Diffuse Alveolar Hemorrhage in the Setting of an Acute Exacerbation of Chronic Hypersensitivity Pneumonitis Due to Drug Rash With Eosinophilia and Systemic Symptoms

There has been a significant increase in electronic cigarette (e-cigarette) use since its introduction in 2007. Ironically, there remains very few published literature on the respiratory complications of e-cigarettes. The use of personalized vaporizers or commonly known as “vaping” has started to overtake standard e-cigarette. Its dynamic vaporizer customization makes it challenging to assess long-term health effects. Case reports on the pulmonary complications of e-cigarettes are limited to bronchiectasis, eosinophilic pneumonia, pleural effusion, and suspected hypersensitivity pneumonitis. Diffuse alveolar hemorrhage (DAH) is bleeding into the alveolar spaces of the lung secondary to disruption of the alveolar-capillary basement membrane. We report a case of young male presenting with subacute respiratory failure. He was later found to have diffuse alveolar hemorrhage syndrome that is likely induced by aggressive vaping. This adds up to the rising concern on the possible serious complications of this innovative technology designed as a safer alternative to traditional cigarettes.

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Anticonvulsant hypersensitivity syndrome or drug rash with eosinophilia and systemic symptoms (DRESS) induced by anticonvulsants

Alergologia ◽

10.26416/aler.1.1.2017.1242 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Cristina Vlaicu

Keyword(s):

Hypersensitivity Syndrome ◽

Anticonvulsant Hypersensitivity Syndrome ◽

Drug Rash ◽

Systemic Symptoms

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Faculty Opinions recommendation of [Severe drug rash with eosinophilia and systemic symptoms after treatment with minocycline].

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1098112.554098 ◽

2007 ◽

Author(s):

Khalid Al Aboud

Keyword(s):

Drug Rash ◽

Systemic Symptoms

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Faculty Opinions recommendation of A haemodynamic study of pulmonary hypertension in chronic hypersensitivity pneumonitis.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718355460.793498260 ◽

2014 ◽

Author(s):

Lars C Huber

Keyword(s):

Pulmonary Hypertension ◽

Hypersensitivity Pneumonitis ◽

Haemodynamic Study ◽

Chronic Hypersensitivity Pneumonitis

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Faculty Opinions recommendation of Chronic Hypersensitivity Pneumonitis With a Usual Interstitial Pneumonia-Like Pattern: Correlation Between Histopathologic and Clinical Findings.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726113335.793524889 ◽

2016 ◽

Author(s):

Jay H Ryu

Keyword(s):

Interstitial Pneumonia ◽

Hypersensitivity Pneumonitis ◽

Usual Interstitial Pneumonia ◽

Clinical Findings ◽

Pattern Correlation ◽

Chronic Hypersensitivity Pneumonitis

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Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

Current Gene Therapy ◽

10.2174/1566523220666201208093325 ◽

2020 ◽

Vol 20 ◽

Author(s):

Hongwei Zhang ◽

Steven Wang ◽

Tao Huang

Keyword(s):

Feature Selection ◽

Expression Analysis ◽

Hypersensitivity Pneumonitis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Support Vector ◽

Svm Classifier ◽

Clinical Tool ◽

Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

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