Evidence map›Paper›PMID 42148262›Full record

ReviewRSC advances2026

Biological treatment strategies for antibiotic contamination: mechanisms, applications, and future perspectives.

Xiangtao Zhao, Enhui Xie, Huina Xie

Abstract readReview
In one paragraph

Review in RSC advances, 2026. 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

3 authors.

Xiangtao ZhaoSchool of Economics and Management, Lanzhou Resource and Environment Vocational and Technical University Lanzhou Gansu China zhaoxt@lzre.edu.cn.
Enhui XieFaculty of Environmental Engineering and Municipal Services, Saint-Petersburg State University of Architecture and Civil Engineering Saint Petersburg Russia.ORCID https://orcid.org/0009-0006-8841-3847
Huina XieSchool of Environmental and Municipal Engineering, Lanzhou Jiaotong University Lanzhou Gansu China xiehuina@mail.lzjtu.cn.ORCID https://orcid.org/0009-0005-3577-8650

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The persistence of antibiotic residues in aquatic environments poses significant environmental and health risks. Biological treatment technologies, owing to the cost-effectiveness and environmental benefits, represent a crucial strategy for addressing this challenge. This review systematically elucidates the degradation mechanisms centered on microbial cometabolism, which involves a series of biochemical reactions mediated by functional microorganisms, including hydrolysis, oxidation, reduction, and side-chain modification and cleavage. It also provides a comprehensive evaluation of treatment systems ranging from conventional processes to emerging enhanced technologies such as bioaugmentation, immobilization, and biomaterial coupling. Two major challenges in this field are clearly identified: the inhibitory effects of antibiotics on functional microorganisms, and the secondary dissemination risk of antibiotic resistance genes (ARGs) dissemination. In response, the paper further summarizes recent advances in frontier approaches, such as process intensification, novel material coupling, synthetic biology tools, resource recovery, and intelligent model-based control. These strategies collectively aim to synergistically improve treatment efficiency and mitigate environmental risks. This review aims to provide a valuable reference for developing more efficient and safer biological treatment technologies for antibiotic-containing wastewater.

Identifiers

PMID42148262
PMCPMC13178247

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.