scholarly journals Refinement of the Peroxidase Peptide Reactivity Assay and Prediction Model for Assessing Skin Sensitization Potential

2020 ◽  
Vol 178 (1) ◽  
pp. 88-103
Author(s):  
Cindy A Ryan ◽  
John A Troutman ◽  
Petra S Kern ◽  
Mike Quijano ◽  
Roy L M Dobson ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Accuracy ◽  
Hazard Identification ◽  
Skin Sensitization ◽  
Reactive Aldehydes ◽  
Reactive Chemicals ◽  
Local Lymph Node ◽  
Sensitivity Specificity ◽  
Activation Component ◽  
Oxidation System

Abstract A peptide reactivity assay with an activation component was developed for use in screening chemicals for skin sensitization potential. A horseradish peroxidase-hydrogen peroxide (HRP/P) oxidation system was incorporated into the assay for characterizing reactivity of hapten and pre-/prohapten sensitizers. The assay, named the Peroxidase Peptide Reactivity Assay (PPRA) had a predictive accuracy of 83% (relative to the local lymph node assay) with the original protocol and prediction model. However, apparent false positives attributed to cysteine depletion at relatively high chemical concentrations and, for some chemicals expected to react with the −NH2 group of lysine, little to no depletion of the lysine peptide were observed. To improve the PPRA, cysteine peptide reactions with and without HRP/P were modified by increasing the number of test concentrations and refining their range. In addition, removal of DL-dithiothreitol from the reaction without HRP/P increased cysteine depletion and improved detection of reactive aldehydes and thiazolines without compromising the assay’s ability to detect prohaptens. Modification of the lysine reaction mixture by changing the buffer from 0.1 M ammonium acetate buffer (pH 10.2) to 0.1 M phosphate buffer (pH 7.4) and increasing the level of organic solvent from 1% to 25% resulted in increased lysine depletion for known lysine reactive chemicals. Refinement of the prediction model improved the sensitivity, specificity, and accuracy for hazard identification. These changes resulted in significant improvement of the PPRA making it is a reliable method for predicting the skin sensitization potential of all chemicals, including pre-/prohaptens and directly reactive haptens.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Predicting Change Prone Classes in Open Source Software

2018 ◽  
Vol 8 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Deepa Godara ◽  
Amit Choudhary ◽  
Rakesh Kumar Singh
Keyword(s):  
Prediction Model ◽  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
New Technology ◽  
Modern Technology ◽  
Time Frequency ◽  
Rigorous Testing ◽  
Technology Changes ◽  
Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

Maternal Serum EG-VEGF as A First Trimester Predictor of Hypertensive Disorders of Pregnancy: A Prospective Cohort Study

2021 ◽  
Author(s):  
Shiyu Zeng ◽  
Ling Yu ◽  
Yiling Ding ◽  
Mengyuan Yang
Keyword(s):  
Cohort Study ◽  
Growth Factor ◽  
Prediction Model ◽  
Pregnant Women ◽  
Predictive Value ◽  
Prospective Cohort ◽  
First Trimester ◽  
Hypertensive Disorders Of Pregnancy ◽  
Hypertensive Disorders ◽  
Sensitivity Specificity

Abstract Background This study aims to explore whether plasma endocrine gland-derived vascular endothelial growth factor (EG-VEGF) in the first trimester can be used as a predictor of hypertensive disorders of pregnancy (HDP), and compare it with placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) to evaluate its prediction of HDP value. Methods This is a prospective cohort study that records the medical history of the pregnant women included in the study at 11–13 weeks’ gestation, and analyzes serum biochemical markers including EG-VEGF, PIGF, sFlt-1 and sFlt-1/PIGF. The predictive values of these tests were determined. We used the receiver operating characteristic (ROC) curve to find the optimal cut-off value for each biomarker and compare the operating characteristics (sensitivity, specificity). Logistic regression analysis was used to create a prediction model for HDP based on maternal characteristics and maternal biochemistry. Results Data were obtained from 205 pregnant women. 17 cases were diagnosed with HDP, the incidence rate was 8.2% (17/205). Women who developed HDP had a significantly higher body mass index (BMI) and mean arterial pressure (MAP). Serum EG-VEGF levels in the first trimester are significantly higher in pregnant women with HDP. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value(NPV) of serum EG-VEGF levels more than 227.83 pg/ml for predicting HDP were 43%, 93%, 86% and 62%, respectively. We established a prediction model in the first trimester include maternal BMI, MAP, and EG-VEGF, with an AUC of 0.8861 (95%CI: 0.7905–0.9818), which is better than using EG-VEGF alone (AUC: 0.66). Conclusion This study demonstrated that serum EG-VEGF is a promising biomarker for predicting HDP in the first trimester. It has better predictive performance compared with the currently used biomarkers like PIGF and sFlt-1. Combining maternal clinical characteristics and biochemical tests at 11–13 weeks can effectively identify women at high risk of HDP.

