Citations

Abstract Alveolar macrophages (AMs) are major lung tissue-resident macrophages capable of proliferating and self-renewal in situ. AMs are vital in pulmonary antimicrobial immunity and surfactant clearance. The mechanisms regulating AM compartment formation and maintenance remain to be fully elucidated currently. In this study, we have explored the roles of mitochondrial transcription factor A (TFAM)–mediated mitochondrial fitness and metabolism in regulating AM formation and function. We found that TFAM deficiency in mice resulted in significantly reduced AM numbers and impaired AM maturation in vivo. TFAM deficiency was not required for the generation of AM precursors nor the differentiation of AM precursors into AMs, but was critical for the maintenance of AM compartment. Mechanistically, TFAM deficiency diminished gene programs associated with AM proliferation and self-renewal and promoted the expression of inflammatory genes in AMs. We further showed that TFAM-mediated AM compartment impairment resulted in defective clearance of cellular debris and surfactant in the lung and increased the host susceptibility to severe influenza virus infection. Finally, we found that influenza virus infection in AMs led to impaired TFAM expression and diminished mitochondrial fitness and metabolism. Thus, our data have established the critical function of TFAM-mediated mitochondrial metabolism in AM maintenance and function.

Abstract Funding Acknowledgements Type of funding sources: None. Background Aortic regurgitation (AR) is a common valvular lesion associated with left ventricular (LV) enlargement and dysfunction and increased risk of death with onset of symptoms, significant LV enlargement, or systolic dysfunction. The current guidelines are based on symptoms, LV size and LV function to evaluate the candidacy for surgery in patients with severe AR. Purpose We propose a machine-learning (ML) based algorithm to identify patients at risk for death from AR independent of aortic valve replacement (AVR) surgery (patients were censored at AVR event unless the operation occurred within 100 days of the first visit, in which case baseline AVR is included as a predictor). Methods The model was trained with 5-fold cross-validation on a dataset of 1035 patients. Model performance is reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a Conditional Random Survival Forest model. A subset of 19 / 41 variables were selected for inclusion in the final model. Variable selection was performed with 10-fold cross validation using Random Survival Forest model; variables that were on an average selected by less than 6/10 splits were excluded. Results The mean age of patients was 60 ± 17 years and 198 (18%) were females. The variables included in the models were age, end-diastolic volume, end systolic dimension, body surface area, ejection fraction, NYHA class, body mass index, diastolic blood pressure, bicuspid valve, regurgitant volume, filling pressure, mid ascending aorta diameter, mitral tissue early relaxation velocity, pulmonary artery systolic pressure, and comorbidities, and the relative variable importances averaged across five splits of cross validation in each repeat were evaluated (Figure). The predictive AUC for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using commonly observed echo parameters and patient characteristics we successfully trained a ML model to predict survival in patients with severe AR. This technique could identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes. Abstract Figure.

Abstract IntroductionLiver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds.MethodsWe utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis.ResultsA 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted.ConclusionCNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.

Abstract Background To describe response to therapy of small bowel (SB) Crohn’s disease (CD) at CT or MR enterography (CTE/MRE) in patients on vedolizumab. Methods Patients with SB CD who underwent CTE/MRE exams greater than 12 months apart on vedolizumab therapy were included. Length (in cm) and inflammation severity (EMBARK score) of inflamed SB segments were assessed. Changes in inflammation length of 3.4 cm or greater or inflammation severity of 2 EMBARK points or greater was categorized as response or progression, as appropriate, with development of newly inflamed segments, strictures, or penetrating complications also indicating progression. Patients not meeting the criteria for response or progression were categorized as having stable disease. Results Of 36 SB CD patients, the large majority had prior surgery (86%; 31), anti-TNF use (92%; 33), and internal penetrating (78%; 28) disease. Thirty-two patients had paired baseline and follow-up CTE/MRE exams without interval surgery, with clinical response observed in 24/32 (75%). Based on imaging response criteria, 22% (7/32; 95% CI: 9%–40%) had response, 50% (16/32; 95% CI: 32%–68%) were stable, and 28% (9/32; 95% CI: 14%–47%) had disease progression. Fifty-six percent of (18/32; 95% CI: 38%–74%) patients had clinical improvement with response or stable disease by imaging. Patients with stable disease had shorter median baseline lengths of SB inflammation (P = .012). Proportion of patients with colonic inflammation, perianal disease, or penetrating complications did not change. Conclusions Most patients on vedolizumab for over 12 months demonstrated response or stable SB disease when using objective cross-sectional radiologic imaging criteria using CTE/MRE.

