Diagnosis of pulmonary hemorrhagic leptospirosis complicated by invasive pulmonary aspergillosis complemented by metagenomic next-generation sequencing: a case report.

Background: Leptospirosis is a bacterial zoonosis with variable clinical manifestations. Pulmonary diffuse hemorrhagic leptospirosis often occurs rapidly and, when not promptly diagnosed and treated, it can be life-threatening. Aspergillus flavus is an opportunistic fungus that is commonly seen in immunosuppressed patients. Invasive pulmonary aspergillosis also progresses rapidly. This case study describes a patient with severe pneumonia caused by pulmonary hemorrhagic leptospirosis combined with invasive pulmonary aspergillosis. We have found almost no clinical reports to date on these two diseases occurring in the same patient. Case presentation: A 73-year-old male arrived at our hospital complaining of fever, general malaise, and hemoptysis that had lasted 4 days. The patient was initially diagnosed with severe pneumonia in the emergency department, but he did not respond well to empiric antibiotics. Subsequently, the patient's condition worsened and was transferred to the ICU ward after emergency tracheal intubation and invasive ventilator. In the ICU, antibacterial drugs were adjusted to treat bacteria and fungi extensively. Although the inflammatory indices decreased, the patient still had recurrent fever, and a series of etiological tests were negative. Finally, metagenomic next-generation sequencing (mNGS) of bronchial alveolar lavage fluid detected Leptospira interrogans and Aspergillus flavus. After targeted treatment with penicillin G and voriconazole, the patient's condition improved rapidly, and he was eventually transferred out of the ICU and recovered. Conclusion: Early recognition and diagnosis of leptospirosis is difficult, especially when a patient is co-infected with other pathogens. The use of mNGS to detect pathogens in bronchial alveolar lavage fluid is conducive to early diagnosis and treatment of the disease, and may significantly improve the prognosis in severe cases.

TNF-α and HMGB1: New Biomarkers of Acute Pulmonary Embolism.

OBJECTIVE: Inflammatory mediators have been implicated in the pathophysiology of acute pulmonary embolism (PE). However, the role of inflammatory mediator activation in the development of pulmonary embolism remains elusive. Here, we determined the reliability of the plasma levels of inflammatory markers tumor necrosis factor-alpha (TNF-α) and high mobility histone 1 (HMGB1) as diagnostic biomarkers of PE. METHODS: Eighty-seven patients with PE and ninety-two healthy adults were enrolled. Plasma levels of TNF-α and HMGB1 were measured before and after anticoagulation treatment using conventional commercialized ELISA. RESULTS: The mean concentrations of plasma TNF-α and HMGB1 in patients with PE before anticoagulation treatment were 3.36- and 2.54-fold higher than those in controls (p<0.0001), respectively. Similar results were obtained in patients with PE before anticoagulation treatment, in which plasma levels of TNF-α and HMGB1 were 3.99- and 1.99-fold higher (p<0.0001), respectively, than in PE patients after anticoagulation treatment. Among the two potential markers, TNF-α performed best in distinguishing patients with PE from controls. A significant positive correlation was found between the two markers' concentrations. CONCLUSIONS: These findings suggest that the plasma levels of TNF-α and HMGB1 may serve as potential biomarkers for PE.

DeepSE: Detecting super-enhancers among typical enhancers using only sequence feature embeddings.

Super-enhancer (SE) is a cluster of active typical enhancers (TE) with high levels of the Mediator complex, master transcriptional factors, and chromatin regulators. SEs play a key role in the control of cell identity and disease. Traditionally, scientists used a variety of high-throughput data of different transcriptional factors or chromatin marks to distinguish SEs from TEs. This kind of experimental methods are usually costly and time-consuming. In this paper, we proposed a model DeepSE, which is based on a deep convolutional neural network model, to distinguish the SEs from TEs. DeepSE represent the DNA sequences using the dna2vec feature embeddings. With only the DNA sequence information, DeepSE outperformed all state-of-the-art methods. In addition, DeepSE can be generalized well across different cell lines, which implied that cell-type specific SEs may share hidden sequence patterns across different cell lines. The source code and data are stored in GitHub (https://github.com/QiaoyingJi/DeepSE).

Development and validation of a novel risk assessment model to estimate the probability of pulmonary embolism in postoperative patients.

