Stem cell research is one of the most rapidly expanding field of medicine which provides significant opportunities for therapeutic and regenerative applications. Different types of stem cells have been isolated investigating their accessibility, control of the differentiation pathway and additional immunomodulatory properties. Bulk of the literature focus has been on the study and potential applications of adult stem cells (ASC) because of their low immunogenicity and reduced ethical considerations. This review paper summarizes the basic available literature on different types of ASC with special focus on stem cells from dental and orofacial origin. ASC have been isolated from different sources, however, isolation of ASC from orofacial tissues has provided a novel promising alternative. These cells offer a great potential in the future of therapeutic and regenerative medicine because of their remarkable availability at low cost while allowing minimally invasive isolation procedures. Furthermore, their immunomodulatory and anti-inflammatory potential is of particular interest. However, there are conflicting reports in the literature regarding their particular biology and full clinical potentials. Sound knowledge and higher control over proliferation and differentiation mechanisms are prerequisites for clinical applications of these cells. Therefore, further standardized basic and translational studies are required to increase the reproducibility and reduce the controversies of studies, which in turn facilitate comparison of related literature and enhance further development in the field.
Adult Stem Cells* ; Adult* ; Biology ; Humans ; Regenerative Medicine* ; Stem Cell Research ; Stem Cells

In this article, the affiliation of the first author should be changed.

OBJECTIVES: A health-promoting lifestyle (HPL) is a factor that affects the quality of life (QoL) in patients with beta-thalassemia (β-thalassemia). Due to the lack of studies of this issue, this study aimed to determine the association between HPL and QoL among adults with β-thalassemia. METHODS: This cross-sectional (descriptive-analytic) study was conducted among 389 adult patients with β-thalassemia in Tehran, Iran. The research instrument included a questionnaire consisting of three parts: demographic items, the Short-Form Health Survey and the Health-Promoting Lifestyle Profile. The data were analyzed using SPSS version 23.0. The results were considered significant at the conventional p<0.05 level. RESULTS: The mean age of the participants was 30.2±8.3 years. The mean score of the HPL dimensions was 127.28±21.53, and the mean score of the QoL domains was 61.44±23.38. The highest and the lowest mean scores of the HPL dimensions were found for spiritual growth (23.96±5.74) and physical activity (11.32±3.95), respectively. The QoL scores in all three domains (total, physical component summary score, and mental component summary score) were moderate. Health responsibility, physical activity, spiritual growth, and interpersonal relations were significant predictive factors of QoL in adults with β-thalassemia; these four dimensions explained 37.9% of the variance in QoL. CONCLUSIONS: QoL and HPL were not at acceptable levels among patients with thalassemia. Therefore, educational interventions emphasizing spiritual growth, physical activity, and interpersonal relations are necessary for patients with thalassemia.
Adult* ; beta-Thalassemia* ; Health Surveys ; Humans ; Interpersonal Relations ; Iran ; Life Style* ; Motor Activity ; Quality of Life* ; Thalassemia

OBJECTIVES: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. METHODS: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. RESULTS: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. CONCLUSIONS: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.
Artificial Intelligence ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Classification ; Decision Making ; Delivery of Health Care ; Diabetes Mellitus ; Diabetes Mellitus, Type 2 ; Diagnosis ; Fasting ; Incidence ; Lipoproteins ; Machine Learning ; Mass Screening ; Methods ; Support Vector Machine ; Triglycerides

OBJECTIVE: To evaluate test-retest reliability of trunk kinematics relative to the pelvis during gait in two groups (males and females) of patients with non-specific chronic low back pain (NCLBP) using three-dimensional motion capture system. METHODS: A convenience sample of 40 NCLBP participants (20 males and 20 females) was evaluated in two sessions. Participants were asked to walk with self-selected speed and kinematics of thorax and lumbar spine were captured using a 6-infrared-cameras motion-analyzer system. Peak amplitude of displacement and its measurement errors and minimal detectable change (MDC) were then calculated. RESULTS: Intraclass correlation coefficients (ICCs) were relatively constant but small for certain variables (lower lumbar peak flexion in female: inter-session ICC=0.51 and intra-session ICC=0.68; peak extension in male: inter-session ICC=0.67 and intra-session ICC=0.66). The measurement error remained constant and standard error of measurement (SEM) difference was large between males (generally ≤4.8°) and females (generally ≤5.3°). Standard deviation (SD) was higher in females. In most segments, females exhibited higher MDCs except for lower lumbar sagittal movements. CONCLUSION: Although ICCs were sufficiently reliable and constant in both genders during gait, there was difference in SEM due to difference in SD between genders caused by different gait disturbance in chronic low back pain. Due to the increasing tendency of measurement error in other areas of men and women, attention is needed when measuring lumbar motion using the method described in this study.
Biomechanical Phenomena* ; Female ; Gait* ; Humans ; Low Back Pain* ; Male ; Methods ; Pelvis* ; Reproducibility of Results ; Spine ; Thorax*