LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment.

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial-Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity.

Physics-informed neural networks as surrogate models of hydrodynamic simulators.

In response to growing concerns surrounding the relationship between climate change and escalating flood risk, there is an increasing urgency to develop precise and rapid flood prediction models. Although high-resolution flood simulations have made notable advancements, they remain computationally expensive, underscoring the need for efficient machine learning surrogate models. As a result of sparse empirical observation and expensive data collection, there is a growing need for the models to perform effectively in 'small-data' contexts, a characteristic typical of many scientific problems. This research combines the latest developments in surrogate modelling and physics-informed machine learning to propose a novel Physics-Informed Neural Network-based surrogate model for hydrodynamic simulators governed by Shallow Water Equations. The proposed method incorporates physics-based prior information into the neural network structure by encoding the conservation of mass into the model without relying on calculating continuous derivatives in the loss function. The method is demonstrated for a high-resolution inland flood simulation model and a large-scale regional tidal model. The proposed method outperforms the existing state-of-the-art data-driven approaches by up to 25 %. This research demonstrates the benefits and robustness of physics-informed approaches in surrogate modelling for flood and hydroclimatic modelling problems.

Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review.

Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient's activity monitoring models. Then, improving such systems' performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area.

Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.

The computational limitations of complex numerical models have led to adoption of statistical emulators across a variety of problems in science and engineering disciplines to circumvent the high computational costs associated with numerical simulations. In flood modelling, many hydraulic and hydrodynamic numerical models, especially when operating at high spatiotemporal resolutions, have prohibitively high computational costs for tasks requiring the instantaneous generation of very large numbers of simulation results. This study examines the appropriateness and robustness of Gaussian Process (GP) models to emulate the results from a hydraulic inundation model. The developed GPs produce real-time predictions based on the simulation output from LISFLOOD-FP numerical model. An efficient dimensionality reduction scheme is developed to tackle the high dimensionality of the output space and is combined with the GPs to investigate the predictive performance of the proposed emulator for estimation of the inundation depth. The developed GP-based framework is capable of robust and straightforward quantification of the uncertainty associated with the predictions, without requiring additional model evaluations and simulations. Further, this study explores the computational advantages of using a GP-based emulator over alternative methodologies such as neural networks, by undertaking a comparative analysis. For the case study data presented in this paper, the GP model was found to accurately reproduce water depths and inundation extent by classification and produce computational speedups of approximately 10,000 times compared with the original simulator, and 80 times for a neural network-based emulator.

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.

Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.

Examining Type 1 Diabetes Mathematical Models Using Experimental Data.

Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with ß-cells and the other with no ß-cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1-4, depending on the diabetes severity of each group, with severity increasing from group 1-4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no ß-cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with ß-cells required more parameters to match the data and we fitted the ß-cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of 1.2, and a difference in BIC of 0.12 for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1-3 ranging from 2.10 to 4.05. Our results suggest that the model without ß-cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.

BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks.

The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

A systematic review and meta-analysis of prevalence of insomnia in the third trimester of pregnancy.

BACKGROUND: Sleep disorders, which are among the foremost important medical care issues, are prevalent in pregnancy. The present study is a meta-analysis of the prevalence of insomnia in the third trimester of pregnancy. This study aims to systematically review the overall prevalence of insomnia in the third trimester of pregnancy through conducting a meta-analysis. METHOD: The literature used in this meta-analysis for the topic discussed above were obtained through searching several databases, including SID, MagIran, IranDoc, Scopus, Embase, Web of Science (WoS), PubMed Science Direct and Google Scholar databases without time limitation until December 2020. Articles developed based on cross-sectional studies were included in the study. The heterogeneity of studies was investigated using the I2 index. Also, the possible effects of heterogeneity in the studied studies are investigated using meta-regression analysis. RESULT: In 10 articles and 8798 participants aged between11-40, the overall prevalence of insomnia in the third trimester of pregnancy based on meta-analysis was 42.4% (95% CI: 32.9-52.5%). It was reported that as the sample size increases, the prevalence of insomnia in the third trimester of pregnancy increases. Conversely, as the year of research increases, the prevalence of insomnia in the third trimester of pregnancy decreases. Both of these differences were statistically significant (P < 0.05). CONCLUSION: Insomnia was highly prevalent in the last trimester of pregnancy. Sleep disorders are neglected among pregnant women, and they are considered natural. While sleep disturbances can cause mental and physical problems in pregnant women, they can consequently cause problems for the fetus. As a result, maintaining the physical and mental health of pregnant mothers is very important. It is thus recommended that in addition to having regular visits during pregnancy, pregnant women should also be continuously monitored for sleep-related disorders.

