Immunogenicity and safety of the BBIBP-CorV vaccine in patients with autoimmune inflammatory rheumatic diseases undergoing immunosuppressive therapy in a monocentric cohort.

INTRODUCTION: Vaccination plays a fundamental role in mastering the COVID-19 pandemic and protecting vulnerable groups. Persons with autoimmune inflammatory rheumatic diseases (AIIRD) requiring immunosuppressive therapies are prioritized for vaccination. However, data concerning immunogenicity and safety of the BBIBP-CorV vaccine in immunosuppressed patients are not found. This study presents data on the efficacy and safety of the BBIBP-CorV vaccine in immunosuppressed patients compared to healthy controls. METHODS: Study population consisted of 100 healthy controls and 100 patients with AIIRD. Vaccination was performed according to national guidelines with the BBIBP-CorV vaccine. SARS-CoV-2 neutralizing antibody titers were quantified by enzyme-linked immunosorbent assay before initial vaccination and 1-3 months after secondary vaccination. Adverse events were assessed before study initiation and 7 days after the second dose. Disease activity was studied before entering the study and 3-8 weeks after the second dose. RESULTS: Vaccination-induced positive immunogenic response rates and SARS-CoV-2 neutralizing antibody titers were significantly lower in the AIIRD patients than healthy subjects (p < .05). There are significant differences in neutralizing antibody titers among patients suffering from rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis, and ankylosing spondylitis (p < .01-.05). The rates of seropositive vaccine responses were similarly distributed across all diseases. Healthy and AIIRD individuals had a similar profile in adverse events. No significant difference was observed in SARS-CoV-2 antibody titers between subjects suffering from side effects and those who did not have. SARS-CoV-2 neutralizing antibody levels were significantly higher in subjects with a history of COVID-19 infection than seronegative individuals (p < .01-0.05). Seropositive subjects had a significant increase in the percentage of vaccine-related adverse events compared to seronegative persons (p < .05). Despite a minor change in the disease activity of patients with RA and SLE, disease activity indices were overall stable in the AIIRD patients. CONCLUSION: These findings revealed that the BBIBP-CorV vaccine is effective in the development of neutralizing antibodies in immunosuppressed patients without considerable reactogenicity or induction of disease flares.

A Step-by-Step Framework on Discrete Events Simulation in Emergency Department; A Systematic Review.

OBJECTIVE: To systematically review the current literature of simulation in healthcare including the structured steps in the emergency healthcare sector by proposing a framework for simulation in the emergency department. METHODS: For the purpose of collecting the data, PubMed and ACM databases were used between the years 2003 and 2013. The inclusion criteria were to select English-written articles available in full text with the closest objectives from among a total of 54 articles retrieved from the databases. Subsequently, 11 articles were selected for further analysis. RESULTS: The studies focused on the reduction of waiting time and patient stay, optimization of resources allocation, creation of crisis and maximum demand scenarios, identification of overcrowding bottlenecks, investigation of the impact of other systems on the existing system, and improvement of the system operations and functions. Subsequently, 10 simulation steps were derived from the relevant studies after an expert's evaluation. CONCLUSION: The 10-steps approach proposed on the basis of the selected studies provides simulation and planning specialists with a structured method for both analyzing problems and choosing best-case scenarios. Moreover, following this framework systematically enables the development of design processes as well as software implementation of simulation problems.

Analyzing a Lung Cancer Patient Dataset with the Focus on Predicting Survival Rate One Year after Thoracic Surgery

Background: Data mining, a new concept introduced in the mid-1990s, can help researchers to gain new, profound insights and facilitate access to unanticipated knowledge sources in biomedical datasets. Many issues in the medical field are concerned with the diagnosis of diseases based on tests conducted on individuals at risk. Early diagnosis and treatment can provide a better outcome regarding the survival of lung cancer patients. Researchers can use data mining techniques to create effective diagnostic models. The aim of this study was to evaluate patterns existing in risk factor data of for mortality one year after thoracic surgery for lung cancer. Methods: The dataset used in this study contained 470 records and 17 features. First, the most important variables involved in the incidence of lung cancer were extracted using knowledge discovery and datamining algorithms such as naive Bayes, maximum expectation and then, using a regression analysis algorithm, a questionnaire was developed to predict the risk of death one year after lung surgery. Outliers in the data were excluded and reported using the clustering algorithm. Finally, a calculator was designed to estimate the risk for one-year post-operative mortality based on a scorecard algorithm. Results: The results revealed the most important factor involved in increased mortality to be large tumor size. Roles for type II diabetes and preoperative dyspnea in lower survival were also identified. The greatest commonality in classification of patients was Forced expiratory volume in first second (FEV1), based on levels of which patients could be classified into different categories. Conclusion: Development of a questionnaire based on calculations to diagnose disease can be used to identify and fill knowledge gaps in clinical practice guidelines.