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Abstract Background Type 2 Diabetes Mellitus (T2DM) has become a major health concern with an increasing prevalence and is now one of the leading attributable causes of death globally. T2DM and cardiovascular disease are strongly associated and T2DM is an important independent risk factor for ischemic heart disease. T-wave abnormalities (TWA) on electrocardiogram (ECG) can indicate several pathologies including ischemia. In this study, we aimed to investigate the association between T2DM and T-wave changes using the Minnesota coding system. Methods A cross-sectional study was conducted on the MASHAD cohort study population. All participants of the cohort population were enrolled in the study. 12-lead ECG and Minnesota coding system (codes 5–1 to 5–4) were utilized for T-wave observation and interpretation. Regression models were used for the final evaluation with a level of significance being considered at p 0.05). Hypertension, age, and body mass index were significantly associated with T2DM (p

Concrete, a widely used construction material, exhibits inherent brittleness and limited tensile strength. To address these shortcomings, the incorporation of fibers has gained prominence, with polyolefin and polypropylene fibers emerging as promising reinforcements. This study experimentally conducted and examined the mechanical characteristics of polyolefin and polypropylene fibers-reinforced concrete. The study involved conducting compressive strength tests and durability tests, including sulfate resistance, acid resistance, chloride ion penetration resistance, and abrasion tests. The results demonstrated a notable enhancement in the compressive strength of both cubic and cylindrical specimens with varying dimensions, owing to the inclusion of polyolefin fibers. Polyolefin fibers exhibited a greater reinforcing effect compared to polypropylene fibers, especially in larger cubic specimens and cylindrical specimens. The quantitative results show that specimens with polypropylene fibers exhibited a 9.67 % improvement in compressive strength, while those with polyolefin fibers showed a 30.47 % enhancement compared to specimens without fibers. The acid resistance test revealed that both types of fiber-reinforced specimens exhibited higher compressive strength in acidic environments compared to plain concrete specimens. In the chloride ion penetration resistance test, the polyolefin fiber-reinforced specimen demonstrated the highest resistance to degradation, while the plain concrete specimen exhibited the lowest resistance. Polyolefin demonstrated superior reinforcement compared to polypropylene, with a 35 % increase in acid resistance and better chloride ion resistance. In contrast, specimens without fibers weakened by 35.36 % in a chloride environment. The sulfate resistance test showed that the polyolefin fiber-reinforced specimen had the highest compressive strength in a sulfate environment, while the plain concrete specimen had the lowest. In sulfate and abrasion tests, polyolefin-fiber-reinforced specimens displayed the highest resistance and the lowest abrasion rate of 34.6 %. These findings highlight the potential of polyolefin fiber-reinforced concrete for enhancing mechanical properties and durability in various applications.

Abstract We aimed to determine the effects of isoproterenol on arrhythmia recurrence in atrioventricular nodal re‐entrant tachycardia (AVNRT) patients treated with catheter ablation. The present randomized controlled clinical trial was conducted on AVNRT patients candidates for radiofrequency ablation (RFA). The patients were randomly assigned to receive isoproterenol (0.5–4 μg/min) or not (control group) for arrhythmia re‐induction after ablation. The results of the electrophysiological (EP) study, the ablation parameters, and the arrhythmia recurrence rate were recorded. We evaluated 206 patients (53 males and 153 females) with a mean (SD) age of 49.87 (15.5) years in two groups of isoproterenol (n = 103) and control (n = 103). No statistically significant difference was observed between the two studied groups in age, gender, EP study, and ablation parameters. The success rate of ablation was 100% in both groups. During ~16.5 months of follow‐up, one patient (1%) in the isoproterenol group and four patients (3.8%) in the control group experienced AVNRT recurrence (HR = 0.245; 95% confidence interval [CI], 0.043–1.418; p = .173). Based on the Kaplan–Meier analysis, there was no significant difference in the incidence rate of arrhythmia recurrence during the follow‐up period between the two studied groups (p = .129). Additionally, there were no significant differences between the arrhythmia's recurrence according to age, gender, junctional rhythm, type of AVNRT arrhythmia, and DAVN persistence after ablation. Although isoproterenol administration for arrhythmia re‐induction after ablation did not alleviate the treatment outcomes and arrhythmia recurrence following RFA in AVNRT patients, further studies with a larger sample size and a longer duration of follow‐up are necessary.

Abstract Background Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations with fractures has a significant impact on reduction of cost of care by preventing future fractures and its corresponding complications. Methods In this study, we developed a rule-based natural language processing (NLP) algorithm for identification of twenty skeletal site-specific fractures from radiology reports. The rule-based NLP algorithm was based on regular expressions developed using MedTagger, an NLP tool of the Apache Unstructured Information Management Architecture (UIMA) pipeline to facilitate information extraction from clinical narratives. Radiology notes were retrieved from the Mayo Clinic electronic health records data warehouse. We developed rules for identifying each fracture type according to physicians’ knowledge and experience, and refined these rules via verification with physicians. This study was approved by the institutional review board (IRB) for human subject research. Results We validated the NLP algorithm using the radiology reports of a community-based cohort at Mayo Clinic with the gold standard constructed by medical experts. The micro-averaged results of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score of the proposed NLP algorithm are 0.930, 1.0, 1.0, 0.941, 0.961, respectively. The F1-score is 1.0 for 8 fractures, and above 0.9 for a total of 17 out of 20 fractures (85%). Conclusions The results verified the effectiveness of the proposed rule-based NLP algorithm in automatic identification of osteoporosis-related skeletal site-specific fractures from radiology reports. The NLP algorithm could be utilized to accurately identify the patients with fractures and those who are also at high risk of future fractures due to osteoporosis. Appropriate care interventions to those patients, not only the most at-risk patients but also those with emerging risk, would significantly reduce future fractures.

Abstract Introduction The development of urbanization has had many negative outcomes in different societies. Population growth and the increase in the transportation are the consequences of urban growth which has resulted into problems. Vehicles are responsible for 90% of air pollution in Iran and it is essential to use authentic models of traffic emissions in accordance with the current conditions to predict this and future emissions. Iran has a lot of different air pollution dispersion parameters compared with the developed countries. Method In this paper air pollution emission parameters in signalized intersections have been modelled and results have been compared to measured concentrations of air pollution in intersections. For this purpose the use of IVE (International Vehicle Emission) model that is common air pollution modelling and besides, Sensitivity Analysis has been performed to show modeling accuracy in comparison with current emissions. Result By modeling and measurement results, it’s easily understood in warm seasons emission concentration is more than cold seasons. Minimum and maximum rate of Carbon-monoxide (CO) and Nitrogen-oxides (NOX) has been evaluated. Conclusion IVE model has shown a bit difference amount of pollutions by comparison with field measurement emission. It could be said it is appropriate for model vehicle emission in Iran.