Background The Dupuytren disease is a benign fibroproliferative disorder that leads to the formation of the collagen knots and fibres in the palmar fascia. The previous studies reveal different levels of Dupuytren’s prevalence worldwide; hence, this study uses meta-analysis to approximate the prevalence of Dupuytren globally. Methods In this study, systematic review and meta-analysis have been conducted on the previous studies focused on the prevalence of the Dupuytren disease. The search keywords were Prevalence, Prevalent, Epidemiology, Dupuytren Contracture, Dupuytren and Incidence. Subsequently, SID, MagIran, ScienceDirect, Embase, Scopus, PubMed and Web of Science databases and Google Scholar search engine were searched without a lower time limit and until June 2020. In order to analyse reliable studies, the stochastic effects model was used and the I2 index was applied to test the heterogeneity of the selected studies. Data analysis was performed within the Comprehensive Meta-Analysis Software version 2.0. Results By evaluating 85 studies (10 in Asia, 56 in Europe, 2 in Africa and 17 studies in America) with a total sample size of 6628506 individuals, the prevalence of Dupuytren disease in the world is found as 8.2% (95% CI 5.7–11.7%). The highest prevalence rate is reported in Africa with 17.2% (95% CI 13–22.3%). According to the subgroup analysis, in terms of underlying diseases, the highest prevalence was obtained in patients with type 1 diabetes with 34.1% (95% CI 25–44.6%). The results of meta-regression revealed a decreasing trend in the prevalence of Dupuytren disease by increasing the sample size and the research year (P < 0.05). Conclusion The results of this study show that the prevalence of Dupuytren disease is particularly higher in alcoholic patients with diabetes. Therefore, the officials of the World Health Organization should design measures for the prevention and treatment of this disease.

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Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.

Background Affective and somatic symptoms of premenstrual syndrome (PMS) may adversely affect many school‐aged females. Different treatment modalities may be used to help alleviate PMS symptoms. Aim This study documented the prevalence and nature of PMS, as well as medication use, among female pharmacy students and pharmacy clients in Iran. Methods A cross‐sectional questionnaire‐based study was conducted among university students and pharmacy clients in Kermanshah Province, eastern Iran, from October 2017 to May 2018. Participants were invited to complete a questionnaire about their PMS symptoms, use of medications, natural remedies and supplements for PMS, and the role of the pharmacist in their medication selection. Results Among the 208 female students and clients who participated in the study, the overall prevalence of PMS was 73.6%. Back pain (59.1%) and mood swings (64.9%) were the most frequently reported physical and affective symptoms, respectively. More than three‐quarters of participants (76.9%) used at least one medication (either prescribed or self‐medicated) during menstruation. Self‐medication was reported by 70.2% of participants, mostly (66.3%) involving over‐the‐counter medications. Non‐steroidal anti‐inflammatory drugs (for analgesia) were used by 49.0% of participants, whereas the oral contraceptive pill was used by only 6.4%. Traditional remedies were used by 42.8% of participants. Overall, community pharmacist involvement in PMS management was reported by only 27.9% of participants. Conclusion Self‐medication for PMS, especially the use of analgesics, was very common among participants. The use of natural remedies was also significant, highlighting the need for studies to further evaluate the ability of such remedies to relieve PMS symptoms. Overall, community pharmacists were not substantially involved in treatment selection.

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This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.