scholarly journals Development of a pest threshold decision support system for minimising damage to winter wheat from wheat bulb fly, Delia coarctata

2021 ◽  
Author(s):  
Daniel J Leybourne ◽  
Kate E Storer ◽  
Pete Berry ◽  
Steve Ellis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Winter Wheat ◽  
Prediction Model ◽  
Support System ◽  
Predictive Accuracy ◽  
Control Strategies ◽  
Wheat Crop ◽  
Shoot Number ◽  
The Uk

Graphical AbstractIn this article we describe two predictive models that can be used for the integrated management of wheat bulb fly. Our first model is a pest level prediction model and our second model predicts the number of shoots a winter wheat crop will achieve by the terminal spikelet developmental stage. We revise and update current wheat bulb fly damage thresholds and combine this with our two models to devise a tolerance-based decision support system that can be used to minimise the risk of crop damage by wheat bulb fly. SummaryWheat bulb fly, Delia coarctata, is an important pest of winter wheat in the UK, causing significant damage of up to 4 t ha-1. Accepted population thresholds for D. coarctata are 250 eggs m-2 for crops sown up to the end of October and 100 eggs m-2 for crops sown from November. Fields with populations of D. coarctata that exceed the thresholds are at higher risk of experiencing economically damaging pest infestations. In the UK, recent withdrawal of insecticides means that only a seed treatment is available for chemical control of D. coarctata, however this is only effective for late-sown crops (November onwards) and accurate estimations of annual population levels are required to ensure a seed treatment is applied if needed. As a result of the lack of post-drilling control strategies, the management of D. coarctata is becoming increasingly reliant on non-chemical methods of control. Control strategies that are effective in managing similar stem-boring pests of wheat include sowing earlier and using higher seed rates to produce crops with more shoots and greater tolerance to shoot damage.In this study we develop two predictive models that can be used for integrated D. coarctata management. The first is an updated pest level prediction model that predicts D. coarctata populations from meteorological parameters with a predictive accuracy of 70%, which represents a significant improvement on the previous D. coarctata population prediction model. Our second model predicts the maximum number of shoots for a winter wheat crop that would be expected at the terminal spikelet development stage. This shoot number model uses information about the thermal time from plant emergence to terminal spikelet, leaf phyllochron length, plant population, and sowing date to predict the degree of tolerance a crop will have against D. coarctata. The shoot number model was calibrated against data collected from five field experiments and tested against data from four experiments. Model testing demonstrated that the shoot number model has a predictive accuracy of 70%. A decision support system using these two models for the sustainable management of D. coarcata risk is described.

scholarly journals The predictive accuracy of hypoxic scoring for prediction of adverse outcome in neonates born with asphyxia

2020 ◽  
Vol 7 (2) ◽  
pp. 333
Author(s):  
Muhammad Saqib ◽  
Safeer A. Jamil ◽  
Usman Arif ◽  
Zubda Anwar ◽  
Sarosh Waheed ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Adverse Outcome ◽  
Birth Asphyxia ◽  
Major Contributor ◽  
Fetal Hypoxia ◽  
Negative Data ◽  
Term Infants ◽  
Study Objective ◽  
The Mean ◽  
Sensitivity Specificity

