Immune Modulation to Improve Survival of Viral Pneumonia in Mice

Postinfectious Pulmonary Complications: Establishing Research Priorities to Advance the Field: An Official American Thoracic Society Workshop Report

Continued improvements in the treatment of pulmonary infections have paradoxically resulted in a growing challenge of individuals with postinfectious pulmonary complications (PIPCs). PIPCs have been long recognized after tuberculosis, but recent experiences such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic have underscored the importance of PIPCs following other lower respiratory tract infections. Independent of the causative pathogen, most available studies of pulmonary infections focus on short-term outcomes rather than long-term morbidity among survivors. In this document, we establish a conceptual scope for PIPCs with discussion of globally significant pulmonary pathogens and an examination of how these pathogens can damage different components of the lung, resulting in a spectrum of PIPCs. We also review potential mechanisms for the transition from acute infection to PIPC, including the interplay between pathogen-mediated injury and aberrant host responses, which together result in PIPCs. Finally, we identify cross-cutting research priorities for the field to facilitate future studies to establish the incidence of PIPCs, define common mechanisms, identify therapeutic strategies, and ultimately reduce the burden of morbidity in survivors of pulmonary infections.

Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays

Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We deployed modified VGG19, ResNet50V2, and DenseNet121 models for feature extraction, followed by five machine learning classifiers (logistic regression, support vector machine, decision tree, random forest, artificial neural network). The approach we suggested displayed remarkable accuracy, with VGG19 and DenseNet121 models obtaining 99.98% accuracy when combined with random forest or decision tree classifiers. ResNet50V2 achieved 99.25% accuracy with random forest. These results illustrate the advantages of merging deep learning models with machine learning classifiers in boosting the speedy and accurate identification of pneumonia. The study underlines the potential of DLxMLC systems in enhancing diagnostic accuracy and efficiency. By integrating these models into clinical practice, healthcare practitioners could greatly boost patient care and results. Future research should focus on refining these models and exploring their application to other medical imaging tasks, as well as including explainability methodologies to better understand their decision-making processes and build trust in their clinical use. This technique promises promising breakthroughs in medical imaging and patient management.

An immunostimulatory glycolipid that blocks SARS-CoV-2, RSV, and influenza infections in vivo

Prophylactic vaccines for SARS-CoV-2 have lowered the incidence of severe COVID-19, but emergence of viral variants that are antigenically distinct from the vaccine strains are of concern and additional, broadly acting preventive approaches are desirable. Here, we report on a glycolipid termed 7DW8-5 that exploits the host innate immune system to enable rapid control of viral infections in vivo. This glycolipid binds to CD1d on antigen-presenting cells and thereby stimulates NKT cells to release a cascade of cytokines and chemokines. The intranasal administration of 7DW8-5 prior to virus exposure significantly blocked infection by three different authentic variants of SARS-CoV-2, as well as by respiratory syncytial virus and influenza virus, in mice or hamsters. We also found that this protective antiviral effect is both host-directed and mechanism-specific, requiring both the CD1d molecule and interferon-[Formula: see text]. A chemical compound like 7DW8-5 that is easy to administer and cheap to manufacture may be useful not only in slowing the spread of COVID-19 but also in responding to future pandemics long before vaccines or drugs are developed.

Innate immune responses in pneumonia

The lungs are an immunologically unique environment; they are exposed to innumerable pathogens and particulate matter daily. Appropriate clearance of pathogens and response to pollutants is required to prevent overwhelming infection, while preventing tissue damage and maintaining efficient gas exchange. Broadly, the innate immune system is the collection of immediate, intrinsic immune responses to pathogen or tissue injury. In this review, we will examine the innate immune responses of the lung, with a particular focus on their role in pneumonia. We will discuss the anatomic barriers and antimicrobial proteins of the lung, pathogen and injury recognition, and the role of leukocytes (macrophages, neutrophils, and innate lymphocytes) and lung stromal cells in innate immunity. Throughout the review, we will focus on new findings in innate immunity as well as features that are unique to the lung.