Citations

Background/Objective: To investigate the effect of mycophenolate mofetil (MMF) and rituximab (RTX) on pulmonary function test (PFT) results in a mixed cohort of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD), longitudinally followed up for 1 year in a single academic center. Methods: Patients with CTD-ILD were identified in electronic medical records from 1 January 2009 to 30 April 2019. Prescribed MMF and RTX doses, dosage changes, and therapy plans were analyzed individually with improvement in PFT outcomes determined using multivariable linear regression models during 12-month follow-up. Results: Forty-seven patients with CTD-ILD, treated with MMF, RTX, or both, were included. Patients on combined MMF and RTX had worse PFT outcomes at baseline compared with patients on monotherapy. Substantial improvement was observed among all PFT outcomes from baseline to 12 months, regardless of medication dosage or therapy plans. The diffusing capacity of the lungs for carbon monoxide (DLCO) worsened by an average of 7.21 mL/(min*mmHg) (95% CI, 4.08–10.33; p < 0.001) among patients on RTX compared to combined therapy. Patients on higher doses of MMF at baseline experienced an average increase of 0.93 (95% CI, 0.04–1.82) units in DLCO from baseline to 6 months (p = 0.04) and a 2.79% (95% CI, 0.61–4.97%) increase in DLCO from 6 to 12 months (p = 0.02) within patients on concurrent RTX at 6-month follow-up. Conclusions: The treatment of CTD-ILD with MMF and/or RTX was associated with overall improvement in PFT outcomes. Combined therapy resulted in significant improvements in DLCO compared with monotherapy. Higher doses of MMF also provided greater improvements in DLCO.

Background White matter hyperintensities (WMH) and their progression are associated with risk of dementia and stroke, so are an important target for clinical trials. The cost of broad magnetic resonance imaging (MRI) screening to identify eligible individuals, however, limits the feasibility of designing clinical trials targeting WMH. A low‐cost retinal or clinical screening measure before MRI could reduce recruitment costs versus an MRI‐only screening design in a hypothetical clinical trial. Methods and Results Data from the Atherosclerosis Risk in Communities study with valid retinal and WMH measurements ( N =1311) were used. To identify a population at greater likelihood of significant WMH on MRI and thus reduce the number of screening MRIs required, we evaluated 3 theoretical prescreening measures: (1) retinal, (2) clinical, (3) combined clinical‐retinal. Given a target sample for clinical trials ( N =646), we calculated screening sample sizes based on the proportion within the population having an elevated score for each prescreening measure (separately) multiplied by the proportion of significant WMH among those with that prescreening feature. Recruitment costs were calculated using estimated retinal and MRI cost estimates. Compared with the estimated cost of MRI‐only screening (>$4.24 million, requiring MRI on 6526 participants), prescreening for a high clinical score resulted in total cost of $2.47 million, with an initial screening group of 52 778 participants, with MRI in 3801. A high clinical‐retinal score cutoff resulted in costs of $2.9 million while requiring 13 572 participants, with 3801 completing MRI. Conclusions A 2‐stage design with low‐cost prescreening measures is a promising approach, resulting in reduced theoretical recruitment costs compared with an MRI‐only design.

Background: Risk stratification for embolism in cardiac myxomas remains poorly explored. Goals: By this meta-analysis we studied the risk factors assicated with embolism among patients with cardiac myxoma. Methods: A comprehensive search was conducted across PubMed, Embase, Cochrane Library, and Google Scholar from their inception until January 2024. Statistical analyses were performed using Cochrane's RevMan 5.4 software. For each risk factor, the pooled odds ratio or mean difference was calculated along with the corresponding 95% confidence interval. Results: We included 18 studies in our analysis with a total population of 2601 out of which 525 patients (20.1%) had at least one episode of embolism. The pooled analyses showed that hypertension (p = 0.001), New York Heart Association I/II (p = 0.03), irregular tumor surface (p <0.00001), hyperlipidemia (p < 0.0001), coronary artery disease (p=0.01), high mean platelet volume (p=0.02), high tumor mobility (p<0.00001), were significantly associated with increased incidence of embolism. Female gender (p = 0.03) was the only risk factor associated with reduced risk. Other factors like smoking, atrial fibrillation, tumor size, age, body mass index (BMI), diabetes, left ventricular ejection fraction (LVEF), left atrial diameter (LAD) were not significantly associated with embolism (p > 0.05). Conclusion: Our study is the first to report significant pooled outcomes for gender, hyperlipidemia, coronary artery disease, mean platelet volume, and tumor mobility in the population discussed. Hypertension, New York Heart Association I/II, irregular tumor surface, hyperlipidemia, coronary artery disease, mean platelet volume and tumor mobility were significantly associated with high risk while female gender was associated with low risk of embolism. Patients with high-risk factors might benefit from early evaluation and definitive treatment (surgery).

