Metapopulation model of phage therapy of an acute Pseudomonas aeruginosa lung infection.

Infections caused by multidrug resistant (MDR) pathogenic bacteria are a global health threat. Bacteriophages ("phage") are increasingly used as alternative or last-resort therapeutics to treat patients infected by MDR bacteria. However, the therapeutic outcomes of phage therapy may be limited by the emergence of phage resistance during treatment and/or by physical constraints that impede phage-bacteria interactions in vivo. In this work, we evaluate the role of lung spatial structure on the efficacy of phage therapy for Pseudomonas aeruginosa infections. To do so, we developed a spatially structured metapopulation network model based on the geometry of the bronchial tree, including host innate immune responses and the emergence of phage-resistant bacterial mutants. We model the ecological interactions between bacteria, phage, and the host innate immune system at the airway (node) level. The model predicts the synergistic elimination of a P. aeruginosa infection due to the combined effects of phage and neutrophils, given the sufficient innate immune activity and efficient phage-induced lysis. The metapopulation model simulations also predict that MDR bacteria are cleared faster at distal nodes of the bronchial tree. Notably, image analysis of lung tissue time series from wild-type and lymphocyte-depleted mice revealed a concordant, statistically significant pattern: infection intensity cleared in the bottom before the top of the lungs. Overall, the combined use of simulations and image analysis of in vivo experiments further supports the use of phage therapy for treating acute lung infections caused by P. aeruginosa, while highlighting potential limits to therapy in a spatially structured environment given impaired innate immune responses and/or inefficient phage-induced lysis. IMPORTANCE: Phage therapy is increasingly employed as a compassionate treatment for severe infections caused by multidrug-resistant (MDR) bacteria. However, the mixed outcomes observed in larger clinical studies highlight a gap in understanding when phage therapy succeeds or fails. Previous research from our team, using in vivo experiments and single-compartment mathematical models, demonstrated the synergistic clearance of acute P. aeruginosa pneumonia by phage and neutrophils despite the emergence of phage-resistant bacteria. In fact, the lung environment is highly structured, prompting the question of whether immunophage synergy explains the curative treatment of P. aeruginosa when incorporating realistic physical connectivity. To address this, we developed a metapopulation network model mimicking the lung branching structure to assess phage therapy efficacy for MDR P. aeruginosa pneumonia. The model predicts the synergistic elimination of P. aeruginosa by phage and neutrophils but emphasizes potential challenges in spatially structured environments, suggesting that higher innate immune levels may be required for successful bacterial clearance. Model simulations reveal a spatial pattern in pathogen clearance where P. aeruginosa are cleared faster at distal nodes of the bronchial tree than in primary nodes. Interestingly, image analysis of infected mice reveals a concordant and statistically significant pattern: infection intensity clears in the bottom before the top of the lungs. The combined use of modeling and image analysis supports the application of phage therapy for acute P. aeruginosa pneumonia while emphasizing potential challenges to curative success in spatially structured in vivo environments, including impaired innate immune responses and reduced phage efficacy.

An Unlikely Case of Blastomycosis in an Elderly Female: A Diagnostic Challenge in a Non-endemic Region.

