Topomer CoMFA, HQSAR Study of Benzimidazole Derivative as NS5B Polymerase Inhibitor

2021 ◽  
Vol 18 ◽  
Author(s):  
Tong Jian-Bo ◽  
Zhang Xing ◽  
Bian Shuai ◽  
Luo Ding ◽  
Wang Tian-Hao
Keyword(s):  
Cross Validation ◽  
Benzimidazole Derivative ◽  
Predictive Ability ◽  
Qsar Model ◽  
Good Prediction ◽  
Topomer Comfa ◽  
Interaction Sites ◽  
Ns5b Polymerase Inhibitor ◽  
The Cross ◽  
Ns5b Polymerase

Background: In recent years, the number of people infected with hepatitis C virus (HCV) has continued to grow, this becoming a major threat to global health, and new anti-HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs. Methods: In order to explore the relationship between the structure of benzimidazole derivatives and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors. Results: The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fits well and has high predictive ability. Through the analysis of contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE. Conclusion: The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors, and for the identification of key residues that the inhibitor binds to NS5B, which helps to better understand its inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

Diagnostic grammar and assessment: translating criteria into questions

1989 ◽  
Vol 19 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Lee N. Robins
Keyword(s):  
Diagnostic Criteria ◽  
Catchment Area ◽  
Predictive Ability ◽  
Diagnostic Interview ◽  
Good Prediction ◽  
Interview Schedule ◽  
Validity Data ◽  
Validity Assessment ◽  
Standardized Interview ◽  
The Relationship

SynopsisThere has been concern about whether standardized psychiatric interviews make valid diagnoses. Agreements between the Diagnostic Interview Schedule (DIS), as an example of a standardized interview, with independent assessments by a clinician are reasonably high in most studies, but the clinical assessment is itself of uncertain validity. Using predictive ability is an alternative way of judging validity. Data are presented to show that the DIS is almost as good at prediction as a clinician's assessment, but here too there are problems. Because prediction is probabilistic (i.e. the same disorder can have multiple outcomes, and different disorders can share outcomes), it is not possible to say how good prediction has to be to demonstrate perfect validity.Across varied methods of validity assessment, some disorders are regularly found more validly diagnosed than others, suggesting that part of the source of invalidity lies in the diagnostic grammar of the systems whose criteria standardized interviews evaluate. Sources of invalidity inherent in the content and structure of a variety of diagnoses in DSM-III and its heir, DSM-III-R, are reviewed and illustrated, in part with results from the Epidemiological Catchment Area study.The relationship between diagnostic criteria and standardized interviews is symbiotic. While attempts to adhere closely to existing diagnostic criteria contribute to the diagnostic accuracy of standardized interviews, the exercise of translating official diagnostic criteria into standardized questions highlights problems in the system's diagnostic grammar, enabling standardized interviews to contribute to improvements in diagnostic nosology.

THE EXPLORATION OF CYP17A1 LIGAND SPACE BY THE QSAR MODEL

2021 ◽  
Author(s):  
Natalia Boboriko ◽  
◽  
He Liying ◽  
Yaraslau Dzichenka
Keyword(s):  
Hydrophobic Effect ◽  
Predictive Ability ◽  
Distance Matrix ◽  
Qsar Model ◽  
Aromatic Rings ◽  
Qsar Study ◽  
Test Set ◽  
Highly Active ◽  
High Efficient ◽  
F Measure

Cytochrome P450 17A1 (CYP17A1) is a critically important enzyme in humans that catalyzes the formation of all endogenous androgens. This enzyme is often considered a molecular target for the development of novel high efficient drugs against prostate cancer. In the present work, the random forest algorithm was used to conduct a QSAR study on 370 CYP17A1 ligands with different structures that were collected from the literature and databases, and a QSAR model was created based on the five important descriptors screened out – 2D adjacency and distance matrix descriptors, 2D atom counts and bond counts and 3D surface area, volume and shape descriptors. The model was verified by the test set (accuracy, specificity, sensitivity, F-measure, MCC, and AUC were calculated). It was revealed that the hydrophobic properties of the vdW surface of the ligand have a significant contribution to the activity prediction. The hydrophobic effect of the molecules may be aroused by the presence of the hydrophobic groups or aromatic rings in the molecules. The created QSAR model shows that the molecules with more aromatic rings have better activity. The accuracy of the model on the test set was 84%, precision – 81%, sensitivity – 93%, specificity – 72%, F-measure – 0.87, MCC – 0.67, AUC – 0.88. The model has good robustness and predictive ability and can be used to screen and discover new highly active CYP17A1 inhibitors.

scholarly journals Hepatitis C virus treatment advances for thalassaemia patients

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
George Papatheodoridis
Keyword(s):  
Liver Disease ◽  
Protease Inhibitor ◽  
Blood Transfusions ◽  
Hcv Treatment ◽  
Once Daily ◽  
Ns5a Inhibitor ◽  
Nucleotide Analogue ◽  
Ns5b Polymerase Inhibitor ◽  
Polymerase Inhibitor ◽  
Ns5b Polymerase

