Respiratory Research

Genome-wide association studies of idiopathic pulmonary fibrosis (IPF) have identified 35 common genetic risk loci associated with IPF susceptibility. In this study, we evaluated the effects of the reported variants in clinically curated non-European individuals. Despite limited sample sizes, we observed partial replication, limited transferability of some variants and evidence of ancestry-specific effects. The MUC5B promoter variant rs35705950 emerged as the dominant and most consistent signal across ancestries. Our findings highlight the need for larger, well-characterised studies in understudied populations to support robust discovery and translation.

Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution networks and machine learning to spatially detect and quantify functional small airways disease (fSAD) and emphysema on paired inspiratory-expiratory CT scans, with enhanced noise robustness. Materials and Methods: In this retrospective analysis of prospectively acquired data (2007-2017), we developed and validated a deep learning-based PRM approach using paired CT scans from 8,972 tobacco-exposed COPDGene participants ([&ge;]10 pack-years; mean age 60.1 {+/-} 8.8 years; 46.5% women), including controls with normal spirometry (n = 3,872; controls), PRISm (n = 1,089), GOLD 1-4 COPD (n = 4,011). Data were stratified into training, validation, and testing sets (24:6:70). PRMD extracts translation-invariant image features using a wavelet scattering network and applies a subspace learning classifier to classify voxels as emphysema or non-emphysematous air trapping (fSAD). PRMD was compared with conventional density-based PRM for voxel-wise agreement, correlation with pulmonary function, robustness to noise, and sensitivity to misregistration using Pearson correlation, Bland-Altman analysis, and paired t tests. Results: PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV1; (emphysema: r = - 0.54; fSAD: r = - 0.51; P < 0.0001) than standard PRM (r = - 0.42 for both; P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by ~15%, whereas PRMD limited error to < 5% (P < 0.001). Conclusion: PRMD provides an interpretable, feature-driven and noise-resilient alternative to traditional PRM for emphysema and fSAD classification, enhancing the reliability of CT-based COPD phenotyping for multi-center studies and low-dose imaging applications.

Purpose: We evaluated healthcare resource utilization (HCRU) and costs in patients with eosinophilic granulomatosis with polyangiitis (EGPA). Methods: Patients with newly diagnosed EGPA (2017--2021), [&ge;]12 months' pre-diagnosis health plan enrollment, and [&ge;]1 inpatient or [&ge;]2 outpatient claims with an EGPA diagnosis were included. Follow-up was from EGPA diagnosis until disenrollment or database end. HCRU and health insurer payment costs during follow-up were compared with those for matched cohorts of general insured patients without EGPA (comparison A) and without EGPA but with severe uncontrolled asthma (SUA; comparison B). Results: In comparison A, all-cause HCRU was higher in the EGPA cohort (n = 213) versus matched patients (n = 779) for all clinical encounters/pharmacy claim types; annualized, mean total all-cause costs were 16-fold higher ($117,563/patient) versus matched patients ($7,520/patient). In comparison B, all-cause HCRU was higher for the EGPA cohort (n = 182) versus the matched SUA cohort (n = 640) for all clinical encounters/pharmacy claim types, with 5-fold higher mean total all-cause costs ($118,127/patient vs $22,286/patient). In both EGPA cohorts, HCRU and associated costs increased between the baseline and follow-up periods. Conclusions: These findings highlight the need for more effective treatments to reduce the clinical and economic burden of EGPA.

