Risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 over 2017-2022 in Sanandaj, an area of Iran.

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM2.5 by age and gender categories. The cumulative influence of PM2.5 particles on respiratory illnesses was statistically significant at 13-16 µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.

Estimation of PM2.5 using high-resolution satellite data and its mortality risk in an area of Iran.

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM2.5 in the model. We statistically found different distributions of the predicted PM2.5 over the region. Compared to the reference value (5 µg/m3), 90th and 95th percentiles had significant adverse effect on total mortality (RR 90th percentile:1.45; CI 95%: 1.08-1.95 and RR 95th percentile:1.53; CI 95%: 1.11-2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies.

Relationship between CT Severity Score and Capillary Blood Oxygen Saturation in Patients with COVID-19 Infection.

Background: COVID-19 pneumonia is responsible for the latest pandemic. Usage of pulmonary computed tomography (CT) scan is known to be an important method in the diagnosis of COVID-19. Here, we aimed to evaluate the relationship between CT severity score and capillary blood oxygen saturation in patients with COVID-19 infection. Methods and material: This is a cross-sectional study performed in 2020 on patients with COVID-19 infection. All patients underwent pulmonary CT scan and CT severity score was calculated. The initial capillary oxygen saturation by the time of admission was also collected. Data were collected and analyzed. Results: A total number of 270 patients diagnosed with COVID-19 infection entered the study. The initial mean capillary oxygen saturation was 89.65 ± 8.09%. The mean score in patients was 15.16 ± 8.08. We also indicated that 27 patients had hypoxia by the time of admission and these patients had significantly higher CT severity scores (p = 0.001). Diabetes mellitus (p = 0.001), hypertension (p = 0.001), and chronic obstructive pulmonary disease (COPD) (p = 0.03) and totally having an underlying disease (p < 0.001) were significantly associated with decreased capillary oxygen saturation. Patients with hypertension (p = 0.001) and patients with the previous medical disease (p = 0.01) had significantly higher CT severity scores than others. There was a significant reverse relationship between CT severity score and capillary oxygen saturation (r = -0.44, p < 0.001). Conclusions: We showed that there was a significant reverse relationship between CT severity score and capillary oxygen saturation which has great clinical importance. Furthermore, we indicated that previous medical history could also play an important role in CT severity score. How to cite this article: Aalinezhad M, Alikhani F, Akbari P, Haji Rezaei M, Soleimani S, Hakamifard A. Relationship between CT Severity Score and Capillary Blood Oxygen Saturation in Patients with COVID-19 Infection. Indian J Crit Care Med 2021;25(3):279-283.

Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model.

The consolidation of Highway-Railroad Grade Crossing (HRGC) is one of the effective approaches to decrease the number of crashes between trains and vehicles. From 2015-2019, there were 57 HRGC crashes at crossings in East Baton Rouge Parish (EBRP), resulting in thirteen injuries with $346,875 cost of vehicle damages. Consolidation programs help to close redundant crossings and thereby decrease the crash risks; however, it is difficult to find the best crossing in a neighborhood for closure. In our previous research working on HRGC consolidation models in 2019, from among four Machine Learning algorithms, eXtreme Gradient Boosting (XGboost) performed better in HRGC prediction models. In continuation of our previous studies on developing a HRGC prediction model, this research employed Text Mining Techniques, and Geospatial Analysis in addition to the XGboost Machine Learning algorithm. The aim was to develop a consolidation model that is customized for local implementation. The results indicated an overall accuracy of 88 % for the proposed model. The relative importance of the variables input to the model was also reported and offers an in-depth understanding of the model's behavior. Considering the different correlation threshold, a sensitivity analysis was also performed on different aggregation gain values. Subsequently, it resulted in the development of a simplified model utilizing 14 variables, with aggregated gain values of 95 % and a correlation threshold of 0.5. Based on this model, 15 % of current highway-rail grade crossings should be closed.

A Comprehensive Railroad-Highway Grade Crossing Consolidation Model: A Machine Learning Approach.

In the United States, there are approximately 212,000 highway-rail grade crossings, some of which experience vehicle-train incidents that often cause a massive financial burden, loss of life, and injury. In 2017, there were 2,108 highway-rail incidents resulting in 827 injuries and 307 fatalities nationwide. To eliminate collision risks, crossing grade separation and active alarm improvement are commonly used. Moreover, crossing closures are considered to be the most effective safety improvement program. While it may be very difficult, and in some cases impossible to close crossings, there are some incentive programs that facilitate the closure process. One of these programs is a working consolidation model that aims to determine crossing closure suitability. Using details of highway-rail grade crossings in the United States and applying an eXtreme Gradient Boosting (XGboost) algorithm, this paper proposes a data-driven consolidation model that takes into consideration a number of engineering variables. The results indicated an overall accuracy of 0.991 for the proposed model. In addition, the developed XGboost consolidation model reported the relative importance of the variables input to the model, offering an in-depth understanding of the model's behavior. Finally, for the practical implementation of the model, a simplified version containing fewer variables was developed. A sensitivity analysis was performed considering the aggregate gain and the different correlation threshold values between variables. This analysis developed a simplified model utilizing 14 variables, with aggregated gain values of 75% and a correlation threshold of 0.9 which would perform similarly to the full model. Based on this model, 62% of current highway-rail grade crossings should be closed.