Chronic infection with hepatitis C virus (HCV) is a major problem for thalassaemia patients, as blood transfusions before 1990 were associated with a high risk of HCV infection. Given the high prevalence of co-morbidities, thalassaemia patients are at an increased risk for dying from end-stage liver disease or hepatocellular carcinoma. HCV treatment in thalassaemia patients was challenging in the interferon-alfa (IFN) era not only due to its unfavourable safety and tolerability profile but due to necessary combined use of ribavirin (RBV) and the subsequent haemolysis and increased need for blood transfusions. The introduction of the current direct acting antivirals (DAAs), which can be used in IFNfree and RBV-free regimens, has dramatically improved the management of all HCV patients including those with thalassaemia. Currently, depending on HCV genotype and availability in each country, the main available DAAs combinations are the co-formulation of sofosbuvir with ledipasvir (nucleotide analogue NS5B polymerase inhibitor/NS5A inhibitor, one tablet of 400/90 mg once daily),, the co-formulation of paritaprevir boosted by ritonavir with ombitasvir (NS3/4 protease inhibitor/ritonavir/NS5A inhibitor, two tablets of 75/50/12.5 mg once daily) perhaps with addition of dasabuvir (non-nucleos(t)ide analogue NS5B polymerase inhibitor, one tablet of 250 mg twice daily), the co-formulation of grazoprevir with elbasvir (NS3/4 protease inhibitor/NS5A inhibitor, one tablet of 100/50 mg once daily) and the co-formulation of sofosbuvir with velpatasvir (nucleotide analogue NS5B polymerase inhibitor/NS5A inhibitor, one tablet of 400/100 mg once daily). In 2017, the co-formulation of glecaprevir with pibrentasvir (NS3/4 protease inhibitor/NS5A inhibitor, three tablets of 100/40 mg once daily) and the co-formulation of sofosbuvir with velpatasvir and voxilaprevir (nucleotide analogue NS5B polymerase inhibitor/NS5A inhibitor/ NS3/4 protease inhibitor, one tablet of 400/100/100 mg once daily) were also approved and started to be used in some countries. According to all international current guidelines, thalassaemia patients do not represent a special group for the current HCV treatment and can be treated with the same indications and regimens used for patients without haemoglobinopathies. However, in countries which still prioritize the use of DAAs according to the severity of liver disease, thalassaemia patients are often excluded from such prioritization and have access to DAAs therapy regardless of their fibrosis severity. Moreover, all guidelines recommend that thalassaemia patients should be preferentially treated not only with IFN-free but RBV-free DAAs regimens too. In a proper clinical trial, only a 12-week regimen of grazoprevir/ elbasvir has been evaluated and proven to be highly efficacious and well tolerated among patients with inherited blood disorders and HCV genotype 1 or 4 infection. In addition, different DAAs regimens have been reported to be safe and effective for the treatment of HCV thalassaemia patiens in clinical practice. Given the availability of the current effective and safe DAAs and the frequent follow-up of thalassaemia patients in a few specific units, such patients could be a targeted population for “HCV micro-elimination” on the road towards the global HCV elimination in each country.

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

scholarly journals Validation of Predictive Ability of Bobi Score in Burn Patients

2021 ◽  
Vol 6 (4) ◽  
Keyword(s):  
Burn Injury ◽  
Prognostic Score ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Good Prediction ◽  
Burn Patients ◽  
Severe Burns ◽  
Inhalation Burn ◽  
Outstanding Result

Introduction: Scoring systems have been used successfully in burn centers to predict the prognosis and take measures for careful monitoring of the burned patient. Belgium Outcome Burn Injury score is one of them which takes into consideration age, burn surface area, and presence of inhalation burn. Objectives: This presentation aims to validate the use of the BOBI prognostic score in our patients. Patients and Methods: The study is a retrospective analytical study that utilized the investigation of the medical charts of 1515 patients hospitalized with severe burns within the ICU of the Service of Burns in Tirana, Albania during 2010-2019. Results: The overall mortality of our patients was 7.06% (107 deaths in 1515 patients). Up to BOBI score 6, we have noticed better mortality than prediction while there is a very good prediction up to score 10. Area Under the Curve was 0.978 (p<0.0001) which is an outstanding result in being a classifier between deaths and survivors. Conclusions: BOBI score is a very good prediction score for mortality in burn patients.

Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Blessing Jaja ◽  
Hester Lingsma ◽  
Ewout Steyerberg ◽  
R. Loch Macdonald ◽  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cross Validation ◽  
Prediction Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Model Performance ◽  
Predictor Variable ◽  
Predictive Ability ◽  
Prognostic Scores ◽  
Operating Characteristics ◽  
Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

Artificial Intelligence–Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors

2021 ◽  
pp. 459-468
Author(s):  
Fatma Güntürkün ◽  
Oguz Akbilgic ◽  
Robert L. Davis ◽  
Gregory T. Armstrong ◽  
Rebecca M. Howell ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Survivors ◽  
Childhood Cancer ◽  
Clinical Features ◽  
Cross Validation ◽  
Childhood Cancer Survivors ◽  
Model Based ◽  
Extreme Gradient Boosting ◽  
The Cross

PURPOSE Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline–recommended baseline ECG to predict cardiomyopathy. MATERIAL AND METHODS Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study. Clinical and echocardiographic assessment of cardiac function was performed at initial and follow-up SJLIFE visits. Cardiomyopathy was defined as an ejection fraction < 50% or an absolute drop from baseline ≥ 10%. Genetic algorithm was used for feature selection, and extreme gradient boosting was applied to predict cardiomyopathy during the follow-up period. Model performance was evaluated by five-fold stratified cross-validation. RESULTS The median age at baseline SJLIFE evaluation was 31.7 years (range 18.4-66.4), and the time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). Two thirds (67.1%) of patients were exposed to chest radiation, and 76.6% to anthracycline chemotherapy. One hundred seventeen (9.6%) patients developed cardiomyopathy during follow-up. In the model based solely on ECG features, the cross-validation area under the curve (AUC) was 0.87 (95% CI, 0.83 to 0.90), whereas the model based on clinical features had an AUC of 0.69 (95% CI, 0.64 to 0.74). In the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI, 0.86 to 0.91), with a sensitivity of 78% and a specificity of 81%. CONCLUSION AI using ECG data may assist in the identification of childhood cancer survivors at increased risk for developing future cardiomyopathy.

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