BACKGROUND: The prevalence of fungi in cystic fibrosis (CF) lung infections is poorly understood and studies have focused on adult patients. We investigated the fungal diversity in children with CF using bronchoalveolar lavage (BAL) and induced sputum (IS) samples to capture multiple lung niches.
METHODS: Sequencing of the fungal ITS2 region and molecular mycobiota diversity analysis was performed on 25 matched sets of BAL-IS samples from 23 children collected as part of the CF-SpIT study (UKCRN14615; ISRCTNR12473810).
RESULTS: Aspergillus and Candida were detected in all samples and were the most abundant and prevalent genera, followed by Dipodascus, Lecanicillium and Simplicillium. The presumptive CF pathogens Exophiala, Lomentospora and Scedosporium were identified at variable abundances in 100 %, 64 %, and 24 % of sample sets, respectively. Fungal pathogens observed at high relative abundance (≥40 %) were not accurately diagnosed by routine culture microbiology in over 50 % of the cohort. The fungal communities captured by BAL and IS samples were similar in diversity and composition, with exception to C. albicans being significantly increased in IS samples. The respiratory mycobiota varied greatly between individuals, with only 13 of 25 sample sets containing a dominant fungal taxon. In 11/25 BAL sample sets, airway compartmentalisation was observed with diverse mycobiota detected from different lobes of the lung.
CONCLUSIONS: The paediatric mycobiota is diverse, complex and inadequately diagnosed by conventional microbiology. Overlapping fungal communities were identified in BAL and IS samples, showing that IS can capture fungal genera associated with the lower airway. Compartmentalisation of the lower airway presents difficulties for consistent mycobiota sampling.

Iron plays a critical role in lung infections due to its function in the inflammatory immune response but also as an important factor for bacterial growth. Iron chelation represents a potential therapeutic approach to inhibit bacterial growth and pathologically increased pro-inflammatory mediator production. The present study was designed to investigate the impact of the iron chelator DIBI in murine lung infection induced by intratracheal Pseudomonas aeruginosa (strain PA14) administration. DIBI is a polymer with a polyvinylpyrrolidone backbone containing nine 3-hydroxy-1-(methacrylamidoethyl)-2-methyl-4(1H) pyridinone (MAHMP) residues per molecule and was given by intraperitoneal injection either as a single dose (80 mg/kg) immediately after PA14 administration or a double dose (second dose 4 h after PA14 administration). The results showed that lung NF-κBp65 levels, as well as levels of various inflammatory cytokines (TNFα, IL-1β, IL-6) both in lung tissue and bronchoalveolar lavage fluid (BALF), were significantly increased 24 h after PA14 administration. Single-dose DIBI did not affect the bacterial load or inflammatory response in the lungs or BALF. However, two doses of DIBI significantly decreased bacterial load, attenuated NF-κBp65 upregulation, reduced inflammatory cytokines production, and relieved lung tissue damage. Our findings support the conclusion that the iron chelator, DIBI, can reduce lung injury induced by P. aeruginosa, via its anti-bacterial and anti-inflammatory effects.

Invasive fungal infection is a life-threatening complication of chemotherapy and neutropaenia in the haematology population. Trichoderma species rarely cause human disease but have been reported to cause invasive infection in the immunosuppressed. We present a case of invasive Trichoderma longibrachiatum pulmonary infection with fatal outcome in a neutropaenic patient with acute myeloid leukaemia. 2012 Elsevier Ltd. All rights reserved.

Cystic fibrosis-related diabetes (CFRD), the most common comorbidity in cystic fibrosis (CF), leads to increased mortality by accelerating the decline in lung function. Scnn1b-Tg transgenic mice overexpressing the epithelial sodium channel β subunit exhibit spontaneous CF-like lung disease, including airway mucus obstruction and chronic inflammation. Here, we established a chronic CFRD-like model utilizing Scnn1b-Tg mice made diabetic by injection of streptozotocin. In Ussing chamber recordings of trachea, Scnn1b-Tg mice exhibited larger amiloride-sensitive currents and forskolin-activated currents, without a difference in ATP-activated currents compared to wildtype (WT) littermates. Both diabetic WT (WT-D) and diabetic Scnn1b-Tg (Scnn1b-Tg-D) mice on the same genetic background exhibited substantially elevated blood glucose at 8 weeks; glucose levels also were elevated in bronchoalveolar lavage fluid (BALF) Bulk lung RNA-seq data showed significant differences between WT-D and Scnn1b-Tg-D mice. Neutrophil counts in BALF were substantially increased in Scnn1b-Tg-D lungs compared to controls (Scnn1b-Tg-con) and compared to WT-D lungs. Lung histology data showed enhanced parenchymal destruction, alveolar wall thickening, and neutrophilic infiltration in Scnn1b-Tg-D mice compared to WT-D mice, consistent with development of a spontaneous lung infection. We intranasally administered Pseudomonas aeruginosa to induce lung infection in these mice for 24 hours, which led to severe lung leukocytic infiltration and an increase in pro-inflammatory cytokine levels in the BALF. In summary, we established a chronic CFRD-like lung mouse model using the Scnn1b-Tg mice. The model can be utilized for future studies toward understanding the mechanisms underlying the lung pathophysiology associated with CFRD and developing novel therapeutics.

