Citations

AbstractDisturbed intrauterine development increases the risk of renal disease. Various studies have reported that Notch signalling plays a significant role in kidney development and kidney diseases. A disintegrin and metalloproteinase domain 10 (ADAM10), an upstream protease of the Notch pathway, is also reportedly involved in renal fibrosis. However, how ADAM10 interacts with the Notch pathway and causes renal fibrosis is not fully understood. In this study, using a prenatal chlorpyrifos (CPF) exposure mouse model, we investigated the role of the ADAM10/Notch axis in kidney development and fibrosis. We found that prenatal CPF‐exposure mice presented overexpression of Adam10, Notch1 and Notch2, and led to premature depletion of Six2+ nephron progenitors and ectopic formation of proximal tubules (PTs) in the embryonic kidney. These abnormal phenotypic changes persisted in mature kidneys due to the continuous activation of ADAM10/Notch and showed aggravated renal fibrosis in adults. Finally, both ADAM10 and NOTCH2 expression were positively correlated with the degree of renal interstitial fibrosis in IgA nephropathy patients, and increased ADAM10 expression was negatively correlated with decreased kidney function evaluated by serum creatinine, cystatin C, and estimated glomerular filtration rate. Regression analysis also indicated that ADAM10 expression was an independent risk factor for fibrosis in IgAN. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

Background Bronchopleural fistula (BPF) is a dreaded complication of pulmonary resection. For high-risk patients, bronchial stump coverage with vascularized tissue has been recommended. The goal of this study was to report our experience with intrathoracic muscle transposition for bronchial stump coverage. Methods A retrospective review of all patients who underwent intrathoracic muscle flap transposition as a prophylactic measure at our institution between 1990 and 2010 was conducted. Demographics, surgical characteristics, and complication rates were abstracted and analyzed. Results A total of 160 patients were identified. The most common lung resections performed were pneumonectomy (n = 69, 43%) and lobectomy (n = 60, 38%). A total of 168 flaps were used where serratus anterior was the most common flap (n = 136, 81%), followed by intercostal (n = 14, 8%), and latissimus dorsi (n = 12, 7%). Ten patients (6%) developed BPF, and empyema occurred in 13 patients (8%). Median survival was 20 months, and operative mortality occurred in 7 patients (4%). Conclusions Reinforcement of the bronchial closure with vascularized muscle is a viable option for potentially decreasing the incidence of BPF in high-risk patients. Further randomized studies are needed to determine the efficacy of this technique for BPF prevention.

Abstract Atrial fibrillation is a common clinical manifestation in hospitalized patients with coronavirus disease 2019 (COVID‐19). Medications used to treat atrial fibrillation, such as antiarrhythmic drugs and anticoagulants, may have significant drug interactions with emerging COVID‐19 treatments. Common unintended nontherapeutic target effects of COVID‐19 treatment include potassium channel blockade, cytochrome P 450 isoenzyme inhibition or activation, and P‐glycoprotein inhibition. Drug‐drug interactions with antiarrhythmic drugs and anticoagulants in these patients may lead to significant bradycardia, ventricular arrhythmias, or severe bleeding. It is important for clinicians to be aware of these interactions, drug metabolism changes, and clinical consequences when choosing antiarrhythmic drugs and anticoagulants for COVID‐19 patients with atrial fibrillation. The objective of this review is to provide a practical guide for clinicians who are managing COVID‐19 patients with concomitant atrial fibrillation.