Background Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are combustion-derived pollutants linked to cardiovascular disease. Prior NHANES analyses have evaluated these chemicals individually, failing to capture the correlated co-exposure structures that characterize real-world environmental burden, thereby underscoring the need for application. In this study, we applied an unsupervised machine learning pipeline to urinary biomarker data to identify multi-chemical exposure clusters and quantify their differential cardiovascular risk profiles in a nationally representative US sample. Methods We analyzed 2,979 participants from NHANES between 2017-2018, representing an estimated 36.8 million US adults after complex survey weighting. Twenty-five urinary biomarkers (6 PAH, 19 VOC metabolites) were log-transformed, imputed using Multivariate Imputation by Chained Equations (MICE), and standardized. Uniform Manifold Approximation and Projection (UMAP) was used for dimensionality reduction, followed by Gaussian Mixture Model (GMM) clustering. Survey-weighted prevalence estimates with 95% confidence intervals (CIs) were calculated for hypertension and high total cholesterol within each cluster. Weighted multivariable logistic regression was used to estimate odds ratios (OR) for hypertension, adjusting for age, sex, race/ethnicity, and income. Results Four exposure clusters were identified with a mean assignment probability of 0.948. The High combustion cluster (n=370; estimated 5.1 million US adults) exhibited the highest multi-chemical burden and a weighted hypertension prevalence of 39.3% (95% CI 37.2-41.4%), compared to 28.7% (95% CI 21.9-35.5%) in the Low exposure reference group. After demographic adjustment, High combustion cluster membership was independently associated with 38.4% higher odds of prevalent hypertension (OR 1.38). The prediction model achieved a cross-validated area under the receiver operating characteristic curve (AUC) of 0.849 (SD 0.017). Non-Hispanic Black participants constituted approximately 40% of the High combustion cluster, exceeding their representation in lower-risk clusters. Conclusions Multi-chemical exposome profiling identifies four cardiovascularly distinct subpopulations in the US adult population. Membership in the High combustion exposure cluster was associated with higher odds of prevalent hypertension and disproportionately affected Non-Hispanic Black participants. These findings support the use of multichemical approaches over single-pollutant analyses and highlight the relevance of environmental exposure patterns for making policy and targeted cardiovascular risk stratification.

1.Etiological diagnosis of lower respiratory tract infections (LRTIs) relies on sputum or bronchoalveolar lavage (BAL), which may be difficult to obtain or invasive. Exhaled breath aerosol (XBA) sampling offers a non-invasive alternative for pathogen detection. We evaluated the performance of the AveloMask, a face mask-based device designed to capture XBAs for molecular testing. In this prospective paired-sample study, hospitalized adults with pneumonia at three hospitals in Switzerland and Georgia provided an XBA sample using the AveloMask and a lower respiratory tract (LRT) specimen (sputum or BAL). XBA samples were analyzed by multiplex PCR using the Roche LightMix(R) panel and LRT samples were tested using the BioFire(R) FilmArray(R) Pneumonia Panel. Concordance between XBA and LRT samples was assessed using positive percent agreement (PPA), negative percent agreement (NPA), and overall percent agreement (OPA). Ninety-three participants were enrolled and 63 participants provided paired samples. AveloMask sampling identified the dominant pathogen (lowest Ct value in the LRT sample) in 40/47 LRT-positive cases (85.1%). Across all targets, PPA was 61% (95%CI, 50-72%), NPA was 100% (95%CI, 99-100%), and OPA was 95% (95% CI, 92-96%). PPA was higher for bacteria than for viruses and lower PPA was largely driven by reduced detection of low-abundance or co-infecting pathogens. In a subset analysis, AveloMask results showed substantial overlap with standard-of-care testing and could have supported antimicrobial de-escalation. Breath aerosol sampling using the AveloMask enabled non-invasive molecular detection of LRT pathogens in pneumonia cases and may complement conventional standard-of-care testing, particularly when sputum is unavailable.

Sarcoidosis is a heterogenous disease of unknown etiology characterized by non-caseating granulomas. Disease prevalence and presentation vary significantly by ancestry and ranges from acute, self-resolving disease to severe, chronic disease. Following previous reports suggesting B cells in the development and pathogenesis of sarcoidosis, we present here results of single-cell RNA sequencing, supporting B cell involvement in sarcoidosis through altered immediate early response, rewiring of MAPK signaling, and ancestry-specific preferential expansion of B cell receptors. Peripheral blood mononuclear cells were obtained from individuals of African or European Ancestry (AA and EA, respectively) including 48 healthy controls, 59 sarcoidosis patients, and 28 systemic lupus erythematosus (SLE) patients. SLE samples were used as a disease control. Differential expression analysis highlighted many differentially expressed genes (DEGs) with almost 5x more in the AA sarcoidosis versus AA control group compared to the EA sarcoidosis versus EA control group. B cells had the most DEGs of all cell types and expression patterns were similar between ancestries, however, sarcoidosis had an opposite transcription pattern than SLE, demonstrating an alternative immune response to acute activation than that seen in a prototypical autoinflammatory disease. This trend was maintained when examining specialized B cell subsets, with the most pronounced effect in the AA sarcoidosis versus AA control comparison. Our results strongly support further investigation of the role of humoral immune response in sarcoidosis and the potential to highlight patient groups likely to benefit from existing B cell therapies.

