Publications by authors named "Debashis Ghosh"

258 Publications

Software engineering principles to improve quality and performance of R software.

PeerJ Comput Sci 2019 4;5:e175. Epub 2019 Feb 4.

University of Colorado Data Science to Patient Value, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Today's computational researchers are expected to be highly proficient in using software to solve a wide range of problems ranging from processing large datasets to developing personalized treatment strategies from a growing range of options. Researchers are well versed in their own field, but may lack formal training and appropriate mentorship in software engineering principles. Two major themes not covered in most university coursework nor current literature are software testing and software optimization. Through a survey of all currently available Comprehensive R Archive Network packages, we show that reproducible and replicable software tests are frequently not available and that many packages do not appear to employ software performance and optimization tools and techniques. Through use of examples from an existing R package, we demonstrate powerful testing and optimization techniques that can improve the quality of any researcher's software.
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http://dx.doi.org/10.7717/peerj-cs.175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924430PMC
February 2019

A general approach to sensitivity analysis for Mendelian randomization.

Stat Biosci 2021 Apr 28;13(1):34-55. Epub 2020 Apr 28.

Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A.

Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.
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http://dx.doi.org/10.1007/s12561-020-09280-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962901PMC
April 2021

Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring.

Commun Stat Theory Methods 2021 15;50(1):216-236. Epub 2019 Jul 15.

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Dependent censoring is common in many medical studies, especially when there are multiple occurrences of the event of interest. Ghosh and Lin (2003) and Hsieh, Ding and Wang (2011) proposed estimation procedures using an artificial censoring technique. However, if covariates are not bounded, then these methods can cause excessive artificial censoring. In this paper, we propose estimation procedures for the treatment effect based on a novel application of propensity scores. Simulation studies show that the proposed method provides good finite-sample properties. The techniques are illustrated with an application to an HIV dataset.
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http://dx.doi.org/10.1080/03610926.2019.1634208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954136PMC
July 2019

Androgen Receptor Regulates CD44 Expression in Bladder Cancer.

Cancer Res 2021 Mar 9. Epub 2021 Mar 9.

Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center

The androgen receptor (AR) is important in the development of both experimental and human bladder cancer. However, the role of AR in bladder cancer growth and progression is less clear, with literature indicating that more advanced stage and grade disease are associated with reduced AR expression. To determine the mechanisms underlying these relationships, we profiled AR-expressing human bladder cancer cells by AR ChIP-seq and complementary transcriptomic approaches in response to in vitro stimulation by the synthetic androgen R1881. In vivo functional genomics consisting of pooled shRNA or pooled ORF libraries to evaluate 97 genes that recapitulate the direction of expression associated with androgen stimulation. Interestingly, we identified CD44, the receptor for hyaluronic acid, a potent biomarker and driver of progressive disease in multiple tumor types, as significantly associated with androgen stimulation. CRISPR-based mutagenesis of androgen response elements (ARE) associated with CD44 identified a novel silencer element leading to the direct transcriptional repression of CD44 expression. In human bladder cancer patients, tumor AR and CD44 mRNA and protein expression were inversely correlated, suggesting a clinically relevant AR-CD44 axis. Collectively, our work describes a novel mechanism partly explaining the inverse relationship between AR and bladder cancer tumor progression and suggests that AR and CD44 expression may be useful for prognostication and therapeutic selection in primary bladder cancer.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-3095DOI Listing
March 2021

Filtering Spatial Point Patterns Using Kernel Densities.

Spat Stat 2021 Mar 10;41. Epub 2020 Dec 10.

Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO.

Understanding spatial inhomogeneity and clustering in point patterns arises in many contexts, ranging from disease outbreak monitoring to analyzing radiologically-based emphysema in biomedical images. This can often involve classifying individual points as being part of a feature/cluster or as being part of a background noise process. Existing methods for this task can struggle when there are differences in the size and/or density of individual clusters. In this work, we propose employing kernel density estimates of the underlying point process intensity function, using an existing data-driven approach to bandwidth selection, to separate feature points from noise. This is achieved by constructing a null distribution, either through asymptotic properties or Monte Carlo simulation, and comparing kernel density estimates to a given quantile of this distribution. We demonstrate that our method, termed Kernel Density and Simulation based Filtering (KDS-Filt), showed superior performance to existing alternative approaches, especially when there is inhomogeneity in cluster sizes and density. We also show the utility of KDS-Filt for identifying clinically relevant information about the spatial distribution of emphysema in lung computed tomography scans. The KDS-Filt methodology is available as part of the sncp R package, which can be downloaded at https://github.com/stop-pre16/sncp.
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http://dx.doi.org/10.1016/j.spasta.2020.100487DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781288PMC
March 2021

More Than Just a Reagent: The Rise of Renewable Organohydrides for Catalytic Reduction of Carbon Dioxide.

