ReviewFrontiers in microbiology2025
Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.
Review in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Multiplex RPA-CRISPR/Cas12a Assay for Rapid Detection of Class D OXA-Type Carbapenem-ResistantBiosensors · 2026Article
- PEPTiGEN: a tool for mining antimicrobial resistance PEPTides using GENe data of public available repositories.Bioinformatics (Oxford, England) · 2026Article
- Evolving Approaches to Bacterial Identification: A Review of Classical and Modern Techniques.International journal of molecular sciences · 2026Review
- Nanomaterial-nucleic acid probe synergy: accelerating rapid pathogen detection and antimicrobial susceptibility testing in bloodstream infections.Folia microbiologica · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Diagnostic Insights Into Pathogen Spectrum and Mixed Microbial Detection in Critically Ill Patients With Pulmonary Infection Using Targeted Next-Generation Sequencing.Canadian respiratory journal · 2026Article
- High-Resolution Melting Curve Analysis (HRMA) for the Identification of Class D β-Lactamases (CHDLs) inInfection and drug resistance · 2026Article
- Fate and Removal of Antibiotics and Antibiotic Resistance Genes in a Rural Wastewater Treatment Plant: A Microbial Perspective of Nature-Based Versus Advanced Technologies.Microorganisms · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic resistance continues to erode the effectiveness of modern medicine, creating an urgent demand for rapid and reliable diagnostic solutions. Conventional diagnostic approaches, including culture-based susceptibility testing, remain the clinical reference standard but are constrained by lengthy turnaround times and limited sensitivity for early detection. In recent years, significant progress has been made with molecular and spectrometry-based methods, such as PCR and next-generation sequencing, MALDI-TOF MS, Raman and FTIR spectroscopy, alongside emerging CRISPR-based platforms. Complementary innovations in biosensors, microfluidics, and artificial intelligence further expand the diagnostic landscape, enabling faster, more sensitive, and increasingly portable assays. This review examines both established and emerging technologies for detecting antibiotic resistance, outlining their respective strengths, limitations, and potential roles across diverse settings. By synthesizing current advances and highlighting future opportunities, this review emphasizes complementarities among detection strategies and their potential integration into practical diagnostic frameworks, including in resource-limited settings.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.