Background: Increased epicardial adipose tissue volume (EATV) is a potentially important risk marker for coronary artery disease (CAD) available from cardiac computed tomography (CT) images. Sex-differences and effects of age and body size on EATV have been insufficiently explored, and no reliable reference values exist. Consequently, EATV has yet to find its deserved use in clinical practice. Objectives: To define normal values by sex and age, the best normalization procedure for EATV to neutralize effects of body-size, explore the relationship between normalized EATV and cardiac risk, and propose a clinically meaningful cut-off. Methods: AI-based automated EATV data from the general population (n=25,155) and a clinical cohort (n=2,482) with suspected CAD was normalized to height, BSA and heart volumes. Correlation between EATV and EAT attenuation was tested with Spearman?s rank correlation and linear regression to find the optimal normalization. Normalized EATV was compared to high-risk by SCORE2 and obstructive CAD in the population cohort. A cut-off including 95% of cases with obstructive CAD was defined in the general population and tested in the clinical cohort. Results: EATV varied with sex and age across cohorts. Normalization of EATV to total heart volume (EATVh) was superior by all metrics and neutralized the effects of sex. High-risk by SCORE2 and the prevalence of obstructive CAD increased over quartiles of EATVh in the population cohort, and significantly higher EATVh was seen with obstructive CAD in both cohorts. A cut-off of 0.1 in EATVh had a negative predictive value for obstructive CAD of 97.1% in the general population and 88.9% in the clinical cohort. Conclusions: EATV varies considerably with sex, age and body size. Normalization to heart volume outperformed other procedures, and EATVh is a useful marker of obstructive CAD in both the general population and symptomatic patients.

Continuous, non-invasive cardiovascular monitoring is limited by the superficial sensing depth of Photoplethysmography (PPG), which is susceptible to peripheral artifacts. This study evaluates a wearable dual-modality prototype integrating dryelectrode Impedance Plethysmography (IPG) and PPG within a smartwatch form factor. Results from a pilot study (N=2) demonstrate that IPG signals exhibit a temporal lead over PPG across ventral and dorsal sites, supporting its greater penetration depth. During brachial artery modulation, IPG showed superior sensitivity to arterial recovery on the ventral forearm. Furthermore, 60-minute napping sessions revealed that while PPG remained morphologically stable, IPG signals underwent significant evolution, capturing distinct pulsewave archetypes. These findings suggest that wearable IPG provides a high-fidelity window into deep systemic hemodynamics typically reserved for clinical instrumentation.

Gingival inflammation is associated with dysbiotic oral biofilms characterized by reduced nitrate-reducing capacity and diminished nitric oxide (NO) bioavailability. While dietary nitrate has been shown to influence oral microbial activity, the effects of sustained, localized nitrate delivery on oral biofilm ecology and gingival inflammation remain incompletely defined. In this randomized, double-blind, placebo-controlled trial, 30 adults with gingival bleeding were assigned to receive localized prebiotic nitrate (~0.989 mmol per dose) or placebo for 21 days. The primary outcome was mean bleeding on probing (mBOP). Secondary outcomes included modified Gingival Index (mGI), Quigley-Hein plaque index (QHPI), salivary nitrite (as a proxy for NO bioavailability), oral pH, and microbiome composition assessed by 16S rRNA gene sequencing. Prebiotic nitrate supplementation formulation delivered in a slow-release chewing gum significantly reduced mBOP (25.7% to 15.3%; p = 0.0002) compared to placebo chewing gum. Salivary nitrite levels and oral pH increased, indicating enhanced nitrate metabolism. Microbiome analysis demonstrated enrichment of nitrate-reducing taxa, including Rothia mucilaginosa and Neisseria spp., and a relative reduction in inflammation-associated genera such as Prevotella and Porphyromonas. Localized prebiotic nitrate formula delivered in a functional chewing gum was associated with reduced gingival inflammation and shifts in oral microbiome composition consistent with enhanced nitrate-reducing capacity critical in nitric oxide formation. These findings support a role for biofilm-directed nutritional modulation as a non-antimicrobial approach for managing gingival inflammation and improving nitric oxide bioavailability.

