Invasive infections due to Magnusiomyces capitatus: case report and review of its prevalence in China.

Magnusiomyces capitatus is an emerging opportunistic yeast, thus far mainly reported from the Western world where fungemia is the most frequent presentation in immunocompromised patients with high mortality. We described a rare case of Magnusiomyces capitatus infection from our hospital in China and reviewed six further cases published to date in Chinese literature. It is noted that half more of the cases (4/7) presented with fungemia in younger, immunosuppressed patients, whereas the remaining cases were with pneumonia in elderly, immunocompetent patients. All seven Chinese cases had favourable outcome with antifungal therapy. Based on the limited in vitro and clinical data, a combination of amphotericin B either with 5-fluorocytosine or voriconazole for fungemia in immunocompromised patients, and although fluconazole is not recommended as first-line therapy in the guideline, in our study, fluconazole alone or with 5-fluorocytosine for local pulmonary infection in immunocompetent patients is effective with good outcome.

Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA).

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

Fuzzy Comprehensive Evaluation Model of M&A Synergy Based on Transfer Learning Graph Neural Network.

With the rapid development of modern China and the influx of capital, the number of companies has gradually increased. However, most companies cannot operate for a long time due to various reasons. Therefore, mergers and acquisitions have occurred. Large companies merge small companies to some extent. The number of employees can be guaranteed, and the market can be stabilized. However, mergers and acquisitions also have higher risks. As the pace of mergers and acquisitions accelerates, there are more and more cases of failed mergers and acquisitions. The synergy effect of mergers and acquisitions is an important indicator to judge the performance of mergers and acquisitions. This article measures the synergy obtained by the main enterprise from the perspective of performance changes, establishes an evaluation model through the rate of change of financial indicators and migration learning, estimates it through a neural network model, and conducts an empirical analysis on it. The transfer learning neural network has been studied in depth. The research of this article is to accurately assess the synergy effect obtained after mergers and acquisitions and to analyze whether the company can profit from mergers and acquisitions, so as to provide a reference for subsequent mergers and acquisitions between companies.

Increased levels of exhaled sICAM1, sVCAM1, and sE-selectin in patients with non-small cell lung cancer.

AIM: The mortality of lung cancer remains high and methods for early diagnosis are still lacking. Recently, exhaled breath condensate (EBC) has been considered a potential tool for obtaining biological information leading to a reliable diagnosis of non-small cell lung cancer (NSCLC). OBJECTIVE: This study assessed the potentials of exhaled and serum concentrations of soluble(s) forms of intercellular adhesion molecule 1 (sICAM1), vascular cell adhesion molecule 1 (sVCAM1), and E-selectin as biomarkers for diagnosis and predicting metastasis in NSCLC patients. METHODS: We enrolled 33 patients with NSCLC, 35 patients with chronic obstructive pulmonary disease (COPD) and 30 healthy controls. EBC and serum samples from subjects were collected at the time of diagnosis and, where applicable, 3 months after surgical treatment. Measurements of sICAM1, sVCAM1, and sE-selectin were determined by enzyme immunoassay. RESULTS: Concentrations of sICAM1, sVCAM1, and sE-selectin in the EBC and sera of NSCLC patients were significantly elevated compared to COPD patients and healthy controls. The exhaled and serum levels of sICAM1 and sVCAM1, but not sE-selectin, decreased significantly after tumor resection from pre-surgery levels. In addition, analyzed results showed a correlation between exhaled sICAM1 levels and disease progression of NSCLC patients. CONCLUSIONS: Our results suggest that the levels of sICAM1, sVCAM1, and sE-selectin in EBC and sera of NSCLC patients are higher than those of COPD patients or healthy controls. Moreover, exhaled sICAM1 may have prognostic value and potential as a biomarker for the diagnosis and prognosis of patients with lung cancer.