Evidence map›Paper›PMID 40871464›Full record

ReviewMicroorganisms2025

Advanced Strategies in Phage Research: Innovations, Applications, and Challenges.

Pengfei Wu, Wanwu Li, Wenlu Zhang, Shasha Li, Bo Deng, Shanghui Xu, Zhongjie Li

Abstract readReview
In one paragraph

Review in Microorganisms, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Pengfei WuMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.ORCID 0000-0003-2618-690X
Wanwu LiMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.
Wenlu ZhangMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.
Shasha LiMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.
Bo DengMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.
Shanghui XuMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.
Zhongjie LiMicrobial Pathogen and Anti-Infection Research Group, School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang 471003, China.

Funding

the Key Science and Technology Program of Henan Province 242102310384
6 · The paper itself

Abstract

The escalating global threat of antimicrobial resistance (AMR) underscores the urgent need for innovative therapeutics. Bacteriophages (phages), natural bacterial predators, offer promising solutions, especially when harnessed through advances in artificial intelligence (AI). This review explores how AI-driven innovations are transforming phage biology, with an emphasis on three pivotal areas: (1) AI-enhanced structural prediction (e.g., AlphaFold); (2) deep learning functional annotation; (3) bioengineering strategies, including CRISPR-Cas. We further discuss applications extending to medical therapy, biosensing, agricultural biocontrol, and environmental remediation. Despite progress, critical challenges persist-including high false-positive rates, difficulties in modeling disordered protein regions, and biosafety concerns remain. Overcoming these requires experimental validation, robust computational frameworks, and global regulatory oversight. AI integration in phage research is accelerating the development of next-generation therapeutics to combat AMR and advance engineered living therapeutics.

Indexed as

artificial intelligencebacteriophagesbiocontrolCRISPRphage-based biosensorphage therapy

Identifiers

PMID40871464
PMCPMC12388022

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.