Blastomyces dermatitidis is a fungus typically found in the soil of endemic regions such as the Midwest, concentrating in areas like Ohio, Mississippi, and the Great Lakes area. The systemic infection caused by inhaling Blastomyces dermatitidis is known as blastomycosis. The frequency of blastomycosis in non-endemic regions is increasing for a variety of speculated reasons, such as higher rates of immunosuppressed individuals and possible climate. Due to clinician unfamiliarity, misdiagnosis of blastomycosis is common, which potentiates worsening systemic infections. This study shows the clinical course of a patient with blastomycosis in a non-endemic region, highlighting the need for education for clinicians in non-endemic areas. A 72-year-old female with a history of chronic obstructive pulmonary disease (COPD), coronary artery disease, a 47-year smoking history, and hypertension presented for outpatient management of COPD. CT three months prior to presentation showed nodular opacities in the lungs. A bronchoscopy was performed and revealed negative findings for malignancy or infection; the patient developed worsening symptoms leading to hospitalization. Subsequent testing revealed Blastomyces dermatitidis. She was promptly treated with a six to 12-month course of itraconazole with close follow-up. The study highlights the need not to rule out causes of infection based on location. Blastomycosis can resemble community-acquired pneumonia. Making the correct diagnosis is paramount, as delays can result in morbidity. Fungal cultures may be the gold standard, but due to the long culture time, there need to be other diagnostic tests like urine antigen testing. This study highlights the need to increase awareness of clinicians who experience blastomycosis patients in a non-endemic region.

Plasma Adsorption with the MTx.100 Column in Critically Ill COVID-19 Patients: A Prospective Study and Propensity Score Analysis.

BACKGROUND: Early in the COVID-19 pandemic, patients with severe disease admitted to the intensive care unit (ICU) had a high incidence of mortality. We aimed to investigate whether plasma adsorption with the MTx.100 Column could improve survival. METHODS: We performed a prospective, single-arm, multicenter, Emergency Use Authorization (EUA) trial in patients admitted to the ICU with severe COVID-19 who were worsening despite standard therapy. The primary outcome was all-cause mortality on day 28. Outcomes were analyzed using both a pre-specified performance goal (PG), and a propensity score-matched (PSM) analysis from the highest enrolling center, in which patients treated with the standard of care (SOC) plus the MTx.100 Column (n = 70) were compared to a contemporaneous cohort treated at the same center with SOC only (n = 244). FINDINGS: Between May 21, 2020, and November 2, 2021, 107 patients with severe COVID-19 (mean age 58.1) at 7 US centers were enrolled and had at least one plasma adsorption treatment initiated. All-cause mortality on day 28 was 37.4% (40/107), an improvement over the prespecified PG (88.1%, p < 0.0001). There were no serious adverse events attributable to the MTx.100 Column or plasmapheresis. Improvements in most metabolic and inflammatory markers were also noted. The PSM analysis showed that survival odds were three times higher for MTx.100 Column-treated patients (95% CI: 1.56-5.88) than for those treated with SOC only. INTERPRETATION: The MTx.100 Column treatment in severe COVID-19 resulted in a lower mortality than SOC by both pre-specified PG and PSM analysis. TRIAL REGISTRATION: clinicaltrials.gov (NCT04358003).

Analysis of pathogen distribution and sTREM-1 and miR-126 levels in patients with pulmonary infection after craniocerebral injury.

BACKGROUND: sTREM-1H and miR-126 play crucial roles in inflammation and immune responses, yet their involvement in patients with pulmonary infection following cranial injury remains understudied. OBJECTIVE: The distribution of pathogens causing infection in patients with pulmonary infection after craniocerebral injury was explored, and the changes in the levels of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) and miR-126 in peripheral blood were analyzed. METHODS: In this study, 60 patients (study group) with postoperative lung infection in craniocerebral injury treated from January 2019 to December 2, 2021, and 60 patients without lung infection were selected as the control group. The study group received anti-infection treatment. The infection pathogen of the study group was tested, and the changes of sTREM-1 and miR-126 levels in the peripheral blood of the study and control groups were recorded to explore the diagnosis and predictive Value of prognostic death. RESULTS: 66 pathogens were detected, including 18 gram-positive bacteria, 42 gram-negative bacteria, and 6 fungi. The sTREM-1 level was higher than the control group, and the miR-126 level was lower than the control group. By ROC curve analysis, the diagnostic AUC values of both patients were 0.907 and 0.848, respectively (P< 0.05). Compared to those in the study group, patients had decreased sTREM-1 levels and increased miR-126 levels after treatment (P< 0.05). Compared with the survival group, patients in the death group had increased sTREM-1 levels and decreased miR-126 levels, and ROC curve analysis, the predicted AUC death values were 0.854 and 0.862, respectively. CONCLUSION: Gram-negative bacteria, with increased peripheral sTREM-1 levels and decreased miR-126 levels. The levels of sTREM-1 and miR-126 have specific diagnostic and prognostic Values for pulmonary infection after craniocerebral injury. However, the study's conclusions are drawn from a limited sample and short-term data, which might limit their broader applicability. Future studies with larger populations and longitudinal designs are required to confirm these findings and determine these biomarkers' robustness across different settings. Further research should also explore how these biomarkers influence patient outcomes in craniocerebral injuries.

Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm.

Purpose: The objective of this study was to create and validate a machine learning (ML)-based model for predicting the likelihood of lung infections following chemotherapy in patients with lung cancer. Methods: A retrospective study was conducted on a cohort of 502 lung cancer patients undergoing chemotherapy. Data on age, Body Mass Index (BMI), underlying disease, chemotherapy cycle, number of hospitalizations, and various blood test results were collected from medical records. We used the Synthetic Minority Oversampling Technique (SMOTE) to handle unbalanced data. Feature screening was performed using the Boruta algorithm and The Least Absolute Shrinkage and Selection Operator (LASSO). Subsequently, six ML algorithms, namely Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (GNB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were employed to train and develop an ML model using a 10-fold cross-validation methodology. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, specificity, F1 score, calibration curve, decision curves, clinical impact curve, and confusion matrix. In addition, model interpretation was performed by the Shapley Additive Explanations (SHAP) analysis to clarify the importance of each feature of the model and its decision basis. Finally, we constructed nomograms to make the predictive model results more readable. Results: The integration of Boruta and LASSO methodologies identified Gender, Smoke, Drink, Chemotherapy cycles, pleural effusion (PE), Neutrophil-lymphocyte count ratio (NLR), Neutrophil-monocyte count ratio (NMR), Lymphocytes (LYM) and Neutrophil (NEUT) as significant predictors. The LR model demonstrated superior performance compared to alternative ML algorithms, achieving an accuracy of 81.80%, a sensitivity of 81.1%, a specificity of 82.5%, an F1 score of 81.6%, and an AUC of 0.888(95%CI(0.863-0.911)). Furthermore, the SHAP method identified Chemotherapy cycles and Smoke as the primary decision factors influencing the ML model's predictions. Finally, this study successfully constructed interactive nomograms and dynamic nomograms. Conclusion: The ML algorithm, combining demographic and clinical factors, accurately predicted post-chemotherapy lung infections in cancer patients. The LR model performed well, potentially improving early detection and treatment in clinical practice.

Respiratory syncytial virus infection provides protection against severe acute respiratory syndrome coronavirus challenge.

Respiratory infections are a major health burden worldwide. Respiratory syncytial virus (RSV) is among the leading causes of hospitalization in both young children and older adults. The onset of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the public health response had a profound impact on the normal seasonal outbreaks of other respiratory viruses. However, little is known about how a prior respiratory virus infection impacts SARS-CoV-2 disease outcomes. In this study, we examine the impact of a previous RSV infection on the disease severity of a subsequent SARS-CoV-2 challenge in BALB/c mice. Mice infected with RSV, followed by a SARS-CoV-2 challenge, 30 days later, exhibited decreased weight loss and increased survival as compared to control groups. Our results suggest a prior RSV infection can provide protection against a subsequent SARS-CoV-2 infection. IMPORTANCE: Severe acute respiratory syndrome coronavirus 2 and respiratory syncytial virus are respiratory viruses that are a major health burden worldwide. Severe acute respiratory syndrome coronavirus 2 and respiratory syncytial virus frequently have peak seasonal outbreaks during the winter months, and are capable of causing severe respiratory disease, often leading to hospitalization. The 2019 pandemic brought attention to the importance of understanding how co-circulating viruses can impact the disease severity of other respiratory viruses. It is known that many hospitalized patients are undergoing multiple viral infections at once, yet not much has been studied to understand the impact this has on other respiratory viruses or patients. How co-circulating viruses impact one another can provide critical knowledge for future interventions of hospitalized patients and potential vaccination strategies.

