Data-Driven Visualization of the Dynamics of Antimicrobial Peptides in Cell Death.

Dan Hou, Jiatong Zhao, Mingshi Guo, Xinran Zhang, Shuiqing Yu, Jiayue Li, Tymour Forouzanfar, Qing Zhang,Janak L Pathak

Probiotics and antimicrobial proteins（2025）

Cited 0|Views0

Abstract

This study explores the current status, research hotspots, and emerging trends in AMP-induced cell death through bibliometric and data-driven visual analysis. The findings aim to provide researchers and clinical professionals with new insights and potential research directions. A total of 1,897 articles and reviews published between 2006 and 2024 were retrieved from the Web of Science Core Collection. Bibliometric and visual analyses were conducted using CiteSpace, VOSviewer, Scimago Graphica, Origin 2022, and WordClouds. The analysis focused on publication trends, contributing institutions, journals, authors, cited references, and keywords. China contributed the largest share of publications (28.15%). The Chinese Academy of Sciences emerged as the most collaborative institution, demonstrating the highest centrality. The author with the highest composite index was Chen, Jyh-Yih (2,985.27). Recent research hotspots have centered on elucidating the mechanisms of AMP-induced cell death and exploring the potential applications of AMPs in cancer therapy. Keywords such as anticancer peptides, mechanism, design, and antibiotic resistance currently dominate the field, reflecting its evolving focus. Research on the application of AMPs in cancer treatment is gaining momentum. The forefront of this field involves modifying and designing AMPs to address antibiotic-resistant bacterial infections and advance cancer therapeutics. However, further investigation is needed to uncover the specific molecular mechanisms underlying AMP-induced cell death, including necrosis, pyroptosis, and ferroptosis.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本研究通过数据驱动可视化方法分析了抗菌肽（AMP）诱导细胞死亡的研究现状、热点及趋势，为研究人员和临床专业人士提供了新见解和潜在研究方向。

【方法】：使用CiteSpace、VOSviewer、Scimago Graphica、Origin 2022和WordClouds等工具对2006至2024年间Web of Science Core Collection中的1,897篇文章和综述进行 bibliometric和可视化分析。

【实验】：通过分析出版物趋势、贡献机构、期刊、作者、引用文献和关键词，发现中国在AMP诱导细胞死亡领域贡献最大（28.15%），中国科学院是最具合作性的机构，且研究热点集中在AMP诱导细胞死亡的机制及其在癌症治疗中的应用。