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Erythema ab igne is a skin condition mainly caused by heat exposure. Erythema ab igne usually follows a favorable prognosis. However, it may increase the risk of developing cutaneous malignancy in the involved skin. Being familiar with the type of cutaneous malignancies that may arise in the site of erythema ab igne is considerably important. To our knowledge, this letter presents the first case that shows the association between erythema ab igne and basal cell carcinoma.

Structure and function are the inherent characteristics of each ecosystem providing various services such as clean air, extreme weather mitigation, and mental and physical well‐being. The objective of this study is to develop a unified model combining Integrated Valuation of Ecosystem Services, ecological network (EN), and correlation analysis to investigate changes in ecosystem structure, function, and process. In this context, carbon sequestration, soil reduction, and flood risk mitigation were quantified from 2000 to 2020 and predicted for 2040 using the cellular automata and Markov chain (CA‐Markov) model. Finally, correlation analysis was used to analyze the relationship over time between the land use (LU) classes and the components of the forest EN that provide and exchange desired ecosystem services (ESs). Thus, the changes in LU in the region in recent years led to significant reduction of ESs in the region as well as changes in the interaction between services. These changes, on the one hand, reduced the area of cores and increased isolated forest patches and, on the other hand, led to the horizontal expansion of cities and agricultural lands. If this trend continues, the decline in services provided by the ecosystem will persist into the future. Consequently, it can be said that structural changes in the ecosystem can lead to changes in the ESs. Integr Environ Assess Manag 2024;20:1099–1111. © 2023 SETAC Key Points: We present an integrated model to investigate structural, functional, and process changes caused by land use and land cover changes in the regions where natural and pristine ecosystems have close and complex interactions with built‐up ecosystems.We evaluated the structural changes related to land use change and its role in ecosystem functioning through changes in ecosystem services by selecting ecosystem services that reflect land use, land cover, vegetation, and ecological network change and soil conditions.We propose that the role of land use in changing ecosystem services represents another difference between this study and similar studies.We forecast the quantity of ecosystem services for future years as a basis for planning. [ABSTRACT FROM AUTHOR]

The effect of fine particulates matter (PM2.5) as the main source of air pollution on human health and the environment is one of the major problems in Tehran. Due to the limited number of ground-level monitoring stations, PM2.5 concentrations are not widely monitored in Tehran. This present study aimed to estimate ground-based PM2.5 concentrations with Merra-2 aerosol components and compare them with Merra-2 PM2.5. In the first, ground-based PM2.5 concentrations from 23 stations of the Air Quality Control Company and Merra-2 PM2.5 components concentrations, Merra-2 PM2.5 concentrations, and aerosol optical thickness from the satellite reanalysis model were recorded over Tehran from 2012 to 2021. Then, we have analyzed the quantified impacts of PM2.5 components (including organic carbon, black carbon, sulfate, sea salt, and dust), meteorological variables (including temperature, wind speed, relative humidity, precipitation, boundary layer height, and cloud cover), and aerosol optical thickness on ground-based and Merra-2 PM2.5 concentrations. Our analysis shows that the Merra-2 PM2.5 concentrations were recorded much lower than the ground-based PM2.5. Also, the annual trend of PM2.5 components decreased except for dust. A strong linear correlation is obtained between Merra-2 PM2.5 concentrations and dust. Boundary layer height among the meteorological variables influencing PM2.5 plays a major role in PM2.5 concentrations. There is a relatively good correlation between ground-based and Merra-2 PM2.5 with aerosol optical thickness. Black carbon has the most significant anti-correlation with boundary layer height. Also, black carbon and relative humidity have a very good correlation. Finally, we validate the estimation of ground-based PM2.5 concentrations with aerosol components using a nonlinear multivariate power regression model ( R 2 = 0.970 , RMSE = 5.942 ). Overall, this study underlines the impact of meteorological variables and anthropogenic emissions on ground-based PM2.5 growth. [ABSTRACT FROM AUTHOR]

