CA19-9 as a Potential Target for Radiolabeled Antibody-Based Positron Emission Tomography of Pancreas Cancer.

Introduction. Sensitive and specific imaging of pancreas cancer are necessary for accurate diagnosis, staging, and treatment. The vast majority of pancreas cancers express the carbohydrate tumor antigen CA19-9. The goal of this study was to determine the potential to target CA19-9 with a radiolabeled anti-CA19-9 antibody for imaging pancreas cancer. Methods. CA19-9 was quantified using flow cytometry on human pancreas cancer cell lines. An intact murine anti-CA19-9 monoclonal antibody was labeled with a positron emitting radionuclide (Iodine-124) and injected into mice harboring antigen positive and negative xenografts. MicroPET/CT were performed at successive time intervals (72 hours, 96 hours, 120 hours) after injection. Radioactivity was measured in blood and tumor to provide objective confirmation of the images. Results. Antigen expression by flow cytometry revealed approximately 1.3 × 10(6) CA19-9 antigens for the positive cell line and no expression in the negative cell line. Pancreas xenograft imaging with Iodine-124-labeled anti-CA19-9 mAb demonstrated an average tumor to blood ratio of 5 and positive to negative tumor ratio of 20. Conclusion. We show in vivo targeting of our antigen positive xenograft with a radiolabeled anti-CA19-9 antibody. These data demonstrate the potential to achieve anti-CA19-9 antibody based positron emission tomography of pancreas cancer

An Open-Source Data Manager for Network Models

Developing simulation and optimisation models for resource networks like water or energy systems increasingly involves integrating multiple data sources and software. Connecting multiple models and managing data accessed by different groups of analysts is a software challenge. Many resource systems are represented in computer models as networks of nodes and links, driven by a range of objectives and rules. We present a data storage platform, written in Python, which exploits the commonality of network representations to store data for multiple model types within a single deployment. This open-source platform provides a common source of data to multiple models using consistent data formats, reducing likelihood of error compared to file based data management. When deployed as a web service, it allows data to be shared securely among authorised users over the internet, facilitating collaboration. A case study describes the hosting of a water utility planning model, with an accompanying worked example

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis: a cohort study in UK Biobank

OBJECTIVE: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer. DESIGN: Population based cohort study. SETTING: Data from the UK Biobank study, collected between March 2006 and July 2010. PARTICIPANTS: 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts. MAIN OUTCOME MEASURES: Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women. RESULTS: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone. CONCLUSIONS: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population

Abstracts

Abstracts of papers about Giuseppe Verdi and his works, presented at joint meetings of the AIVS and Greater NY Chapter of the American Musicological Society, 1979-81 (Hepokoski, Lawton, Chusid, Hornick, Nádas, Tomlinson, Garrison, Powers), at the 1982 national meeting of the American Musicological Society (Harwood), and at an NEH-sponsored summer seminar at NYU in 1980 (Beams, Cole, Cordell, Davis, Fry, King, Mason, McCauley, Town)

Supplement to Christian Chronicle, May 10, 1968

A supplement to the Christian Chronicle, this special issue contains a report of the Race Relations Workshop held March 4-8, 1968, in Nashville, Tennessee at Schrader Lane Church of Christ. Included are full texts of speeches delivered by the workshop theme speakers: David Jones, Jr., Lawrence L. (Bud) Stumbaugh, James Dennis, Sr., Don Finto, leon Hill, Phillip Roseberry, Joseph Tucker, Joe Tomlinson, Perry Wallace, Jim Mayo, Walter E. Burch, and an article by David Lipscomb condensed from the February 21, 1878 Gospel Advocate

Mesenteric rheumatoid nodules masquerading as an intra-abdominal malignancy: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Rheumatoid nodules are the most common extra-articular findings in patients with rheumatoid arthritis. They occur most commonly at pressure points such as the extensor surfaces of the forearms, fingers, and occiput, but have also been reported to occur in unusual locations including the central nervous system, pericardium, pleura, and sclera. We present the unusual case of rheumatoid nodules in the small bowel mesentery masquerading as an intra-abdominal malignancy.</p> <p>Case presentation</p> <p>A 65-year-old-male with a known history of longstanding erosive, nodular, seropositive rheumatoid arthritis was incidentally found to have a mesenteric mass on computed tomography (CT) exam of the abdomen. This mass had not been present on prior imaging studies and was worrisome for a malignancy. Attempts at noninvasive biopsy were nondiagnostic but consistent with a "spindle" cell neoplasm. Laparotomy revealed extensive thickening and fibrosis of the small bowel mesentery along with large, firm nodules throughout the mesentery. A limited bowel resection including a large, partially obstructing, nodule was performed. Pathology was consistent with an unusual presentation of rheumatoid nodules in the mesentery of the small bowel.</p> <p>Conclusion</p> <p>Rheumatoid nodules should be considered in the differential diagnosis of a patient who presents with an intra-abdominal mass and a history of rheumatoid arthritis. Currently, no tests or imaging modality can discriminate with sufficient accuracy to rule out a malignancy in this difficult diagnostic delimma. Hopefully, this case will serve as impetus for further study and biomarker discovery to allow for improved diagnostic power.</p