The fungal diversity in the lungs of children with cystic fibrosis captured by sputum-induction and bronchoalveolar lavage.

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.

Variable fitness effects of bacteriophage resistance mutations in Escherichia coli: implications for phage therapy.

Bacteria exposed to bactericidal treatment, such as antibiotics or bacteriophages (phages), often develop resistance. While phage therapy is proposed as a solution to the antibiotic resistance crisis, the bacterial resistance emerging during phage therapy remains poorly characterized. In this study, we examined a large population of phage-resistant extra-intestinal pathogenic Escherichia coli 536 clones that emerged from both in vitro (non-limited liquid medium) and in vivo (murine pneumonia) conditions. Genome sequencing uncovered a convergent mutational pattern in phage resistance mechanisms under both conditions, particularly targeting two cell-wall components, the K15 capsule and the lipopolysaccharide (LPS). This suggests that their identification in vivo could be predicted from in vitro assays. Phage-resistant clones exhibited a wide range of fitness according to in vitro tests, growth rate, and resistance to amoeba grazing, which could not distinguish between the K15 capsule and LPS mutants. In contrast, K15 capsule mutants retained virulence comparable to the wild-type strain, whereas LPS mutants showed significant attenuation in the murine pneumonia model. Additionally, we observed that resistance to the therapeutic phage through a nonspecific mechanism, such as capsule overproduction, did not systematically lead to co-resistance to other phages that were initially capable or incapable of infecting the wild-type strain. Our findings highlight the importance of incorporating a diverse range of phages in the design of therapeutic cocktails to target potential future phage-resistant clones effectively. IMPORTANCE: This study isolated more than 50 phage-resistant mutants from both in vitro and in vivo conditions, exposing an extra-intestinal pathogenic Escherichia coli strain to a single virulent phage. The characterization of these clones revealed several key findings: (1) mutations occurring during phage treatment affect the same pathways as those identified in vitro; (2) the resistance mechanisms are associated with the modification of two cell-wall components, with one involving receptor deletion (phage-specific mechanism) and the other, less frequent, involving receptor masking (phage-nonspecific mechanism); (3) an in vivo virulence assay demonstrated that the absence of the receptor abolishes virulence while masking the receptor preserves it; and (4) clones with a resistance mechanism nonspecific to a particular phage can remain susceptible to other phages. This supports the idea of incorporating diverse phages into therapeutic cocktails designed to collectively target both wild-type and phage-resistant strains, including those with resistance mechanisms nonspecific to a phage.

Dry Powder Inhaler Formulation of Lactobacillus rhamnosus GG Targeting Pseudomonas aeruginosa Infection in Bronchiectasis Maintenance Therapy.

The inhaled delivery of lactic acid bacteria (LAB) probiotics has been demonstrated to exert therapeutic benefits to the lungs due to LAB's immunomodulatory activities. The development of inhaled probiotics formulation, however, is in its nascent stage limited to nebulized LAB. We developed a dry powder inhaler (DPI) formulation of lactobacillus rhamnosus GG (LGG) intended for bronchiectasis maintenance therapy by spray freeze drying (SFD). The optimal DPI formulation (i.e., LGG: mannitol: lactose: leucine = 35: 45: 15: 5 wt.%) was determined based on the aerosolization efficiency (86% emitted dose and 26% respirable fraction) and LGG cell viability post-SFD (7 log CFU/mL per mg powder). The optimal DPI formulation was evaluated and compared to lyophilized naked LGG by its (1) adhesion capacity and cytotoxicity to human lung epithelium cells (i.e., A549 and 16HBE14o- cells) as well as its (2) effectiveness in inhibiting the growth and adhesion of Pseudomonas aeruginosa to lung cells. The optimal DPI of LGG exhibited similar non-cytotoxicity and adhesion capacity to lung cells to naked LGG. The DPI of LGG also inhibited the growth and adhesion of P. aeruginosa to the lung cells as effectively as the naked LGG. The present work established the feasibility of delivering the LAB probiotic by the DPI platform without adversely affecting LGG's anti-pseudomonal activities.

