Basal ganglia calcification occurs in 73·8% of patients with IH and correlates with the duration of hypocalcaemia, choroid plexus calcification, seizures and cataract. The progression of BGC is related to the calcium/phosphorus ratio during follow-up. This brings forth the importance of adequate phosphorus control in the management of hypoparathyroidism.
We report the fabrication of low cost highly structured silver (Ag) capped aluminium (Al) nanorods (NRs) as surface enhanced Raman spectroscopy (SERS) substrate utilising the glancing angle deposition technique. The nano-capping of silver onto the Al NRs can concentrate the local electric field within the minimal volume that can serve as hotspots. The average size of the Ag nanocaps was 50 nm. The newly proposed nanoporous Ag capped Al NRs as SERS substrate could detect the Raman signal of rhodamine 6G (R6G) up to 10 −15 molar concentration. The significant enhancement in the Raman signal of 10 7 was achieved for Ag capped Al NRs considering R6G as a probe molecule. Using the developed SERS substrate, we recorded Raman spectra for Escherichia coli bacteria with its concentration varying from 10 8 colony forming units per ml (CFU ml −1 ) up to 10 2 CFU ml −1 . All the reported Raman spectra were acquired by a portable handheld Raman spectrometer. Hence, this newly proposed low cost, effective SERS substrate can be used commercially for the onsite detection of clinical pathogens. The 3D finite difference time domain simulation model was performed for Ag capped Al nanostructure to understand the generation of hotspots. The simulated results show excellent agreement with the experimental results. We fabricated uncapped Ag nanorods of similar dimensions and performed the experimental measurements and simulations for comparison. We found a significant enhancement in Ag capped Al NRs compared to the long Ag NRs. The description of the Raman signal enhancement has been elaborated.
Antimicrobial resistance (AMR) is a major health threat
worldwide
and the culture-based bacterial detection methods are slow. Surface-enhanced
Raman spectroscopy (SERS) can be used to identify target analytes
in real time with sensitivity down to the single-molecule level, providing
a promising solution for the culture-free bacterial detection. We
report the fabrication of SERS substrates having tightly packed silver
(Ag) nanoparticles loaded onto long silicon nanowires (Si NWs) grown
by the metal-assisted chemical etching (MACE) method for the detection
of bacteria. The optimized SERS chips exhibited sensitivity down to
10–12 M concentration of R6G molecules and detected
reproducible Raman spectra of bacteria down to a concentration of
100 colony forming units (CFU)/mL, which is a thousand times lower
than the clinical threshold of bacterial infections like UTI (105 CFU/mL). A Siamese neural network model was used to classify
SERS spectra from bacteria specimens. The trained model identified
12 different bacterial species, including those which are causative
agents for tuberculosis and urinary tract infection (UTI). Next, the
SERS chips and another Siamese neural network model were used to differentiate
AMR strains from susceptible strains of Escherichia
coli (E. coli). The enhancement offered
by SERS chip-enabled acquisitions of Raman spectra of bacteria directly
in the synthetic urine by spiking the sample with only 103 CFU/mL E. coli. Thus, the present
study lays the ground for the identification and quantification of
bacteria on SERS chips, thereby offering a potential future use for
rapid, reproducible, label-free, and low limit detection of clinical
pathogens.
We present a fast, accurate, and molecular specific detection technique of dyes at an ultra-trace level using surface-enhanced Raman spectroscopy (SERS). A highly sensitive Ag - TiO2 thin film having...
We have investigated the enhanced Raman spectra of AMR bacteria strains of E. coli using silver coated silicon nanowires SERS assay. Three different E. coli strains, E. coli CCUG17620, NCTC 13441, and A239, were detected using two different excitation laser wavelengths. We found stable and enhanced SERS spectrum using 785 nm laser as opposed to 532 nm. Future development of SERS-chip could offer a reliable platform for direct identification of the pathogen in bio-fluid samples at strains level.
The world health organization considers antimicrobial resistance (AMR) to be a critical global public health problem. Conventional culture-based methods that are used to detect and identify bacterial infection are slow. Thus, there is a growing need for the development of robust, cost-effective, and fast diagnostic solutions for the identification of pathogens. Surface-enhanced Raman spectroscopy (SERS) can be used to identify target analytes with sensitivity down to the single-molecule level. Here, we developed a SERS chip by optimizing the entire fabrication pipeline of the metal-assisted chemical etching (MACE) method. The MACE approach offers a large-scale, densely packed silver (Ag) nanostructure on top of silicon nanowires (Si-NWs) with a large aspect ratio that significantly enhances the Raman signal due to localised surface plasmonic enhancement. The optimised SERS chips exhibited sensitivity down to 10^(-12) M concentration of R6G molecule and detected reproducible Raman spectra of bacteria down to a concentration of 100 colony forming units (CFU)/ml, which is a thousand times lower than the clinical threshold of bacterial infections like UTI (10^5 CFU/ml). A Siamese neural network model was used to classify SERS Raman spectra from bacteria specimens. The trained model identified 12 different bacterial species, including those which are causative agents for tuberculosis and urinary tract infection (UTI). Next, the SERS chips and another Siamese neural network model were used to differentiate antibiotic-resistant strains from susceptible strains of E. coli. The enhancement offered by SERS chip enabled acquisitions of Raman spectra of bacteria directly in the synthetic urine by spiking the sample with only 10^3 CFU/ml E. coli. Thus, the present study lays the ground for the identification and quantification of bacteria on SERS chips, thereby offering a potential future use for rapid, reproducible, label-free, and low limit detection of clinical pathogens.
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