The effectiveness of telehealth interventions for people with multiple sclerosis: A systematic review and meta-analysis protocol.

Objective: Multiple sclerosis (MS) is a chronic neurological condition that significantly impacts the quality of life of affected individuals. Telehealth interventions (TIs) have emerged as a promising avenue for providing healthcare services remotely, potentially improving outcomes and enhancing the quality of life for people with MS. This systematic review aims to assess the impact and efficacy of TIs on outcomes of people with MS by synthesizing and comparing outcomes from related randomized controlled trials. Methods: The systematic review protocol will involve searching electronic databases (PubMed, Web of Science, Scopus, EMBASE, CENTRAL), gray literature resources, and key journals from 1997 to June 2023 for keywords related to Telehealth and MS. The collection of research-related articles will be followed by the removal of duplicates before they are included in the study. During the screening stage, the titles and abstracts of the articles will be reviewed, and irrelevant articles will be excluded. The full text of relevant articles will be independently evaluated by two researchers, and data will be extracted from the final articles. The findings will be classified and recorded in an extraction table. The risk of bias will be assessed using the Cochrane Collaboration tool. The data will be analyzed using STATA version 14.2. The PROSPERO ID for this study is CRD42023450081. Discussion: This study aims to address the current knowledge gap by investigating the comparative effectiveness of TIs. The findings of this research will provide valuable insights and evidence regarding the effectiveness of these interventions, assess their feasibility and accessibility, examine patient outcomes, identify research gaps, and offer guidance for clinical practice and policy-making.

Self-management interventions for people with multiple sclerosis: A systematic review and meta-analysis protocol.

Background and Aims: Educational self-management interventions (SMI) have an important role in improving symptom management, preventing relapse of multiple sclerosis (MS) and promoting quality of life (QoL) of these patients; since there is little knowledge about overall effectiveness of MS self-management programs and which types of SMI improves the outcomes, this research aims to assess the efficacy of structured SMI in improving health outcomes in people with MS (PwMS) by synthesizing and compare outcomes from related randomized controlled trials. Methods: In the present systematic review protocol, the keywords related to self-management and MS will be searched in electronic databases including (PubMed, Web of Science, Scopus, EMBASE, Cochrane Central Register of Controlled Trials [CENTRAL]), gray literature resources and key journals from 2000 to July 2023. Research-related articles will be collected and after removing duplicate articles, will be included in the study. In the screening step, titles and abstracts of articles will be reviewed and after deleting irrelevant articles, the full text of related articles will be evaluated independently by two researchers and data will be extracted from final articles and the findings will be categorized in an extraction table. Risk of bias will be assessed by using the Cochrane collaboration's tool. If possible, the data will be analyzed using random effect models and the statistical analysis will be performed using STATA software (version 14.2) developed by StataCorp. Discussion: Comparative effectiveness of SMI is currently unknown. We will analyze outcome measures used to assess effectiveness of self-management education in improving QoL, depression, self-efficacy, pain, and fatigue. These findings will help identify the most promising components of SMIs, guiding targeted interventions for specific subpopulations, and facilitating the design of better interventions.

Improving service efficiency and throughput of cardiac surgery patients using Monte Carlo simulation: a queueing setting.

Bed occupancy rate (BOR) is important for healthcare policymakers. Studies showed the necessity of using simulation approach when encountering complex real-world problems to plan the optimal use of resources and improve the quality of services. So, the aim of the present study is to estimate average length of stay (LOS), BOR, bed blocking probability (BBP), and throughput of patients in a cardiac surgery department (CSD) using simulation models. We studied the behavior of a CSD as a complex queueing system at the Farshchian Hospital. In the queueing model, customers were patients and servers were beds in intensive care unit (ICU) and post-operative ward (POW). A computer program based on the Monte Carlo simulation, using Python software, was developed to evaluate the behavior of the system under different number of beds in ICU and POW. The queueing simulation study showed that, for a fixed number of beds in ICU, BOR in POW decreases as the number of beds in POW increases and LOS in ICU increases as the number of beds in POW decreases. Also, based on the available data, the throughput of patients in the CSD during 800 days was 1999 patients. Whereas, the simulation results showed that, 2839 patients can be operated in the same period. The results of the simulation study clearly demonstrated the behavior of the CSD; so, it must be mentioned, hospital administrators should design an efficient plan to increase BOR and throughput of patients in the future.