OBJECTIVE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality.
METHODS: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction.
RESULTS: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889.
CONCLUSIONS: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE .

Mycobacterium abscessus pulmonary infections are increasingly problematic, especially for immunocompromised individuals and those with underlying lung conditions. Currently, there is no reliable standardized treatment, underscoring the need for improved preclinical drug testing. We present a simplified immunosuppressed mouse model using only four injections of cyclophosphamide, which allows for sustained M. abscessus lung burden for up to 16 days. This model proved effective for antibiotic efficacy evaluation, as demonstrated with imipenem or amikacin.

Aim: Torquetenovirus (TTV) was a promising biomarker for immunity, while lung regional TTV for evaluating the opportunistic infection among immunocompromised hosts (ICH) was unclear. Materials & methods: In the ICH and non-ICH populations, we compared the susceptibility to opportunistic infections, clinical severity and the prognosis between subgroups, respectively. Results: ICH with detectable bronchoalveolar lavage fluid (BALF)-TTV were more susceptible to lung aspergillosis and Mycobacterium infections. Furthermore, our data demonstrated that the ICH cohort with detectable BALF-TTV represented a higher clinical severity and a worse prognosis, while the above findings were not found in the non-ICH population. Conclusion: Our findings demonstrated that the BALF-TTV could act as an effective predictor for opportunistic infection for ICH that complemented the CD4+ T cell counts.
[Box: see text].

OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.
METHODS: The clinical data of 1415 patients diagnosed with CHF complicated by lung infections were obtained from the MIMIC-IV database. According to the pathogen type, the patients were categorized into bacterial pneumonia and non-bacterial pneumonia groups, and their risks of in-hospital death were compared using Kaplan-Meier survival curves. Univariate analysis and LASSO regression were used to select the features for constructing LR, AdaBoost, XGBoost, and LightGBM models, and their performance was compared in terms of accuracy, precision, F1 value, and AUC. External validation of the models was performed using the data from eICU-CRD database. SHAP algorithm was applied for interpretive analysis of XGBoost model.
RESULTS: Among the 4 constructed models, the XGBoost model showed the highest accuracy and F1 value for predicting the risk of in-hospital death in CHF patients with lung infections in the training set. In the external test set, the XGBoost model had an AUC of 0.691 (95% CI: 0.654-0.720) in bacterial pneumonia group and an AUC of 0.725 (95% CI: 0.577-0.782) in non-bacterial pneumonia group, and showed better predictive ability and stability than the other models.
CONCLUSIONS: The overall performance of the XGBoost model is superior to the other 3 models for predicting the risk of in-hospital death in CHF patients with lung infections. The SHAP algorithm provides a clear interpretation of the model to facilitate decision-making in clinical settings.

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

OBJECTIVE: Slow-growing nontuberculous mycobacteria (NTMs) are highly prevalent and routinely cause opportunistic intracellular infectious disease in immunocompromised hosts.
METHODS: The activity of the triple combination of antibiotics, clarithromycin (CLR), rifabutin (RFB), and clofazimine (CFZ), was evaluated and compared with the activity of single antibiotics as well as with double combinations in an in vitro biofilm assay and an in vivo murine model of Mycobacterium avium subsp. hominissuis (M. avium) lung infection.
RESULTS: Treatment of 1-week-old biofilms with the triple combination exerted the strongest effect of all (0.12 ± 0.5 × 107 CFU/mL) in reducing bacterial growth as compared to the untreated (5.20 ± 0.5 × 107/mL) or any other combination (≥0.75 ± 0.6 × 107/mL) by 7 days. The treatment of mice intranasally infected with M. avium with either CLR and CFZ or the triple combination provided the greatest reduction in CLR-sensitive M. avium bacterial counts in both the lung and spleen compared to any single antibiotic or remaining double combination by 4 weeks posttreatment. After 4 weeks of treatment with the triple combination, there were no resistant colonies detected in mice infected with a CLR-resistant strain. No clear relationships between treatment and spleen or lung organ weights were apparent after triple combination treatment.
CONCLUSIONS: The biofilm assay data and mouse disease model efficacy results support the further investigation of the triple-antibiotic combination.