Quantification of Antiretroviral Drug Emtricitabine in Human Plasma by Surface Enhanced Raman SpectroscopyClick to copy article linkArticle link copied!
- Marguerite R. ButlerMarguerite R. ButlerDepartment of Chemistry and Biochemistry, Old Dominion University, Norfolk, Virginia 23529, United StatesMore by Marguerite R. Butler
- Terry A. JacotTerry A. JacotCONRAD, Eastern Virginia Medical School, Norfolk, Virginia 23507, United StatesMore by Terry A. Jacot
- Sucharita M. DuttaSucharita M. DuttaCONRAD, Eastern Virginia Medical School, Norfolk, Virginia 23507, United StatesMore by Sucharita M. Dutta
- Gustavo F. DoncelGustavo F. DoncelCONRAD, Eastern Virginia Medical School, Norfolk, Virginia 23507, United StatesMore by Gustavo F. Doncel
- John B. Cooper*John B. Cooper*Email: [email protected]Department of Chemistry and Biochemistry, Old Dominion University, Norfolk, Virginia 23529, United StatesMore by John B. Cooper
Abstract
In this study, reproducible label-free detection and quantification of the antiretroviral drug emtricitabine (FTC) down to 78 ng/mL in human plasma by surface enhanced Raman spectroscopy (SERS) is presented. A novel plasma sample pretreatment method using silver nitrate and silver colloidal nanoparticles (Ag CNPs) was used to prepare the plasma samples for analysis. The pretreated plasma samples were evaporated to dryness on an aluminum surface and a computer-controlled Raman scanning system was used to collect spatially resolved SERS spectra of the entire surface. Calibration curves of commercial human plasma samples containing FTC in a concentration range of 5000 to 78 ng/mL were calculated using three different methods. First, a conventional approach was taken, where all the spectra collected for each concentration were averaged, then the SERS intensity of a known FTC peak (792 cm–1) was used for calibrations (total population method). This approach was refined by utilizing a figure-of-merit (FOM) quality index (Qi) to sample spectra from each concentration that contained the highest signal-to-noise (S/N), before averaging and calculating the SERS intensity of the 792 cm–1 FTC peak (Qi sample method). Finally, the distribution of all Qi values for each concentration were modeled using cumulative distribution functions (CDFs) and were used for calibrations (CDF method). The CDF method exhibited the highest analytical sensitivity (slope = 3702.47) compared to the Qi sample method (slope = 1591.05) and the total population method (slope = 754.21). The Qi sample method exhibited the highest linearity (R2 = 0.99) compared to the CDF method (R2 = 0.95) and the total population average (R2 = 0.97). The CDF method exhibited the highest S/N in the concentration range of 5000 to 312 ng/mL (S/N range of 31.5–16.6). The Qi sample method exhibited the highest S/N for concentrations 156 and 78 ng/mL (S/N = 9.7 and 7.4, respectively). These results show that the Qi sample method is advantageous over all other methods when approaching the LOQ while the CDF method is advantageous over all methods at higher concentrations. The LOQ (78 ng/mL) was confirmed by principal component analysis (PCA). Together these results show that statistical treatment of a large population of SERS spectra, where the analyte signal intensity follows an exponential distribution, is superior to standard methods of averaging populations of spectra in terms of analytical sensitivity, linearity, and S/N. Additionally, it was found that the background signal had no interference with the quantitative data calculated for the total population and Qi sample methods after repeating both analyses with baseline-subtracted spectra. The results and methodology presented in this study establish a framework for integrating SERS into drug adherence monitoring for FTC-based treatment and prevention of infections by demonstrating consistent SERS detection and quantification of FTC in human plasma at therapeutically relevant concentrations.
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License Summary*
You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:
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Special Issue
Published as part of ACS Omega special issue “Celebrating 50 Years of Surface Enhanced Spectroscopy”.
1. Introduction
2. Materials and Methods
2.1. Synthesis of Silver Colloidal Nanoparticles
Figure 1
Figure 1. Workflow of the plasma sample treatment protocol used in this study.
