Rat Insulin ELISA Kit (DEIA1655)

Regulatory status: For research use only, not for use in diagnostic procedures.

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serum, plasma
Species Reactivity
Intended Use
The Ultrasensitive Rat Insulin ELISA provides a method for determination of insulin in rat serum or plasma.
Contents of Kit
1. Coated Plate: 1 x 96 wells
2. Calibrators 1, 2, 3, 4, 5: 5 x 1000 μL
3. Calibrator 0: 1 x 5 mL
4. Enzyme Conjugate(11X): 1 x 1.3 mL
5. Enzyme Conjugate Buffer: 1 vial, 13 mL
6. Wash Buffer(21 x ): 1 x 50 mL
7. Substrate TMB: 1 x 22 mL
8. Stop Solution: 1 x 7 mL
Store kit at 2-8°C or -20°C upon arrival up to the expiration date. Avoid multiple freeze/thaw cycles. For more detailed information, please download the following document on our website.
Intra-assay Precision: 2.0%-2.9%
Inter-assay Precision: 3.8%-5.0%
Standard Curve


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Cross-reaction of POC-CCA urine test for detection ofSchistosoma mekongiin Lao PDR: a cross-sectional study


Authors: Homsana, Anousin; Odermatt, Peter; Southisavath, Phonesavanh; Yajima, Aya; Sayasone, Somphou

Background The point-of-care circulating cathodic antigen (POC-CCA) test is increasingly used as a rapid diagnostic method forSchistosoma mansoniinfection. The test has good sensitivity, although false positive results have been reported among pregnant women and patients with urine infections and hematuria. We validated the POC-CCA test's ability to diagnoseSchistosoma mekongiinfection in Lao People's Democratic Republic (Lao PDR), whereS. mekongiis endemic. Of particular interest was the test's specificity and possible cross-reactivity with other helminth infections. Methods We conducted a cross-sectional study of children and adults in the provinces of Champasack (Schistosoma mekongiandOpisthorchis viverriniendemic), Savannakhet (O. viverriniendemic) and Luang Prabang (soil-transmitted helminths endemic) between October 2018 and April 2019.POC-CCA and urine dipstick tests were administered to all study participants, while an additional pregnancy test was offered to women. Two stool samples were collected from participants and examined with a Kato-Katz test (two smears per stool). Logistic regression was used to associate potential confounding factors (predictors) with POC-CCA test results (outcome). Results InS. mekongi-endemic Champasack, 11.5% (n = 366) and 0.5% (n = 2) of study participants had positive POC-CCA and Kato-Katz test results, respectively. Only one of the two Kato-Katz positive patients was also POC-CCA positive. In Champasack and Luang Prabang, whereS. mekongiis not endemic, the POC-CCA test yielded (presumably) false positive results for 6.0% (n = 22) and 2.5% (n = 9) of study participants, respectively, while all of the Kato-Katz tests were negative. POC-CCA positive test results were significantly associated withO. viverriniinfection (1.69, 95% confidence interval (CI): 1.02-2.77,P = 0.042), increased leukocytes (adjusted Odds Ratio(aOR) = 1.58, 95%CI: 1.15-2.17,P = 0.005) and hematuria (aOR = 1.50, 95%CI: 1.07-2.10,P = 0.019) if the observed trace was counted as a positive test result. Two pregnant women from Champasack province had POC-CCA positive tests. Conclusions We observed a cross-reaction between the POC-CCA test andO. viverriniinfection. To some extent, we can confirm previous observations asserting that POC-CCA provides false positive results among patients with urinary tract infections and hematuria. InS. mekongi-endemic areas, POC-CCA can be applied cautiously for surveillance purposes, keeping in mind the considerable risk of false positive results and its unknown sensitivity.

Process mining as support to simulation modeling: A hospital-based case study


Authors: Tamburis, Oscar; Esposito, Christian

The purpose of this paper is to show how the knowledge of Process Mining techniques can provide a robust premise to build a Discrete Event Simulation (DES) model of a healthcare process. In order to analyze some specific processes of an ophthalmology ward of a large Italian hospital, ProM6 framework was implemented, which supports process mining techniques in form of plug-ins; the plug-ins process data, in form of Event Log, in order to extract information about the process. The DES model based on such information was run via a commercial tool, Simul8, which allows building sophisticated process models. A timely algorithm was then deployed to adapt the ProM6 information to the DES model. Following, a conformance analysis was conducted by comparing the original Event Log, and the Simulation tool data, taking into account the main case-related model attributes (routing probabilities profile, time perspective, and resources perspective). The paper aims at developing the line of inquiry for what concerns the deployment of approaches to set forth the link between Process mining and Simulation modeling.

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