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Excessive tissue scarring, or fibrosis, is a critical contributor to end stage renal disease, but current clinical tests are not sufficient for assessing renal fibrosis. Quantitative magnetization transfer (qMT) MRI provides indirect information about the macromolecular composition of tissues. We evaluated measurements of the pool size ratio (PSR, the ratio of immobilized macromolecular to free water protons) obtained by qMT as a biomarker of tubulointerstitial fibrosis in a well-established murine model with progressive renal disease. MR images were acquired from 16-week-old fibrotic hHB-EGF mice and normal wild-type (WT) mice (N = 12) at 7 T. QMT parameters were derived using a two-pool five-parameter fitting model. A normal range of PSR values in the cortex and outer stripe of outer medulla (CR + OSOM) was determined by averaging across voxels within WT kidneys (mean ± 2SD). Regions in diseased mice whose PSR values exceeded the normal range above a threshold value (tPSR) were identified and measured. The spatial distribution of fibrosis was confirmed using picrosirius red stains. Compared with normal WT mice, scattered clusters of high PSR regions were observed in the OSOM of hHB-EGF mouse kidneys. Moderate increases in mean PSR (mPSR) of CR + OSOM regions were observed across fibrotic kidneys. The abnormally high PSR regions (% area) detected by the tPSR were significantly increased in hHB-EGF mice, and were highly correlated with regions of fibrosis detected by histological fibrosis indices measured from picrosirius red staining. Renal tubulointerstitial fibrosis in OSOM can thus be assessed by qMT MRI using an appropriate analysis of PSR. This technique may be used as an imaging biomarker for chronic kidney diseases.
© 2019 John Wiley & Sons, Ltd.
Placental dysfunction is implicated in many pregnancy complications, including preeclampsia and preterm birth (PTB). While both these syndromes are influenced by environmental risk factors, they also have a substantial genetic component that is not well understood. Precisely controlled gene expression during development is crucial to proper placental function and often mediated through gene regulatory enhancers. However, we lack accurate maps of placental enhancer activity due to the challenges of assaying the placenta and the difficulty of comprehensively identifying enhancers. To address the gap in our knowledge of gene regulatory elements in the placenta, we used a two-step machine learning pipeline to synthesize existing functional genomics studies, transcription factor (TF) binding patterns, and evolutionary information to predict placental enhancers. The trained classifiers accurately distinguish enhancers from the genomic background and placental enhancers from enhancers active in other tissues. Genomic features collected from tissues and cell lines involved in pregnancy are the most predictive of placental regulatory activity. Applying the classifiers genome-wide enabled us to create a map of 33,010 predicted placental enhancers, including 4,562 high-confidence enhancer predictions. The genome-wide placental enhancers are significantly enriched nearby genes associated with placental development and birth disorders and for SNPs associated with gestational age. These genome-wide predicted placental enhancers provide candidate regions for further testing in vitro, will assist in guiding future studies of genetic associations with pregnancy phenotypes, and aid interpretation of potential mechanisms of action for variants found through genetic studies.
OBJECTIVE - Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification.
MATERIALS AND METHODS - A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals.
RESULTS - The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82).
CONCLUSION - This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.
Copyright © 2018 Elsevier Inc. All rights reserved.
The AJCC recently published the 8th edition of its cancer staging system. Significant changes were made to the staging algorithm for soft tissue sarcoma (STS) of the extremities or trunk, including the addition of 2 additional T (size) classifications in lieu of tumor depth and grouping lymph node metastasis (LNM) with distant metastasis as stage IV disease. Whether these changes improve staging system performance is questionable. This retrospective cohort analysis of 21,396 adult patients with STS of the extremity or trunk in the SEER database compares the AJCC 8th edition staging system with the 7th edition and a newly proposed staging algorithm using a variety of statistical techniques. The effect of tumor size on disease-specific survival was assessed by flexible, nonlinear Cox proportional hazard regression using restricted cubic splines and fractional polynomials. The slope of covariate-adjusted log hazards for sarcoma-specific survival decreases for tumors >8 cm in greatest dimension, limiting prognostic information contributed by the new T4 classification in the AJCC 8th edition. Anatomic depth independently provides significant prognostic information. LNM is not equivalent to distant, non-nodal metastasis. Based on these findings, an alternative staging system is proposed and demonstrated to outperform both AJCC staging schemes. The analyses presented also disclose no evidence of improved clinical performance of the 8th edition compared with the previous edition. The AJCC 8th edition staging system for STS is no better than the previous 7th edition. Instead, a proposed staging system based on histologic grade, tumor size, and anatomic depth shows significantly higher predictive accuracy, with higher model concordance than either AJCC staging system. Changes to existing staging systems should improve the performance of prognostic models. Until such improvements are documented, AJCC committees should refrain from modifying established staging schemes.