Aim: To comprehensively characterize the salivary proteome in periodontitis using Orbitrap Astral data-independent acquisition mass spectrometry (DIA-MS), identify an atlas of differentially expressed proteins (DEPs), and develop a machine learning-derived multi-protein biomarker panel for non-invasive diagnosis of stage III/IV periodontitis. Materials and Methods: Unstimulated saliva samples from 199 participants (periodontal health/gingivitis, n=120; stage III/IV periodontitis, n=79) were analyzed by Orbitrap Astral DIA-MS. DEPs were identified, and pathway enrichment analysis was performed. A two-tier machine learning pipeline, integrating pathway-based feature selection with cross-validated evaluation, was applied to identify the optimal diagnostic panel. Results: Orbitrap Astral DIA-MS quantified 5,597 salivary proteins and 1,966 DEPs (|log2FC|>0.5, FDR<0.05). Pathway analysis identified 14 periodontitis-relevant KEGG pathways, including Th17 cell differentiation, IL-17 signaling, neutrophil extracellular trap formation, and complement and coagulation cascades. A four-protein panel (TEC, RAC1, MAPK14, KRT17) achieved an area under the curve (AUC) of 0.985 plus-or-minus sign 0.010, with 83% sensitivity and 100% specificity. The panel was corroborated using public datasets. Conclusions: To our knowledge, this study represents the first application of Orbitrap Astral DIA mass spectrometry in periodontitis research, establishing a disease-specific DEPs atlas and a salivary biomarker panel with high diagnostic accuracy for stage III/IV periodontitis, providing a foundation for future external validation studies.

Background: We aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients. Methods: We studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients. Findings: Four distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine month graft loss (72.3%, 31.1%, 2.2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0.85) and consistent class assignment and trajectory patterns when applied to CTOT-20. Interpretation: We identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians.

PurposeObstructive sleep apnea (OSA) is a common comorbidity in heart failure (HF) patients with prevalence increasing as HF severity worsens. While CPAP/BiPAP has been shown to reduce disease burden and mortality in the general HF population, it is unclear whether these benefits extend to patients with left ventricular assist devices (LVADs). We sought to determine whether OSA affects long-term survival in newly implanted LVAD patients and whether CPAP/BiPAP treatment confers mortality benefits. MethodsThis single-center retrospective study included patients who underwent LVAD implantation between January 2007 and February 2022. Recipients were stratified by OSA status (OSA vs No-OSA), and those with OSA were further categorized based on CPAP/BiPAP compliance. Comparative statistics and Kaplan-Meier survival analyses were performed, with log-rank tests used to compare groups and assess survival differences. A Cox proportional hazards model was conducted to evaluate the association between risk factors and survival among patients with OSA and No-OSA. ResultsBefore LVAD implantation, patients with OSA had higher body mass index, hypertension, and a higher rate of implantable cardioverter-defibrillator placement than those without OSA. OSA was not associated with increased postoperative complications. Although survival did not differ significantly between OSA and No-OSA patients (p=0.33), CPAP/BiPAP-compliant OSA patients had significantly better survival than noncompliant patients (p=0.0099). ConclusionsLVAD patients with OSA who consistently use CPAP/BiPAP have better survival than those who do not. CPAP/BiPAP is a simple, low-risk treatment that can reduce mortality in this population. Therefore, increased perioperative screening for OSA should be considered for patients receiving LVADs. Multicenter studies are needed to confirm our findings further.

