Citations

Abstract This International evidence-based nomenclature and classification consensus on the congenital bicuspid aortic valve and its aortopathy recognizes 3 types of bicuspid aortic valve: 1. Fused type, with 3 phenotypes: right-left cusp fusion, right-non cusp fusion and left-non cusp fusion; 2. 2-sinus type with 2 phenotypes: Latero-lateral and antero-posterior; and 3. Partial-fusion or forme fruste. This consensus recognizes 3 bicuspid-aortopathy types: 1. Ascending phenotype; root phenotype; and 3. extended phenotypes.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Abstract Pyoderma gangrenosum (PG) is a rare, inflammatory dermatologic condition characterized by painful cutaneous ulcerations. Herein, we describe the third documented case of PG arising in an elective plastic surgery patient who had undergone an otherwise uncomplicated facelift. We describe the course of her diagnosis and management of PG, which involved her face and neck and then progressed to her lower extremities. Although the etiology remains unknown, PG often arises in a host with another autoimmune disease. In the case described, the patient was diagnosed with an immunoglobulin A gammopathy shortly after she developed PG. Following the case report, the pathogenesis, diagnosis, and treatment strategy of PG is briefly reviewed. Level of Evidence: 5

PURPOSE SJMB03 (ClinicalTrials.gov identifier: NCT00085202 ) was a phase III risk-adapted trial that aimed to determine the frequency and clinical significance of biological variants and genetic alterations in medulloblastoma. PATIENTS AND METHODS Patients 3-21 years old were stratified into average-risk and high-risk treatment groups based on metastatic status and extent of resection. Medulloblastomas were molecularly classified into subgroups (Wingless [WNT], Sonic Hedgehog [SHH], group 3, and group 4) and subtypes based on DNA methylation profiles and overlaid with gene mutations from next-generation sequencing. Coprimary study end points were (1) to assess the relationship between ERBB2 protein expression in tumors and progression-free survival (PFS), and (2) to estimate the frequency of mutations associated with WNT and SHH tumors. Clinical and molecular risk factors were evaluated, and the most robust were used to model new risk-classification categories. RESULTS Three hundred thirty eligible patients with medulloblastoma were enrolled. Five-year PFS was 83.2% (95% CI, 78.4 to 88.2) for average-risk patients (n = 227) and 58.7% (95% CI, 49.8 to 69.1) for high-risk patients (n = 103). No association was found between ERBB2 status and PFS in the overall cohort ( P = .74) or when patients were stratified by clinical risk ( P = .71). Mutations in CTNNB1 (96%), DDX3X (37%), and SMARCA4 (24%) were most common in WNT tumors and PTCH1 (38%), TP53 (21%), and DDX3X (19%) in SHH tumors. Methylome profiling classified 53 WNT (17.4%), 48 SHH (15.7%), 65 group 3 (21.3%), and 139 group 4 (45.6%) tumors. A comprehensive clinicomolecular risk factor analysis identified three low-risk groups (WNT, low-risk SHH, and low-risk combined groups 3 and 4) with excellent (5-year PFS > 90%) and two very high-risk groups (high-risk SHH and high-risk combined groups 3 and 4) with poor survival (5-year PFS < 60%). CONCLUSION These results establish a new risk stratification for future medulloblastoma trials.

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

AbstractOne of the many challenges that has befallen the Infectious Diseases and Graduate Medical Education communities during the coronavirus disease 2019 (COVID-19) pandemic is the maintenance of continued effective education and training of the future leaders of our field. With the remarkable speed and innovation that has characterized the responses to this pandemic, educators everywhere have adapted existing robust and safe learning environments to meet the needs of our learners. This paper will review distinct aspects of education and training of the Infectious Diseases fellows we believe the COVID-19 pandemic has impacted most, including mentoring, didactics, and wellness. We anticipate that several strategies developed in this context and described herein will help to inform training and best practices during the pandemic and beyond.