Daily Respiratory Research Analysis

BACKGROUND: Studies show associations between air pollution exposure and coronavirus 2019 (COVID19) hospitalizations, but have not substantially explored regional differences. In this study, we estimate associations between shorter-term exposure to fine particulate matter (PM METHODS: This study utilized data from 72,385 patients (78,504 hospitalizations) with a hospital-confirmed SARS-CoV-2 infection between January 1, 2020 and December 31, 2020. Daily PM RESULTS: In the random effects meta-analysis, a 1 µg/m CONCLUSIONS: Higher concentrations of PM Exposure to air pollution is shown to have a negative impact on human health. For example, air pollution increases the risk of cardiovascular- and respiratory-related hospitalizations and deaths. Little is known about how air pollution might affect the health of individuals with COVID19. Using hospitalization data from 57 cities across the United States (US), we assess whether individuals who tested positive for COVID19 were more likely to be hospitalized after multiple days of higher air pollution exposures. We find that higher levels of fine particulate matter, a common air pollutant, are associated with increased likelihood of COVID19-related hospitalization, and that the relationship between particulate matter and COVID19 differs across the US regions and cities we examine. The short- and long-term health impacts of air pollution exposure in individuals with COVID19 merits further research and should be considered in public health interventions and planning health care capacity.

BACKGROUND: Reliable biomarkers to identify inoperable limited stage small-cell lung cancer (LS-SCLC) benefiting from post-definitive chemoradiotherapy (dCRT) immunotherapy is valuable. This study aims to develop a circulating tumor DNA (ctDNA)-based algorithm to stratify progression risk and predict survival benefit from consolidation immunotherapy. METHODS: Baseline tumor tissues from 203 consecutive LS-SCLC receiving dCRT and a cBioPortal LS-SCLC cohort (n = 218) were analyzed to identify tissue-based prognostic genetic alterations. Plasma ctDNA after induction chemotherapy initiation (post-ICT) and/or during subsequent thoracic radiotherapy (TRT) were collected from two independent dCRT-only cohorts (training, n = 49; test, n = 32) and from 86 patients receiving post-dCRT consolidation immunotherapy, for prognostic algorithm development and utility investigation. RESULTS: Tissue-based prognostic biomarkers were generally rare except for PTEN, whose mutations were associated with antigen processing and presentation pathway enrichment (p.adjust = 0.008), and better progression-free survival (PFS, p = 0.047) and overall survival (p = 0.040). A Bayesian inference prognostic algorithm, combining post-ICT and TRT ctDNA detection, receipt of prophylactic cranial irradiation, and post-ICT shrinkage, accurately predicted 3-year progression (time-dependent AUC = 0.796) and stratified the training cohort into two subgroups exhibiting significantly different PFS (p = 0.008), with consistent performance in the test cohort (time-dependent AUC = 0.745; PFS, p = 0.098). The posterior Bayesian algorithm involving the test cohort revealed ctDNA-based risk classification as an independent predictor of PFS (p < 0.001). Notably, significantly improved PFS under consolidation immunotherapy was exclusively observed in patients predicted as high-risk (p = 0.004), with increasing benefit observed at higher high-risk thresholds. CONCLUSIONS: Serial ctDNA monitoring during dCRT is a critical approach to predicting inoperable LS-SCLC progression and consolidation immunotherapy benefit identification.

PURPOSE: To develop and evaluate a memory-efficient and accelerated reconstruction framework for respiratory-resolved 4D lung MRI using coil sketching and Toeplitz approximation, enabling high-quality motion-compensated low-rank (MoCo-LR) reconstructions on clinically accessible GPU hardware. THEORY AND METHODS: Respiratory-resolved 4D MRI enables non-invasive assessment of pulmonary structure and function but is limited by computational and memory demands from large matrices and long acquisitions. We extend the coil sketching framework-previously proposed for 3D imaging-to 4D (3D + time), allowing the data consistency term of compressed-sensing objective functions to be solved with reduced GPU memory consumption and reconstruction duration while preserving image accuracy. Additionally, we implement Toeplitz approximation to accelerate repeated applications of the normal encoding operator, further reducing computational demand. Finally, we outline a MoCo-LR regularization technique that uses only forward deformations, improving reconstruction stability over many iterations. Reconstructions were performed across several regularization schemes using a 3D stack-of-spirals acquisition and evaluated for memory footprint, speed, and image accuracy. RESULTS: The proposed 4D coil sketching method reduced memory usage by ˜3-fold compared to fully sampled reconstructions and enabled high-resolution MoCo-LR reconstructions in < 10 min on < 48 GB GPUs. Toeplitz approximation further reduced runtime with minimal impact on image quality. Compared to conventional coil compression, coil sketching preserved parenchymal signal and structural fidelity in low-SNR regions. CONCLUSION: Coil sketching and Toeplitz approximation provide a generalizable, hardware-efficient solution for 4D lung MRI reconstruction. These methods reduce computational barriers, improve reconstruction speed, and maintain image quality, offering a path toward broader clinical adoption of respiratory-resolved lung MRI.