Figure 2
Figure 2. Calibration curves prepared using the total population method for three replicate experiments. (A–C) Averaged SERS spectra for all concentrations and corresponding SERS intensity calibration curves beneath. Each spectrum shown is an average of 9030 spectra (1806 spectra from each concentration replicate). Each black data point represents the difference in SERS intensities at 792 and 723 cm–1 for each concentration replicate. Linear regression lines were calculated using the average of all concentration replicates (red data points). The regression line, equation, and correlation coefficient for each replicate experiment are shown in red. Spectra were offset for clarity.
2.2. SERS Surface Preparation
2.3. Plasma Sample Preparation
2.4. Instrumentation
2.5. Quality Index (Qi) Calculations
2.6. Cumulative Distribution Function Calculations
3. Results
3.1. Calibration Curves
3.1.1. Total Population Method
3.1.2. Qi Sample Method
Figure 3
Figure 3. Calibration curves prepared using the Qi sample method for three replicate experiments. (A–C) Averaged SERS spectra for all concentrations and corresponding SERS intensity calibration curves beneath. Each spectrum shown is an average of 100 spectra (20 spectra from each replicate corresponding to the highest 792 cm–1 Qi). Each black data point represents the difference in SERS intensities at 792 and 723 cm–1 for each concentration replicate. Linear regression lines were calculated using the average of all concentration replicates (red data points). The regression line, equation, and correlation coefficient for each replicate experiment are shown in red. Spectra were offset for clarity.
3.1.3. CDF Method
Figure 4
Figure 4. Calibration curves prepared using the CDF method for three replicate experiments. (A–C) Model CDFs of each FTC concentration and corresponding calibration curves beneath. The CDFs were constructed based on the Qi of the 792 cm–1 spectral region. A 4th order polynomial was fitted to each CDF in the probability range of 0.6–0.9. The Σ ΔQCDF was calculated for each concentration (see eq 2) and plotted as a function of the logarithm of FTC concentration. The Σ ΔQCDF values of the model CDFs (red data points) were used for linear regression. Black data points represent the Σ ΔQCDF of concentration replicates. Each data point in the calibration curves was increased by the absolute value of the smallest data point, ensuring all values are positive and maintain the same intervals between points. The regression line, equation, and correlation coefficient for each replicate experiment are shown in red.
3.2. Comparisons Between the Methods of Quantification
quantitative method | |||
---|---|---|---|
total population | Qi sample | CDF | |
slope | 754.21 | 1591.05 | 3702.47 |
R2 | 0.97 | 0.99 | 0.95 |
quantitative method | |||
---|---|---|---|
concentration (ng/mL) | total population | Qi sample | CDF |
5000 | 0.09 | 0.15 | 0.05 |
2500 | 0.06 | 0.08 | 0.03 |
1250 | 0.07 | 0.09 | 0.04 |
625 | 0.11 | 0.09 | 0.06 |
312 | 0.07 | 0.07 | 0.06 |
156 | 0.15 | 0.10 | 0.17 |
78 | 0.16 | 0.13 | 0.56 |
For each method, the quantitative information of the three replicate experiments (see Figures 2–4) were averaged prior to RSD calculations. The RSD values were calculated by dividing the average response of all concentration replicates by the standard deviation. The cells corresponding to the lowest RSD of each concentration are highlighted in bold.
quantitative method | |||
---|---|---|---|
concentration (ng/mL) | total population | Qi sample | CDF |
5000 | 10.7 | 6.8 | 20.8 |
2500 | 16.2 | 13.2 | 31.5 |
1250 | 14.9 | 10.7 | 25.5 |
625 | 9.1 | 10.6 | 16.6 |
312 | 14.5 | 13.9 | 17.4 |
156 | 6.7 | 9.7 | 5.7 |
78 | 6.2 | 7.4 | 1.8 |
For each method, the quantitative information of the three replicate experiments (see Figures 2–4) were averaged prior to S/N calculations. The S/N values were calculated by computing the reciprocal of the concentration RSD values shown in Table 2. The cells corresponding to the highest S/N of each concentration are highlighted in bold.
