Citations

Novel therapeutic strategies are needed for the treatment of rhabdomyosarcoma (RMS), the most common soft-tissue sarcoma in children. By using a combination of cell surface proteomics and transcriptomic profiling of RMS and normal muscle, we generated a catalog of targetable cell surface proteins enriched in RMS tumors. Among the top candidates, we identified B7-H3 as the major immunoregulatory molecule expressed by RMS tumors. By using a large cohort of tissue specimens, we demonstrated that B7-H3 is expressed in a majority of RMS tumors while not detected in normal human tissues. Through a deconvolution analysis of the RMS tumor RNA-seq data, we showed that B7-H3-rich tumors are enriched in macrophages M1, NK cells, and depleted in CD8+-T cells. Furthermore, in vitro functional assays showed that B7-H3 knockout in RMS tumor cells increases T-cell mediated cytotoxicity. Altogether, our study uncovers new potential targets for the treatment of RMS and provides the first biological insights into the role of B7-H3 in RMS biology, paving the way for the development of next-generation immunotherapies.

Objective: To evaluate geographic and temporal trends in Medicare fee-for-service (FFS) billing and reimbursements across female otolaryngologists (ORL). Methods: We performed a cross-sectional, retrospective analysis of the 2017 Medicare Physician and Other Suppliers Aggregate File. We analyzed differences in the number of services, patients, reimbursements, unique Current Procedural Terminology (CPT) codes used, and services billed per patient among female ORLs. Results: Female ORLs accounted for 15.2% of the 8453 Medicare-reimbursed ORLs. Female ORLs who graduated between 2000 and 2010 were reimbursed a median of $58 031.9 (IQR: $32 286.5-$91 512.2) and performed a median of 702 (IQR: 359.5-1221.5) services, significantly less than those who graduated between 1990 and 1999 (median: $67 508.9; IQR: 37 018.0-110 471.5; P < .001; median: 1055.5; IQR: 497.3-1944; P < .001). Female ORLs who graduated between 2000 and 2010 saw a median of 232 patients (IQR: 130.5-368), significantly less than those who graduated between 1990 and 1999 (median: 308; IQR: 168.3-496; P < .001) patients, significantly more than those. Female ORLs in urban settings performed a median of 795 (IQR: 364-1494.3) services and billed for a median of 42 (IQR: 28-58) unique codes, significantly fewer than their counterparts in rural settings (median: 1096; IQR: 600-2192.5; P = .002; median: 54; IQR: 31.5-64.5; P = .001). Conclusions: Medicare reimbursements and billing patterns across female ORLs varied by graduation decade and geography. Female ORLs further along in their careers may be reimbursed more with greater clinical volume and productivity. Those practicing in urban settings may have practices with decreased procedural diversity and lower clinical volume compared to their counterparts in rural areas.

Abstract Aims Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. Methods and results We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic 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. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, &lt;45%, and &lt;50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF &lt; 45%), and 0.87 (LVEF &lt; 50%). For LVEF of 35% or less, 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 to 0.86 and 0.72, respectively. Conclusions An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

AbstractThe study aimed at widening the clinical and genetic spectrum of ASXL3‐related syndrome, a neurodevelopmental disorder, caused by truncating variants in the ASXL3 gene. In this international collaborative study, we have undertaken a detailed clinical and molecular analysis of 45 previously unpublished individuals with ASXL3‐related syndrome, as well as a review of all previously published individuals. We have reviewed the rather limited functional characterization of pathogenic variants in ASXL3 and discuss current understanding of the consequences of the different ASXL3 variants. In this comprehensive analysis of ASXL3‐related syndrome, we define its natural history and clinical evolution occurring with age. We report familial ASXL3 pathogenic variants, characterize the phenotype in mildly affected individuals and discuss nonpenetrance. We also discuss the role of missense variants in ASXL3. We delineate a variable but consistent phenotype. The most characteristic features are neurodevelopmental delay with consistently limited speech, significant neuro‐behavioral issues, hypotonia, and feeding difficulties. Distinctive features include downslanting palpebral fissures, hypertelorism, tubular nose with a prominent nasal bridge, and low‐hanging columella. The presented data will inform clinical management of individuals with ASXL3‐related syndrome and improve interpretation of new ASXL3 sequence variants.

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.

The NCCN Guidelines for Uterine Neoplasms provide recommendations for diagnostic workup, clinical staging, and treatment options for patients with endometrial cancer or uterine sarcoma. These NCCN Guidelines Insights focus on the recent addition of molecular profiling information to aid in accurate diagnosis, classification, and treatment of uterine sarcomas.