Evidence map›Paper›PMID 39229986›Full record

ReviewCurrent medicinal chemistry2025

Competence Regulation in

Yapeng Zhang, Shaowei Guo, Yimin Li, Linying Zhou, Yong Wu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

5 authors.

Yapeng ZhangThe Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha, 410005, China.
Shaowei GuoHengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Yimin LiThe Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha, 410005, China.
Linying ZhouThe Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha, 410005, China.
Yong WuThe Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha, 410005, China.

Funding

Key Research and Development Program of Hunan Province of China 2022SK2116National Natural Science Foundation of China 82072350Natural Science Foundation of Changsha kq2403197Natural Science Foundation of Hunan Province 2022JJ70046
6 · The paper itself

Abstract

Natural transformation refers to the process in which bacteria acquire new traits by uptaking naked DNA from the environment and integrating it into their genome through homologous recombination when they are in the specialized physiological state of competence. The natural transformation was first described in Streptococcus pneumoniae. Since Frederick Griffith first described natural transformations in S. pneumoniae in 1928, this phenomenon has been studied extensively. Induction of competence before natural transformation has been reported to involve about 10% of the pneumococcal genome. In addition to natural transformation, multiple physiological processes are involved, including biofilm formation, bacteriocin production, and fratricide. In this review, we summarized current knowledge about natural transformation in S. pneumoniae and described its competence regulation mechanism. This review also introduces the development of novel drugs and vaccines against S. pneumoniae infection by utilizing the existing knowledge of competence and natural transformation.

Indexed as

Anti-Bacterial AgentsPneumococcal InfectionsStreptococcus pneumoniaeBiofilmsHumansTransformation, BacterialAnti-Bacterial Agentsanti-microbial therapeuticsgenomemicroflora.natural transformationnovel drugsStreptococcus pneumoniae

Identifiers

PMID39229986

What Socratic holds

Textmetadata
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 Socratic graph.