4. Discussion
Figure 5
Figure 5. PCA of the matrix blank (blue) and 78 ng/mL (red) SERS spectra used for calibrations in the (A–C) Qi sample method and (D–F) total population method. The Python library sklearn was used for PCA. (35) The spectra were preprocessed by first truncating the spectral region (585.48 to 1710.01 cm–1) followed by applying an improved asymmetrically reweighted penalized least-squares (IarPLS) background correction algorithm (36,37) (see Figure S10). The first two PC scores were plotted against each other, and the explained variance ratios for each PC are shown on corresponding axes. A 95% confidence ellipsoid of each group is shown. A detailed description of the PCA workflow is described in Figure S11.
Figure 6
Figure 6. SERS spectra of aqueous 1250 ng/mL FTC (blue), plasma containing 1250 ng/mL of FTC (black), and nonspiked plasma (red). Relevant peaks from each spectrum are noted by a vertical line with their wavenumber written at the top in the color corresponding to the sample type. Each spectrum shown is an average of 25 spectra, each acquired using 15 mW of laser power and 800 ms integration time.
5. Conclusion
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.4c06162.
DLS of nanoparticles, UV–vis of nanoparticles, image of aluminum well plate, schematic of Qi calculation process, process diagram of CDF calculation workflow, calibration curves calculated using the total population method after subtracting the matrix blank SERS spectrum, calibration curves calculated using the Qi sample method after subtracting the matrix blank SERS spectrum, unfitted CDFs of the entire probability range (0–1), CDF method analysis using the SERS intensity at 792 cm–1 to construct CDFs, preprocessed SERS spectra used for PCA, overview of PCA workflow, histograms of the Qi distribution of all data sets, chemical structure of FTC, peak assignments of an aqueous FTC SERS spectrum (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Acknowledgments
The first author would like to thank Jana Hrncirova for preparing the graphics of laboratory equipment seen in Figure 1.
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- 15Tian, M.; Wang, J.; Li, C.; Wang, Z.; Liu, G.; Lv, E.; Zhao, X.; Li, Z.; Cao, D.; Liu, H. Qualitative and quantitative detection of microcystin-LR based on SERS-FET dual-mode biosensor. Biosens. Bioelectron. 2022, 212, 114434, DOI: 10.1016/j.bios.2022.114434Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhsFyrsbrI&md5=a0ae1f69e407d64bbf85353f80a71115Qualitative and quantitative detection of microcystin-LR based on SERS-FET dual-mode biosensorTian, Meng; Wang, Jihua; Li, Chonghui; Wang, Zhenxing; Liu, Guofeng; Lv, Enguang; Zhao, Xiaofei; Li, Zhen; Cao, Dongyan; Liu, Huilan; Zhang, Chao; Xu, Shicai; Man, BaoyuanBiosensors & Bioelectronics (2022), 212 (), 114434CODEN: BBIOE4; ISSN:0956-5663. (Elsevier B.V.)Microcystin-LR (MC-LR), a kind of hepatoxin produced by cyanobacteria blooms, can promote liver cancer through long-term exposure even at low concns. In this study, a novel biosensor based on surface-enhanced Raman scattering (SERS) and field effect transistor (FET) dual sensing mode was developed by using gold nanoparticles (AuNPs)/graphene composite as sensing material. Based on the SERS sensing mode, the Raman fingerprint spectrum of MC-LR was obtained through the specific combination of MC-LR aptamer and MC-LR. The SERS enhanced effect of the AuNPs was also verified by theor. simulation. By using FET sensing mode, the graphene field effect transistor (G-FET) biosensor resp. exhibited the detection limit as low as 0.62 aM and 0.91 aM in phosphate buffered saline (PBS) and human serum, and showed a good linear relationship in a wide range of 1 x 10-18 to 1 x 10-8 M in both solns. Meanwhile, the sensor was utilized for the detection of MC-LR in actual water samples, and the complex components in the water did not interfere with MC-LR detection, indicating a significant high specificity of the sensor. The SERS-FET dual-mode biosensor can provide more detection options and improve the reliability of measurement results, which may has a great application prospect in the field of water environment detection.
