In vivo Raman measurement of levofloxacin lactate in blood using a nanoparticle-coated optical fiber probe

Liu, Shupeng; Rong, Ming; Zhang, Heng; Chen, Na; Pang, Fufei; Chen, Zhenyi; Yan, Jing

doi:10.1364/boe.7.000810

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Figure 5b demonstrates the most informative Raman spectral features for sitagliptin are 409 cm −1 (C‐N‐C deformation), [ 37 ] 580 cm −1 (NH 2 wag), [ 38 ] 615 cm −1 (C=C‐C bending), [ 34 ] 728 cm −1 (C‐F symmetric stretching vibrations), 760 cm −1 (stretching vibrations of C‐F bonds), 904 cm −1 (C‐O‐C asymmetric vibrations), [ 39 ] 939 cm −1 (C‐N‐C stretching modes), 1019 cm −1 (aromatic benzene ring breathing), [ 32 ] 1522 cm −1 (stretching vibrations of C=C bond in aromatic ring), and 1673 cm −1 (C=C stretching). [ 40 ] These most informative features were extracted to train a PLSR model and all other features were eliminated.…”

Section: Resultsmentioning

confidence: 99%

Development of optimized regression model for Raman spectral analysis of pharmaceutical drugs cefixime and sitagliptin

Bajwa

Hussain

Nawaz

et al. 2022

J Raman Spectroscopy

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To build a robust model for the quantification of pharmaceutical drugs, different regression algorithms and preprocessing techniques are employed. In this study, Raman spectroscopy is used for the analysis of solid dosage formulations of two drugs, cefixime and sitagliptin. Using the different amounts of excipients and active pure ingredients (API), the different concentrations of cefixime and sitagliptin are made. The principal component regression (PCA), partial least‐squares regression (PLSR), and PLSR based on feature selection by variable importance in projection (VIP) scores are used to regress the model to quantify the API concentration of cefixime and sitagliptin in a mixture of excipients. These models are built on both normalized and unnormalized data of cefixime and sitagliptin to check the performance of the constructed models. By comparing errors of all trained models, it is observed that the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were minimum in the case of PLSR model construction on unnormalized data of both cefixime and sitagliptin.

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Section: Resultsmentioning

confidence: 99%

Development of optimized regression model for Raman spectral analysis of pharmaceutical drugs cefixime and sitagliptin

Bajwa

Hussain

Nawaz

et al. 2022

J Raman Spectroscopy

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“…Nevertheless, further steps are required to move this technology toward animal models. These steps include: i) understanding whether the mechanical interaction between the brain tissue and the probe will introduce severe damages, since the tissue is intrinsically different from liquid phase environments, where nanoparticle-based SERS probes can be applied; [65][66][67] ii) testing the potential toxicity due to gold ions or NIs release from the fiber surface; iii) boosting the temporal response of the approach; and iv) developing a method to discriminate across multiple chemical compounds, for example using barcoding data processing [43] or to functionalize the structured fiber probes to be able to catch the target analyte. [68,69] In this view, we believe that the SERS-active TFs neural probes have the potential to complement existing methods to detect neurotransmitters, currently employing GEI or cyclic voltammetry.…”

Section: Discussionmentioning

confidence: 99%

Toward Plasmonic Neural Probes: SERS Detection of Neurotransmitters through Gold‐Nanoislands‐Decorated Tapered Optical Fibers with Sub‐10 nm Gaps

et al. 2023

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Integration of plasmonic nanostructures with fiber‐optics‐based neural probes enables label‐free detection of molecular fingerprints via surface‐enhanced Raman spectroscopy (SERS), and it represents a fascinating technological horizon to investigate brain function. However, developing neuroplasmonic probes that can interface with deep brain regions with minimal invasiveness while providing the sensitivity to detect biomolecular signatures in a physiological environment is challenging, in particular because the same waveguide must be employed for both delivering excitation light and collecting the resulting scattered photons. Here, a SERS‐active neural probe based on a tapered optical fiber (TF) decorated with gold nanoislands (NIs) that can detect neurotransmitters down to the micromolar range is presented. To do this, a novel, nonplanar repeated dewetting technique to fabricate gold NIs with sub‐10 nm gaps, uniformly distributed on the wide (square millimeter scale in surface area), highly curved surface of TF is developed. It is experimentally and numerically shown that the amplified broadband near‐field enhancement of the high‐density NIs layer allows for achieving a limit of detection in aqueous solution of 10−7 m for rhodamine 6G and 10−5 m for serotonin and dopamine through SERS at near‐infrared wavelengths. The NIs‐TF technology is envisioned as a first step toward the unexplored frontier of in vivo label‐free plasmonic neural interfaces.

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“…The quantification of tetracycline hydrochloride and oxytetracycline hydrochloride in buffer solutions using a molecular printed polymer (MIP) has been reported based on various binding sites/nanocavities having the complementary form of the functional groups of the target molecules on its surface. This method raised LODs of 4.23 ng/mL for tetracycline and 4.05 ng/mL for oxytetracycline [ 296 ]. Likewise, traces of erythromycin in milk and honey have been quantified with LODs of 47.41 ng/mL and 28.48 ng/mL, respectively [ 256 ].…”

Section: Nanobiosensors As Bioanalytic Applications In the Quantifmentioning

confidence: 99%

Personalized Medicine for Antibiotics: The Role of Nanobiosensors in Therapeutic Drug Monitoring

Garzón

Bustos

Pinacho

2020

JPM

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Due to the high bacterial resistance to antibiotics (AB), it has become necessary to adjust the dose aimed at personalized medicine by means of therapeutic drug monitoring (TDM). TDM is a fundamental tool for measuring the concentration of drugs that have a limited or highly toxic dose in different body fluids, such as blood, plasma, serum, and urine, among others. Using different techniques that allow for the pharmacokinetic (PK) and pharmacodynamic (PD) analysis of the drug, TDM can reduce the risks inherent in treatment. Among these techniques, nanotechnology focused on biosensors, which are relevant due to their versatility, sensitivity, specificity, and low cost. They provide results in real time, using an element for biological recognition coupled to a signal transducer. This review describes recent advances in the quantification of AB using biosensors with a focus on TDM as a fundamental aspect of personalized medicine.

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In vivo Raman measurement of levofloxacin lactate in blood using a nanoparticle-coated optical fiber probe

Cited by 10 publications

References 26 publications

Development of optimized regression model for Raman spectral analysis of pharmaceutical drugs cefixime and sitagliptin

Development of optimized regression model for Raman spectral analysis of pharmaceutical drugs cefixime and sitagliptin

Toward Plasmonic Neural Probes: SERS Detection of Neurotransmitters through Gold‐Nanoislands‐Decorated Tapered Optical Fibers with Sub‐10 nm Gaps

Personalized Medicine for Antibiotics: The Role of Nanobiosensors in Therapeutic Drug Monitoring

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