4500 Background: Trastuzumab is a monoclonal antibody against human epidermal growth factor receptor 2 (HER2). The primary objective of RTOG 1010 was to determine if trastuzumab increases disease-free survival (DFS) when combined with trimodality treatment for patients with HER2 overexpressing esophageal adenocarcinoma. Methods: This open label, randomized phase III trial included patients with newly diagnosed stage T1N1-2, T2-3N0-2 adenocarcinoma of the esophagus involving the mid, distal, or esophagogastric junction and up to 5cm of the stomach. All patients received chemotherapy (C) of paclitaxel, 50mg/m2 and carboplatin AUC = 2, weekly for 6 weeks, with radiation (XRT: 3D-CRT or IMRT, 50.4 Gy in 28 fractions) followed by surgery. Patients were randomized 1:1 to receive weekly trastuzumab 4mg/kg week 1 then 2mg/kg/weekly x 5 during CXRT then 6 mg/kg for 1 dose prior to surgery and 6mg/kg every 3 weeks for 13 treatments after surgery. HER2 status was determined by IHC and gene amplification by FISH. With a 2-sided alpha of 0.05, 162 DFS events provide 90% power to detect a signal for an increase in median DFS from 15 to 25 months. DFS and overall survival (OS) were estimated by the Kaplan-Meier method. and arms were compared using the log rank test. The Cox proportional hazards model was used to analyze treatment effect. Results: 571 patients were entered for assessment of HER2 expression, 203 HER2+ patients randomized. The median follow-up for alive patients is 5.0 years. The estimated 2, 3, and 4-year DFS (95% CI) for the CXRT +trastuzumab arm were 41.8% (31.8%, 51.7%), 34.3% (24.7%, 43.9%), and 33.1% (23.6%, 42.7%), respectively, and for the CXRT arm were 40.0% (30.0%, 49.9%), 33.4% (23.8%, 43.0%), and 30.1% (20.7%, 39.4%), respectively; log-rank p = 0.85. The median DFS time is 19.6 months (13.5-26.2) for the CXRT +trastuzumab arm compared to 14.2 months (10.5-23.0) for the CXRT arm. The hazard ratio (95% CI) comparing the DFS of CXRT+trastuzumab arm to the CXRT arm was 0.97 (0.69, 1.36). The median OS time was 38.5 months (26.2-70.4) for the CXRT+trastuzumab arm compared to 38.9 months (29.0-64.5) for the CXRT arm, hazard ratio (95% CI): 1.01 (0.69, 1.47). There was no statistically significant increase in treatment-related toxicities with the addition of trastuzumab including no increase in cardiac events. Conclusions: The addition of trastuzumab to trimodality treatment did not improve DFS for patients with HER2 overexpressing esophageal adenocarcinoma. Supported by NCI grants U10CA180868, UG1CA189867, U10CA180822 and Genentech. Clinical trial information: NCT01196390 .

1512 Background: Tobacco smoking profoundly impacts LC risk; however, data are limited as to what extent SC prior to diagnosis impacts LC overall survival (OS) and lung cancer specific survival (LCSS). LC screening offers a possible teachable moment, but there is uncertainty of SC benefits after a lifetime of smoking. We use the ILCCO database to answer if SC prior to LC dx is associated with better OS and LCSS, considering time since smoking cessation (TSSC). Methods: Using individual data, analysis was performed on 17 ILCCO studies with available TSSC to estimate survival using univariable analysis and models of stage-adjusted and cumulative smoking-adjusted multivariable analysis. Adjusted Hazard Ratios (aHR) from Cox models, cubic spline smooth curves and Kaplan-Meier curves were created. Sensitivity analysis was performed for TSSC and LCSS on 13 studies. Results: Of 34649 patients, 14322 (41%) were current smokers 14273 (41%) ex-smokers and 6054 (18%) never smokers at diagnosis. We confirmed that ex-smokers (aHR 0.88 CI 0.86-0.91) and never smokers (aHR 0.76 CI 0.73-0.8) improved OS compared to current smokers. Amongst ex-smokers, < 2y TSSC (aHR 0.88 CI 0.82-0.94), 2-5y TSSC (aHR 0.83 CI 0.77-0.90) and > 5y TSSC (aHR 0.8 CI 0.76-0.84) had improved OS compared to CS. Sensitivity analysis showed a trend towards improved LCSS survival for < 2y TSSC (aHR 0.95 CI 0.86-1.05) and 2-5y TSSC (aHR 0.93 CI 0.83-1.04), whereas > 5y TSSC significantly improved LCSS by 15% (aHR 0.85 CI 0.78-0.92). To mimic the LC screening participants, in analysis of > 30 pack-years individuals, associations were strikingly strong: < 2y TSSC had improved OS by 14% (aHR 0.86 CI 0.80-0.93); 2-5y TSSC by 17% (aHR 0.83 CI 0.76-0.90); and > 5 TSSC by 22% (aHR 0.78 CI 0.74-0.83), compared to current smokers; for < 30 packs-years, a trend towards better OS was observed for < 2y TSSC (aHR 0.95 CI 0.92-1.02) and 2-5y TSSC (aHR 0.86 CI 0.74-1.01), whereas > 5y TSSC improved OS by 23% (aHR 0.77 CI 0.72-0.82). Conclusions: Among ex-smokers, the risk of overall death was reduced by 12% on < 2y TSSC, 17% on 2-5y TSSC and 20% > 5y TSSC, whereas for LCSS, the benefit was significant only for > 5y TSCC, compared to current smokers at time of diagnosis. Here we demonstrate that convincing screening participants to quit smoking at any point of their trajectory, even just prior to dx such as < 2y TSSC, improved OS, and LCSS benefit was present beyond 5y of quitting. These relationships are independent of pack-years, age, across all stages and other prognostic variables.

