Prevalence of Catheter-associated bacteriuria in patients who received short-term catheterization in the northeast of Iran

Background: Catheter-associated (CA) bacteriuria is a result of the extensive usage of urinary catheterization. Once a catheter is placed, many patients achieve bacteriuria, even with the use of greatest consideration and care of the catheter. In this study, we decided to evaluate the prevalence of Catheter-associated bacteriuria in patients who received short-term catheterization in the northeast of Iran.Materials and Methods: In this cross-sectional study during one year (among 2014-2015) 275 patients who have admitted recently and have no history of catheterization and drug consumption were included. Three samples were taken from patients before, one day after catheterization and after removal of the catheter. The urine samples were analyzed and cultured on the suitable media. Antibiotics susceptibility testing was performed by disk diffusion method. Then, data analyzed using SPSS software by Student t-test. In addition, the p values less than 0.05 were considered as significant.Results: In general, the rate of catheter-associated bacteriuria in these hospitals was 68% (187 cases of 275). The mean age of the participants and patients with bacteriuria were 41±1.2 and 24.8±6.2 years old, respectively. The most common isolated bacteria were Escherichia coli (50.6%) followed by Staphylococcus aureus and Klebsiella pneumonia (21.6%). The highest sensitivity was reported against kanamycin (68.9%) and highest resistance was observed against ampicillin with a rate of 96.3%.Conclusion: For prevention of healthcare-associated UTI, correct catheterization and use of the closed catheter system is recommended. In addition, before prescribing any antibiotics it should be paying attention to the antibiotics susceptibility testing results

Learning to rank with click-through features in a reinforcement learning framework

Purpose - Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information. Design/methodology/approach - The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA. Findings - The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system. Originality/value - In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function

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

Abstract 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