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Objective. To summarize the clinical characteristics and treatment experiences of patients with plastic bronchitis (PB). Methods. All patients who were diagnosed with PB by bronchoscopic removal of tree-like casts at a single institution from January 2012 to May 2022 were retrospectively reviewed. Demographic and clinical data were retrieved from electronic patient records. Results. A total of 55 patients, with a median age of 5.3 years, were eligible for the study. Nineteen cases had underlying diseases, among which asthma was the most common. The median course of the disease before admission was 11 days. Clinical symptoms were characterized by cough and fever, while moist rales (78.2%) and dyspnea (61.8%) were the most common signs. The most common laboratory finding was elevated C-reactive protein (58.2%). Patchy opacity was the most frequent radiographic finding (81.2%), followed by consolidation (60.0%) and pleural effusion (43.6%). Respiratory pathogens were detected in 41 cases, and M. pneumoniae was the most common one (41.8%), followed by adenovirus (20.0%) and influenza B virus (10.9%). The casts were removed by alveolar lavage, combined with ambroxol immersion (63.6%) and forceps (30.9%). Patients received an average of 2.3 bronchoscopies, and the median time for the first procedure was 3 days after admission. Antibiotics were given to all patients, methylprednisolone to 33 (60.0%), and gamma globulin to 25 (45.5%). A total of 53 cases were improved with an overall mortality rate of 3.6%. Conclusions. PB in children is characterized by airway obstruction, mostly caused by respiratory infections, and timely removal of the cast by bronchoscopy is the most effective treatment.

IntroductionLand use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized.MethodsA quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted.ResultsThe quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm.ConclusionThe findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area.

Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.