Details of patients hospitalized for asthma exacerbation in mainland China are lacking. To improve disease control and reduce economic burden, a large sample survey among this patient population is indispensable. This study aimed to investigate the clinical characteristics and outcomes of such patients. A retrospective study was conducted on patients hospitalized for asthma exacerbation in 29 hospitals of 29 regions in mainland China during the period 2013 to 2014. Demographic features, pre-admission conditions, exacerbation details, and outcomes were summarized. Risk factors for exacerbation severity were analyzed. There were 3,240 asthmatic patients included in this study (57.7% females, 42.3% males). Only 28.0% used daily controller medications; 1,287 (39.7%) patients were not currently on inhaled corticosteroids. Acute upper airway infection was the most common trigger of exacerbation (42.3%). Patients with severe to life-threatening exacerbation tended to have a longer disease course, a smoking history, and had comorbidities such as hypertension, chronic obstructive pulmonary disease (COPD), and food allergy. The multivariate analysis showed that smoking history, comorbidities of hypertension, COPD, and food allergy were independent risk factors for more severe exacerbation. The number of patients hospitalized for asthma exacerbation varied with seasons, peaking in March and September. Eight patients died during the study period (mortality 0.25%). Despite enhanced education on asthma self-management in China during recent years, few patients were using daily controller medications before the onset of their exacerbation, indicating that more educational efforts and considerations are needed. The findings of this study may improve our understanding of hospital admission for asthma exacerbation in mainland China and provide evidence for decision-making.

Watersheds are critical natural systems that serve as the foundation for sustaining life on Earth

Semantic segmentation of high spatial resolution remote sensing images presents unique challenges due to the imbalanced foreground-background distribution and large intra-class variance. This study proposes a novel semantic segmentation algorithm based on non-learnable prototypes, named RSProtoSeg. This approach optimizes the spatial relationship between foreground-background prototypes and intra-class prototypes. Specifically, we propose a foreground-background distance optimization loss function to enhance sparsity between these phototypes, effectively mitigating foreground-background distribution imbalances. Moreover, we introduce an online discrete clustering module that represents each class with a set of prototypes and adds an adaptive regular term penalty to promote sparse structure and reduce the variance issue. Evaluation on three remote sensing datasets (iSAID, ISPRS Potsdam, and Vaihingen) demonstrates significant accuracy improvements, aligning our approach with state-of-the-art methods. Our non-learnable prototype-based approach offers a promising solution for semantic segmentation in high spatial resolution remote sensing images.