Evidence map›Paper›PMID 42505440›Full record

ReviewBiosensors2026

Advances in Detecting Viable/Dead Foodborne Microorganisms Using Diverse Functional Nucleic Acid-Based Molecular Recognition.

Yanger Liu, Huifu Yuan, Juan Zhang, Xiaoyun Sun, Peili Wang, Pazilaiti Yiming, Ailiang Chen, Yanyang Xu

Abstract readReview
In one paragraph

Review in Biosensors, 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.

Yanger LiuState Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Huifu YuanCollege of Agriculture and Forestry, Hebei North University, Zhangjiakou 075000, China.
Juan ZhangState Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Xiaoyun SunState Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Peili WangState Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China.ORCID 0009-0009-4768-0879
Pazilaiti YimingXinjiang Institute of Veterinary Medicine and Feed Supervision, Urumqi 830063, China.
Ailiang ChenState Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China.ORCID 0000-0003-2203-2823
Yanyang XuState Key Laboratory for Quality and Safety of Agro-Products, Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, China.ORCID 0000-0001-5193-9286

Funding

National Key Research and Development Program 2025YFD2100200
6 · The paper itself

Abstract

Accurately detecting viable foodborne pathogenic bacteria is essential for food safety risk assessments and public health interventions. Traditional plate counting is time-consuming and operationally cumbersome. Immunological assays are unable to distinguish viable from dead cells, whereas conventional nucleic acid amplification is often affected by residual DNA originating from dead bacteria. These limitations render conventional approaches inadequate for rapid and precise field detection. Functional nucleic acids (FNAs) offer a promising alternative for viability detection because of their high sensitivity, specificity, target diversity, and programmable integrability. This review provides a systematic overview of molecular recognition strategies and FNA-based detection technologies for identifying viable foodborne microorganisms. We categorize the biomarkers targeted by FNAs into nucleic acids, surface structures, and metabolic activities. Building on this categorization, we examine the core principles and technological evolution of primers, aptamers, DNAzymes, guide nucleic acids, and oligonucleotide probes in viability discrimination. We then outline the practical applications of these technologies across the food supply chain and discuss the remaining challenges and future directions in the field. Ultimately, this work provides a theoretical reference and practical guidance for ensuring food safety and advancing precise microbial risk management.

Indexed as

BacteriaBiosensing TechniquesFood MicrobiologyNucleic AcidsFood SafetyHumansNucleic Acidsdead bacteria detectionFNAfoodborne pathogensfood safetyviable bacteria detection

Identifiers

PMID42505440
PMCPMC13406421

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.