Citations

Abstract Rationale Impaired lung health may serve as a risk factor for, and a post-acute sequela of, SARS-CoV-2 infection. Yet most prior studies of lung health sequelae of COVID-19 lacked information on pre-infection lung health, a major antecedent confounder. To identify associations of COVID-19 with changes in lung health, we leveraged paired pre- and post-pandemic data on respiratory symptoms, lung function, and lung structure in a general population-based cohort. Methods We studied participants in the Multi-Ethnic Study of Atherosclerosis Lung Study with paired pre-pandemic (2016-2018) and post-pandemic (2022-2024) assessments of respiratory symptoms, spirometry, and lung CT. SARS-CoV-2 infection (2020-2023) was ascertained using a composite of questionnaires, serology, and medical record adjudication, and classified as severe (requiring hospitalization) or non-severe (not requiring hospitalization). Lung health was assessed by questionnaire (mMRC grade ≥2 dyspnea, chronic cough, chronic phlegm, chronic bronchitis), pre-bronchodilator spirometry (FEV1, FVC, FEV1:FVC), and lung density on non-contrast lung CT (% low attenuation areas, % high attenuation areas). Each post-pandemic lung health measure was regressed on infection history and the corresponding pre-pandemic lung health measure, adjusting for age at post-pandemic exam, sex, race/ethnicity, smoking status, and vaccination status at infection. Sensitivity analyses were included- (a) restriction to participants without pre-pandemic respiratory symptoms and (b) with additional adjustment for time since infection and variant wave. Results Among 2,015 participants (mean age 77.2±7.5 years; 55% female; 40% non-Hispanic White; 25% Black; 11% Asian; 23% Hispanic/Latino), 59 (3%) experienced severe and 506 (25%) non-severe SARS-CoV-2 infection. The prevalence of respiratory symptoms was similar or slightly higher at the post- vs. pre-pandemic exam (grade ≥2 dyspnea: 14% vs. 10%; chronic cough: 10% vs. 8%; chronic phlegm: 9% vs. 8%; chronic bronchitis: 4% vs. 4%; p-values <0.0001). Compared with participants without infection, those with severe infection had higher odds of chronic cough (OR:2.9, 95%CI:1.3-6.3) and chronic bronchitis (OR:3.3, 95%CI:1.2-9.0) at the post-pandemic exam (Table). Associations were stronger among individuals infected >90 days prior to the post-pandemic assessment and during pre-Omicron waves. Results were similar when restricted to participants without pre-pandemic symptoms. No significant associations were observed for non-severe infection (vs. no infection) or for any other lung health outcomes. Conclusions Controlling for pre-pandemic lung health, severe COVID-19 was associated with increased odds of chronic cough and chronic bronchitis but not with changes in lung function or structure in a general population-based sample. Non-severe COVID-19 was not associated with lung health outcomes. This abstract is funded by: OT2HL156812, R21HL165405, R01HL077612, 75N92025D00022, 75N92025D00026, 75N92020D00002, 75N92025D00024, 75N92025D00027, 75N92025D00025, 75N92025D00028

