Quality of Life and Mental Health in Iranian Transgender Women

Background: Mental health issues and quality of life are among the critical items of general health in individuals, especially transgender subjects. The present study aimed to assess the quality of life, depression, anxiety, and stress in transgender women and determine the factors contributing to them.Methods: A cross-sectional study was performed on 127 Iranian transgender women using a convenience sampling method from August 2019 to April 2020. The Quality of Life (QOL) was determined using the World Health Organization (WHO) Questionnaire (WHOQOL-BREF). The DASS-21 questionnaire was employed to evaluate the subjects’ anxiety, depression, and stress.Results: In total, 48% of the individuals had a moderate QOL. The highest score was achieved in the physical health dimension and the lowest in social relationships. Depression, anxiety, and stress were severe and extremely severe in 22%, 20.4%, and 17.3% of the subjects, respectively. A significant relationship was observed between the overall perception of QOL and depression, anxiety, and stress (P<0.001).A significant positive association was observed between the subscales of QOL and education and favorable economic status among transgender women. Furthermore, a significant negative correlation existed between the subscales of QOL with age and sexual violence. Regarding mental health, education had a significant relationship with reduced stress and anxiety, and good economic status had a significant relationship with reduced depression. Still, sexual violence was associated with increased stress in individuals.Conclusion: The present study results emphasize that transgender women are at risk of mental health disorders, including depression, anxiety, and stress. These conditions are in close association with the quality of life in this population. Moreover, considering the high sexual violence in transgender women of the present study and its considerable effects on mental health disorders, there is a strong need to develop violence prevention services in the community and legal protections in this area. The culture of preventing violence against women in society should be emphasized, and education to families should be considered the first line of prevention

Fatal cases of strychnine ingestion referred to Fars Legal Medicine Organization; Autopsy findings and Analytical methods used in strychnine detection

Strychnine is a toxic alkaloid which might be the source of poisoning with homicidal and suicidal purposes. Eleven fatal cases of strychnine ingestion were considered in this study. Strychnine was isolated from biological samples using the solid phase extraction (SPE) procedure and detected by analytical methods such as thin layer chromatography (TLC), gas chromatography/mass spectrometry (GC-MS) as well as high performance liquid chromatography (HPLC). Eleven cases of strychnine ingestion including 8 men and 3 women with minimum and maximum ages of 17 and 56 years old have been studied. Most of them had an education level of high school and had neither criminal history nor psychological disorders except for 3 cases that were using psychiatric drugs. Facial and ocular congestion, facial cyanosis, lung edema and hemorrhage were found in all of the cases. Hyperemic kidney, brain, liver and meninges and lung collapse were other pathologic findings encountered. Reactive gliosis, subarachnoid focal hemorrhage and 6 cm laceration on the left side of the face were separately found in three cases. Considering highly fatal effects of strychnine and its potential suicidal use, it should be strictly and severely prohibited and watched out for its misuse. On the other hand, the analytical methods used, indicated their reliable, simple, specific and sensible application in forensic and clinical investigations.</p

The seven-year epidemiological study of legal abortion caused by heart disease, blood disorders, diabetes and hypertension as referred to forensic medicine centers in Fars Province

common risk factors for high risk pregnancies and spontaneous or therapeutic abortions. Objectives. To investigate the legal abortion caused by heart disease, blood disorders, diabetes and hypertension as referred to forensic medicine centers in Fars Province from 2007 to 2013. Material and methods. In a retrospective, cross-sectional study, samples consisted of all documents of people referred to forensic medicine centers in Shiraz since 2007 to 2013, comprising of 1664 files. Data collection tools included a demographic forum and the checklist of abortion causes. SPSS.16.0 was applied to analyze the data through descriptive statistical analysis. Results. The most frequent age group was 25–29 years at 31.5% (n = 522) and the lowest was over 40 years old at 4.15% (n = 70). The statistical report of the reasons for legal abortion permission were 19% (n = 63), 24.4% (n = 81), 10.54% (n = 35), and 8.13% (n = 27) due to heart problems, blood disorders, hypertension, and diabetes mellitus, respectively. Most frequent legal abortion permits by forensic medicine due to maternal causes were between the years of 2011–2012 at 17.8–28% (n = 59–93). The relationship between legal abortion permission at The Forensic Medicine Center at different years and maternal ages was statistically significant (p < 0.00001). Conclusions. The most common prevalent reason of abortion was Blood Disorder – 81 patients (24.4%) and heart disease – 63 cases (19%). It is essential that family education and prevention of repeated pregnancies be done with high-risk women. Also, initiation of pregnancy care at lower gestational age in identifying risky pregnancies and timely control of complications must also be undertake

Strontium- and Cobalt-Doped Multicomponent Mesoporous Bioactive Glasses (MBGs) for Potential Use in Bone Tissue Engineering Applications