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- 17Reese, T.; Suarez, C.; Premasiri, W. R.; Shaine, M. L.; Ingraham, H.; Brodeur, A. N.; Ziegler, L. D. Surface enhanced Raman scattering specificity for detection and identification of dried bloodstains. Forensic Sci. Int. 2021, 328, 111000, DOI: 10.1016/j.forsciint.2021.111000Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXisV2lsr3F&md5=8eea8f7392d717aaf86ffd7dcc1a8cdfSurface enhanced Raman scattering specificity for detection and identification of dried bloodstainsReese, T.; Suarez, C.; Premasiri, W. R.; Shaine, M. L.; Ingraham, H.; Brodeur, A. N.; Ziegler, L. D.Forensic Science International (2021), 328 (), 111000CODEN: FSINDR; ISSN:0379-0738. (Elsevier Ltd.)Surface enhanced Raman spectroscopy (SERS) provides highly specific vibrational signatures identifying dried blood for a variety of forensic applications. SERS spectra on Au nanoparticle substrates excited at 785 nm are found to identify dried stains of human and nonhuman blood from seven animals, and distinguish stains due to menstrual and peripheral blood. In addn., the unique SERS bloodstain spectrum is distinct from the SERS spectra of thirty red-brown stains of potential household substances that could be visually mistaken for bloodstains and from food stains that have been shown to give pos. results with presumptive colorimetric blood tests. Finally, a SERS swab procedure has been developed and demonstrates that the substrates that a blood sample dried on does not offer any Raman or fluorescence interference for the SERS identification of dried blood. Such bloodstains on porous and nonporous materials are all identical and exclusively due to the heme moiety of Hb. Optimized selection of the extn. solvent is found to control the chem. compn. of mol. components appearing in the SERS spectrum of complex, multicomponent biol. mixts., such as body fluids.
- 18Shi, L.; Li, Y.; Li, Z. Early cancer detection by SERS spectroscopy and machine learning. Light: Sci. Appl. 2023, 12 (1), 234, DOI: 10.1038/s41377-023-01271-7Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXhvFarsrjI&md5=517e0219428ac674c35680f686862366Early cancer detection by SERS spectroscopy and machine learningShi, Lingyan; Li, Yajuan; Li, ZhiLight: Science & Applications (2023), 12 (1), 234CODEN: LSAIAZ; ISSN:2047-7538. (Nature Portfolio)Abstr.: A new approach for early detection of multiple cancers is presented by integrating SERS spectroscopy of serum mol. fingerprints and machine learning.
- 19Bonifacio, A.; Dalla Marta, S.; Spizzo, R.; Cervo, S.; Steffan, A.; Colombatti, A.; Sergo, V. Surface-enhanced Raman spectroscopy of blood plasma and serum using Ag and Au nanoparticles: a systematic study. Anal. Bioanal. Chem. 2014, 406 (9–10), 2355– 2365, DOI: 10.1007/s00216-014-7622-1Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhslSku78%253D&md5=655abe670985fc2e97778b18ecf4ae46Surface-enhanced Raman spectroscopy of blood plasma and serum using Ag and Au nanoparticles: a systematic studyBonifacio, Alois; Dalla Marta, Silvia; Spizzo, Riccardo; Cervo, Silvia; Steffan, Agostino; Colombatti, Alfonso; Sergo, ValterAnalytical and Bioanalytical Chemistry (2014), 406 (9-10), 2355-2365CODEN: ABCNBP; ISSN:1618-2642. (Springer)Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on biofluids in point-of-care or screening tests. In particular, label-free SERS spectra of blood serum and plasma, two biofluids widely used in diagnostics, could be used as a metabolic fingerprinting approach for biomarker discovery. This study aims at a systematic evaluation of SERS spectra of blood serum and plasma, using various Ag and Au aq. colloids, as SERS substrates, in combination with three excitation lasers of different wavelengths, ranging from the visible to the near-IR. The anal. of the SERS spectra collected from 20 healthy subjects under a variety of exptl. conditions revealed that intense and repeatable spectra are quickly obtained only if proteins are filtered out from samples, and an excitation in the near-IR is used in combination with Ag colloids. Moreover, common plasma anticoagulants such as EDTA and citrate are found to interfere with SERS spectra; accordingly, filtered serum or heparin plasma are the samples of choice, having identical SERS spectra. Most bands obsd. in SERS spectra of these biofluids are assigned to uric acid, a metabolite whose blood concn. depends on factors such as sex, age, therapeutic treatments, and various pathol. conditions, suggesting that, even when the right exptl. conditions are chosen, great care must be taken in designing studies with the purpose of developing diagnostic tests.
