Evidence map›Paper›PMID 42095490›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Metabolism-Based Biomarkers for Rapid Phenotypic Antibiotic Susceptibility Testing.

Sha Yu, Bayinqiaoge, Xi Lu, Xin Wang, Rongfeng Wang, Yi Li, Shi-Yang Tang, Chengchen Zhang

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Sha YuDigital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton, UK.
BayinqiaogeDigital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton, UK.
Xi LuDigital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton, UK.
Xin WangDigital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton, UK.
Rongfeng WangSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Yi LiKey Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, China.ORCID https://orcid.org/0000-0003-2226-5918
Shi-Yang TangDigital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton, UK.
Chengchen ZhangDigital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton, UK.

Funding

The Royal Society, UK IEC∖NSFC∖233339The Royal Society, UK IES∖R2∖252009The Royal Society, UK RG∖R1∖241228UK Research and Innovation (UKRI), UK APP34994Wessex Medical Trust, UK AF06
6 · The paper itself

Abstract

The accelerating global crisis of antimicrobial resistance (AMR) demands rapid and accurate methods for antibiotic susceptibility testing (AST). Conventional phenotypic assays remain the gold standard but are hindered by long culture times, while genotypic tests cannot reliably predict phenotypic resistance. In recent years, metabolism-based AST has emerged as a promising alternative, enabling the rapid detection of bacterial responses to antibiotics through shifts in metabolic activity. These approaches bridge molecular speed with phenotypic precision, allowing susceptibility determination within hours, or even minutes, without requiring cell proliferation. In this review, we summarize the latest advances in metabolism-based biomarkers for rapid AST. First, we discuss how antibiotics influence bacterial metabolism, linking resistance mechanisms to metabolic activities. We then summarize emerging metabolic biomarkers, categorized by their physiological underpinnings: nutrient uptake, respiratory activity, metabolic reprogramming, and enzymatic function. Finally, we list key challenges and future directions toward deployable metabolism-based AST platforms.

Indexed as

Anti-Bacterial AgentsBacteriaBiomarkersDrug Resistance, BacterialHumansMicrobial Sensitivity TestsPhenotypeAnti-Bacterial AgentsBiomarkersantibiotic susceptibility testingbacteriabiomarkermetabolism

Identifiers

PMID42095490
PMCPMC13271646

What Socratic 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 Socratic graph.