The prevalence of metabolic syndrome in cardiovascular patients in Iran: a systematic review and meta-analysis.

BACKGROUND: Cardiovascular disease is the cause of more than 50% of mortalities globally, and this rate has grown by 8.6% since the 60 s. One of the risk factors associated with cardiovascular disease and its resulting mortality rate is the metabolic syndrome. Different studies have reported inconsistent rates for the metabolic syndrome. However, no comprehensive study has been conducted to combine the results of existing studies. Thus, the present study was performed with the aim of determining the prevalence of metabolic syndrome among cardiovascular patients in Iran through a systematic review and meta-analysis. METHOD: In this review study, the Scientific Information Database, Google Scholar, Science Direct, Scopus, PubMed, and Web of Science (ISI), databases were searched from January 2005 and until May 2020, to identify and extract related articles. To conduct the analysis, a random effects model was used, and the heterogeneity of the studies was examined using the I2 index. Data analysis was performed within Comprehensive Meta-Analysis (version 2) software. RESULTS: The prevalence of metabolic syndrome in cardiovascular patients in Iran in the 27 papers examined with a sample size of 44,735 patients was 34.2% (95% CI: 26.8-42.6%). A sensitivity analysis was performed to ensure the stability of the results, these results show that by omitting the prevalence from each study, the overall prevalence (34.2%) does not change significantly. the highest prevalence of metabolic syndrome in studies conducted in the period between 2015 and 2020, and this was reported as 55.3 (95% CI: 47.9-62.3) and the highest prevalence of metabolic syndrome in studies conducted in the methods of diagnosis IDF, and the rate was reported as 48 (95% CI: 36.5-59.8). based on meta-regression as the year of research increased, the prevalence of metabolic syndrome in cardiovascular patients in Iran also increased. However, with the increase in sample size, this prevalence decreased (p < 0.05). CONCLUSIONS: The results of this study indicate that metabolic syndrome is high in cardiovascular patients in Iran. Accordingly, by understanding its etiology and supervision at all levels, suitable solutions could be offered by providing feedback to hospitals.

The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: a systematic review and meta-regression.

BACKGROUND: Stress, anxiety, and depression are some of the most important research and practice challenges for psychologists, psychiatrists, and behavioral scientists. Due to the importance of issue and the lack of general statistics on these disorders among the Hospital staff treating the COVID-19 patients, this study aims to systematically review and determine the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients. METHODS: In this research work, the systematic review, meta-analysis and meta-regression approaches are used to approximate the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients. The keywords of prevalence, anxiety, stress, depression, psychopathy, mental illness, mental disorder, doctor, physician, nurse, hospital staff, 2019-nCoV, COVID-19, SARS-CoV-2 and Coronaviruses were used for searching the SID, MagIran, IranMedex, IranDoc, ScienceDirect, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases. The search process was conducted in December 2019 to June 2020. In order to amalgamate and analyze the reported results within the collected studies, the random effects model is used. The heterogeneity of the studies is assessed using the I2 index. Lastly, the data analysis is performed within the Comprehensive Meta-Analysis software. RESULTS: Of the 29 studies with a total sample size of 22,380, 21 papers have reported the prevalence of depression, 23 have reported the prevalence of anxiety, and 9 studies have reported the prevalence of stress. The prevalence of depression is 24.3% (18% CI 18.2-31.6%), the prevalence of anxiety is 25.8% (95% CI 20.5-31.9%), and the prevalence of stress is 45% (95% CI 24.3-67.5%) among the hospitals' Hospital staff caring for the COVID-19 patients. According to the results of meta-regression analysis, with increasing the sample size, the prevalence of depression and anxiety decreased, and this was statistically significant (P < 0.05), however, the prevalence of stress increased with increasing the sample size, yet this was not statistically significant (P = 0.829). CONCLUSION: The results of this study clearly demonstrate that the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients is high. Therefore, the health policy-makers should take measures to control and prevent mental disorders in the Hospital staff.