- 20Wang, Y.; Yu, C.; Ji, H.; Liu, Z.; Wang, X.; Ji, Y.; Sun, X.; Zhao, Y.; Qiu, X.; Zhang, T. Label-free therapeutic drug monitoring in human serum by the 3-step surface enhanced Raman spectroscopy and multivariate analysis. Chem. Eng. J. 2023, 452, 139588, DOI: 10.1016/j.cej.2022.139588Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xis1WlsLnI&md5=16fd06fe1bca8ef5d13edc320a5ca85aLabel-free therapeutic drug monitoring in human serum by the 3-step surface enhanced Raman spectroscopy and multivariate analysisWang, Yunpeng; Yu, Cuiwei; Ji, Haoyu; Liu, Zhehan; Wang, Xiaotong; Ji, Yinghe; Sun, Xiaomeng; Zhao, Yue; Qiu, Xiaohong; Zhang, Ting; Li, Jing; Liu, Xin; Lv, Xinpeng; Cai, Benzhi; Zhao, Yingqi; Huang, Jian-An; Li, YangChemical Engineering Journal (Amsterdam, Netherlands) (2023), 452 (Part_4), 139588CODEN: CMEJAJ; ISSN:1385-8947. (Elsevier B.V.)Surface enhanced Raman spectroscopy (SERS) is widely used in drug mol. detection. However, SERS detections of drug mols. in serum with high sensitivity and reproducibility remains extremely challenging due to signal interference of complex constituents of serum. The latter presents a high SERS background noise that buries the signals of the drug mols. Here, we report a 3-step method to make SERS system of silver nanoparticle clusters to overcome the interference and achieve quant. SERS anal. of drugs in serum by 1 proteins removal from serum; 2 enhanced drug adsorption on the nanoparticles; and 3 background suppression by internal std. in nanoparticle aggregation. By careful selection of the aggregation agents and internal std., clear SERS peaks of the internal std. and six different drug analytes were obsd. for pesticide identification in human serum. Significantly, the SERS peak ratio of the internal std. and drug analytes has achieved univariate quant. monitoring of drug metab. in mice serum, which is in agreement with anal. by the multivariate curve resoln.-alternating least squares method. Our method shows great clin. application potential in therapeutic drug monitoring and personalized medicine.