Mesoporous bioactive glasses (MBGs) offer suitable platforms for drug/ion delivery in tissue engineering strategies. The main goal of this study was to prepare strontium (Sr)- and cobalt (Co)-doped MBGs; strontium is currently used in the treatment of osteoporosis, and cobalt is known to exhibit pro-angiogenic effects. Sr- and Co-doped mesoporous glasses were synthesized for the first time in a multicomponent silicate system via the sol&ndash;gel method by using P123 as a structure-directing agent. The glassy state of the Sr- and Co-doped materials was confirmed by XRD before immersion in SBF, while an apatite-like layer was detected onto the surface of samples post-immersion. The textural characteristics of MBGs were confirmed by nitrogen adsorption/desorption measurements. In vitro experiments including MTT assay, Alizarin red staining, and cell attachment and migration showed the cytocompatibility of all the samples as well as their positive effects on osteoblast-like cell line MG-63. Early experiments with human umbilical vein endothelial cells also suggested the potential of these MBGs in the context of angiogenesis. In conclusion, the prepared materials were bioactive, showed the ability to improve osteoblast cell function in vitro and could be considered as valuable delivery vehicles for therapeutics, like Co2+ and Sr2+ ions

Demographic Evaluation of Oro-Dental Self-Injury for Insurance Deception; Evaluation of the Cases Referred to Shiraz Forensic Medicine Center

Statement of the Problem: Nonsuicidal self-inflicted injuries are socially unacceptable and may cause mild to severe damages. Purpose: This study aimed to evaluate the demographic features of the subjects with orodental self-injuries referred to a forensic medicine center in Shiraz, Iran. Materials and Method: This cross-sectional study evaluated 51 participants (49 men and 2 women) with orodental injuries referred to forensic medicine administration. Orodental self-injury was detected in the subjects, based on the last forensic criterion of self-injuries, considering their history, clinical examinations, and panoramic radiographs. Results: The findings of this study revealed that dental self-injuries were more prevalent among married men from urban areas with secondary education levels. Most of the cases were due to the monetary compensation received. In the majority of cases, a hard object was used for this self-injury. Moreover, no statistical association was observed between the economic status and orodental self-injury. Conclusion: This study concluded that dental self-injury could be regarded as an unplanned incident because no significant correlation was observed between the participants, their economic status, and the type of dental trauma. Furthermore, detailed investigations on the latent variables are required

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Several researchers benefited from the EU supported project Sus-tainable Process Integration Laboratory - SPIL funded as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL project.This work was also partly supported by the Ministerio de Ciencia e Innovacion (Espana) /FEDER under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 and A-TIC-080-UGR18 projects.The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.European Commission CZ.02.1.01/0.0/0.0/15_003/0000456Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL projectMinisterio de Ciencia e Innovacion (Espana) /FEDER RTI2018-098913-B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC-080-UGR1

Prognosis of COVID‐19 patients using lab tests: A data mining approach

Abstract Background The rapid prevalence of coronavirus disease 2019 (COVID‐19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID‐19 patients using data mining techniques. Methods In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion Data mining methods have the potential to be used for predicting outcomes of COVID‐19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID‐19 patients

Baseline Characteristics and Associated Factors of Mortality in COVID-19 Patients; an Analysis of 16000 Cases in Tehran, Iran

Introduction: Given the importance of evidence-based decision-making, this study aimed to evaluate epidemiological and clinical characteristics as well as associate factors of mortality among admitted COVID-19 cases. Methods: This multicenter, cross-sectional study was conducted on confirmed and suspected COVID-19 cases who were hospitalized in 19 public hospitals affiliated to Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran, between February 19 and May 12, 2020. Epidemiological and clinical characteristics of the infected cases were compared between the deceased and survivors after discharge. Case fatality rates (CFRs) were calculated across all study variables. Single and multiple logistic regressions were used to explore the risk factors associated with COIVD-19 mortality. Results: Out of the 16035 cases that referred to the hospitals affiliated to SBMU, 16016 patients (99.93 of Confirmed and 99.83 of suspected cases) were hospitalized. 1612 patients died with median hospitalization days of 5 (interquartile range (IQR): 2-9) and 3 (1-7) for confirmed and suspected COVID-19 cases, respectively. The highest death rate was observed among ages>65 (63.4 of confirmed cases, 62.3 of suspected cases) and intensive care unit (ICU)/critical care unit (CCU) patients (62.7 of confirmed cases, 52.2 of suspected cases). Total case fatality rate (CFR) was 10.05 (13.52 and 6.37 among confirmed and suspected cases, respectively). The highest total CFR was observed in patients with age>65 years (25.32), underlying comorbidities (25.55), and ICU/CCU patients (41.7). The highest CFR was reported for patients who had diabetes and cardiovascular diseases (38.46) as underlying non-communicable diseases (NCDs), and patients with cancer (35.79). Conclusion: This study showed a high CFR among suspected and confirmed COVID-19 cases, and highlighted the main associated risk factors including age, sex, underlying NCDs, and ICU/CCU admission affecting survival of COVID-19 patients © 2020, Archives of Academic Emergency Medicine. All Rights Reserved