The Effect of Lavender (Lavandula stoechas L.) on Reducing Labor Pain: A Systematic Review and Meta-Analysis.

BACKGROUND: Labor pain is one of the most severe pains, which most of women experience. By using novel supportive methods, the labor pain can be reduced, which makes this event pleasant and delightful. Several original studies have been conducted in regard to the effect of lavender on reducing labor pain, whose results are controversial. One of the applications of meta-analysis studies is to respond to these hypotheses and remove controversies; therefore, this study aimed to determine the effect of lavender on labor pain in Iran by using meta-analysis. METHODS: In this study, to find published articles electronically from 2006 to 2019, the published articles in national and international databases of SID, MagIran, IranMedex, IranDoc, Google Scholar, Cochrane Library, Embase, ScienceDirect, Scopus, PubMed, and Web of Science (ISI) were used. Heterogenic index between studies was determined by Cochrane test (Q)c and I 2. Due to heterogeneity, the random effects model was used to estimate standardize difference of the mean score of lavender test in order to assess the labor pain between intervention and control group. RESULTS: In this meta-analysis and systematic review, finally 13 eligible articles met the inclusion criteria of the study. The sample size from original studies enrolled in the meta-analysis entered in the intervention group was 794 individuals and in the control group was 795 individuals. Mean score for pain in the control group was 7.2 ± 0.42 and in the intervention group was 5.4 ± 0.58 and this difference was statistically significant (p ≤ 0.001). CONCLUSION: The results of this study showed that lavender can reduce labor pain, which can be considered by health policy makers and gynecologists.

The effect of exercise on anxiety in the elderly worldwide: a systematic review and meta-analysis.

BACKGROUND: Physical activity and exercise are among the most important, simplest, and cheapest approaches to anxiety treatment, especially for the elderly. Their positive effects on improvement of mental disorders in the elderly have attracted a considerable level of attention. Therefore, the present study was conducted to determine the effect of sport on reducing anxiety in the elderly using meta-analysis. METHODS: In this study, national and international databases of SID, MagIran, IranMedex, IranDoc, Cochrane, Embase, ScienceDirect, Scopus, PubMed, and Web of Science were searched to find studies published electronically from 1999 to 2019. Heterogeneity between the collected studies was determined using the Cochran's test (Q) and I2. Due to presence of heterogeneity, the random effects model was used to estimate the standardized mean difference of sport test scores obtained from the measurement of anxiety reduction among the elderly, between the intervention group before and after the test. RESULTS: In this meta-analysis and systematic review, 19 papers finally met the inclusion criteria. The overall sample size of all collected studies for the meta-analysis was 841 s. Mean anxiety score before and after intervention were 38.7 ± 5.6 33.7 ± 3.4 respectively, denoting a decrease in anxiety score after intervention. CONCLUSION: Results of this study indicates that Sport significantly reduces Anxiety in the Elderly. Therefore, a regular exercise program can be considered as a part of the elderly care program.

The Effect of Exercise on the Older Adult's Blood Pressure Suffering Hypertension: Systematic Review and Meta-Analysis on Clinical Trial Studies.

BACKGROUND: Senescence refers to spontaneous and progressive irreversible degenerative changes in which both the physical and psychological power diminish significantly. Hypertension is the most common cardiovascular disease in the elderly. Several studies have been conducted regarding the effect of exercise on reducing the blood pressure of the elderly, which have found contradictory results. One of the uses of meta-analysis study is responding to these assumptions and resolving the discrepancies. Accordingly, the aim of the present study is to determine the impact of exercise on the blood pressure of older adults. METHOD: In this research, in order to find electronic published papers from 1992 to 2019, the papers published in both domestic and foreign databases including SID, MagIran, IranMedex, IranDox, Gogole Scholar, Cohrane, Embase, Science Direct, Scopus, PubMed, and Web of Science (ISI) were used. Heterogeneity index between the studies was determined based on Cochran test Q(c) and I 2. Considering existence of heterogeneity, random effects model was employed to estimate the standardized subtraction of the mean exercise test score for reduction of blood pressure in the older adults across the intervention group before and after the test. RESULTS: In this meta-analysis and systematic review, eventually 69 papers met the inclusion criteria. The total number of participants was 2272 in the pre- and postintervention groups when examining the systolic changes and 2252 subjects in the pre- and postintervention groups when inspecting the diastolic changes. The standardized mean difference in examining the systolic changes before the intervention was 137.1 ± 8.09 and 132.98 ± 0.96 after the intervention; when exploring the diastolic changes, the pre- and postintervention values were 80.3 ± 0.85 and 76.0 ± 6.56, respectively, where these differences were statistically significant (P < 0.01). CONCLUSION: The results of this study indicated that exercise leads to significant reduction in both systolic and diastolic blood pressure. Accordingly, regular exercise can be part of the treatment plan for hypertensive elderly.

