Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy toward this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of hematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We identified the transcript coding for the S100 calcium-binding protein B (S100B) as a potential new biomarker for the diagnosis of IA. Considering the expression of this gene, we were able to classify samples from patients with IA with 82.3% sensitivity and 74.6% specificity. Moreover, we validated the expression of S100B in a real-time reverse transcription polymerase chain reaction (RT-PCR) assay and we also found a down-regulation of S100B in A. fumigatus stimulated DCs. An influence on the IL1B and CXCL1 downstream levels was demonstrated by this S100B knockdown. In conclusion, this study covers an effective feature selection revealing a key regulator of the human immune response during IA. S100B may represent an additional diagnostic marker that in combination with the established techniques may improve the accuracy of IA diagnosis.
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http://dx.doi.org/10.3389/fmicb.2016.00320 | DOI Listing |
Microlife
December 2024
Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Adolf-Reichwein-Str. 23, 07745 Jena, Germany.
The polyene antimycotic amphotericin B (AmB) and its liposomal formulation AmBisome belong to the treatment options of invasive aspergillosis caused by . Increasing resistance to AmB in clinical isolates of species is a growing concern, but mechanisms of AmB resistance remain unclear. In this study, we conducted a proteomic analysis of exposed to sublethal concentrations of AmB and AmBisome.
View Article and Find Full Text PDFBMJ Case Rep
January 2025
Pulmonary, Critical Care and Sleep Medicine, ESICPGIMSR, New Delhi, Delhi, India.
Allergic bronchopulmonary aspergillosis (ABPA) is a disease of immunocompetent patients, and invasive pulmonary aspergillosis is seen in immunocompromised patients. Hence, pulmonary overlap syndrome presenting with ABPA and invasive aspergillosis is extremely rare. We report a case of well-controlled bronchial asthma who presented with acute exacerbation and hypoxaemic respiratory failure.
View Article and Find Full Text PDFCells
December 2024
Department of Dermatology, University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany.
The mRNA-binding protein KSRP (KH-type splicing regulatory protein) is known to modulate immune cell functions post-transcriptionally, e.g., by reducing the mRNA stability of cytokines.
View Article and Find Full Text PDFis the etiologic agent of invasive aspergillosis, a life- threatening fungal pneumonia that is initiated by the inhalation of conidia (spores) into the lung. If the conidia are not cleared, they secrete large quantities of hydrolytic enzymes and toxins as they grow, resulting in extensive damage to pulmonary tissue. Stromal fibroblasts are central responders to tissue damage in many organs, but their functional response to pulmonary injury caused by has not been explored.
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January 2025
National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
In clinical practice, differentiating among pulmonary mucormycosis (PM), invasive pulmonary aspergillosis (IPA), and pulmonary tuberculosis (PTB) can be challenging. This study aimed to evaluate the performance of chest CT-based artificial intelligence (AI) models in distinguishing among these three diseases. Patients with confirmed PM, IPA, or PTB were retrospectively recruited from three tertiary hospitals.
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