- 21Lin, D.; Pan, J.; Huang, H.; Chen, G.; Qiu, S.; Shi, H.; Chen, W.; Yu, Y.; Feng, S.; Chen, R. Label-free blood plasma test based on surface-enhanced Raman scattering for tumor stages detection in nasopharyngeal cancer. Sci. Rep. 2014, 4 (1), 4751, DOI: 10.1038/srep04751Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXovV2isrw%253D&md5=07a576168d2d0ab919a3315d2073d40fLabel-free blood plasma test based on surface-enhanced Raman scattering for tumor stages detection in nasopharyngeal cancerLin, Duo; Pan, Jianji; Huang, Hao; Chen, Guannan; Qiu, Sufang; Shi, Hong; Chen, Weiwei; Yu, Yun; Feng, Shangyuan; Chen, RongScientific Reports (2014), 4 (), 4751/1-4751/8CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)This study aims to evaluate the feasibility of a label-free nanobiosensor based on blood plasma surface-enhanced Raman spectroscopy (SERS) method for exploring variability of different tumor (T) stages in nasopharyngeal cancer (NPC). Au nanoparticles as the SERS-active nanostructures were directly mixed with human blood plasma to enhance the Raman scattering signals. High quality SERS spectra can be acquired from blood plasma samples belong to 60 healthy volunteers, 25 NPC patients with T1 stage and 75 NPC patients with T2-T4 stage. A diagnostic accuracy of 83.5% and 93.3%, resp., can be achieved for classification between early T (T1) stage cancer and normal; and advanced T (T2-T4) stage cancer and normal blood groups. This exploratory study demonstrates that the nanobiosensor based on SERS technique in conjunction with PCA-LDA has great potential as a clin. complement for different T stages detection in nasopharyngeal cancer.
- 22Gao, S.; Lin, Y.; Zhao, X.; Gao, J.; Xie, S.; Gong, W.; Yu, Y.; Lin, J. Label-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancers. Spectrochim. Acta, Part A 2022, 267, 120605, DOI: 10.1016/j.saa.2021.120605Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXisFChu7bO&md5=1da5274ca180553f5157c1fa49b769adLabel-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancersGao, Siqi; Lin, Yamin; Zhao, Xin; Gao, Jiamin; Xie, Shusen; Gong, Wei; Yu, Yun; Lin, JuqiangSpectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (2022), 267 (Part_2), 120605CODEN: SAMCAS; ISSN:1386-1425. (Elsevier B.V.)Surface-enhanced Raman spectroscopy (SERS) is considered as an ultrasensitive, non-invasive as well as rapid detection technol. for cancer diagnosis. In this study, we developed a novel blood serum anal. strategy using coffee ring effect-assisted label-free SERS for different types of cancer screening. Addnl., the pretreated Ag nanoparticles (Ag NPs) were mixed with the serum from liver cancer patients (n = 40), prostate cancer patients (n = 32) and healthy volunteers (n = 30) for SERS measurement. The droplets of Ag NPs-serum mixt. formed the coffee ring on the peripheral after air-drying, and thus extremely enhancing Raman signal and ensuring the stability and reliability of SERS detection. Partial least square (PLS) and support vector machine (SVM) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying, yielding the high diagnostic accuracy of 98.04% for normal group and two types of cancers simultaneously distinguishing. More importantly, for the unknown testing set, an ideal diagnostic accuracy of 100% could be achieved by PLS-SVM algorithm for differentiating cancers from the normal group. The results from this exploratory work demonstrate that serum SERS detection combined with PLS-SVM diagnostic algorithm and coffee ring effect has great potential for the noninvasive and label-free detection of cancer.
- 23Peng, S.; Lu, D.; Zhang, B.; You, R.; Chen, J.; Xu, H.; Lu, Y. Machine learning–assisted internal standard calibration label-free SERS strategy for colon cancer detection. Anal. Bioanal. Chem. 2023, 415 (9), 1699– 1707, DOI: 10.1007/s00216-023-04566-1Google ScholarThere is no corresponding record for this reference.
- 24Schinazi, R. F.; Patel, D.; Ehteshami, M. The best backbone for HIV prevention, treatment, and elimination: Emtricitabine+tenofovir. Antivir. Ther. 2022, 27 (2), 13596535211067599, DOI: 10.1177/13596535211067599Google ScholarThere is no corresponding record for this reference.
- 25Muller, J. T.; Al Khalili, Y. Emtricitabine. In StatPearls [Internet]; StatPearls Publishing, 2023.Google ScholarThere is no corresponding record for this reference.