Copyright © 2018 by the National Comprehensive Cancer Network.
BACKGROUND - Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease.
RESULTS - To illustrate this approach in a clinical application, we analyzed 13 candidate missense variants in regulator of telomere elongation helicase 1 (RTEL1) identified in patients with Familial Interstitial Pneumonia (FIP). We curated pathogenic and neutral RTEL1 variants from the literature and public databases. We then used homology modeling to construct a 3D structural model of RTEL1 and mapped known variants into this structure. We next developed a pathogenicity prediction algorithm based on proximity to known disease causing and neutral variants and evaluated its performance with leave-one-out cross-validation. We further validated our predictions with segregation analyses, telomere lengths, and mutagenesis data from the homologous XPD protein. Our algorithm for classifying RTEL1 VUS based on spatial proximity to pathogenic and neutral variation accurately distinguished 7 known pathogenic from 29 neutral variants (ROC AUC = 0.85) in the N-terminal domains of RTEL1. Pathogenic proximity scores were also significantly correlated with effects on ATPase activity (Pearson r = -0.65, p = 0.0004) in XPD, a related helicase. Applying the algorithm to 13 VUS identified from sequencing of RTEL1 from patients predicted five out of six disease-segregating VUS to be pathogenic. We provide structural hypotheses regarding how these mutations may disrupt RTEL1 ATPase and helicase function.
CONCLUSIONS - Spatial analysis of missense variation accurately classified candidate VUS in RTEL1 and suggests how such variants cause disease. Incorporating spatial proximity analyses into other pathogenicity prediction tools may improve accuracy for other genes and genetic diseases.
The recent Zika virus (ZIKV) outbreak demonstrates that cost-effective clinical diagnostics are urgently needed to detect and distinguish viral infections to improve patient care. Unlike dengue virus (DENV), ZIKV infections during pregnancy correlate with severe birth defects, including microcephaly and neurological disorders. Because ZIKV and DENV are related flaviviruses, their homologous proteins and nucleic acids can cause cross-reactions and false-positive results in molecular, antigenic, and serologic diagnostics. We report the characterization of monoclonal antibody pairs that have been translated into rapid immunochromatography tests to specifically detect the viral nonstructural 1 (NS1) protein antigen and distinguish the four DENV serotypes (DENV1-4) and ZIKV without cross-reaction. To complement visual test analysis and remove user subjectivity in reading test results, we used image processing and data analysis for data capture and test result quantification. Using a 30-μl serum sample, the sensitivity and specificity values of the DENV1-4 tests and the pan-DENV test, which detects all four dengue serotypes, ranged from 0.76 to 1.00. Sensitivity/specificity for the ZIKV rapid test was 0.81/0.86, respectively, using a 150-μl serum input. Serum ZIKV NS1 protein concentrations were about 10-fold lower than corresponding DENV NS1 concentrations in infected patients; moreover, ZIKV NS1 protein was not detected in polymerase chain reaction-positive patient urine samples. Our rapid immunochromatography approach and reagents have immediate application in differential clinical diagnosis of acute ZIKV and DENV cases, and the platform can be applied toward developing rapid antigen diagnostics for emerging viruses.
Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.
BACKGROUND - The objective of this study is to evaluate use of the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) online risk calculator for estimating common outcomes after operations for gallbladder cancer and extrahepatic cholangiocarcinoma.
METHODS - Subjects from the United States Extrahepatic Biliary Malignancy Consortium (USE-BMC) who underwent operation between January 1, 2000 and December 31, 2014 at 10 academic medical centers were included in this study. Calculator estimates of risk were compared to actual outcomes.
RESULTS - The majority of patients underwent partial or major hepatectomy, Whipple procedures or extrahepatic bile duct resection. For the entire cohort, c-statistics for surgical site infection (0.635), reoperation (0.680) and readmission (0.565) were less than 0.7. The c-statistic for death was 0.740. For all outcomes the actual proportion of patients experiencing an event was much higher than the median predicted risk of that event. Similarly, the group of patients who experienced an outcome did have higher median predicted risk than those who did not.
