Evidence map›Paper›PMID 40124357›Full record

ArticleFrontiers in immunology2025

Screening and preclinical assessment of novel

Xichao Ou, Eryong Liu, Faiqa Rashid, Shaojun Pei, Guoqin Zhang, Richard Anthony, Ruida Xing, Fei Huang, Hui Xia, Yang Zheng and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Recent Characterization of Key Proteins to Combat Tuberculosis.Advances in experimental medicine and biology · 2026
    Review
  3. 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

17 authors.

Xichao Ou *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Eryong Liu *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Faiqa Rashid *Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan.
Shaojun PeiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Guoqin ZhangDepartment for Tuberculosis Control Research, Tianjin Center for Tuberculosis Control, Tianjin, China.
Richard AnthonyNational Tuberculosis Reference Laboratory, National Institute for Public Health and the Environment, Bilthoven, Netherlands.
Ruida XingNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Fei HuangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Hui XiaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Yang ZhengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuanyuan SongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Yang ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Bing ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Shengfen WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Minggui LinBeijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Lin ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Yanlin ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: A new class of Methods: We constructed Results: Out of 24 tested recombinant MTB antigens, only three (E-M, E-C-M, and E-7.7-C) met the criteria for skin test reaction interpretation for preclinical trials. Among these, the mean values of skin reaction from E-M were found to be comparable to EC (P>0.05), with no cross-reaction with BCG. Additionally, E-M exhibited a strong safety preclinical profile with no significant abnormalities in physiological, biochemical, or histopathological assessments, supporting its suitability for clinical evaluation. It also displayed high specificity by differentiating MTB infection from BCG vaccination and NTM infection, with no cross-reactivity observed in sensitized guinea pigs. Discussion: Our results indicate that the E-M recombinant antigen possesses promising characteristics for the detection of tuberculosis infection, demonstrating good safety and efficacy at the preclinical level. Further clinical trials are required to assess its clinical safety, efficacy, and feasibility.

Indexed as

Antigens, BacterialMycobacterium tuberculosisTuberculin TestTuberculosisAnimalsBCG VaccineDisease Models, AnimalEscherichia coliFemaleGuinea PigsHumansMiceRecombinant ProteinsAntigens, BacterialBCG VaccineMycobacterium tuberculosis antigensRecombinant ProteinsEMMTB recombinant antigenspreclinical efficacyscreeningtuberculin skin test

Identifiers

PMID40124357
PMCPMC11925772

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.