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

Abstract High grade gliomas (HGGs) are aggressive brain tumors that are difficult to remove completely, leading to poor survival rates. Contrast enhancement on T1Gd magnetic resonance imaging (MRI) has traditionally been used to guide resection, however it often underestimates true tumor burden, adding a layer of uncertainty for surgeons during pre-operative planning. Fluorescence guided surgery using 5-aminolevulinic acid (5-ALA) has emerged as an intraoperative solution and understanding it in conjunction with MRI features can address the limitations of routine T1Gd imaging during surgical planning. Here we investigate sex-specific patterns in HGGs using 5-ALA fluorescence and imaging features. We analyzed a patient dataset of 202 multiregional MRI-localized biopsies from 42 HGG patients (20 male and 22 female). Data included patient demographics, 5-ALA status (positive or negative), MRI annotations (CE = contrast enhancement, NE = non-enhancement, BAT = brain around tumor non-enhancement), and survival outcomes. Statistical analyses included Wilcoxon rank-sum tests, Kruskal-Wallis tests, and chi-squared tests to assess differences in imaging annotations and 5-ALA positive vs negative status among sex cohorts. Among all samples, CE and BAT were associated with higher 5-ALA positivity, while NE was associated with higher 5-ALA negativity (p = 0.02119). Female patients were less likely to have detectable 5-ALA in their multiregional biopsy samples than males (p &lt; 0.0001). CE at the biopsy location was more likely to be indicative of 5-ALA positivity in males than females (p = 0.001927); whereas NE was associated with higher 5-ALA negativity in females than males (p = 0.03426). BAT was associated with higher 5-ALA positivity in males than females (p = 0.03895). Our study reveals sex-specific patterns in 5-ALA fluorescence and image features among HGG patients. These unique features may serve as potential pre-surgical markers for tumor regions that are more likely to benefit from 5-ALA-guided resection.

Abstract The intra- and inter-patient heterogeneity in high-grade glioma (HGG) continues to contribute to its poor prognosis. Clinical biopsies are often harvested from limited regions and typically are not image localized. Thus, they fail to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in tumor development. It is important to gain an understanding of these subpopulation ecologies, their spatial resolution, and interactions between them that may then be exploited for future therapeutic benefit. Further, these may differ by patient characteristics such as sex, age at diagnosis and treatment status. Using an ongoing image-localized biopsy collection protocol, we have so far evaluated the bulk transcriptomics of 202 multi-regional biopsies from 58 patients to characterize HGG heterogeneity. These samples were processed through Monocle, a reverse graph embedding algorithm that groups samples into states and orders them along developmental trajectories. Deconvolution methods were previously used to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample, which we have now overlaid on the Monocle graph. Monocle classified HGG into 4 main states along a three-pronged trajectory. These states reveal distinct population ecologies with associated enriched gene pathways. We also note significant immune pathway enrichments that differ between state and patient-reported sex. Together, these algorithms reveal a simple transcriptomic trajectory that helps us understand the development and evolution of HGG. Characterizing the in vivo diversity within and between high grade gliomas is important for understanding prognosis, stratifying future treatments and ultimately improving patient outcome.

Abstract Magnetic resonance imaging (MRI) is key to clinically managing brain tumor patients, however connecting the biology to imaging remains challenging. We previously developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Using this model, we previously identified the fatty acid metabolism (FAM) and oxidative phosphorylation (OxPhos) pathways as sex-distinct, with both pathways amplified for high edema in males and low edema in females. The purpose of this project was to further delineate sex-distinct biology associated with MRI-estimated brain tumor edema abundance. We analyzed 179 multiregional samples (Female: 75; Male: 104) from 55 high grade glioma patients (Female: 21; Male: 34) for bulk RNA-Seq. Patients’ pre-surgical multiparametric MRIs were preprocessed and segmented for abnormal regions and normal tissue. Utilizing the segmentations and preprocessed images we estimated the relative fractions of extracellular and intracellular space based on the edema mathematical model. Samples were characterized by their edema scores, which were analyzed using differential expression for high and low edema, gene set enrichment analysis (GSEA) using MSigDB hallmarks, and leading edge interpretation. Based on transcriptomic leading edge analyses of high and low edema samples from sex-separated cohorts, we identified common and sex-distinct enrichment of the FAM and OxPhos pathways underlying edema patterns. Of the OxPhos pathway leading edge genes, 45 were common, 36 were unique to females, and 66 were unique to males. Of the FAM pathway leading edge genes, 8 were common, 39 were unique to females, and 29 were unique to males. Notably, expression of both IDH1 and IDH2 were increased for males in regions of high edema in the OxPhos pathway. IDH3a was decreased for females in regions of low edema in the OxPhos pathway. These data suggest that there may be sex-distinct metabolism underlying MRI measurable edema formation.

Abstract Aims Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS. Methods and results A comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained to identify progression to severe stenosis and all-cause mortality and tested in their performance for endpoints at 2 and 5 years. In the independent test group, the AS progression model differentiated those with progression to severe AS within 2 and 5 years with an area under the curve (AUC) of 0.86 for both. The feature of greatest importance was aortic valve mean gradient, followed by other valve haemodynamic measurements including valve area and dimensionless index. The mortality model identified those with mortality within 2 and 5 years with an AUC of 0.84 and 0.87, respectively. Smaller reduced-input validation models had similarly robust findings. Conclusion Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.

Abstract The Midwest Aging Consortium (MAC) has emerged as a critical collaborative initiative aimed at advancing our understanding of aging and developing strategies to combat the rising prevalence of age-related diseases. Founded in 2019, MAC brings together researchers from various disciplines and institutions across the Midwestern United States to foster interdisciplinary geroscience research. This report summarizes the highlights of the Fourth Annual Symposium of MAC, which was held at Iowa State University in May 2023. The symposium featured presentations on a wide array of topics, including studies on slow-aging animals, cellular senescence and senotherapeutics, the role of the immune system in aging, metabolic changes in aging, neuronal health in aging, and biomarkers for measuring the aging process. Speakers shared findings from studies involving a variety of animals, ranging from commonly used species such as mice, rats, worms, yeast, and fruit flies, to less-common ones like naked mole-rats, painted turtles, and rotifers. MAC continues to emphasize the importance of supporting emerging researchers and fostering a collaborative environment, positioning itself as a leader in aging research. This symposium not only showcased the current state of aging biology research but also highlighted the consortium’s role in training the next generation of scientists dedicated to improving the healthspan and well-being of the aging population.