Objectives. To determine the efficacy of platelet-rich plasma in treating alopecia areata. Methods. A systematic search was carried out in PubMed, Embase, and the Cochrane Library databases to identify any article evaluating the efficacy of platelet-rich plasma for the treatment of alopecia areata and comparing platelet-rich plasma with other treatment modalities. Results. Nine studies were included based on our inclusion criteria with a total of 616 patients. Various evaluations of alopecia areata treatment efficacy with platelet-rich plasma, including the comparison between platelet-rich plasma and triamcinolone acetonide, minoxidil, placebo, and other methods, such as fractional carbon dioxide laser and microneedling, were conducted in the included studies. The main results revealed that platelet-rich plasma and triamcinolone acetonide are both effective in the treatment of alopecia areata. However, the treatment response was in favor of platelet-rich plasma. Also, minoxidil showed positive effects on the treatment of alopecia areata alongside platelet-rich plasma. Platelet-rich plasma also has significantly better effects on alopecia areata compared to placebo. Most of the side effects of treatment of alopecia areata with platelet-rich plasma were minor, including burning sensation, pain during injection, erythema, edema, ecchymosis, crust formation, and headache. Conclusion. Based on the evidence reviewed, it is suggested that platelet-rich plasma is a safe and effective treatment option for alopecia areata. Furthermore, platelet-rich plasma has the advantage of being a steroid-sparing therapy, reducing the reliance on corticosteroids. The use of platelet-rich plasma is associated with fewer complications compared to other treatment modalities. [ABSTRACT FROM AUTHOR]

Delijan, the main production center production of bitumen insulation, is one of the most polluted cities in Iran. Therefore, the measurement and analysis of emissions and the optimization of energy consumption, and, thus the reduction of emissions of these pollutants are particularly important. This study aimed to measure the gaseous pollutants emitted from the chimneys of one of the bitumen insulation production units in the industrial city of Delijan and also to improve the performance of the exhaust gas cleaning systems. For this purpose, first, the flow parameters of the chimney are measured with the KIMO gas analyzer, model KIGAS300, and then the pollutants Carbon monoxide, Hydrocarbons, and Nitrogen oxides are optimized of two methods in the MATLAB software using the Genetic Algorithm method and python software using the multiple regression with Sklearn and Statsmodels approach. An objective function was formed that has a non-linear relationship between the preheater outlet air temperature, the percent excess air, and the production of CO, CxHy and NOX pollutants. Statsmodels python regression were presented showing the effect of increasing or decreasing the preheater air temperature and the percent excess air (parameters for which the optimization is carried out) on the amount of pollutants production. According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. Moreover, R2 values are at the 0.89 level and above. Finally, the proposed method can solve optimization problems and support environmental management decisions in the insulation industry. Delijan, the main production center production of bitumen insulation, is one of the most polluted cities in Iran. Therefore, the measurement and analysis of emissions and the optimization of energy consumption, and, thus the reduction of emissions of these pollutants are particularly important. This study aimed to measure the gaseous pollutants emitted from the chimneys of one of the bitumen insulation production units in the industrial city of Delijan and also to improve the performance of the exhaust gas cleaning systems. For this purpose, first, the flow parameters of the chimney are measured with the KIMO gas analyzer, model KIGAS300, and then the pollutants Carbon monoxide, Hydrocarbons, and Nitrogen oxides are optimized of two methods in the MATLAB software using the Genetic Algorithm method and python software using the multiple regression with Sklearn and Statsmodels approach. An objective function was formed that has a non-linear relationship between the preheater outlet air temperature, the percent excess air, and the production of CO, CxHy and NOX pollutants. Statsmodels python regression were presented showing the effect of increasing or decreasing the preheater air temperature and the percent excess air (parameters for which the optimization is carried out) on the amount of pollutants production. According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. Moreover, R2 values are at the 0.89 level and above. Finally, the proposed method can solve optimization problems and support environmental management decisions in the insulation industry. Delijan, the main production center production of bitumen insulation, is one of the most polluted cities in Iran. Therefore, the measurement and analysis of emissions and the optimization of energy consumption, and, thus the reduction of emissions of these pollutants are particularly important. This study aimed to measure the gaseous pollutants emitted from the chimneys of one of the bitumen insulation production units in the industrial city of Delijan and also to improve the performance of the exhaust gas cleaning systems. For this purpose, first, the flow parameters of the chimney are measured with the KIMO gas analyzer, model KIGAS300, and then the pollutants Carbon monoxide, Hydrocarbons, and Nitrogen oxides are optimized of two methods in the MATLAB software using the Genetic Algorithm method and python software using the multiple regression with Sklearn and Statsmodels approach. An objective function was formed that has a non-linear relationship between the preheater outlet air temperature, the percent excess air, and the production of CO, CxHy and NOX pollutants. Statsmodels python regression were presented showing the effect of increasing or decreasing the preheater air temperature and the percent excess air (parameters for which the optimization is carried out) on the amount of pollutants production. According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. Moreover, R2 values are at the 0.89 level and above. Finally, the proposed method can solve optimization problems and support environmental management decisions in the insulation industry. Delijan, the main production center production of bitumen insulation, is one of the most polluted cities in Iran. Therefore, the measurement and analysis of emissions and the optimization of energy consumption, and, thus the reduction of emissions of these pollutants are particularly important. This study aimed to measure the gaseous pollutants emitted from the chimneys of one of the bitumen insulation production units in the industrial city of Delijan and also to improve the performance of the exhaust gas cleaning systems. For this purpose, first, the flow parameters of the chimney are measured with the KIMO gas analyzer, model KIGAS300, and then the pollutants Carbon monoxide, Hydrocarbons, and Nitrogen oxides are optimized of two methods in the MATLAB software using the Genetic Algorithm method and python software using the multiple regression with Sklearn and Statsmodels approach. An objective function was formed that has a non-linear relationship between the preheater outlet air temperature, the percent excess air, and the production of CO, CxHy and NOX pollutants. Statsmodels python regression were presented showing the effect of increasing or decreasing the preheater air temperature and the percent excess air (parameters for which the optimization is carried out) on the amount of pollutants production. According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. Moreover, R2 values are at the 0.89 level and above. Finally, the proposed method can solve optimization problems and support environmental management decisions in the insulation industry. [ABSTRACT FROM AUTHOR]

