BackgroundAspergillus diseases are frequently encountered in patients who are immunocompromised. Without a prompt diagnosis, the clinical consequences may be lethal. Aspergillus-specific antibodies have been widely used to facilitate the diagnosis of Aspergillus diseases. To date, universally standardized cut-off values have not been established. This study aimed to investigate the cut-off values of Aspergillus-specific antibodies and perform a narrative review to depict the geographic differences in the Taiwanese population.MethodsWe analyzed enrolled 118 healthy controls, 29 patients with invasive aspergillosis (IA), chronic pulmonary aspergillosis (CPA), and allergic bronchopulmonary aspergillosis (ABPA) and 99 with disease control, who were tested for Aspergillus fumigatus and Aspergillus niger-specific IgG and IgE using ImmunoCAP. 99 participants not fulfilling the diagnosis of IA, CPA, and ABPA were enrolled in the disease control group. The duration of retrieval of medical records from June 2018 to September 2021. Optimal cut-offs and association were determined using receiver operating characteristic curve (ROC) analysis.ResultsWe found that patients with CPA had the highest A. fumigatus-specific IgG levels while patients with ABPA had the highest A. fumigatus-specific IgE, and A. niger-specific IgG and IgE levels. In patients with CPA and ABPA, the optimal cut-offs of A. fumigatus-specific IgG and A. niger-specific IgG levels were 41.6, 40.8, 38.1, and 69.9 mgA/l, respectively. Geographic differences in the cut-off values of A. fumigatus-specific IgG were also noted. Specifically, the levels were different in eco-climatic zones.ConclusionWe identified the optimal cut-offs of Aspergillus-specific antibodies to facilitate a precise diagnosis of aspergillosis. The observed geographic differences of the antibody levels suggest that an eco-climatic-specific reference is needed to facilitate a prompt and accurate diagnosis of aspergillosis.

Information about financial risk almost always contains a problem of class imbalance. Class-imbalanced data refers to the asymmetric categories of data, and it is divided into a major class and a minor class. If we guide all information into the training sample to model of this situation, it may happen that the accuracy rate of the major class is high, but the accuracy rate of the minor class is too low. Many risk assessment models have been developed in many studies, but most of them only use sampling methods to deal with the class-imbalanced data; this may cause the distortion of information. In order to effectively solve the problem of class imbalance in credit risk assessment, this paper proposed a novel credit risk assessment model using a granular computing technique to construct a risk assessment model to provide a better insight into the essence of data and effectively solve class imbalance problems. On the other hand, in order to improve the lack of granular computing and enhance the efficiency of the credit risk assessment model, this paper adds a new index, “% of minor class (PM),” to avoid a situation in which minor class data spread to the major class granular. Finally, this paper also compares the results of the area under the receiver operating characteristic curve (AUC) and G-means methods for dealing with class-imbalanced data. The results demonstrate that the proposed granular computing credit assessment model would have better results than other sampling models.

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Purpose: The goal of this research is to use the correlation analysis method (CAM) to find out the investors can invest with simple messages. Results: This research's empirical results indicate the following: (1) Based on our empirical results obtained from using the correlation analysis method (CAM) method, the highest price, lowest price, and closing price can affect the opening price, and there is a significant and positive relationship. Moreover, the stock trading volume is an insignificant positive correlation, and the rate of price spread is an insignificant negative correlation. (2) This research assumes no external interference and government protection. And that stock investors do not have any technical analysis and other conditions. Whether the company can make a profit can reflect the value of bonds and stocks through public information. Therefore, investors can invest based on simple information. The grey relational analysis (GRA) research shows that that both the highest price and the lowest price displays were significant. The closing price was strong. In contrast, the study found that the stock trading volume and price spread displays are weak, and the analysis results showed almost no effect. Unique contribution to theory, policy and practice: Therefore, if you play day trader it is feasible to use the high and low stock prices to make a remuneration profit.