ChemSusChem 2021 Feb 23;14(3):824-841. Epub 2020 Dec 23.

Institute for Integrated Cell-Material Sciences (KUIAS/iCeMS), Kyoto University, Yoshida, Sakyo-ku, Kyoto, 606-8501, Japan.

Stoichiometric carbon dioxide reduction to highly reduced C1 molecules, such as formic acid (2e ), formaldehyde (4e ), methanol (6e ) or even most-reduced methane (8e ), has been successfully achieved by using organosilanes, organoboranes, and frustrated Lewis Pairs (FLPs) in the presence of suitable catalyst. The development of renewable organohydride compounds could be the best alternative in this regard as they have shown promise for the transfer of hydride directly to CO . Reduction of CO by two electrons and two protons to afford formic acid by using renewable organohydride molecules has recently been investigated by various groups. However, catalytic CO reduction to ≥2e -reduced products by using renewable organohydride-based molecules has rarely been explored. This Minireview summarizes important findings in this regard, encompassing both stoichiometric and catalytic CO reduction.
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http://dx.doi.org/10.1002/cssc.202002660DOI Listing
February 2021

Subjects at-risk for future development of rheumatoid arthritis demonstrate a PAD4-and TLR-dependent enhanced histone H3 citrullination and proinflammatory cytokine production in CD14 monocytes.

J Autoimmun 2021 Feb 9;117:102581. Epub 2020 Dec 9.

University of Colorado School of Medicine, Department of Immunology and Microbiology, Aurora, CO, USA; University of Colorado School of Medicine, Children's Hospital Colorado, Department of Pediatrics, Section of Allergy & Immunology, Aurora, CO, USA.

The presence of anti-citrullinated protein/peptide antibodies (ACPA) and epitope spreading across the target autoantigens is a unique feature of rheumatoid arthritis (RA). ACPA are present in the peripheral blood for several years prior to the onset of arthritis and clinical classification of RA. ACPA recognize multiple citrullinated proteins, including histone H3 (H3). Intracellular citrullination of H3 in neutrophils and T cells is known to regulate immune cell function by promoting neutrophil extracellular trap formation and citrullinated autoantigen release as well as regulating the Th2/Th17 T cell phenotypic balance. However, the roles of H3 citrullination in other immune cells are not fully elucidated. We aimed to explore H3 citrullination and cytokine/metabolomic signatures in peripheral blood immune cells from subjects prior to and after the onset of RA, at baseline and in response to ex vivo toll-like receptor (TLR) stimulation. Here, we analyzed 13 ACPA (+) subjects without arthritis but at-risk for future development of RA, 14 early RA patients, and 13 healthy controls. We found significantly elevated H3 citrullination in CD14 monocytes, as well as CD1c dendritic cells and CD66 granulocytes. Unsupervised analysis identified two distinct subsets in CD14 monocytes characterized by H3 modification and unique cytokine/metabolomic signatures. CD14 monocytes with elevated TLR-stimulated H3 citrullination were significantly increased in ACPA (+) at-risk subjects. These cells were skewed to produce TNFα, MIP1β, IFNα, and partially IL-12. Additionally, they demonstrate peptidyl arginine deiminase 4 (PAD4) mediated upregulation of the glycolytic enzyme PFKFB3. These CD14 monocytes with elevated H3 citrullination morphologically formed monocyte extracellular traps (METs). Taken together, dysregulated PAD4-driven cytokine production as well as MET formation in CD14 monocytes in ACPA (+) at-risk subjects likely plays an important role in the development of RA via promoting and perpetuating inflammation and generation of citrullinated autoantigens.
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http://dx.doi.org/10.1016/j.jaut.2020.102581DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855988PMC
February 2021

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

IEEE Trans Med Imaging 2020 Dec 7;PP. Epub 2020 Dec 7.

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.
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http://dx.doi.org/10.1109/TMI.2020.3042789DOI Listing
December 2020

Characterisation of MRI Indeterminate Breast Lesions Using Dedicated Breast PET and Prone FDG PET-CT in Patients with Breast Cancer-A Proof-of-Concept Study.

J Pers Med 2020 Sep 25;10(4). Epub 2020 Sep 25.

Radiology Department, Royal Free Hospital NHS Trust, London NW3 2QG, UK.

