ReviewBiosensors2024
Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering.
Review in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Research progress of AI assisted nano-SERS technology in rapid identification of multi species oral pathogenic biofilms.Mikrochimica acta · 2026Review
- Discrimination of Closely RelatedBiology · 2026Article
- Ethanol-assisted electrochemical deposition of silver nanoflowers on silicon for highly sensitive SERS detection ofRSC advances · 2026Article
- An LPS-Immobilized Affinity Screening Platform Identifies Lipopolysaccharide Binding Phytochemicals fromBiology · 2026Article
- Next-Generation SERS Probes: Engineering Hotspots, Intelligent Molecular Targeting, and AI-Driven Spectral Analysis for Emerging Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Laboratory strategies for the identification of Burkholderia species: From classical phenotyping to advanced genomic and proteomic approaches.Antonie van Leeuwenhoek · 2026Review
- Spectroscopic Techniques in Bacterial Analysis: Applications of FTIR and Raman-Review.Foods (Basel, Switzerland) · 2026Review
- Recent Advancements in the SERS-Based Detection ofSensors (Basel, Switzerland) · 2026Review
- Machine Learning-Assisted SERS Platform for Rapid and Quantitative Discrimination of Shiga Toxin-ProducingBiosensors · 2025Article
- White Light Spectroscopy for Sampling-Free Bacterial Contamination Detection During CAR T-Cells Production: Towards an On-Line and Real-Time System.Biosensors · 2025Article
- Lighting the Path: Raman Spectroscopy's Journey Through the Microbial Maze.Molecules (Basel, Switzerland) · 2024Article
- HypervirulentPathogens & immunity · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.