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Rosa canina L. (Rosaceae), commonly known as the rose hip, is originated from Europe, Africa, and Asia with a long history in medicinal applications. This study aimed to analyze the morphological traits, fatty acids profile, and content of phenolic compounds, anthocyanins, vitamin C, total carotenoid, total phenol, total flavonoid, and antioxidant activity of the fruits of eleven Iranian R. canina ecotypes (RCEs). The highest coefficient of variation was obtained in 1000 seed weight (46.57%). The seed oil varied from 8.08 ± 0.17% to 16.91 ± 0.35%. Linoleic (35.41 ± 0.78% to 49.59 ± 0.96%) and eicosanoic (17.67 ± 0.06% to 25.36 ± 0.54%) acids were the predominant fatty acids in the studied samples. The anthocyanin content in the fruits was ranged from 0.98 ± 0.03 to 4.41 ± 0.04 mg cyanidin 3-glucoside/100 g of dry weight (mg C3G/100 g DW). The high content of vitamin C (103.51 ± 1.24-419.70 ± 3.12 mg/100 g DW), total carotenoid (111.22 ± 0.78-206.98 ± 1.25 mg β-carotene equivalents per g of dry weight (mg β-CARE/g DW)), total phenol (52.87 ± 0.82-104.52 ± 0.23 mg GAE/g DW), and total flavonoid (14.20 ± 0.12-25.18 ± 0.47 mg RE/g DW) were observed in the studied samples. Catechin (20.42 ± 0.47-19.22 ± 0.13 µg/g DW) was the major phenolic compound. The high antioxidant activity in the fruits of the plant was recorded in the studied RCEs (IC 50  = 12.54 ± 0.18-26.33 ± 0.13 μg/ml). A significant correlation between some phytochemical compounds (dependent variable) and morphological features (independent variable) was found. Based on our findings, the fruit of the studied ecotypes can be used for future breeding programs and drug development., (© 2023. The Author(s).)

Aging as a major challenge can affect the development and growth of countries all around the world. This study aimed to identify the subgroups of the elderly based on the quality of life (Qol), sleep quality, and common mental disorders and assess the role of demographic characteristics on the membership of participants in each latent class. This cross-sectional study was conducted on 1064 people over the age of 60 years. The sample was selected through cluster sampling in northern Iran. All participants completed six sets of checklists and questionnaires. Data analysis was performed using latent class analysis. Three latent classes were identified; namely, (1) healthy (66.8%), (2) anxious and with poor sleep quality (28.6%), and unhealthy (4.6%). Being Female significantly increased the odds of membership in classes 2 and 3 compared to class 1. Furthermore, living in urban areas increased the odds of belonging to class 2 and class 3 compared to class 1. Illiteracy was also shown to increase the odds of being in class 3 in comparison to class 1. Results from the present indicate that the co-occurrence of health problems in 33.2% of the elderly was observed in various forms. The results of this study can be used in prioritizing health programs for the elderly and emphasizing high-risk groups., (© 2022. The Author(s).)

Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs., (© 2022. The Author(s).)