Repair and regeneration of a diseased lung using stem cells or bioengineered tissues is an exciting therapeutic approach for a variety of lung diseases and critical illnesses. Over the past decade, increasing evidence from preclinical models suggests that mesenchymal stromal cells, which are not normally resident in the lung, can be used to modulate immune responses after injury, but there have been challenges in translating these promising findings to the clinic. In parallel, there has been a surge in bioengineering studies investigating the use of artificial and acellular lung matrices as scaffolds for three-dimensional lung or airway regeneration, with some recent attempts of transplantation in large animal models. The combination of these studies with those involving stem cells, induced pluripotent stem cell derivatives, and/or cell therapies is a promising and rapidly developing research area. These studies have been further paralleled by significant increases in our understanding of the molecular and cellular events by which endogenous lung stem and/or progenitor cells arise during lung development and participate in normal and pathological remodeling after lung injury. For the 2023 Stem Cells, Cell Therapies, and Bioengineering in Lung Biology and Diseases Conference, scientific symposia were chosen to reflect the most cutting-edge advances in these fields. Sessions focused on the integration of “omics” technologies with function, the influence of immune cells on regeneration, and the role of the extracellular matrix in regeneration. The necessity for basic science studies to enhance fundamental understanding of lung regeneration and to design innovative translational studies was reinforced throughout the conference.

The first licensed malaria vaccine, RTS,S/AS01, confers moderate protection against symptomatic disease. Because many malaria infections are asymptomatic, we conducted a large-scale longitudinal parasite genotyping study of samples from a clinical trial exploring how vaccine dosing regimen affects vaccine efficacy.

Pyroptosis is an inflammatory form of programmed cell death that is distinct from necrosis and apoptosis. Pyroptosis is primarily mediated by the gasdermin family of proteins (GSDMA-E and PVJK), which, when activated by proteolytic cleavage, form pores in the plasma membrane, leading to cell death. While much of the past research on pyroptosis has focused on its role in cancer, metabolic disorders, and infectious diseases, recent experimental and observational studies have begun to implicate pyroptosis in allergic diseases. These studies suggest that gasdermin-mediated pyroptosis contributes to the development of allergic conditions and could offer novel targets for therapy. Here, we review our current understanding of pyroptosis with an emphasis on the role of gasdermins as executioners of pyroptosis and potential mediators to allergic disease. We highlight new discoveries that establish a mechanistic link between the biochemical actions of gasdermins and the onset of allergic diseases. Additionally, we discuss how pyroptosis and gasdermins might contribute to the dysfunction of epithelial barrier, a key factor believed to initiate the progression of various allergic diseases.

During the COVID-19 pandemic, novel nanoparticle-based mRNA vaccines were developed. A small number of individuals developed allergic reactions to these vaccines although the mechanisms remain undefined.

Antiretroviral therapy (ART) is recommended for all people with HIV. Understanding ART use among Medicare beneficiaries with HIV is therefore critically important for improving quality and equity of care among the growing population of older adults with HIV. However, a comprehensive national evaluation of filled ART prescriptions among Medicare beneficiaries is lacking.

The number of new cancer cases in Commonwealth countries rose by 35% between 2008 and 2018, but progress in cancer control has been slow in many low-income and lower-middle-income member states. We aimed to examine cancer outcomes and priority areas in the Commonwealth to provide insight and guidance on prioritisation of efforts to improve cancer survival and make the best use of scarce resources.

Background It is increasingly recognized that interstitial lung abnormalities (ILAs) detected at CT have potential clinical implications, but automated identification of ILAs has not yet been fully established. Purpose To develop and test automated ILA probability prediction models using machine learning techniques on CT images. Materials and Methods This secondary analysis of a retrospective study included CT scans from patients in the Boston Lung Cancer Study collected between February 2004 and June 2017. Visual assessment of ILAs by two radiologists and a pulmonologist served as the ground truth. Automated ILA probability prediction models were developed that used a stepwise approach involving section inference and case inference models. The section inference model produced an ILA probability for each CT section, and the case inference model integrated these probabilities to generate the case-level ILA probability. For indeterminate sections and cases, both two- and three-label methods were evaluated. For the case inference model, we tested three machine learning classifiers (support vector machine [SVM], random forest [RF], and convolutional neural network [CNN]). Receiver operating characteristic analysis was performed to calculate the area under the receiver operating characteristic curve (AUC). Results A total of 1382 CT scans (mean patient age, 67 years ± 11 [SD]; 759 women) were included. Of the 1382 CT scans, 104 (8%) were assessed as having ILA, 492 (36%) as indeterminate for ILA, and 786 (57%) as without ILA according to ground-truth labeling. The cohort was divided into a training set ( = 96; ILA, = 48), a validation set ( = 24; ILA, = 12), and a test set ( = 1262; ILA, = 44). Among the models evaluated (two- and three-label section inference models; two- and three-label SVM, RF, and CNN case inference models), the model using the three-label method in the section inference model and the two-label method and RF in the case inference model achieved the highest AUC, at 0.87. Conclusion The model demonstrated substantial performance in estimating ILA probability, indicating its potential utility in clinical settings. © RSNA, 2024 See also the editorial by Zagurovskaya in this issue.