BATF regulates ILC2-mediated tissue repair and inflammation resolution during acute respiratory virus infection.

Abstract Aim To evaluate whether short‐term treatment with a selective 11β‐Hydroxysteroid dehydrogenase‐1 (11β‐HSD1) inhibitor, AZD4017, would block hepatic cortisol production and thereby decrease hepatic fat in patients with nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH), with or without type 2 diabetes (T2D). Materials and Methods This was a randomized, double‐blind, placebo‐controlled, phase 2 study conducted at two sites. Key inclusion criteria were the presence of NAFLD or NASH on magnetic resonance imaging (MRI) or recent biopsy positive for NASH. Enrolled patients were randomly assigned (1:1) to AZD4017 or placebo for 12 weeks. Primary outcomes were between‐group differences in mean change from baseline to week 12 in liver fat fraction (LFF) and conversion of 13 C cortisone to 13 C cortisol in the liver. Results A total of 93 patients were randomized; 85 patients completed treatment. The mean (standard deviation [SD]) change in LFF was −0.667 (5.246) and 0.139 (4.323) in the AZD4017 and placebo groups ( P = 0.441). For patients with NASH and T2D, the mean (SD) change in LFF was significantly improved in the AZD4017 versus the placebo group (−1.087 [5.374] vs. 1.675 [3.318]; P = 0.033). Conversion of 13 C cortisone to 13 C cortisol was blocked in all patients in the AZD4017 group. There were no significant between‐group differences (AZD4017 vs. placebo) in changes in fibrosis, weight, levels of liver enzymes or lipids, or insulin sensitivity. Conclusion Although the study did not meet one of the primary outcomes, AZD4017 blocked the conversion of 13 C cortisone to 13 C cortisol in the liver in all patients who received the drug. In patients with NASH and T2D, AZD4017 improved liver steatosis versus placebo.

Background and objectives Outcomes reported in trials in adults with glomerular disease are often selected with minimal patient input, are heterogeneous, and may not be relevant for clinical decision making. The Standardized Outcomes in Nephrology–Glomerular Disease (SONG-GD) initiative aimed to establish a core outcome set to help ensure that outcomes of critical importance to patients, care partners, and clinicians are consistently reported. Design, setting, participants, and measurements We convened two 1.5-hour workshops in Melbourne, Australia, and Washington, DC, United States. Attendees were identified purposively with 50 patients/care partners and 88 health professionals from 19 countries; 51% were female. Patients and care partners were from the United States, Australia, and Canada, and had experience of a glomerular disease with systemic features ( n =9), kidney-limited nephrotic disease ( n =9), or other kidney-limited glomerular disease ( n =8). Attendees reviewed the results of the SONG-GD Delphi survey and aims of the workshop and then discussed potential core outcomes and their implementation in trials among moderated breakout groups of eight to 12 people from diverse backgrounds. Transcripts of discussions were analyzed thematically. Results Three themes were identified that supported the proposed core outcomes: limiting disease progression, stability and control, and ensuring universal relevance ( i.e. , applicable across diverse populations and settings). The fourth theme, preparedness for implementation, included engaging with funders and regulators, establishing reliable and validated measures, and leveraging existing endorsements for patient-reported outcomes. Conclusions Workshop themes demonstrated support for kidney function, disease activity, death, life participation, and cardiovascular disease, and these were established as the core outcomes for trials in adults with glomerular disease. Future work is needed to establish the core measures for each domain, with funders and regulators central to the uptake of the core outcome set in trials.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.