The associations between components of metabolic syndrome (MetS), insulin resistance (IR), and several malignancies have been reported. However, the possible link between IR and dermal malignancies, including non-melanoma skin cancers (NMSCs), has not been investigated to date. In this study, we aimed to examine the possible association between components of MetS, IR, adhesion molecules, and NMSC for the first time. This was a case-control study comprising 73 confirmed cases of NMSC and 72 unrelated healthy controls. Anthropometric and biochemical assessments including fasting blood lipid profile, glucose and insulin assays were performed. To evaluate IR, HOMA-IR formula was used. Though fasting serum glucose showed no significant between-group difference, serum concentrations of insulin (p = 0.048) as well as HOMA-IR (p = 0.037) were both significantly higher in NMSC group than in controls. Logistic regression analysis revealed significant associations between waist circumference (OR: 1.04, 95% CI: 1.007-1.080, p = 0.018), percent of visceral fat (OR: 1.10, 95% CI: 1.024-1.190, p = 0.01), HOMA-IR (OR: 1.169, 95% CI: 1.004-1.360, p = 0.044), circulating VCAM-1 concentrations (OR: 1.005, 95% CI: 1.003-1.007, p < 0.001) and NMSC risk. Interestingly, the occurrence of MetS was significantly higher in subjects with NMSC than in healthy controls (p = 0.038). MetS and its components were associated with increased NMSC risk. [ABSTRACT FROM AUTHOR]

Global fire cases in recent years and their vast damages are vivid reasons to study the wildfires more deeply. A 25-year period natural wildfire database and a wide array of environmental variables are used in this study to develop an artificial neural network model with the aim of predicting potential fire spots. This study focuses on non-human reasons of wildfires (natural) to compute global warming effects on wildfires. Among the environmental variables, this study shows the significance of temperature for predicting wildfire cases while other parameters are presented in a next study. The study area of this study includes all natural forest fire cases in United States from 1992 to 2015. The data of eight days including the day fire occurred and 7 previous days are used as input to the model to forecast fire occurrence probability of that day. The climatic inputs are extracted from ECMWF. The inputs of the model are temperature at 2 meter above surface, relative humidity, total pressure, evaporation, volumetric soil water layer, snow melt, Keetch-Byram drought index, total precipitation, wind speed, and NDVI. The results show there is a transient temperature span for each forest type which acts like a threshold to predict fire occurrence. In temperate forests, a 0.1-degree Celsius increase in temperature relative to 7-day average temperature before a fire occurrence results in prediction model output of greater than 0.8 for 4.75% of fire forest cases. In Boreal forests, the model output for temperature increase of less than 1 degree relative to past 7-day average temperature represents no chance of wildfire. But the non-zero fire forest starts at 2 degrees increase of temperature which ends to 2.62% of fire forest cases with model output of larger than 0.8. It is concluded that other variables except temperature are more determinant to predict wildfires in temperate forests rather than in boreal forests. [ABSTRACT FROM AUTHOR]