Background. Patients with heart failure and reduced ejection fraction (HFrEF) commonly show signs of systemic inflammation. Interleukin-1 (IL-1) is a pro-inflammatory cytokine, known to modulate cardiac function. We aimed to determine the effects of treatment with anakinra, recombinant IL-1 receptor antagonist (IL-1Ra), on plasma IL-1Ra levels. Methods. We measured IL-1Ra levels at baseline and longest available follow-up to 24 weeks in 63 patients (44 males, 40 self-identified Black-Americans) with recent hospitalization for HFrEF, and systemic inflammation (C reactive protein [CRP] levels >2 mg/L) who were assigned to anakinra (N=42 [66.7%]) or placebo (N=21 [33.3%]) as part of the REDHART2 clinical trial (NCT0014686). Cardiorespiratory fitness was measured as peak oxygen consumption (peak VO2). Results. Baseline plasma IL-1Ra levels were 380 pg/ml (290 to 1046). On-treatment IL-1Ra levels were significantly higher in the patients treated with anakinra vs placebo (3,994 pg/ml [3,372 to 5,000] vs 492 pg/ml [304 to 1370], P<0.001). The longest available follow-up was 6 weeks in 10 patients (15.9%), 12 weeks in 12 patients (19%) and 24 weeks in 41 patients (65.1%). On-treatment IL-1Ra levels and interval change in IL-1Ra showed a modest inverse correlation with on-treatment CRP levels (R=-0.269, P=0.033 and R=-0.355, P=0.004, respectively) and no statistically significant correlations with peak VO2 values (P>0.05). Conclusions. Patients with recently decompensated HFrEF and systemic inflammation treated with recombinant IL-1Ra, anakinra, have a significant several-fold increase in plasma IL-1Ra levels. On-treatment IL-1Ra levels however show only a modest correlation with CRP levels and not with peak VO2.

BackgroundChemokine receptor type 5 (CCR5) is expressed on hepatic stellate cells (HSCs), which, together with fibroblasts, are major producers of extracellular matrix during liver fibrosis. Leronlimab is a humanized IgG4{kappa} monoclonal antibody that binds to CCR5. The objective of the present study was to evaluate the antifibrotic effects of leronlimab in three independent preclinical studies using two mouse models of liver fibrosis. MethodsIn STAM (Stelic Animal Model) model 1, leronlimab was administered at doses of 5 or 10 mg/kg/week for 3 weeks. STAM model 2 was conducted as a confirmatory study to validate the antifibrotic effect observed with the 10 mg/kg/week dose in STAM model 1. In a third study, a carbon tetrachloride (CCl)-induced liver fibrosis mouse model was used to evaluate leronlimab administered at 10 mg/kg/week for 3 weeks. An isotype-matched control antibody was included in all studies for comparison. Evaluations included liver enzymes and histological assessment of liver fibrosis. ResultsIn STAM model 1, leronlimab at 10 mg/kg/week significantly reduced fibrosis area compared with the isotype control (p = 0.0005). These findings were confirmed in STAM model 2 (p < 0.0001). Consistent antifibrotic effects were also observed in the CCl-induced liver fibrosis model (p = 0.0006). ConclusionsCollectively, these preclinical results demonstrate that CCR5 blockade by leronlimab is associated with a significant reduction of established liver fibrosis in multiple mouse models and support further evaluation of leronlimab as a potential therapeutic option, either as monotherapy or in combination regimens, for chronic liver diseases with fibrosis.

ObjectiveOnly preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study DesignRetrospective video-based study for classification development and reliability validation. MethodsVideo recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. ResultsThe final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7/65) of cases. Of those incorrect, 57% (4/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. ConclusionTissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.

Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. Liver function biomarkers (LFBs), particularly gamma - glutamyl transferase (GGT), have been linked to cardiovascular outcomes, yet their contribution to hard ASCVD risk prediction is not well defined. Methods: This study analyzed data from the National Health and Nutrition Examination Survey (NHANES, 2005 - 2018) to assess cross - sectional associations between LFBs and 10 - year hard ASCVD risk estimated by the ACC/AHA Pooled Cohort Equations. Multivariable regression, restricted cubic splines, and mediation analyses were applied to examine independent and dose - response relationships. External validation was performed in the China Health and Retirement Longitudinal Study (CHARLS) and NHANES using machine learning models (CoxBoost, Naive Bayes and Random Forest). Results: Among 5,731 NHANES participants, GGT showed an independent linear association with hard ASCVD risk (P - trend = 0.003), partly mediated by systolic blood pressure (44.8%), HbA1c (19.0%), and high density lipoprotein cholesterol (13.4%). Machine learning (ML) models incorporating GGT, alkaline phosphatase (ALP), and globulin alongside traditional risk factors improved predictive accuracy, with Naive Bayes achieving an AUC of 0.751 in NHANES validation. Conclusions: GGT is an independent and biologically plausible biomarker of hard ASCVD risk, acting through cardiometabolic pathways. Incorporating LFBs into risk prediction models, particularly with machine learning, enhances risk stratification and may facilitate early identification of high - risk individuals.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.

