RESUMO
Pollinosis is one of the common allergic diseases, and its morbidity continues to increase. Studies have demonstrated that air pollution is a key environmental factor that leads to the increased prevalence of pollinosis. Air pollutants and pollen allergens exert synergistic effects in stimulating allergic responses in susceptible individuals. In this article, we analyzed the relationship between air pollution and pollinosis based on the latest studies, and elaborated potential mechanisms on how air pollution increases the incidence of pollinosis and aggravates allergic reactions. Air pollutants can increase both pollen production and the levels of allergenic proteins, and enhance allergenicity of pollen allergens through structural alterations or chemical modifications. The potential mechanisms of air pollutants exacerbating pollen allergies are as follows: Air pollutants may disrupt the barrier function of the respiratory epithelium and facilitate the penetration of pollen allergens into deeper tissues. Additionally, they may accelerate the process of the release of pollen allergy-related cytokines, promoting T helper 2 (Th2) cell differentiation and exacerbating inflammatory responses in the airways. Given the limitations of existing research, future prospective studies are needed to explore the effects of mixed pollutants and different types of pollutants on pollen, and the response mechanisms of allergy-related cells and cytokines to different pollutant categories. The findings would provide a comprehensive understanding of the impacts of air pollution on pollen allergies and scientific evidence for effective protection of the heath of pollinosis patients.
RESUMO
Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.