ArticleJournal of visualized experiments : JoVE2018
Histological Quantification to Determine Lung Fungal Burden in Experimental Aspergillosis.
Article in Journal of visualized experiments : JoVE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- Gas6/Axl-mediated macrophage hyporesponsiveness drives severe pulmonary mucormycosis in diabetic mice.Frontiers in cellular and infection microbiology · 2026Article
- Adiponectin pathway activation dampens inflammation and enhances alveolar macrophage fungal killing via LC3-associated phagocytosis.PLoS pathogens · 2025Article
- Precision-cut lung slices in air-liquid interface (PCLS-ALI): A novelbioRxiv : the preprint server for biology · 2024Article
- Adiponectin pathway activation dampens inflammation and enhances alveolar macrophage fungal killing via LC3-associated phagocytosis.bioRxiv : the preprint server for biology · 2024Article
- Immunogenicity and protective efficacy of a pan-fungal vaccine in preclinical models of aspergillosis, candidiasis, and pneumocystosis.PNAS nexus · 2022Article
- The Lysine Demethylases KdmA and KdmB Differently Regulate Asexual Development, Stress Response, and Virulence inJournal of fungi (Basel, Switzerland) · 2022Article
- Vaccine-Induced Protection in Two Murine Models of Invasive Pulmonary Aspergillosis.Frontiers in immunology · 2021Article
- RgsA Attenuates the PKA Signaling, Stress Response, and Virulence in the Human Opportunistic PathogenInternational journal of molecular sciences · 2019Article
- Single-Step Enrichment of a TAP-Tagged Histone Deacetylase of the Filamentous Fungus Aspergillus nidulans for Enzymatic Activity Assay.Journal of visualized experiments : JoVE · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
The quantification of lung fungal burden is critical for the determination of the relative levels of immune protection and fungal virulence in mouse models of pulmonary fungal infection. Although multiple methods are used to assess fungal burden, quantitative polymerase chain reaction (qPCR) of fungal DNA has emerged as a technique with several advantages over previous culture-based methods. Currently, a comprehensive assessment of lung pathology, leukocyte recruitment, fungal burden, and gene expression in mice with invasive aspergillosis (IA) necessitates the use of a significant number of experimental and control animals. Here the quantification of lung histological staining to determine fungal burden using a reduced number of animals was examined in detail. Lung sections were stained to identify fungal structures with Gomori's modified methanamine silver (GMS) staining. Images were taken from the GMS-stained sections from 4 discrete fields of each formalin-fixed paraffin-embedded lung. The GMS stained areas within each image were quantified using an image analysis program, and from this quantification, the mean percentage of stained area was determined for each sample. Using this strategy, eosinophil-deficient mice exhibited decreased fungal burden and disease with caspofungin therapy, while wild-type mice with IA did not improve with caspofungin. Similarly, fungal burden in mice lacking γδ T cells were also improved by caspofungin, as measured by qPCR and GMS quantification. GMS quantification is therefore introduced as a method for the determination of relative lung fungal burden that may ultimately reduce the quantity of experimental animals required for comprehensive studies of invasive aspergillosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.