The worldwide prevalence of the Dupuytren disease: a comprehensive systematic review and meta-analysis.

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.

The prevalence of sleep disturbances among physicians and nurses facing the COVID-19 patients: a systematic review and meta-analysis.

BACKGROUND: In all epidemics, healthcare staff are at the centre of risks and damages caused by pathogens. Today, nurses and physicians are faced with unprecedented work pressures in the face of the COVID-19 pandemic, resulting in several psychological disorders such as stress, anxiety and sleep disturbances. The aim of this study is to investigate the prevalence of sleep disturbances in hospital nurses and physicians facing the COVID-19 patients. METHOD: A systematic review and metanalysis was conducted in accordance with the PRISMA criteria. The PubMed, Scopus, Science direct, Web of science, CINHAL, Medline, and Google Scholar databases were searched with no lower time-limt and until 24 June 2020. The heterogeneity of the studies was measured using I2 test and the publication bias was assessed by the Egger's test at the significance level of 0.05. RESULTS: The I2 test was used to evaluate the heterogeneity of the selected studies, based on the results of I2 test, the prevalence of sleep disturbances in nurses and physicians is I2: 97.4% and I2: 97.3% respectively. After following the systematic review processes, 7 cross-sectional studies were selected for meta-analysis. Six studies with the sample size of 3745 nurses were examined in and the prevalence of sleep disturbances was approximated to be 34.8% (95% CI: 24.8-46.4%). The prevalence of sleep disturbances in physicians was also measured in 5 studies with the sample size of 2123 physicians. According to the results, the prevalence of sleep disturbances in physicians caring for the COVID-19 patients was reported to be 41.6% (95% CI: 27.7-57%). CONCLUSION: Healthcare workers, as the front line of the fight against COVID-19, are more vulnerable to the harmful effects of this disease than other groups in society. Increasing workplace stress increases sleep disturbances in the medical staff, especially nurses and physicians. In other words, increased stress due to the exposure to COVID-19 increases the prevalence of sleep disturbances in nurses and physicians. Therefore, it is important for health policymakers to provide solutions and interventions to reduce the workplace stress and pressures on medical staff.

Clinical effects of curcumin in enhancing cancer therapy: A systematic review.

BACKGROUND: Curcumin is herbal compound that has been shown to have anti-cancer effects in pre-clinical and clinical studies. The anti-cancer effects of curcumin include inhibiting the carcinogenesis, inhibiting angiogenesis, and inhibiting tumour growth. This study aims to determine the Clinical effects of curcumin in different types of cancers using systematic review approach. METHODS: A systematic review methodology is adopted for undertaking detailed analysis of the effects of curcumin in cancer therapy. The results presented in this paper is an outcome of extracting the findings of the studies selected from the articles published in international databases including SID, MagIran, IranMedex, IranDoc, Google Scholar, ScienceDirect, Scopus, PubMed and Web of Science (ISI). These databases were thoroughly searched, and the relevant publications were selected based on the plausible keywords, in accordance with the study aims, as follows: prevalence, curcumin, clinical features, cancer. RESULTS: The results are derived based on several clinical studies on curcumin consumption with chemotherapy drugs, highlighting that curcumin increases the effectiveness of chemotherapy and radiotherapy which results in improving patient's survival time, and increasing the expression of anti-metastatic proteins along with reducing their side effects. CONCLUSION: The comprehensive systematic review presented in this paper confirms that curcumin reduces the side effects of chemotherapy or radiotherapy, resulting in improving patients' quality of life. A number of studies reported that, curcumin has increased patient survival time and decreased tumor markers' level.