Magnetic resonance imaging (MRI) in patients with breast cancer to assess extent of disease or multifocal disease can demonstrate indeterminate lesions requiring second-look ultrasound and ultrasound or MRI-guided biopsies. Prone positron emission tomography-computed tomography (PET-CT) is a dedicated acquisition performed with a breast-supporting device on a standard PET-CT scanner. The MAMmography with Molecular Imaging (MAMMI, Oncovision, Valencia, Spain) PET system (PET-MAMMI) is a true tomographic ring scanner for the breast. We investigated if PET-MAMMI and prone PET-CT were able to characterise these MRI- indeterminate lesions further. A total of 10 patients with breast cancer and indeterminate lesions on breast MRI were included. Patients underwent prone PET-MAMMI and prone PET-CT after injection of FDG subsequently on the same day. Patients then resumed their normal pathway, with the clinicians blinded to the results of the PET-MAMMI and prone PET-CT. Of the MRI-indeterminate lesions, eight were histopathologically proven to be malignant and two were benign. PET-MAMMI and prone PET-CT only were able to demonstrate increased FDG uptake in 1/8 and 0/8 of the MRI-indeterminate malignant lesions, respectively. Of the MRI-indeterminate benign lesions, both PET-MAMMI and prone PET-CT demonstrated avidity in 1/2 of these lesions. Our findings do not support the use of PET-MAMMI to characterise indeterminate breast MRI lesions requiring a second look ultrasound.
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http://dx.doi.org/10.3390/jpm10040148DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712852PMC
September 2020

Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67.

JCO Clin Cancer Inform 2020 07;4:666-679

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO.

Purpose: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)-stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning-based domain adaptation is helpful for nucleus recognition when image annotations are unavailable in target data sets.

Methods: We considered 2 different institutional pancreatic NET data sets: one (ie, source) containing 38 cases with 114 annotated images and the other (ie, target) containing 72 cases with 20 annotated images. The gold standards were manually annotated by 1 pathologist. We developed a novel deep learning-based domain adaptation framework to count different types of nuclei (ie, immunopositive tumor, immunonegative tumor, nontumor nuclei). We compared the proposed method with several recent fully supervised deep learning models, such as fully convolutional network-8s (FCN-8s), U-Net, fully convolutional regression network (FCRN) A, FCRNB, and fully residual convolutional network (FRCN). We also evaluated the proposed method by learning with a mixture of converted source images and real target annotations.

Results: Our method achieved an score of 81.3% and 62.3% for nucleus detection and classification in the target data set, respectively. Our method outperformed FCN-8s (53.6% and 43.6% for nucleus detection and classification, respectively), U-Net (61.1% and 47.6%), FCRNA (63.4% and 55.8%), and FCRNB (68.2% and 60.6%) in terms of score and was competitive with FRCN (81.7% and 70.7%). In addition, learning with a mixture of converted source images and only a small set of real target labels could further boost the performance.

Conclusion: This study demonstrates that deep learning-based domain adaptation is helpful for nucleus recognition in Ki-67 IHC stained images when target data annotations are not available. It would improve the applicability of deep learning models designed for downstream supervised learning tasks on different data sets.
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http://dx.doi.org/10.1200/CCI.19.00108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397778PMC
July 2020

Reply to Letter to the Editor.

J Pediatr Surg 2020 11 25;55(11):2537-2538. Epub 2020 Jun 25.

Department of Surgery, Division of Urology, University of Colorado School of Medicine and the Children's Hospital of Colorado, Aurora, CO. Electronic address:

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http://dx.doi.org/10.1016/j.jpedsurg.2020.06.026DOI Listing
November 2020

Increased Odds of Ventricular Arrhythmias Associated with Selective Serotonin Reuptake Inhibitor Use among the Pediatric and Young Adult Population: A Case-Control Study.

J Pediatr 2020 Jul 6. Epub 2020 Jul 6.

Department of Clinical Pharmacy, Center for Pharmaceutical Outcomes (CePOR), Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora, CO.

Objective: To measure the association between selective serotonin reuptake inhibitor (SSRI) use and out-of-hospital ventricular arrhythmia among the pediatric and young adult population.

Study Design: Case-control study using US claims data from 2007 to 2018. Cases were subjects with at least 1 event between ages 2 and 24 years. Controls (matched 10:1 on index date, age, sex, and continuous enrollment) had no events during study period. Independent association between current SSRI use (prescription fill with continuous exposure ending on, or after, the index date) and incident out-of-hospital ventricular arrhythmia (hospitalization or emergency room encounter with primary diagnostic code for ventricular arrhythmia) was estimated using multivariable conditional logistic regression. Separate analyses were performed for pediatric (2-17 years of age) vs young adult (18-24 years of age) subjects and between citalopram/escitalopram vs other SSRIs.

Results: During the study period, 237 eligible cases were identified with 2370 matched controls. Cases were more likely to have government insurance and have a mental health, cardiac, or other complex chronic condition. Thirteen cases (5%) and 15 controls (<1%) had current SSRI exposure. After adjustment for mental health and chronic conditions, there was an increased odds of current SSRI use among cases compared with controls (OR 5.11, 95% CI 1.22-21.37). No difference was observed between pediatric and young adult ages, nor between citalopram/escitalopram and other SSRIs.

