Advances in Usage of Venom Proteins as Diagnostics and Therapeutic Mediators

Menaa, Farid; A, Barkat; Khan, Sardar

doi:10.2174/092986652507180813110453

Cited by 4 publications

(1 citation statement)

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“…Melittin induces endogenous enzymes by binding to membranes through hydrophobic interactions with zwitterionic phospholipids, disrupting the orientation of the phosphate group to alter properties of phospholipids and ultimately weakening the barrier function of the plasmalemma. Formation of melittin-phospholipid domains on the surface of erythrocytes causes hemolysis (94)(95)(96)(97)(98)(99)(100).…”

Section: Molecular Structure and Mechanism Of Action Of Toxins Of Ven...mentioning

confidence: 99%

Effect of animal venom toxins on the main links of the homeostasis of mammals (Review)

Matkivska,

Samborska,

Maievskyi

2023

Biomed Rep

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The human body is affected by environmental factors. The dynamic balance between the organism and its environment results from the influence of natural, anthropogenic and social aspects. The factors of exogenous origin determine development of adaptive changes. The present article summarises the mechanisms of animal venom toxins and homeostasis disruption in the body of mammals. The mechanisms underlying pathological changes are associated with shifts in biochemical reactions. Components of the immune, nervous and endocrine systems are key in the host defense and adaptation processes in response to venom by triggering signalling pathways (PI3kinase pathway, arachidonic acid cascade). Animal venom toxins initiate the development of inflammatory processes, the synthesis of pro-inflammatory mediators (cytokines), ROS, proteolytic enzymes, activate the migration of leukocytes and macrophages. Keratinocytes and endothelial cells act as protective barriers under the action of animal venom toxins on the body of mammals. In addition, the formation of pores in cell membranes, structural changes in cell ion channels are characteristic of the action of animal venom toxins. Contents 1. Introduction 2. Mechanisms of disruption of cellular homeostasis of mammals following animal venom toxin exposure 3. Molecular structure and mechanism of action of toxins of venomous animals 4. Conclusion and future perspectives

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Section: Molecular Structure and Mechanism Of Action Of Toxins Of Ven...mentioning

confidence: 99%

Effect of animal venom toxins on the main links of the homeostasis of mammals (Review)

Matkivska,

Samborska,

Maievskyi

2023

Biomed Rep

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Efficient gradient boosting for prognostic biomarker discovery

Yao

Zhang

et al. 2022

Bioinformatics

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Motivation A gradient boosting decision tree (GBDT) is a powerful ensemble machine learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as Extreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB), have rendered the GBDT training more efficient, scalable, and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies. Results In this paper, we present a new R package “Xsurv” as an integrated solution that applies two modern GBDT training frameworksnamely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package “gbm”. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research. Availability “Xsurv” is freely available as an R package at: https://github.com/topycyao/Xsurv Supplementary information Supplementary data are available at Bioinformatics online.

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Efficient gradient boosting for prognostic biomarker discovery

Yao

Zhang

et al. 2021

Preprint

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Motivation: Gradient boosting decision tree (GBDT) is a powerful ensemble machine learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as Extreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. These modern techniques, however, have not yet been widely adopted in biomarkers discovery based on patient survival data, which are key clinical outcomes or endpoints in cancer studies. Results: In this paper, we present a new R package Xsurv as an integrated solution which ap-plies two modern GBDT training framework namely, XGB and LGB, for the modeling of censored survival outcomes. Based on a comprehensive set of simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package gbm. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate cancer translational and clinical research. Availability: Xsurv is freely available as an R package at: https://github.com/topycyao/Xsurv

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Advances in Usage of Venom Proteins as Diagnostics and Therapeutic Mediators

Cited by 4 publications

References 0 publications

Effect of animal venom toxins on the main links of the homeostasis of mammals (Review)

Effect of animal venom toxins on the main links of the homeostasis of mammals (Review)

Efficient gradient boosting for prognostic biomarker discovery

Efficient gradient boosting for prognostic biomarker discovery

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