Daily Respiratory Research Analysis

Seasonal coronaviruses, similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), only cause severe respiratory symptoms in a small fraction of infected individuals. However, the host factors that determine the variable responses to coronavirus infection remain unclear. Here, we use seasonal human coronavirus OC43 (HCoV-OC43) infection as an asymptomatic model that triggers both innate and adaptive immune responses in mice. Interestingly, innate sensing pathways as well as adaptive immune cells are not essential in protection against HCoV-OC43. Instead, alveolar macrophage (AMΦ) deficiency in mice results in COVID-19-like severe pneumonia post HCoV-OC43 infection, with abundant neutrophil infiltration, neutrophil extracellular trap (NET) release, and exaggerated pro-inflammatory cytokine production. Mechanistically, AMΦ efficiently phagocytose HCoV-OC43, effectively blocking virus spread, whereas, in their absence, HCoV-OC43 triggers Toll-like receptor (TLR)-dependent chemokine production to cause pneumonia. These findings reveal the central role of AMΦ in defending against seasonal HCoV-OC43 with clinical implications for human immunopathology associated with coronavirus infection.

BACKGROUND: Among 1 billion patients worldwide with OSA, 90% remain undiagnosed. The main barrier to diagnosis is the overnight polysomnogram, which requires specialized equipment, skilled technicians, and inpatient beds available only in tertiary sleep centers. Recent advances in artificial intelligence (AI) have enabled OSA detection using breathing sound recordings. RESEARCH QUESTION: What is the diagnostic accuracy of and how can we optimize machine listening for OSA? STUDY DESIGN AND METHODS: PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases were systematically searched. Two masked reviewers selected studies comparing the patient-level diagnostic performance of AI approaches using overnight audio recordings vs conventional diagnosis (apnea-hypopnea index) using a train-test split or k-fold cross-validation. Bayesian bivariate meta-analysis and meta-regression were performed. Publication bias was assessed by using a selection model. Risk of bias and evidence quality were assessed by using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Grading of Recommendations, Assessment, Development, and Evaluation tools. RESULTS: From 6,254 records, 16 studies (41 models) trained on 4,864 participants and tested on 2,370 participants were included. No study had a high risk of bias. Machine listening achieved a pooled sensitivity (95% credible interval) of 90.3% (86.9%-93.1%), a specificity of 86.7% (83.1%-89.7%), a diagnostic OR of 60.8 (39.4-99.9), and positive and negative likelihood ratios of 6.78 (5.34-8.85) and 0.113 (0.079-0.152), respectively. At apnea-hypopnea index cutoffs of ≥ 5, ≥ 15, and ≥ 30 events per hour, sensitivities were 94.3% (90.3%-96.8%), 86.3% (80.1%-90.9%), and 86.3% (79.2%-91.1%); and specificities were 78.5% (68.0%-86.9%), 87.3% (81.8%-91.3%), and 89.5% (84.8%-93.3%). Meta-regression identified increased sensitivity for the following: higher audio sampling frequencies, non-contact microphones, higher OSA prevalence, and train-test split model evaluation. Accuracy was equal regardless of home smartphone vs in-laboratory professional microphone recordings, deep learning vs traditional machine learning, and variations in age and sex. Publication bias was not evident, and the evidence was of high quality. INTERPRETATION: In this study, machine listening achieved excellent diagnostic accuracy, superior to the STOP-Bang (snoring, tiredness, observed apnea, BP, BMI, age, neck size, gender) questionnaire and comparable to common home sleep tests. Digital medicine should be further explored and externally validated for accessible and equitable OSA diagnosis. CLINICAL TRIAL REGISTRATION: PROSPERO database; No.: CRD42024534235; URL: https://www.crd.york.ac.uk/PROSPERO/).

BACKGROUND: Circulating tumor DNA (ctDNA) has the potential to become a reliable biomarker for identifying minimal residual disease (MRD) and predicting recurrence in patients with non-small cell lung cancer (NSCLC) following curative treatment. However, there is a lack of studies that investigate the clinical validity of ctDNA using a tumor-agnostic approach, which can provide significant clinical benefits. METHODS: We analyzed samples from 45 NSCLC patients recruited in a prospective national multicenter study, all of whom had undergone curative treatment. A total of 38 pre-treatment plasma samples and 76 post-treatment plasma samples were examined using a commercially available cancer personalized profiling by deep sequencing (CAPP-seq) strategy, and a tumor-agnostic approach. Post-treatment samples were collected at two distinct landmark time points: Follow-up 1 (0.5-4.5 months post-treatment) and Follow-up 2 (4.5-7.5 months post-treatment). RESULTS: Detectable ctDNA post-treatment was significantly associated with increased risk of tumor recurrence and shorter recurrence-free survival (RFS). Using only a single blood sample taken from Follow-up 2, we correctly identified MRD in 50% of the patients who later experienced recurrence. However, subgroup analysis further revealed that in patients treated with radiotherapy or chemoradiotherapy (CRT), ctDNA detection was significantly linked to shorter RFS in the MRD analysis from Follow-up 2, but not in the MRD analysis from Follow-up 1. CONCLUSION: These findings suggest that post-treatment ctDNA, detected using a tumor-agnostic approach, is a reliable biomarker for predicting recurrence in NSCLC patients following curative treatment. However, the optimal timing for blood sampling to detect MRD appears to depend on the type of curative treatment received.