Prediction the survival of patients with breast cancer using random survival forests for competing risks.

Objectives: Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this study was to identify important prognostic factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks. Also, its performance was compared with cause-specific hazard model. Methods: This retrospective cohort study assessed 222 patients with BC who were admitted to Ayatollah Khansari hospital in Arak, a major industrial city and the capital of Markazi province in Iran. The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients. Results: The mean and median survival duration of the patients were 90.71 (95%CI: 83.8-97.6) and 100.73 (95%CI: 89.2-121.5) months, respectively. The cause-specific model indicated that type of surgery and HER2 had statistically significant effects on the risk of death of BC. Moreover, the RSF model identified that HER2 was the most important variable for the event of interest. Conclusion: According to the results of this study, the performance of the RSF model was better than the cause-specific hazard model. Moreover, HER2 was the most important variable for death of BC in both of the models.

The relationship between health-related quality of life of students at Tehran University of Medical Sciences and their knowledge, attitudes, and practices regarding COVID-19 in 2020.

BACKGROUND: Present study attempts to investigate health-related quality of life (HRQoL) and its relation with knowledge, attitudes, and practices (KAP) of students of Tehran University of Medical Sciences (TUMS) during this pandemic. MATERIALS AND METHODS: In this cross-sectional study which was conducted between 23 may to 21 June 2020, 470 students in different levels of TUMS were included to the study randomly. participants completed validate, designed online questionnaire which assessed KAP towards coronavirus disease 2019 (COVID-19) and HRQoL. All statistical tests were applied, including Chi-square and Fisher's exact test, Partial correlation, analysis of variance, multiple linear regression, multiple binary and multinomial logistic regression models (P < 0.05) and were performed in SPSS 16, R 4.0.2, and GraphPad Prism 6.0 softwares. RESULTS: A total of 470 students were included in the study. The overall correct answer rate of the COVID-19 knowledge questionnaire was 74.43% and total score of the HRQoL was 72.50 (14.85). 61.7% of the students were agreed that COVID-19 will finally be successfully controlled, 44.3% had confidence that Iran can win the battle against the COVID-19, and 92.6% agreed that Quarantine will reduce the prevalence of COVID-19. Most of them adhered to health protocols and about a relation between HRQoL and knowledge we have a weak positive and unsignificant correlation between them (r = 0.05, P = 0.27). CONCLUSIONS: TUMS students showed expected levels of knowledge, proper attitudes, and preemptive practices regarding COVID-19, whereas COVID-19 outbreak substantially affected the physical and mental health but, the students were in a way better physical health rather than mental. Therefore, motivational planning and other related intervention to improve mental health can be noticeable.

The emerging role of microRNAs in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

The novel coronavirus disease 2019 (COVID-19) pandemic has imposed significant public health problems for the human populations worldwide after the 1918 influenza A virus (IVA) (H1N1) pandemic. Although numerous efforts have been made to unravel the mechanisms underlying the coronavirus, a notable gap remains in our perception of the COVID-19 pathogenesis. The innate and adaptive immune systems have a pivotal role in the fate of viral infections, such as COVID-19 pandemic. MicroRNAs (miRNAs) are known as short noncoding RNA molecules and appear as indispensable governors of almost any cellular means. Several lines of evidence demonstrate that miRNAs participate in essential mechanisms of cell biology, regulation of the immune system, and the onset and progression of numerous types of disorders. The immune responses to viral respiratory infections (VRIs), including influenza virus (IV), respiratory syncytial virus (RSV), and rhinovirus (RV), are correlated with the ectopic expression of miRNAs. Alterations of the miRNA expression in epithelial cells may contribute to the pathogenesis of chronic and acute airway infections. Hence, analyzing the role of these types of nucleotides in antiviral immune responses and the characterization of miRNA target genes might contribute to understanding the mechanisms of the interplay between the host and viruses, and in the future, potentially result in discovering therapeutic strategies for the prevention and treatment of acute COVID-19 infection. In this article, we present a general review of current studies concerning the function of miRNAs in different VRIs, particularly in coronavirus infection, and address all available therapeutic prospects to mitigate the burden of viral infections.

Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach.

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.