CONCLUSIONS - The ACS NSQIP risk calculator is easy to use but requires further modifications to more accurately estimate outcomes for some patient populations and operations for which validation studies show suboptimal performance.
Copyright © 2017 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.
Background - Recent trials suggest procalcitonin-based guidelines can reduce antibiotic use for respiratory infections. However, the accuracy of procalcitonin to discriminate between viral and bacterial pneumonia requires further dissection.
Methods - We evaluated the association between serum procalcitonin concentration at hospital admission with pathogens detected in a multicenter prospective surveillance study of adults hospitalized with community-acquired pneumonia. Systematic pathogen testing included cultures, serology, urine antigen tests, and molecular detection. Accuracy of procalcitonin to discriminate between viral and bacterial pathogens was calculated.
Results - Among 1735 patients, pathogens were identified in 645 (37%), including 169 (10%) with typical bacteria, 67 (4%) with atypical bacteria, and 409 (24%) with viruses only. Median procalcitonin concentration was lower with viral pathogens (0.09 ng/mL; interquartile range [IQR], <0.05-0.54 ng/mL) than atypical bacteria (0.20 ng/mL; IQR, <0.05-0.87 ng/mL; P = .05), and typical bacteria (2.5 ng/mL; IQR, 0.29-12.2 ng/mL; P < .01). Procalcitonin discriminated bacterial pathogens, including typical and atypical bacteria, from viral pathogens with an area under the receiver operating characteristic (ROC) curve of 0.73 (95% confidence interval [CI], .69-.77). A procalcitonin threshold of 0.1 ng/mL resulted in 80.9% (95% CI, 75.3%-85.7%) sensitivity and 51.6% (95% CI, 46.6%-56.5%) specificity for identification of any bacterial pathogen. Procalcitonin discriminated between typical bacteria and the combined group of viruses and atypical bacteria with an area under the ROC curve of 0.79 (95% CI, .75-.82).
Conclusions - No procalcitonin threshold perfectly discriminated between viral and bacterial pathogens, but higher procalcitonin strongly correlated with increased probability of bacterial pathogens, particularly typical bacteria.
© The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: firstname.lastname@example.org.
BACKGROUND & AIMS - There is controversy regarding the role of the type 2 immune response in the pathogenesis of ulcerative colitis (UC)-few data are available from treatment-naive patients. We investigated whether genes associated with a type 2 immune response in the intestinal mucosa are up-regulated in treatment-naive pediatric patients with UC compared with patients with Crohn's disease (CD)-associated colitis or without inflammatory bowel disease (IBD), and whether expression levels are associated with clinical outcomes.
METHODS - We used a real-time reverse-transcription quantitative polymerase chain reaction array to analyze messenger RNA (mRNA) expression patterns in rectal mucosal samples from 138 treatment-naive pediatric patients with IBD and macroscopic rectal disease, as well as those from 49 children without IBD (controls), enrolled in a multicenter prospective observational study from 2008 to 2012. Results were validated in real-time reverse-transcription quantitative polymerase chain reaction analyses of rectal RNA from an independent cohort of 34 pediatric patients with IBD and macroscopic rectal disease and 17 controls from Cincinnati Children's Hospital Medical Center.
RESULTS - We measured significant increases in mRNAs associated with a type 2 immune response (interleukin [IL]5 gene, IL13, and IL13RA2) and a type 17 immune response (IL17A and IL23) in mucosal samples from patients with UC compared with patients with colon-only CD. In a regression model, increased expression of IL5 and IL17A mRNAs distinguished patients with UC from patients with colon-only CD (P = .001; area under the receiver operating characteristic curve, 0.72). We identified a gene expression pattern in rectal tissues of patients with UC, characterized by detection of IL13 mRNA, that predicted clinical response to therapy after 6 months (odds ratio [OR], 6.469; 95% confidence interval [CI], 1.553-26.94), clinical response after 12 months (OR, 6.125; 95% CI, 1.330-28.22), and remission after 12 months (OR, 5.333; 95% CI, 1.132-25.12).
CONCLUSIONS - In an analysis of rectal tissues from treatment-naive pediatric patients with IBD, we observed activation of a type 2 immune response during the early course of UC. We were able to distinguish patients with UC from those with colon-only CD based on increased mucosal expression of genes that mediate type 2 and type 17 immune responses. Increased expression at diagnosis of genes that mediate a type 2 immune response is associated with response to therapy and remission in pediatric patients with UC.
Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.