Allergic bronchopulmonary aspergillosis with marked peripheral blood eosinophilia and pulmonary eosinophilia: A case report.

BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) is an immune-related pulmonary disease caused by sensitization of airway by Aspergillus fumigatus. The disease manifests as bronchial asthma and recurring pulmonary shadows, which may be associated with bronchiectasis. The diagnosis of ABPA mainly depends on serological, immunological, and imaging findings. Pathological examination is not necessary but may be required in atypical cases to exclude pulmonary tuberculosis, tumor, and other diseases through lung biopsy. CASE SUMMARY: An 18-year-old man presented with recurrent wheezing, cough, and peripheral blood eosinophilia. Chest computed tomography showed pulmonary infiltration. There was a significant increase in eosinophils in bronchoalveolar lavage fluid. There was no history of residing in a parasite-endemic area or any evidence of parasitic infection. Pathologic examination of bronchoalveolar lavage fluid excluded fungal and mycobacterial infections. The patient was receiving medication for comorbid diseases, but there was no temporal correlation between medication use and clinical manifestations, which excluded drug-induced etiology. Histopathological examination of lung biopsy specimen showed no signs of eosinophilic granulomatosis with polyangiitis, IgG4-related diseases, or tumors. The diagnosis of ABPA was considered based on the history of asthma and the significant increase in serum Aspergillus fumigatus-specific immunoglobulin (Ig)E. Eosinophil-related diseases were excluded through pathological biopsy, which showed typical pathological manifestations of ABPA. CONCLUSION: The possibility of ABPA should be considered in patients with poorly controlled asthma, especially those with eosinophilia, lung infiltration shadows, or bronchiectasis. Screening for serum IgE, Aspergillus fumigatus-specific IgE and IgG, and alveolar lavage can help avoid misdiagnosis.

Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores.

BACKGROUND: Early identification of severe/critical coronavirus disease 2019 (COVID-19) is crucial for timely treatment and intervention. Chest computed tomography (CT) score has been shown to be a significant factor in the diagnosis and treatment of pneumonia, however, there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution. AIM: To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels. METHODS: This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients. The study also took into consideration the general clinical indicators such as dyspnea, oxygen saturation, alternative lengthening of telomeres (ALT), and androgen suppression treatment (AST), which are commonly associated with severe/critical COVID-19 cases. The study employed lasso regression to evaluate and rank the significance of different disease characteristics. RESULTS: The results showed that blood oxygen saturation, ALT, IL-6/IL-10, combined score, ground glass opacity score, age, crazy paving mode score, qsofa, AST, and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases. The study established a COVID-19 severe/critical early warning system using various machine learning algorithms, including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier. The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution. CONCLUSION: The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

Rationale and design of REGULATE: an observational study protocol for relationship between plasma metabolome and the efficacy of systemic glucocorticoid in acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) significantly increases the mortality of patients with COPD. Guidelines have recommended systemic glucocorticoid as a regular treatment. Recently, evidence has shown that systemic glucocorticoid cannot be a benefit to all of the patients with AECOPD. Thus, the problem that how the clinicians can screen the patients who can benefit from systemic glucocorticoid needs to be solved urgently. This study is aimed to detect the metabolic biomarkers and metabolic pathways that are related to the efficacy of systemic glucocorticoid and contribute to the precise treatment of COPD. METHODS AND DESIGN: In this study, we will utilize ultraperformance liquid chromatography/mass spectrometry (LC-MS) and gas chromatography/mass spectrometry (GC-MS) methods for the analysis of the metabolites in AECOPD patients and compare the metabolites profiles between patients with systemic glucocorticoid treatment success group and treatment failure group. We aim to detect the metabolic biomarkers and metabolic pathways that are related to the efficacy of systemic glucocorticoid and contribute to the precise treatment of COPD. DISCUSSION: Previous studies have found that plasma metabolome changed significantly after dexamethasone treatment in healthy participants. Furthermore, inter-person variability was high and remained uninfluenced by treatment, suggesting the potential of metabolomics for predicting the efficacy and side effects of systemic glucocorticoid. Therefore, we hypothesized that metabolome changes in patients with AECOPD may be associated with the efficacy of systemic glucocorticoid. Trial registration Clinicaltrials.gov registration number NCT04710849. Registered 15 January 2021, https://clinicaltrials.gov/ct2/show/NCT04710849 .