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Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

ABSTRACT: All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations--such as the creation of a dilution series with a robotic liquid handler--can significantly amplify imprecision and even contribute substantially to bias. To illustrate these techniques, we review an example of how the choice of dispensing technology can impact assay measurements, and show how large contributions to discrepancies between assays can be easily understood and potentially corrected for. These simple modeling techniques--illustrated with an accompanying IPython notebook--can allow modelers to understand the expected error and bias in experimental datasets, and even help experimentalists design assays to more effectively reach accuracy and imprecision goals.

SUBMITTER: Hanson SM

PROVIDER: S-EPMC4696763 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

Hanson Sonya M SM Ekins Sean S Chodera John D JD

Journal of computer-aided molecular design 20151217 12

All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations--such as the creation of a dilution series with a robotic liquid handler--can significantly amplify imprecision and even contribute substantially ...[more]

PMID: 26678597

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Project description:BackgroundBaseline physical activity in patients when they initiate cardiac rehabilitation is poorly understood. We used mobile health technology to understand baseline physical activity of patients initiating cardiac rehabilitation within a clinical trial to potentially inform personalized care.MethodsThe VALENTINE (Virtual Application-Supported Environment to Increase Exercise During Cardiac Rehabilitation Study) is a prospective, randomized-controlled, remotely administered trial designed to evaluate a mobile health intervention to supplement cardiac rehabilitation for low- and moderate-risk patients. All participants receive a smartwatch and usual care. Baseline physical activity was assessed remotely after enrollment and included (1) 6-minute walk distance, (2) daily step count, and (3) daily exercise minutes, both over 7 days and for compliant days, defined by >8 hours of watch wear time. Multivariable linear regression identified patient-level features associated with these 3 measures of baseline physical activity.ResultsFrom October 2020 to March 2022, 220 participants enrolled in the study. Participants are mostly White (184 [83.6%]); 67 (30.5%) are female and 84 (38.2%) are >65 years old. Most participants enrolled in cardiac rehabilitation after percutaneous coronary intervention (105 [47.7%]) or coronary artery bypass surgery (39 [17.7 %]). Clinical diagnoses include coronary artery disease (78.6%), heart failure (17.3%), and valve repair or replacement (26.4%). Baseline mean 6-minute walk distance was 489.6 (SD, 143.4) meters, daily step count was 6845 (SD, 3353), and exercise minutes was 37.5 (SD, 33.5). In a multivariable model, 6-minute walk distance was significantly associated with age and sex, but not cardiac rehabilitation indication. Sex but not age or cardiac rehabilitation indication was significantly associated with daily step count and exercise minutes.ConclusionsBaseline physical activity varies substantially in low- and moderate-risk patients enrolled in cardiac rehabilitation. Future studies are warranted to explore whether personalizing cardiac rehabilitation programs using mobile health technologies could optimize recovery.RegistrationURL: https://www.Clinicaltrialsgov; Unique identifier: NCT04587882.

Project description:Background:Community pharmacies are easily accessible outlets providing medicines to the general public in Nepal, but it is known that irrational dispensing of antibiotics from such outlets contributes to antimicrobial resistance. Objective:To assess the understanding of community pharmacy personnel around antibiotic-dispensing in Eastern Nepal and the relationship between this understanding and their personal characteristics. Methods:A cross-sectional survey was conducted on 312 pharmacy personnel working in community pharmacies of three districts within Eastern Nepal using a self-administered questionnaire. Descriptive statistics were used to analyze participants' characteristics and their understanding of antibiotic dispensing. The relationships between their understanding of antibiotic dispensing and their characteristics were determined using Chi-square tests. Results:Most of the pharmacy personnel considered that dispensing antibiotics without a valid prescription is a problem (76.9%), and that it would not be legal to do so (86.9%). In the survey, 34.9% of participants agreed that they had dispensed antibiotics without prescription, and 26.9% disagreed with the assertion that inappropriate dispensing of antibiotics could promote antimicrobial resistance. Most (94.5%) reported that they would advise patients to follow their antibiotic dosage regimen, but 34.3% reported that they believed antibiotics to reduce pain and inflammation. Bivariate analysis showed that the level of understanding about antibiotic indication was significantly associated with age (p<0.001), work experience (p<0.001) and qualifications (p=0.017) of the pharmacy personnel. Work experience and qualifications also had significant but independent relationships with the level of understanding that irrational dispensing of antibiotics promotes antimicrobial resistance (p=0.018 and p=0.004) and is on the need for patient follow-up after dispensing antibiotics (p<0.001 and p=0.042). Conclusion:The understanding of community pharmacy personnel about antibiotic dispensing in Eastern Nepal requires significant improvement. Degree of understanding of some aspects of antibiotic dispensing was influenced by age, work experience and qualifications.

Dataset Information

Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

Publications

Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

Similar Datasets

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