Conclusions: These findings demonstrate increased odds of out-of-hospital ventricular arrhythmia associated with SSRI use in the pediatric and young adult population, suggesting a need for heightened awareness and ongoing monitoring of this potential adverse effect.
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http://dx.doi.org/10.1016/j.jpeds.2020.07.007DOI Listing
July 2020

Evaluation of Rapid Extraction Methods Coupled with a Recombinase Polymerase Amplification Assay for Point-of-Need Diagnosis of Post-Kala-Azar Dermal Leishmaniasis.

Trop Med Infect Dis 2020 Jun 5;5(2). Epub 2020 Jun 5.

Nutrition and Clinical Service Division (NCSD), International Centre for Diarrhoeal Disease Research, Bangladesh, (icddr,b), Dhaka 1212, Bangladesh.

To detect Post-kala-azar leishmaniasis (PKDL) cases, several molecular methods with promising diagnostic efficacy have been developed that involve complicated and expensive DNA extraction methods, thus limiting their application in resource-poor settings. As an alternative, we evaluated two rapid DNA extraction methods and determined their impact on the detection of the parasite DNA using our newly developed recombinase polymerase amplification (RPA) assay. Skin samples were collected from suspected PKDL cases following their diagnosis through national guidelines. The extracted DNA from three skin biopsy samples using three different extraction methods was subjected to RPA and qPCR. The qPCR and RPA assays exhibited highest sensitivities when reference DNA extraction method using Qiagen (Q) kit was followed. In contrast, the sensitivity of the RPA assay dropped to 76.7% and 63.3%, respectively, when the boil & spin (B&S) and SpeedXtract (SE) rapid extraction methods were performed. Despite this compromised sensitivity, the B&S-RPA technique yielded an excellent agreement with both Q-qPCR (k = 0.828) and Q-RPA (k = 0.831) techniques. As expected, the reference DNA extraction method was found to be superior in terms of diagnostic efficacy. Finally, to apply the rapid DNA extraction methods in resource-constrained settings, further methodological refinement is warranted to improve DNA yield and purity through rigorous experiments.
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http://dx.doi.org/10.3390/tropicalmed5020095DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344569PMC
June 2020

Retinoic acid signaling within pancreatic endocrine progenitors regulates mouse and human β cell specification.

Development 2020 06 22;147(12). Epub 2020 Jun 22.

Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA

Retinoic acid (RA) signaling is essential for multiple developmental processes, including appropriate pancreas formation from the foregut endoderm. RA is also required to generate pancreatic progenitors from human pluripotent stem cells. However, the role of RA signaling during endocrine specification has not been fully explored. In this study, we demonstrate that the disruption of RA signaling within the NEUROG3-expressing endocrine progenitor population impairs mouse β cell differentiation and induces ectopic expression of crucial δ cell genes, including somatostatin. In addition, the inhibition of the RA pathway in hESC-derived pancreatic progenitors downstream of NEUROG3 induction impairs insulin expression. We further determine that RA-mediated regulation of endocrine cell differentiation occurs through Wnt pathway components. Together, these data demonstrate the importance of RA signaling in endocrine specification and identify conserved mechanisms by which RA signaling directs pancreatic endocrine cell fate.
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http://dx.doi.org/10.1242/dev.189977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328135PMC
June 2020

Quantifying the incremental value of deep learning: Application to lung nodule detection.

PLoS One 2020 14;15(4):e0231468. Epub 2020 Apr 14.

Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America.

We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some domains; however, implementation of these methods is in early development. Often these methods are only directly compared to existing methods; here we present a framework for assessing the value of a machine learning model by assessing the incremental value. We developed a machine learning model to identify and classify lung nodules and assessed the incremental value added to existing risk prediction models. Multiple external datasets were used for validation. We found that our image model, trained on a dataset from The Cancer Imaging Archive (TCIA), improves upon existing models that are restricted to patient characteristics, but it was inconclusive about whether it improves on models that consider nodule features. Another interesting finding is the variable performance on different datasets, suggesting population generalization with machine learning models may be more challenging than is often considered.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231468PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156089PMC
July 2020

Validity of administrative claims-based algorithms for ventricular arrhythmia and cardiac arrest in the pediatric population.

Pharmacoepidemiol Drug Saf 2020 11 13;29(11):1499-1503. Epub 2020 Apr 13.

Department of Pediatrics, Cardiology Section, School of Medicine, University of Colorado, Aurora, Colorado, USA.

Purpose: Identify administrative claims-based algorithms for capturing out-of-hospital ventricular arrhythmias (VA) and cardiac arrests (CA) due to cardiac causes in the pediatric population with high positive-predictive value (PPV).

Methods: Within a single pediatric center, a retrospective cohort of patients hospitalized or seen in the emergency room for VA or CA were identified from the electronic health records. Eligible encounters were blindly reviewed and linked to administrative data, including ICD-9/ICD-10 codes. Test characteristics, including PPV, for different diagnostic and procedure codes were generated using a 50% training sample. The gold standard was definite or suspected out-of-hospital VA or CA due to cardiac cause verified based on clinical criteria. Algorithms with the highest PPV were then applied to a 50% validation sample to validate performance.

