ReviewBiosensors2022
Optical Methods for Label-Free Detection of Bacteria.
Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 40 citations in OpenAlex.
- A Review of SERS-Based Bacterial Detection from Nanomaterials to Integrated Clinical Platforms.Biosensors · 2026Review
- RapidFoods (Basel, Switzerland) · 2025Article
- Review
- Lighting the Path: Raman Spectroscopy's Journey Through the Microbial Maze.Molecules (Basel, Switzerland) · 2024Article
- Electronic structure, global reactivity descriptors and nonlinear optical properties of glycine interacted with ZnO, MgO and CaO for bacterial detection.Scientific reports · 2024Article
- Comparison of Culture-Dependent and Culture-Independent Methods for Routine Identification of Airborne Microorganisms in Speleotherapeutic Caves.Microorganisms · 2024Article
- Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms.Molecules (Basel, Switzerland) · 2024Review
- Rapid discrimination between wild and cultivatedCurrent research in food science · 2024Article
- Optical Sensors for Bacterial Detection.Sensors (Basel, Switzerland) · 2023Review
- A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification.Mikrochimica acta · 2023Review
- Recent Advances in Optical Sensing for the Detection of Microbial Contaminants.Micromachines · 2023Review
- Label-Free Biosensor.Biosensors · 2023Article
- Absorption/Attenuation Spectral Description of ESKAPEE Bacteria: Application to Seeder-Free Culture Monitoring, Mammalian T-Cell and Bacteria Mixture Analysis and Contamination Description.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments.
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.