Pulmonary embolism (PE) is a leading cause of mortality in postoperative patients. Numerous PE prevention clinical practice guidelines are available but not consistently implemented. This study aimed to develop and validate a novel risk assessment model to assess the risk of PE in postoperative patients. Patients who underwent Grade IV surgery between September 2012 and January 2020 (n = 26,536) at the Affiliated Dongyang Hospital of Wenzhou Medical University were enrolled in our study. PE was confirmed by an identified filling defect in the pulmonary artery system in CT pulmonary angiography. The PE incidence was evaluated before discharge. All preoperative data containing clinical and laboratory variables were extracted for each participant. A novel risk assessment model (RAM) for PE was developed with multivariate regression analysis. The discrimination ability of the RAM was evaluated by the area under the receiver operating characteristic curve, and model calibration was assessed by the Hosmer-Lemeshow statistic. We included 53 clinical and laboratory variables in this study. Among them, 296 postoperative patients developed PE before discharge, and the incidence rate was 1.04%. The distribution of variables between the training group and the validation group was balanced. After using multivariate stepwise regression, only variable age (OR 1.070 [1.054-1.087], P < 0.001), drinking (OR 0.477 [0.304-0.749], P = 0.001), malignant tumor (OR 2.552 [1.745-3.731], P < 0.001), anticoagulant (OR 3.719 [2.281-6.062], P < 0.001), lymphocyte percentage (OR 2.773 [2.342-3.285], P < 0.001), neutrophil percentage (OR 10.703 [8.337-13.739], P < 0.001), red blood cell (OR 1.872 [1.384-2.532], P < 0.001), total bilirubin (OR 1.038 [1.012-1.064], P < 0.001), direct bilirubin (OR 0.850 [0.779-0.928], P < 0.001), prothrombin time (OR 0.768 [0.636-0.926], P < 0.001) and fibrinogen (OR 0.772 [0.651-0.915], P < 0.001) were selected and significantly associated with PE. The final model included four variables: neutrophil percentage, age, malignant tumor and lymphocyte percentage. The AUC of the model was 0.949 (95% CI 0.932-0.966). The risk prediction model still showed good calibration, with reasonable agreement between the observed and predicted PE outcomes in the validation set (AUC 0.958). The information on sensitivity, specificity and predictive values according to cutoff points of the score in the training set suggested a threshold of 0.012 as the optimal cutoff value to define high-risk individuals. We developed a new approach to select hazard factors for PE in postoperative patients. This tool provided a consistent, accurate, and effective method for risk assessment. This finding may help decision-makers weigh the risk of PE and appropriately select PE prevention strategies.

Clinical symptoms and related risk factors in pulmonary embolism patients and cluster analysis based on these symptoms.

Pulmonary embolism (PE) remains largely underdiagnosed due to nonspecific symptoms. This study aims to evaluate typical symptoms of PE patients, their related predictors, and to differentiate typical clusters of patients and principal components of PE symptoms. Clinical data from a total of 551 PE patients between January 2012 and April 2016 were retrospectively reviewed. PE was diagnosed according to the European Society of Cardiology Guidelines. Logistic regression models, system clustering method, and principal component analysis were used to identify potential risk factors, different clusters of the patients, and principal components of PE symptoms. The most common symptoms of PE were dyspnea, cough, and tachypnea in more than 60% of patients. Some combined chronic conditions, laboratory and clinical indicators were found to be related to these clinical symptoms. Our study also suggested that PE is associated with a broad list of symptoms and some PE patients might share similar symptoms, and some PE symptoms were usually cooccurrence. Based on ten symptoms generated from our sample, we classified the patients into five clusters which represent five groups of PE patients during clinical practice, and identified four principal components of PE symptoms. These findings will improve our understanding of clinical symptoms and their potential combinations which are helpful for clinical diagnosis of PE.

Eosinophilic biomarkers for detection of acute exacerbation of chronic obstructive pulmonary disease with or without pulmonary embolism.

Eosinophilia has been implicated in the pathophysiology of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, the role of eosinophil activation in the development of AECOPD remains unclear. In the present study, the reliability of plasma levels of eosinophil activation markers, including eosinophil cationic protein (ECP), major basic protein (MBP), eosinophil-derived neurotoxin (EDN) and eosinophil peroxidase (EPX), were measured and used as diagnostic biomarkers of AECOPD with or without pulmonary embolism (PE). A total of 47 patients with AECOPD, 30 patients with AECOPD/PE and 35 healthy adults were enrolled in the present study. Plasma levels of ECP, EDN, EPX and MBP were measured using commercial ELISA kits. The mean concentrations of plasma ECP, EDN, EPX and MBP in the patients with AECOPD was significantly 2.87-, 3.06-, 1.60- and 1.92-fold higher, respectively, compared with the control group (P<0.05). Similar results were obtained in patients with AECOPD/PE, for whom plasma levels of ECP, EDN, EPX and MBP were significantly 2.06-, 2.21-, 1.42- and 2.42-fold higher, respectively, compared with the controls (P<0.05). No significant differences were observed in the levels of these proteins between patients with AECOPD or AECOPD/PE. Among the four potential markers, ECP was determined to be the optimal marker for distinguishing patients with AECOPD or AECOPD/PE from the controls. No significant correlation was observed between marker concentrations and gender, age or disease severity. The results of the present study may have clinical applications in the diagnosis of AECOPD using these novel biomarkers.