Results: From 2004-2017, 598 encounters met eligibility criteria. 174 (29%) had an outcome of interest, with remainder being an inpatient event or CA due to other cause. Within the training sample (n = 263), VA codes in primary position had a PPV 94% (95%CI 81%-99%) with low sensitivity (44%, 95%CI 33%-56%). CA codes in any position or VA codes in nonprimary positions had low PPV (18%-19%, 31% respectively). Applying the top three performing algorithms to the validation sample (n = 252) yielded similar PPV values.

Conclusions: Contrary to adults, algorithms including a CA code do not perform well for identifying out-of-hospital VA and CA due to cardiac cause in the pediatric populations. Researchers should be aware of the potential implications for future pediatric drug safety studies for these outcomes.
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http://dx.doi.org/10.1002/pds.5001DOI Listing
November 2020

Safety and Outcomes of Permanent and Retrievable Inferior Vena Cava Filters in the Oncology Population.

Int J Vasc Med 2020 5;2020:6582742. Epub 2020 Feb 5.

University of Colorado School of Medicine, Department of Medicine, Division of Hematology, USA.

Background: The role for inferior vena cava (IVC) filters in the oncology population is poorly defined.

Objectives: Our primary endpoint was to determine the rate of filter placement in cancer patients without an absolute contraindication to anticoagulation and the rate of recurrent VTE after filter placement in both retrievable and permanent filter groups. /.

Methods: A single-institution, retrospective study of patients with active malignancies and acute VTE who received a retrievable or permanent IVC filter between 2009-2013. Demographics and outcomes were confirmed on independent chart review. Cost data were obtained using Current Procedural Terminology (CPT) codes.

Results: 179 patients with retrievable filters and 207 patients with permanent filters were included. Contraindication to anticoagulation was the most cited reason for filter placement; however, only 76% of patients with retrievable filters and 69% of patients with permanent filters had an absolute contraindication to anticoagulation. 20% of patients with retrievable filters and 24% of patients with permanent filters had recurrent VTE. The median time from filter placement to death was 8.9 and 3.2 months in the retrievable and permanent filter groups, respectively. The total cost of retrievable filters and permanent filters was $2,883,389 and $3,722,688, respectively.

Conclusions: The role for IVC filters in cancer patients remains unclear as recurrent VTE is common and time from filter placement to death is short. Filter placement is costly and has a clinically significant complication rate, especially for retrievable filters. More data from prospective, randomized trials are needed to determine the utility of IVC filters in cancer patients.
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http://dx.doi.org/10.1155/2020/6582742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025073PMC
February 2020

Predictive Modeling for Metabolomics Data.

Methods Mol Biol 2020 ;2104:313-336

Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.
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http://dx.doi.org/10.1007/978-1-0716-0239-3_16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423323PMC
January 2021

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.

JAMA Netw Open 2020 01 3;3(1):e1919396. Epub 2020 Jan 3.

Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora.

Importance: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements in outcomes through the appropriate prescription of anticoagulation medication. Although a variety of methods exist for screening for AF, a targeted approach, which requires an efficient method for identifying patients at risk, would be preferred.

Objective: To examine machine learning approaches applied to electronic health record data that have been harmonized to the Observational Medical Outcomes Partnership Common Data Model for identifying risk of AF.

Design, Setting, And Participants: This diagnostic study used data from 2 252 219 individuals cared for in the UCHealth hospital system, which comprises 3 large hospitals in Colorado, from January 1, 2011, to October 1, 2018. Initial analysis was performed in December 2018; follow-up analysis was performed in July 2019.

Exposures: All Observational Medical Outcomes Partnership Common Data Model-harmonized electronic health record features, including diagnoses, procedures, medications, age, and sex.

Main Outcomes And Measures: Classification of incident AF in designated 6-month intervals, adjudicated retrospectively, based on area under the receiver operating characteristic curve and F1 statistic.

Results: Of 2 252 219 individuals (1 225 533 [54.4%] women; mean [SD] age, 42.9 [22.3] years), 28 036 (1.2%) developed incident AF during a designated 6-month interval. The machine learning model that used the 200 most common electronic health record features, including age and sex, and random oversampling with a single-layer, fully connected neural network provided the optimal prediction of 6-month incident AF, with an area under the receiver operating characteristic curve of 0.800 and an F1 score of 0.110. This model performed only slightly better than a more basic logistic regression model composed of known clinical risk factors for AF, which had an area under the receiver operating characteristic curve of 0.794 and an F1 score of 0.079.

Conclusions And Relevance: Machine learning approaches to electronic health record data offer a promising method for improving risk prediction for incident AF, but more work is needed to show improvement beyond standard risk factors.
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http://dx.doi.org/10.1001/jamanetworkopen.2019.19396DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991266PMC
January 2020

Leveraging Family History in Case-Control Analyses of Rare Variation.

