members harboring the microduplication.

Genetic disorders are the main causes of many other diseases. Integrating genetic data into Electronic Health Records (EHRs) can facilitate the management of genetic information and care of patients in clinical practices. The aim of this study was to identify the main requirements for integrating genetic data into the EHR system from the medical geneticists' perspectives. The research was completed in 2018 and consisted of two phases. In the first phase, the main requirements for integrating genetic data into the EHR system were identified by reviewing the literature. In the second phase, a 5-point Likert scale questionnaire was developed based on the literature review and the results derived from the first phase. Then, the Delphi method was applied to reach a consensus about the integration requirements. The findings of the first phase showed that data elements, including patients' and healthcare providers' personal data, clinical and genetic data, technical infrastructure, security issues and functional requirements, should be taken into account before data integration. In the second phase, a consensus was reached for most of the items (mean ≥3.75). The items with a mean value of less than 2.5 did not achieve a consensus and were removed from the final list. The integration of genetic data into the EHRs can provide a ground for increasing accuracy and precision in the diagnosis and treatment of genetic disorders. Such integration requires adequate investments to identify users' requirements as well as technical and non-technical issues.