Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection

Zidan, Ahmad; Saad, Eman A.; Ibrahim, Nasser E.; Hashem, Medhat H.; Mahmoud, Amal; Hemeida, Alaa A.

doi:10.1016/j.heliyon.2021.e06908

Cited by 2 publications

(3 citation statements)

References 74 publications

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“…This kind of data may have originated from improper procedures or wrong data collection. However, it can be handled by implementing the methods such as regression, clustering or binning ( 18 – 20 ).…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Onyema

Dalal

Dhaou

et al. 2022

Front. Public Health

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Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Onyema

Dalal

Dhaou

et al. 2022

Front. Public Health

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“…This safer second and third generation PIs were followed by daclatasvir and sofosbuvir ( 5 ) (Figure ) used as NS5A and NS5B polymerase inhibitors, respectively. In particular, treatment of naı̈ve patients with sofosbuvir ( 5 ) in combination with peg-IFN and ribavirin allowed to achieve an SVR rate of 90% after only 12 weeks. , These drugs showed to be effective against gt1 subclasses (gt1a and gt1b). However, some patients affected by gt2 and gt3 achieved a high SVR rate after dual therapy of sofosbuvir ( 5 ) and ribavirin .…”

Section: Hcv Infection and Current Treatmentmentioning

confidence: 99%

“…33) was selected as a clinical candidate. Unfortunately, in October 2016, vedroprevir failed the phase II 116,…”

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confidence: 99%

Hepatitis C: The Story of a Long Journey through First, Second, and Third Generation NS3/4A Peptidomimetic Inhibitors. What Did We Learn?

Martino,

Petri,

De Rosa

2024

J. Med. Chem.

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Hepatitis C viral (HCV) infection is the leading cause of liver failure and still represents a global health burden. Over the past decade, great advancements made HCV curable, and sustained viral remission significantly improved to more than 98%. Historical treatment with pegylated interferon alpha and ribavirin has been displaced by combinations of direct-acting antivirals. These regimens include drugs targeting different stages of the HCV life cycle. However, the emergence of viral resistance remains a big concern. The design of peptidomimetic inhibitors (PIs) able to fit and fill the conserved substrate envelope region within the active site helped avoid contact with the vulnerable sites of the most common resistance-associated substitutions Arg155, Ala156, and Asp168. Herein, we give an overview of HCV NS3 PIs discovered during the past decade, and we deeply discuss the rationale behind the structural optimization efforts essential to achieve pangenotypic activity.

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Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection

Cited by 2 publications

References 74 publications

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Hepatitis C: The Story of a Long Journey through First, Second, and Third Generation NS3/4A Peptidomimetic Inhibitors. What Did We Learn?

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