Torquetenovirus from bronchoalveolar lavage fluid as a biomarker for lung infection among immunocompromised hosts.

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.

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Chronic hyperglycemia aggravates lung function in a Scnn1b-Tg murine model.

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 ß 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.

A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.

PURPOSE: 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. CONCLUSION: 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 .

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

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. CONCLUSION: 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.

Invasive Trichoderma longibrachiatum infection in a neutropaenic patient.

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.

The Hydroxypyridinone Iron Chelator DIBI Reduces Bacterial Load and Inflammation in Experimental Lung Infection.

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-1ß, 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.

An optimized cyclophosphamide-treated mouse model of Mycobacterium abscessus pulmonary infection.

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.

Triple-Antibiotic Combination Exerts Effective Activity against Mycobacterium avium subsp. hominissuis Biofilm and Airway Infection in an In Vivo Murine Model.

OBJECTIVES: 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.

Detection of pathogenic bacteria and biomarkers in lung specimens from cystic fibrosis patients.

Diagnosing lung infections is often challenging because of the lack of a high-quality specimen from the diseased lung. Since persons with cystic fibrosis are subject to chronic lung infection, there is frequently a need for a lung specimen. In this small, proof of principle study, we determined that PneumoniaCheckTM, a non-invasive device that captures coughed droplets from the lung on a filter, might help meet this need. We obtained 10 PneumoniaCheckTMcoughed specimens and 2 sputum specimens from adult CF patients hospitalized with an exacerbation of their illness. We detected amylase (upper respiratory tract) with an enzymatic assay, surfactant A (lower respiratory tract) with an immunoassay, pathogenic bacteria by PCR, and markers of inflammation by a Luminex multiplex immunoassay. The amylase and surfactant A levels suggested that 9/10 coughed specimens were from lower respiratory tract with minimal upper respiratory contamination. The PCR assays detected pathogenic bacteria in 7 of 9 specimens and multiplex Luminex assay detected a variety of cytokines or chemokines. These data indicate that the PneumoniaCheckTMcoughed specimens can capture good quality lower respiratory tract specimens that have the potential to help in diagnosis, management and understanding of CF exacerbations and other lung disease.

Diagnostic Challenge for Positive 1,3-ß-D-Glucan in an Immunocompromised Patient Receiving Intravenous Immunoglobulin Presenting With Respiratory Failure.

Diagnosing Pneumocystis jirovecii pneumonia (PJP) can be complex, particularly in cases of significant respiratory failure. The 1,3-ß-D-glucan (BDG) serum assay has emerged as a promising non-invasive diagnostic tool for detecting fungal infections, including PJP. However, factors that can confound the interpretation of BDG levels by causing elevation in serum levels have been documented. Here, we present the case of 51-year-old woman with underlying autoimmune disorder, hematologic malignancy, and chronic steroid use, who was admitted for acute hypoxemic respiratory failure. Obtaining the BDG assay after the administration of intravenous immunoglobulin (IVIG) posed a diagnostic challenge, as the patient was unable to undergo bronchoscopy. This circumstance led to a debate regarding the possibility of a false-positive BDG due to IVIG use or the presence of PJP. Ultimately, the patient was empirically treated for PJP. This case underscores the importance of comprehending factors that may contaminate BDG results, particularly in immunocompromised individuals.

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning.

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.