Genetics 2020 02 16;214(2):295-303. Epub 2019 Dec 16.

Department of Human Genetics, Emory University, Atlanta, 30030 Georgia

Standard methods for case-control association studies of rare variation often treat disease outcome as a dichotomous phenotype. However, both theoretical and experimental studies have demonstrated that subjects with a family history of disease can be enriched for risk variation relative to subjects without such history. Assuming family history information is available, this observation motivates the idea of replacing the standard dichotomous outcome variable used in case-control studies with a more informative ordinal outcome variable that distinguishes controls (0), sporadic cases (1), and cases with a family history (2), with the expectation that we should observe increasing number of risk variants with increasing category of the ordinal variable. To leverage this expectation, we propose a novel rare-variant association test that incorporates family history information based on our previous GAMuT framework for rare-variant association testing of multivariate phenotypes. We use simulated data to show that, when family history information is available, our new method outperforms standard rare-variant association methods, like burden and SKAT tests, that ignore family history. We further illustrate our method using a rare-variant study of cleft lip and palate.
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http://dx.doi.org/10.1534/genetics.119.302846DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017020PMC
February 2020

Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification.

Med Image Comput Comput Assist Interv 2019 Oct 10;11764:740-749. Epub 2019 Oct 10.

Depatment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus.

Cell or nucleus quantification has recently achieved state-of-the-art performance by using convolutional neural networks (CNNs). In general, training CNNs requires a large amount of annotated microscopy image data, which is prohibitively expensive or even impossible to obtain in some applications. Additionally, when applying a deep supervised model to new datasets, it is common to annotate individual cells in those target datasets for model re-training or fine-tuning, leading to low-throughput image analysis. In this paperSSS, we propose a novel adversarial domain adaptation method for cell/nucleus quantification across multimodality microscopy image data. Specifically, we learn a fully convolutional network detector with task-specific cycle-consistent adversarial learning, which conducts pixel-level adaptation between source and target domains and completes a cell/nucleus detection task. Then we generate pseudo-labels on target training data using the detector trained with adapted source images and further fine-tune the detector towards the target domain to boost the performance. We evaluate the proposed method on multiple cross-modality microscopy image datasets and obtain a significant improvement in cell/nucleus detection compared to the reference baselines and a recent state-of-the-art deep domain adaptation approach. In addition, our method is very competitive with the fully supervised models trained with all real target training labels.
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http://dx.doi.org/10.1007/978-3-030-32239-7_82DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6903918PMC
October 2019

Using a spatial point process framework to characterize lung computed tomography scans.

Spat Stat 2019 Mar 31;29:243-267. Epub 2018 Dec 31.

Department of Radiology, National Jewish Health, Denver, CO, USA.

Pulmonary emphysema is a destructive disease of the lungs that is currently diagnosed via visual assessment of lung Computed Tomography (CT) scans by a radiologist. Visual assessment can have poor inter-rater reliability, is time consuming, and requires access to trained assessors. Quantitative methods that reliably summarize the biologically relevant characteristics of an image are needed to improve the way lung diseases are characterized. The goal of this work was to show how spatial point process models can be used to create a set of radiologically derived quantitative lung biomarkers of emphysema. We formalized a general framework for applying spatial point processes to lung CT scans, and developed a Shot Noise Cox Process to quantify how radiologically based emphysematous tissue clusters into larger structures. Bayesian estimation of model parameters was done using spatial Birth-Death MCMC (BD-MCMC). In simulations, we showed the BD-MCMC estimation algorithm is able to accurately recover model parameters. In an application to real lung CT scans from the COPDGene cohort, we showed variability in the clustering characteristics of emphysematous tissue across disease subtypes that were based on visual assessments of the CT scans.
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http://dx.doi.org/10.1016/j.spasta.2018.12.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867806PMC
March 2019

Wavelet-based Benjamini-Hochberg procedures for multiple testing under dependence.

Authors:
Debashis Ghosh

Math Biosci Eng 2019 09;17(1):56-72

Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA.

Multiple comparisons methodology has experienced a resurgence of interest due to the increase in high-dimensional datasets generated from various biological, medical and scientific fields. An outstanding problem in this area is how to perform testing in the presence of dependence between the p-values. We propose a novel approach to this problem based on a spacings-based representation of the Benjamini-Hochberg procedure. The representation leads to a new application of the wavelet transform to effectively decorrelate p-values. Theoretical justification for the procedure is shown. The power gains of the proposed methodology relative to existing procedures is demonstrated using both simulated and real datasets.
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http://dx.doi.org/10.3934/mbe.2020003DOI Listing
September 2019

Minimizing Batch Effects in Mass Cytometry Data.

Front Immunol 2019 15;10:2367. Epub 2019 Oct 15.

Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States.