Correlation Assessment of Climate and Geographic Distribution of Tuberculosis Using Geographical Information System (GIS).

BACKGROUND: Tuberculosis (TB) spread pattern is influenced by geographic and social factors. Nowadays Geographic Information System (GIS) is one of the most important epidemiological instrumentation identifying high-risk population groups and geographic areas of TB. The aim of this study was to determine the correlation between climate and geographic distribution of TB in Khuzestan Province using GIS during 2005-2012. METHODS: Through an ecological study, all 6363 patients with definite diagnosis of TB from 2005 until the end of September 2012 in Khuzestan Province, southern Iran were diagnosed. Data were recorded using TB- Register software. Tuberculosis incidence based on the climate and the average of annual rain was evaluated using GIS. Data were analyzed through SPSS software. Independent t-test, ANOVA, Linear regression, Pearson and Eta correlation coefficient with a significance level of less than 5% were used for the statistical analysis. RESULTS: The TB incidence was different in various geographic conditions. The highest mean of TB cumulative incidence rate was observed in extra dry areas (P= 0.017). There was a significant inverse correlation between annual rain rate and TB incidence rate (R= -0.45, P= 0.001). The lowest TB incidence rate (0-100 cases per 100,000) was in areas with the average of annual rain more than 1000 mm (P= 0.003). CONCLUSION: The risk of TB has a strong relationship with climate and the average of annual rain, so that the risk of TB in areas with low annual rainfall and extra dry climate is more than other regions. Services and special cares to high-risk regions of TB are recommended.

Association between promoter region of the uPAR (rs344781) gene polymorphism in genetic susceptibility to migraine without aura in three Iranian hospitals.

INTRODUCTION: Migraine is a chronic neurological disorder. Inflammation has a key role in migraine pathophysiology. Urokinase plasminogen activator receptor (uPAR) directly involves in inflammatory conditions by facilitating migration of inflammatory cells to different tissues. The aim of this study was to investigate whether uPAR rs344781, common genetic polymorphism in the uPAR promoter region, might be associated with migraine without aura susceptibility in an Iranian population. METHODS: We enrolled 103 newly diagnosed patients with migraine and 100 healthy controls. Peripheral blood sample was used for DNA extraction and uPAR rs344781 gene polymorphism was determined. Patients filled HIT-6 as a tool to evaluate headache severity. RESULTS: The genotype frequency of uPAR is significantly different between migraine patients and control subjects. Heterozygote genotype (AG) was statistically more frequent in the patients than the controls (P=0.001; OR=2.67, 95% CI=1.51-4.7). Also G allele was more frequent in the patients. Total HIT-6 score was not significantly different between heterozygote and homozygote patients (55.50±2.22 vs. 49.60±3.68 respectively, P=0.075). CONCLUSION: In conclusion, our study showed a significant association between uPAR rs344781 gene promoter polymorphism and migraine without aura susceptibility but not with headache severity.

Investigation of chemokine receptor CCR2V64Il gene polymorphism and migraine without aura in the Iranian population.

BACKGROUND AND OBJECTIVES: Migraine is a multifactorial common neurovascular disease with a polygenic inheritance. Inflammation plays an important part in migraine pathophysiology. C-C chemokine receptor 2 (CCR2) is an important chemokine for monocyte aggregation and transendothelial monocyte migration. The aim of our study was to investigate the association of migraine with CCR2V64Il polymorphism in the Iranian population. METHODS: We assessed 103 patients with newly diagnosed migraine and 100 healthy subjects. Genomic DNA samples were extracted from peripheral blood and genotypes of CCR2V64Il gene polymorphism were determined. For measuring the severity of headache, every patient filled out the MIGSEV questionnaire. RESULTS: There were no significant differences in the distribution of both 64Il allele and heterozygote (GA) genotype of CCR2 gene polymorphism (P = 0.396; OR = 0.92, 95% CI = 0.50-1.67 and P = 0.388; OR = 0.91, 95% CI = 0.47-1.73, resp.) between case and control groups. There was no significant difference of alleles frequency between three grades of MIGSEV (P = 0.922). CONCLUSIONS: In conclusion our results revealed no association between CCR2V64Il polymorphism and susceptibility to migraine and also headache severity in the Iranian population.