Objectives To assess the incidence of ocular manifestations of Kawasaki disease (KD) in children and to evaluate the relationship between ocular manifestations and the other clinical manifestations, laboratory findings, and echocardiographic findings. Methods Complete ophthalmologic examination and echocardiography were performed in 36 patients with KD during the acute phase before starting the treatment. Clinical manifestations and laboratory data including white blood cell (WBC) count, neutrophil-to-lymphocyte ratio, platelet count, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were obtained from all the patients. All the clinical and laboratory findings were compared between the group with ocular involvement and the one without ocular involvement. Results The incidence of bilateral non-exudative conjunctivitis was 63.9%. It was significantly higher in patients with skin rashes ( P < 0.05). The incidence of uveitis was 36.1%, which consisted primarily of grade 1+ or 2+ acute anterior uveitis. Neutrophil count and CRP levels were significantly higher in the uveitis group than in the group without uveitis ( P < 0.05). Coronary artery dilatation showed significant correlation with uveitis ( P < 0.05). Uveitis did not show a significant correlation with other clinical manifestations, ESR, ALT level, AST level, and platelet count ( P > 0.05). Conclusion In children with Kawasaki disease, uveitis is associated with coronary artery dilatation, higher neutrophil count, and higher CRP level.

Coronavirus disease 2019 (COVID-19) has spread across the globe producing hundreds of thousands of deaths, shutting down economies, closing borders and causing havoc on an unprecedented scale. Its potent effects have earned the attention of researchers in different fields worldwide. Among them, authors from different countries have published numerous research articles based on the environmental concepts of COVID-19. The environment is considered an essential receptor in the COVID-19 pandemic, and it is academically significant to look into publications to follow the pathway of hot topics of research and upcoming trends in studies. Reviewing the literature can therefore provide valuable information regarding the strengths and weaknesses in facing the COVID-19 pandemic, considering the environmental viewpoint. The present study categorizes the understanding caused by environmental and COVID-19-related published papers in the Scopus metadata from 2020 to 2021. VOSviewer is a promising bibliometric tool used to analyze the publications with keywords “COVID-19*” and “Environment.” Then, a narrative evaluation is utilized to delineate the most interesting research topics. Co-occurrence analysis is applied in this research, which further characterizes different thematic clusters. The published literature mainly focused on four central cluster environmental concepts: air pollution, epidemiology and virus transmission, water and wastewater, and environmental policy. It also reveals that environmental policy has gained worldwide interest, with the main keyword “management” and includes keywords like waste management, sustainability, governance, ecosystem, and climate change. Although these keywords could also appear in other environmental policy-related research studies, the importance of the COVID-19 pandemic requires such comprehensive research. The fourth cluster involves governance and management concerns encountered during the pandemic. Mapping the research topics in different clusters will pave the way for researchers to view future potential ideas and studies better. The scope for further research needs from the perspective of environmental concepts is reviewed and recommended, which can expand the vital role and value of environmental sciences in alerting, observing, and COVID-19 prediction for all four clusters. In other words, the research trend would shift from qualitative studies and perspectives to quantitative ones.

&lt;p&gt;Groundwater level (GWL) is a promising indicator for monitoring sustainable groundwater management, and GWL modeling is a critical way to predict GWL changes. There are different ways to simulate the GWL, including numerical and machine learning (ML) models. The present research used a series of supervised ML methods to predict the GWL fluctuations in the Unconfined Tehran aquifer, Iran, using 14 years of monthly GWL and other hydrological and meteorological datasets. The wavelet transform (WT) method was also applied to enhance the GWL prediction accuracy and precision three months ahead. The standalone ML approaches, including Artificial Neural Network, Fuzzy Logic (FL), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Square Support Vector Machine, were employed and evaluated with the hybrid-wavelet conjunction methods. The accuracy and precision of the used models were assessed based on Root Means Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R) and Nash&amp;#8211;Sutcliffe efficiency (NSE). The comparison revealed that the hybrid-wavelet ML significantly increased the standalone model&#039;s accuracy and precision. The coupled WT-FL and WT-ANFIS method was superior to other WT hybrid and standalone ML methods to predict GWL for the employed database. The optimum GWL predictions were obtained from the WT-FL method outcomes from input scenario one. The method generated RMSE, MAE, R and NSE values as 0.25, 0.20, 0.99 and 0.99 for one month ahead of GWL prediction in monitoring well number 84 (P84) and 0.48, 0.35, 0.99 and 0.98 for monitoring well number 71 (P71). The optimal GWL predictions were obtained from the WT-ANFIS method outcomes from input scenario 1, and this method created RMSE MAE, R and NSE as 0.25, 0.20, 0.99 and 0.99 for one month ahead GWL prediction in P84 and 0.48, 0.35, 0.99 and 0.98 for P71. The overall results of the used models showed that the WT hybrid and standalone ML methods are reliable in predicting GWL in the Unconfined Tehran aquifer, Iran.&lt;/p&gt;