Patients with idiopathic pulmonary fibrosis (IPF) are highly vulnerable to respiratory virus infections, but the cellular mechanisms linking fibrotic remodeling to impaired local antiviral defense remain unclear. Here, we investigated how cellular senescence shapes the response of patient-derived healthy and IPF primary lung fibroblasts to influenza A virus (IAV) infection. Transcriptomic profiling identified infection as the driver of gene expression in both DNA damage-induced senescent healthy and IPF fibroblasts and revealed induction of canonical antiviral pathways in both cell states. However, senescent IPF fibroblasts adopted a distinct antiviral response state characterized by a broader set of uniquely induced genes and differential coordination of antiviral transcriptional networks. Functionally, senescence increased viral titers in healthy and IPF fibroblasts, while senescent IPF fibroblasts displayed an altered inflammatory response. Network analysis linked viral response- and cell cycle-associated modules specifically to the senescent healthy infected state, whereas these programs were weaker in senescent IPF fibroblasts. Transcription factor inference identified IRF3 and STAT1 as candidate regulators of this altered antiviral state in both senescent healthy and IPF fibroblasts. Consistent with the network and transcription factor analyses, siRNA-mediated depletion of IRF3 or STAT1 significantly reduced IFN-{beta} secretion in senescent healthy fibroblasts, whereas IPF fibroblasts showed only milder effects, indicating a disease-specific dependence on these pathways for antiviral control. Together, these findings show that the combination of cellular senescence and fibrotic fibroblast identity creates a dysfunctional antiviral state that may help explain the high susceptibility of IPF patients to virus-associated acute exacerbations and disease worsening.

Coronary artery disease is a leading cause of morbidity and mortality. Invasive coronary angiography is currently the gold standard in disease diagnosis. Several studies have attempted to use artificial intelligence (AI) to automate their interpretations with varying levels of success. However, most existing studies cannot generate detailed angiographic reports beyond simple classification or segmentation. This study aims to fine-tune and evaluate the performance of a Vision-Language Model (VLM) in coronary angiogram interpretation and report generation. Using twenty-thousand angiogram keyframes of 1987 patients collated across four unique datasets, we finetuned InternVL2-4B model with Low-Rank Adaptor weights that can perform stenosis detection, anatomy labelling, and report generation. The fine-tuned VLM achieved a precision of 0.56, recall of 0.64, and F1-score of 0.60 for stenosis detection. In anatomy segmentation, it attained a weighted precision of 0.50, recall of 0.43, and F1-score of 0.46, with higher scores in major vessel segments. Report generation integrating multiple angiographic projection views yielded an accuracy of 0.42, negative predictive value of 0.58 and specificity of 0.52. This study demonstrates the potential of using VLM to streamline angiogram interpretation to rapidly provide actionable information to guide management, support care in resource-limited settings, and audit the appropriateness of coronary interventions. AUTHOR SUMMARYCoronary artery disease has heavy disease burden worldwide and coronary angiogram is the gold standard imaging for its diagnosis. Interpreting these complex images and producing clinical reports require significant expertise and time. In this study, we fine-tuned and investigated an open-source VLM, InternVL2-4B, to interpret and report coronary angiogram images in key tasks including stenosis detection, anatomy identification, as well as full report generation. We also referenced the fine-tuned InternVL2-4B against state-of-the-art segmentation model, YOLOv8x, which was evaluated on the same test sets. We examined how machine learning metrics like the intersection over union score may not fully capture the clinical accuracy of model predictions and discussed the limitations of relying solely on these metrics for evaluating clinical AI systems. Although the model has not yet achieved expert-level interpretation, our results demonstrate the potential and feasibility of automating the reporting of coronary angiograms. Such systems could potentially assist cardiologists by improving reporting efficiency, highlightning lesions that may require review, and enabling automated calculations of clinical scores such as the SYNTAX score.