Background: Birth asphyxia is a major contributor to neonatal mortality. Fetal hypoxia followed by asphyxia is common cause of brain injury in term infants. Hypoxia score has shown to be accurate enough to predict adverse outcome in asphyxiated neonates. But controversies exist regarding predictive accuracy of hypoxia score. So we conducted this study. Objective to assess the predictive accuracy of hypoxic scoring for prediction of adverse outcome in neonates born with asphyxia.Methods: 170 neonates were screed for hypoxia score. Neonates were labelled as positive or negative. Then all neonates were followed-up for 7 days. If neonate died within 7days, then case was confirmed as positive or negative. Data was analysed by using SPSS 20. 2x2 table was developed to calculate sensitivity, specificity, PPV, NPV and predictive accuracy of hypoxia score.Results: The mean Apgar score at birth was 5.01±0.83. The sensitivity of hypoxia score was 87.8%, specificity was 90.9%, PPV was 90%, NPV was 88.9% while predictive accuracy was 89.4% taking actual adverse outcome as gold standard.Conclusions: The predictive accuracy of hypoxia score was high for prediction of adverse outcome in asphyxiated neonates.

scholarly journals Development and Validation of a Clostridium difficile Infection Risk Prediction Model

2011 ◽  
Vol 32 (4) ◽  
pp. 360-366 ◽  
Author(s):  
Erik R. Dubberke ◽  
Yan Yan ◽  
Kimberly A. Reske ◽  
Anne M. Butler ◽  
Joshua Doherty ◽  
...  
Keyword(s):  
High Risk ◽  
Clostridium Difficile ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Tertiary Care ◽  
Risk Prediction Model ◽  
Brier Score ◽  
Tertiary Care Medical Center

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

Application of the Random Forest Method in Studies of Local Lymph Node Assay Based Skin Sensitization Data.

ChemInform ◽  
2005 ◽  
Vol 36 (39) ◽  
Author(s):  
Shengqiao Li ◽  
Adam Fedorowicz ◽  
Harshinder Singh ◽  
Sidney C. Soderholm
Keyword(s):  
Lymph Node ◽  
Random Forest ◽  
Skin Sensitization ◽  
Local Lymph Node Assay ◽  
Random Forest Method ◽  
Local Lymph Node

scholarly journals A Study of the Diagnostic Accuracy of an Existing Multivariable Test to Predict Shoulder Dystocia

2019 ◽  
Vol 09 (03) ◽  
pp. e262-e267
Author(s):  
Henry Alexander Easley ◽  
Todd Michael Beste
Keyword(s):  
Diagnostic Accuracy ◽  
Prediction Model ◽  
Study Design ◽  
False Positive ◽  
False Positive Rate ◽  
Shoulder Dystocia ◽  
The Other ◽  
Predictive Values ◽  
Positive Rate ◽  
Sensitivity Specificity

Objectives To evaluate the diagnostic accuracy of a multivariable prediction model, the Shoulder Screen (Perigen, Inc.), and compare it with the American College of Obstetricians and Gynecologists (ACOG) guidelines to prevent harm from shoulder dystocia. Study Design The model was applied to two groups of 199 patients each who delivered during a 4-year period. One group experienced shoulder dystocia and the other group delivered without shoulder dystocia. The model's accuracy was analyzed. The performance of the model was compared with the ACOG guideline. Results The sensitivity, specificity, positive, and negative predictive values of the model were 23.1, 99.5, 97.9, and 56.4%, respectively. The sensitivity of the ACOG guideline was 10.1%. The false-positive rate of the model was 0.5%. The accuracy of the model was 61.3%. Conclusion A multivariable prediction model can predict shoulder dystocia and is more accurate than ACOG guidelines.

scholarly journals QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Medicines ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Junko Nagai ◽  
Mai Imamura ◽  
Hiroshi Sakagami ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Anticancer Drugs ◽  
Predictive Accuracy ◽  
Cell Cancer ◽  
External Validation ◽  
Selective Toxicity ◽  
Normal Cells ◽  
Validation Set ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.

Evaluation of skin sensitization potential of chemicals by local lymph node assay using 5-bromo-2-deoxyuridine with flow cytometry

2019 ◽  
Vol 107 ◽  
pp. 104401 ◽  
Author(s):  
Byeol-I. Han ◽  
Jung-Sun Yi ◽  
Souk Jin Seo ◽  
Tae Sung Kim ◽  
Ilyoung Ahn ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Lymph Node ◽  
Skin Sensitization ◽  
Local Lymph Node Assay ◽  
Local Lymph Node
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