- 26Ciccullo, A.; Baldin, G.; Putaggio, C.; Di Giambenedetto, S.; Borghetti, A. Comparative safety review of recommended, first-line single-tablet regimens in patients with HIV. Expert Opin. Drug Saf. 2021, 20 (11), 1317– 1332, DOI: 10.1080/14740338.2021.1931115Google ScholarThere is no corresponding record for this reference.
- 27Butler, M. R.; Hrncirova, J.; Clark, M.; Dutta, S.; Cooper, J. B. Quantification of antiviral drug tenofovir (TFV) by surface-enhanced Raman spectroscopy (SERS) using cumulative distribution functions (CDFs). ACS Omega 2024, 9 (1), 1310– 1319, DOI: 10.1021/acsomega.3c07641Google ScholarThere is no corresponding record for this reference.
- 28Butler, M. R.; Hrncirova, J.; Jacot, T. A.; Dutta, S.; Clark, M. R.; Doncel, G. F.; Cooper, J. B. Detection and quantification of antiviral drug tenofovir using silver nanoparticles and surface enhanced Raman spectroscopy (SERS) with spatially resolved hotspot selection. Front. Nanotechnol. 2023, 5, 1270474, DOI: 10.3389/fnano.2023.1270474Google ScholarThere is no corresponding record for this reference.
- 29Hrncirova, J.; Butler, M. R.; Dutta, S.; Clark, M. R.; Cooper, J. B. Cumulative distribution function and spatially resolved surface-enhanced Raman spectroscopy for the quantitative analysis of emtricitabine. Appl. Spectrosc. Pract. 2024, 2 (1), 1– 11, DOI: 10.1177/27551857241235972Google ScholarThere is no corresponding record for this reference.
- 30Leopold, N.; Lendl, B. A new method for fast preparation of highly surface-enhanced Raman scattering (SERS) active silver colloids at room temperature by reduction of silver nitrate with hydroxylamine hydrochloride. J. Phys. Chem. B 2003, 107 (24), 5723– 5727, DOI: 10.1021/jp027460uGoogle Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXjvFSntr0%253D&md5=08cf57bc48c7a5cd1b537bb873e31d7cA New Method for Fast Preparation of Highly Surface-Enhanced Raman Scattering (SERS) Active Silver Colloids at Room Temperature by Reduction of Silver Nitrate with Hydroxylamine HydrochlorideLeopold, Nicolae; Lendl, BernhardJournal of Physical Chemistry B (2003), 107 (24), 5723-5727CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)A very effective and simple way to produce silver colloids for surface-enhanced Raman scattering (SERS) is reported. Redn. of silver nitrate with hydroxylamine hydrochloride at alk. pH and at room temp. yields highly sensitive SERS colloids within a short time. The so-produced colloids can be used for SERS spectroscopy immediately after prepn. The overall procedure is fast, simple, and characterized by a high prepn. success rate. Changing the mixing order and rate of the two involved solns., silver nitrate and hydroxylamine hydrochloride contg. sodium hydroxide, one can control the size and dispersion of the produced colloids. The obtained colloids have been characterized by UV-vis spectroscopy, transmission electron microscopy, and SERS using a 1064 nm laser line on a Fourier transform and a 785 nm laser line on a dispersive Raman spectrometer. The SERS enhancement factor of the hydroxylamine-reduced silver colloids was tested using crystal violet, rhodamine 6G, methylene blue, and 9-aminoacridine. It was found that for both excitation lines sensitivities comparable to those achievable with a Lee-Meisel silver colloid were obtained thus rendering the new colloid advantageous because of its significantly simpler and faster synthesis.
- 31Darragh-Hickey, C.; Flowers, K. C.; Shipman, A. R.; Allen, G. T.; Kaur, S.; Shipman, K. E. Investigative algorithms for disorders affecting plasma chloride: a narrative review. J. Lab. Precis. Med. 2022, 7, 22, DOI: 10.21037/jlpm-22-7Google ScholarThere is no corresponding record for this reference.
- 32Chang, T. M. S. Chapter 45─Nanodimension biodegradable polymeric membrane artificial red blood cells. In Blood Substitutes; Winslow, R. M., Ed.; Academic Press, 2006; pp 523– 531.Google ScholarThere is no corresponding record for this reference.