Abstract Rationale Chronic bronchitis (CB), a common phenotype of chronic obstructive pulmonary disease (COPD), is characterized by pathologic mucus hypersecretion and is associated with substantial morbidity, impaired quality of life, and progressive functional decline, with prior studies demonstrating an average worsening of approximately one point/year on the COPD Assessment Test (CAT). Bronchial rheoplasty (BR) with RheOx® combines mechanical mucous plug disimpaction with pulsed electric field (PEF) energy to ablate and restore the airway mucosa. The RheSolve study was designed to provide evidence of safety and efficacy versus mechanical disimpaction alone. Primary endpoint results have been previously reported; final 2-year outcomes are reported here. Methods RheSolve was a prospective, randomized, double-blind study enrolling 273 patients with COPD and moderate-to-severe CB across 37 centers in the United States and Europe. Participants were randomized 2:1 to BR (n = 185) or a mechanical control procedure (n = 88). Following unblinding at 12 months, 75% of control patients crossed over to BR, precluding valid randomized comparisons beyond that time point; therefore, longer-term outcomes were evaluated using within-patient changes from baseline in the BR cohort. Outcomes included CAT, St George’s Respiratory Questionnaire (SGRQ), Cough and Sputum Assessment Questionnaire (CASA-Q), and pulmonary function tests (PFTs). Results Patients in the BR group represented a moderate to severe COPD CB phenotype at baseline (FEV1 60.9% predicted, emphysema (-950 HU) 11.0%, CAT 27.2 points, SGRQ 64.4) with most patients (93%) receiving a combination of ≥ 2 long-acting medications (LABA, LAMA, inhaled corticosteroids). The BR procedure was well-tolerated with no SAEs related to the device or procedure beyond 12 months. Both groups demonstrated clinically meaningful improvements in CAT, SGRQ, and CASA-Q at 12 months (P < 0.001), which were sustained in the BR group through 24 months. Follow-up was limited to 1 year in the mechanical control group, with outcomes demonstrating a modest regression towards baseline at 12 months (Figure). PFTs were also stable to improved in the BR group (changes from baseline to 24 months: FEV1 % Predicted 0.9 (±10.8) P = 0.319, FVC % Predicted 2.8% (±14.0) P = 0.02, FEV1/FVC -0.5% (±7.2) P = 0.449). Conclusions BR with RheOx® (e.g.,mechanical mucous plug disimpaction and PEF delivery) demonstrated clinically meaningful symptom improvement that was sustained through 24 months supporting BR as a therapeutic option for patients with persistent symptoms despite appropriate medical therapy. This abstract is funded by: Galvanize Therapeutics

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Abstract Senescent cells accumulate with age and contribute to tissue dysfunction, yet their identification in tissues is challenging due to low abundance, heterogeneous phenotypes, and the lack of specific markers. Senescence-associated features span multiple subcellular compartments, including nuclear DNA damage foci, cytosolic protein changes, and perinuclear alterations, each requiring tailored detection strategies. To overcome these challenges, we developed SenoQuant ( https://github.com/HaamsRee/senoquant ), a versatile software designed for comprehensive, accurate, and unbiased spatial quantification and prediction of senescence markers across diverse tissue contexts. Utilizing AI models, SenoQuant enables precise nuclear and cytoplasmic segmentation and detection of senescence markers across low- and high-plex imaging modalities, applicable to cultured cells and tissue sections from mice and humans. The platform also supports custom AI models; for example, we built SenCeption, a proof-of-concept predictor of single-cell p21 status from DAPI-stained nuclei in human skin. Available as a free napari plugin, SenoQuant is widely accessible to researchers. By providing a unified approach to senescence analysis and prediction, SenoQuant opens new opportunities for exploring the complex biology of senescence and its impacts on aging and disease.

Abstract Large Language Models (LLMs) demonstrate strong capabilities in healthcare applications. However, recruiting human evaluators to assess usability and effectiveness becomes challenging in the large-scale LLM-generated outcomes. Existing metrics are often outdated for evaluating LLM-based healthcare technologies. This paper proposes a Research through Evaluation (RtE) approach that can refine metrics and improve assessments of LLM-generated outputs. We deploy RtE in a retrospective comparative study of prostate cancer patient inquiries, comparing responses from human clinical care teams with those generated by a GPT-4-based in-basket bot. Through three rounds of co-evaluations with clinical professionals and an LLM-as-graders study with four LLMs, the RtE method shows that starting with small, iterative evaluations helps human graders adjust metrics to suit their daily practices better. Additionally, in the LLM-as-graders study, LLMs exhibit similar behavior, preferring the in-basket bot's responses over the clinical care team. Our methodology and findings demonstrate that the RtE method can enhance the transparency and robustness of evaluation metrics for LLM-generated content, with the potential to generalize to evaluations in other clinical practices.