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates.
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http://dx.doi.org/10.3389/fimmu.2019.02367DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803429PMC
October 2020

Phosphorylation of human placental aromatase CYP19A1.

Biochem J 2019 11;476(21):3313-3331

Keck MS & Proteomics Resource, WM Keck Biotechnology Resource Laboratory, New Haven, CT 06510, U.S.A.

Aromatase CYP19A1 catalyzes the synthesis of estrogens in endocrine, reproductive and central nervous systems. Higher levels of 17β-estradiol (E2) are associated with malignancies and diseases of the breast, ovary and endometrium, while low E2 levels increase the risk for osteoporosis, cardiovascular diseases and cognitive disorders. E2, the transcriptional activator of the estrogen receptors, is also known to be involved in non-genomic signaling as a neurotransmitter/neuromodulator, with recent evidence for rapid estrogen synthesis (RES) within the synaptic terminal. Although regulation of brain aromatase activity by phosphorylation/dephosphorylation has been suggested, it remains obscure in the endocrine and reproductive systems. RES and overabundance of estrogens could stimulate the genomic and non-genomic signaling pathways, and genotoxic effects of estrogen metabolites. Here, by utilizing biochemical, cellular, mass spectrometric, and structural data we unequivocally demonstrate phosphorylation of human placental aromatase and regulation of its activity. We report that human aromatase has multiple phosphorylation sites, some of which are consistently detectable. Phosphorylation of the residue Y361 at the reductase-coupling interface significantly elevates aromatase activity. Other sites include the active site residue S478 and several at the membrane interface. We present the evidence that two histidine residues are phosphorylated. Furthermore, oxidation of two proline residues near the active site may have implications in regulation. Taken together, the results demonstrate that aromatase activity is regulated by phosphorylation and possibly other post-translational modifications. Protein level regulation of aromatase activity not only represents a paradigm shift in estrogen-mediated biology, it could also explain unresolved clinical questions such as aromatase inhibitor resistance.
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http://dx.doi.org/10.1042/BCJ20190633DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069221PMC
November 2019

Risk and subtypes of secondary primary malignancies in diffuse large B-cell lymphoma survivors change over time based on stage at diagnosis.

Cancer 2020 01 11;126(1):189-201. Epub 2019 Sep 11.

Department of Medicine, Division of Hematology, University of Colorado School of Medicine, Aurora, Colorado.

Background: Previous studies have shown an increased risk of secondary primary malignancies (SPMs) after diffuse large B-cell lymphoma (DLBCL) treatment. Whether stage of DLBCL at diagnosis affects the subtypes of SPMs that occur has not been previously described.

Methods: The Surveillance, Epidemiology, and End Results database was queried for patients aged >18 years diagnosed with primary DLBCL from 1973 to 2010 and categorized by early stage (ES) (stage I-II) or advanced stage (AS) (stage III-IV) disease. Differences in overall and location-specific SPM incidence by stage and time since diagnosis were assessed in 5-year intervals using a Fine-Gray hazards model. Overall survival was compared using the log-rank test. A Cox proportional hazards model was used to assess differences in survival.

Results: In total, 26,038 patients with DLBCL were identified, including 14,724 with ES and 11,314 with AS disease. The median follow-up was 13.3 years. Overall, 13.0% of patients developed SPM, with a higher but nonsignificantly increased risk of SPM development in those who had ES disease compared with those who had AS disease (14% vs 11.6%; P = .14). During the first 5 years after diagnosis, patients who had ES disease had a higher risk of SPM than those who had AS disease, specifically colorectal, pancreas, breast, and prostate SPMs. During the period from 10 to 15 years after diagnosis, patients who had AS disease had a higher risk of SPM than those who had ES disease, specifically hematologic SPMs. Development of SPM was found to significantly increase the risk of death regardless of stage at diagnosis.

Conclusions: In this large, population-based study, distinctly different subtypes and temporal patterns of SPM development were identified based on stage of DLBCL at diagnosis. The current study merits consideration of tailored site-specific and time-specific surveillance for patients with DLBCL according to stage and time interval since diagnosis.
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http://dx.doi.org/10.1002/cncr.32513DOI Listing
January 2020

Structural and Functional Analyses of an Allosteric EYA2 Phosphatase Inhibitor That Has On-Target Effects in Human Lung Cancer Cells.

Mol Cancer Ther 2019 09 8;18(9):1484-1496. Epub 2019 Jul 8.

Experimental Drug Discovery Centre, A*STAR, Singapore, Singapore.