Abstract Objective As coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine-readable dataset known as the COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection among COVID-19–related biomedical entities. Materials and Methods Leveraging a Linked Data version of CORD-19 (ie, CORD-19-on-FHIR), we first utilized SPARQL to extract co-occurrences among chemicals, diseases, genes, and mutations and build a co-occurrence network. We then trained the representation of the derived co-occurrence network using node2vec with 4 edge embeddings operations (L1, L2, Average, and Hadamard). Six algorithms (decision tree, logistic regression, support vector machine, random forest, naïve Bayes, and multilayer perceptron) were applied to evaluate performance on link prediction. An unsupervised learning strategy was also developed incorporating the t-SNE (t-distributed stochastic neighbor embedding) and DBSCAN (density-based spatial clustering of applications with noise) algorithms for case studies. Results The random forest classifier showed the best performance on link prediction across different network embeddings. For edge embeddings generated using the Average operation, random forest achieved the optimal average precision of 0.97 along with a F1 score of 0.90. For unsupervised learning, 63 clusters were formed with silhouette score of 0.128. Significant associations were detected for 5 coronavirus infectious diseases in their corresponding subgroups. Conclusions In this study, we constructed COVID-19–centered co-occurrence network embeddings. Results indicated that the generated embeddings were able to extract significant associations for COVID-19 and coronavirus infectious diseases.

Abstract Objective The n2c2/UMass Lowell spin-off shared task focused on medical concept normalization (MCN) in clinical records. This task aimed to assess state-of-the-art methods for matching salient medical concepts from clinical records to a controlled vocabulary. We describe the task and the dataset used, compare the participating systems, and identify the strengths and limitations of the current approaches and directions for future research. Materials and Methods Participating teams were asked to link preselected text spans in discharge summaries (henceforth referred to as concept mentions) to the corresponding concepts in the SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and RxNorm vocabularies from the Unified Medical Language System. The shared task used the MCN corpus created by the organizers, which maps all mentions of problems, treatments, and tests in the 2010 i2b2/VA challenge data to the Unified Medical Language System concepts. Submitted systems represented 4 broad categories of approaches: cascading dictionary matching, cosine distance, deep learning, and retrieve-and-rank systems. Disambiguation modules were common across all approaches. Results A total of 33 teams participated in the shared task. The best-performing team achieved an accuracy of 0.8526. The median and mean performances among all teams were 0.7733 and 0.7426, respectively. Conclusions Overall performance among the top 10 teams was high. However, particularly challenging for all teams were mentions requiring disambiguation of misspelled words, acronyms, abbreviations, and mentions with more than 1 possible semantic type. Complex mentions of long, multiword terms were also challenging and, in the future, will require better methods for learning contextualized representations of concept mentions and better use of domain knowledge.