The prevalence of Restless Legs Syndrome/Willis-ekbom disease (RLS/WED) in the third trimester of pregnancy: a systematic review.

BACKGROUND: RLS is known as one of the most common movement disorders during pregnancy, which is most aggravated in the third trimester of pregnancy and can affect up to one-third of pregnant women. This study intends to determine the total prevalence of RLS in the third trimester of pregnancy through a systematic review. METHODS: The present study was conducted via meta-analysis method up to 2019. The papers related to the subject of interest were obtained through searching in SID, MagIran, IranDoc, Scopus, Embase, Web of Science (ISI), PubMed, Science Direct, and Google Scholar databases. Heterogeneity of the studies was examined via I2 index, and the data were analyzed in Comprehensive meta-analysis software. RESULTS: In investigating 10 papers capturing 2431 subjects within the age range of 25-39 years, the total prevalence of RLS in the third trimester of pregnancy based on meta-analysis was obtained as 22.9% (95% CI: 14.7-33.8%). Further, as the sample size increased, the RLS prevalence diminished, while with increase in years, this prevalence increased, where this difference was statistically significant (P < 0.05). CONCLUSION: Prevalence of RLS in the third trimester of pregnancy is high, healthcare policymakers should organize educational classes to improve the life dimensions among this group of pregnant women.

The impact of physical exercise on the fatigue symptoms in patients with multiple sclerosis: a systematic review and meta-analysis.

BACKGROUND: Despite many benefits of the physical activity on physical and mental health of patients with Multiple Sclerosis (MS), the activity level in these patients is still very limited, and they continue to suffer from impairment in functioning ability. The main aim of this study is thus to closely examine exercise's effect on fatigue of patients with MS worldwide, with particular interest on Iran based on a comprehensive systematic review and meta-analysis. METHODS: The studies used in this systematic review were selected from the articles published from 1996 to 2019, in national and international databases including SID, Magiran, Iranmedex, Irandoc, Google Scholar, Cochrane, Embase, ScienceDirect, Scopus, PubMed and Web of Science (ISI). These databases were thoroughly searched, and the relevant ones were selected based on some plausible keywords to the aim of this study. Heterogeneity index between studies was determined using Cochran's test and I2. Due to heterogeneity in studies, the random effects model was used to estimate standardized mean difference. RESULTS: From the systematic review, a meta-analysis was performed on 31 articles which were fulfilled the inclusion criteria. The sample including of 714 subjects was selected from the intervention group, and almost the same sample size of 720 individuals were selected in the control group. Based on the results derived from this meta-analysis, the standardized mean difference between the intervention group before and after the intervention was respectively estimated to be 23.8 ± 6.2 and 16.9 ± 3.2, which indicates that the physical exercise reduces fatigue in patients with MS. CONCLUSION: The results of this study extracted from a detailed meta-analysis reveal and confirm that physical exercise significantly reduces fatigue in patients with MS. As a results, a regular exercise program is strongly recommended to be part of a rehabilitation program for these patients.

The prevalence of severe depression in Iranian older adult: a meta-analysis and meta-regression.

BACKGROUND: Depression is one of the most common psychiatric disorders in the older adult and one of the most common risk factors for suicide in the older adult. Studies show different and inconsistent prevalence rates in Iran. This study aims to determine the prevalence of severe depression in Iranian older adult through a meta-analysis approach. METHODS: The present meta-analysis was conducted between January 2000-August 2019. Articles related to the subject matter were obtained by searching Scopus, Sciencedirect, SID, magiran, Barakat Knowledge Network System, Medline (PubMed), and Google Scholar databases. The heterogeneity of the studies was evaluated using I2 index and the data were analyzed in Comprehensive Meta-Analysis software. RESULTS: In a study of 3948 individuals aged 50-90 years, the overall prevalence of severe depression in Iranian older adult was 8.2% (95% CI, 4.14-6.3%) based on meta-analysis. Also, in order to investigate the effects of potential factors (sample size and year of study) on the heterogeneity of severe depression in Iranian older adult, meta-regression was used. It was reported that the prevalence of severe depression in Iranian older adult decreased with increasing sample size and increasing years of the study, which is significantly different (P < 0.05). CONCLUSION: Considering the high prevalence of severe depression in Iranian older adult, it is necessary for health policy makers to take effective control measures and periodic care for the older adult.