EYA proteins (EYA1-4) are critical developmental transcriptional cofactors that contain an EYA domain (ED) harboring Tyr phosphatase activity. EYA proteins are largely downregulated after embryogenesis but are reexpressed in cancers, and their Tyr phosphatase activity plays an important role in the DNA damage response and tumor progression. We previously identified a class of small-molecule allosteric inhibitors that specifically inhibit the Tyr phosphatase activity of EYA2. Herein, we determined the crystal structure of the EYA2 ED in complex with NCGC00249987 (a representative compound in this class), revealing that it binds to an induced pocket distant from the active site. NCGC00249987 binding leads to a conformational change of the active site that is unfavorable for Mg binding, thereby inhibiting EYA2's Tyr phosphatase activity. We demonstrate, using genetic mutations, that migration, invadopodia formation, and invasion of lung adenocarcinoma cells are dependent on EYA2 Tyr phosphatase activity, whereas growth and survival are not. Further, we demonstrate that NCGC00249987 specifically targets migration, invadopodia formation, and invasion of lung cancer cells, but that it does not inhibit cell growth or survival. The compound has no effect on lung cancer cells carrying an EYA2 F290Y mutant that abolishes compound binding, indicating that NCGC00249987 is on target in lung cancer cells. These data suggest that the NCGC00249987 allosteric inhibitor can be used as a chemical probe to study the function of the EYA2 Tyr phosphatase activity in cells and may have the potential to be developed into an antimetastatic agent for cancers reliant on EYA2's Tyr phosphatase activity.
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http://dx.doi.org/10.1158/1535-7163.MCT-18-1239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6726557PMC
September 2019

Kung Faux Pandas Simplifying privacy protection.

AMIA Jt Summits Transl Sci Proc 2019 6;2019:267-274. Epub 2019 May 6.

CU Data Science to Patient Value (D2V), Anschutz Medical Campus, Aurora, CO.

There are many barriers to data access and data sharing, especially in the domain of computational research using health care data. Legal constraints, such as HIPAA, protect patient privacy but slow access to data and limit reproducibility. We provide a description of an end-to-end system called Kung Faux Pandas for easily generating de-identified or synthetic data which is statistically similar to real data but lacks sensitive information. This system focuses on data synthesis and de-identification narrowed to a specific research question to allow for self-service data access without the complexities required to generate an entire population of data that is not needed for a given research project. Kung Faux Pandas is an open source publicly available system that lowers barriers to HIPAA- and GDPR-compliant data sharing for enabling reproducibility and other purposes.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568075PMC
May 2019

How many lymph nodes are enough? Assessing the adequacy of lymph node yield for staging in favorable histology wilms tumor.

J Pediatr Surg 2019 Nov 20;54(11):2331-2335. Epub 2019 Jun 20.

Department of Surgery, Division of Urology, University of Colorado School of Medicine, Aurora, CO. Electronic address:

Purpose: Current investigational priorities in the treatment of favorable histology Wilms tumor (FHWT) center on accurate staging and risk-stratification. The extent of lymph node (LN) sampling has not been clearly defined; its importance cannot be overstated as it guides adjuvant therapy. The identification of a minimum LN yield to minimize the risk of harboring occult metastatic disease could help development of surgical guidelines. This study focuses on using the beta-binomial distribution to estimate the risk of occult metastatic disease in patients with FHWT.

Materials & Methods: The National Cancer Database was queried for patients with unilateral FHWT from 2004 to 2013. Data were used to characterize nodal positivity for patients who underwent surgery and had ≥1 positive LN and ≥2 LNs examined. The probability of missing a positive LN (i.e., false negative) for a given LN yield was calculated using an empirical estimation and the beta-binomial model. Patients were then stratified by tumor size.

Results: 422 patients met study criteria. To limit the chance of missing a positive LN to ≤10%, the empirical estimation and beta-binomial model estimated that 6 and 10 LNs needed to be sampled, respectively. Tumor size did not influence the result. Internal validation showed little variation to maintain a false negative rate ≤ 10%.

Conclusions: Using mathematical modeling, it appears that the desired LN yield in FHWT to reduce the risk of false-negative LN sampling to ≤10% is between 6 and 10. The current analysis represents an objective attempt to determine the desired surgical approach to LN sampling to accurately stage patients with FHWT.

Level Of Evidence: II.
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http://dx.doi.org/10.1016/j.jpedsurg.2019.06.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881092PMC
November 2019

Rhodium-Catalyzed Directed C-H Amidation of Imidazoheterocycles with Dioxazolones.

Org Lett 2019 Jun 12;21(12):4905-4909. Epub 2019 Jun 12.

Department of Chemistry , Visva-Bharati (A Central University) , Santiniketan 731235 , India.

A Rh(III)-catalyzed directed ortho-amidation of 2-arylimidazoheterocycles using dioxazolone as an amidating reagent has been developed. This protocol is a simple, straightforward, and economic was to afford a variety of N-(2-(imidazo[1,2- a]pyridin-2-yl)phenyl)acetamide derivatives with excellent yields. A mechanistic study reveals that a reversible cleavage of C-H bond might be involved in the reaction.
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http://dx.doi.org/10.1021/acs.orglett.9b01832DOI Listing
June 2019