- 33Hrncirova, J.; Butler, M. R.; Clark, M. R.; Doncel, G. F.; Cooper, J. B. A new approach for discriminating spatially acquired SERS spectra using antiretroviral drug emtricitabine as a test sample. J. Raman Spectrosc. 2024, 55, 1129– 1138, DOI: 10.1002/jrs.6721Google ScholarThere is no corresponding record for this reference.
- 34Zhang, Y.; Zhao, C.; Tian, G.; Lu, C.; Li, Y.; He, L.; Xiao, H.; Zheng, J. Simultaneous characterization of chemical structures and bioactivities of citrus-derived components using SERS barcodes. Food Chem. 2018, 240, 743– 750, DOI: 10.1016/j.foodchem.2017.07.103Google Scholar34https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtlSltLfF&md5=9a321578e802cef6059d2f3e15e03a1bSimultaneous characterization of chemical structures and bioactivities of citrus-derived components using SERS barcodesZhang, Ye; Zhao, Chengying; Tian, Guifang; Lu, Chang; Li, Yuzhi; He, Lili; Xiao, Hang; Zheng, JinkaiFood Chemistry (2018), 240 (), 743-750CODEN: FOCHDJ; ISSN:0308-8146. (Elsevier Ltd.)Rapid detection of bioactive components in food has attracted great attention. Herein, we report a novel method through surface-enhanced Raman spectroscopy (SERS) spectra based barcodes for simultaneous characterization of chem. structures and bioactivities of nine citrus components for the first time. SERS barcodes were successfully used to characterize and discriminate all the components with high sensitivity down to 40-60 ng. Importantly, SERS barcodes exhibited the 'identity' characteristics. Beyond the mol. structure information, bioactivity information can also be read from the barcodes according to the bioactivity assay and structure-activity relationship. Hence, a simple and intuitive SERS barcoding approach used for simultaneous characterization of chem. structures and bioactivities was established. With a large database of barcodes, there is high potential that the SERS barcoding approach could be further developed to be a rapid, simple and effective foodomics-like approach for bioactive compd. identification from a complex food matrix.
- 35Fabian, P. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825– 2830Google ScholarThere is no corresponding record for this reference.
- 36Ye, J.; Tian, Z.; Wei, H.; Li, Y. Baseline correction method based on improved asymmetrically reweighted penalized least squares for the Raman spectrum. Appl. Opt. 2020, 59 (34), 10933– 10943, DOI: 10.1364/AO.404863Google ScholarThere is no corresponding record for this reference.
- 37Erb, D. pybaselines: A Python library of algorithms for the baseline correction of experimental data. In Zenodo , 2022.Google ScholarThere is no corresponding record for this reference.
- 38Billinghurst, B. E.; Oladepo, S. A.; Loppnow, G. R. pH-Dependent UV resonance Raman spectra of cytosine and uracil. J. Phys. Chem. B 2009, 113 (20), 7392– 7397, DOI: 10.1021/jp811327wGoogle Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXltVChtLo%253D&md5=390453b5fca0695f36062b5f332f5cdfpH-Dependent UV Resonance Raman Spectra of Cytosine and UracilBillinghurst, Brant E.; Oladepo, Sulayman A.; Loppnow, Glen R.Journal of Physical Chemistry B (2009), 113 (20), 7392-7397CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)Cytosine is a nucleobase found in both DNA and RNA, while uracil is found only in RNA. Uracil has abstractable protons at N3 and N1. Cytosine has only one abstractable proton at N1 but can also accept a proton at N3. The pKa values of these protons are well-known, but the effect of the change in protonation on the rest of the mol. is not well understood and is very important in base stacking, base pairing, and protein-nucleic acid interactions. In this paper, UV resonance Raman (UVRR) spectroscopy is used to probe the structures of both cytosine and uracil at varying pH to det. the structural changes that take place. The results show that cytosine has increased electronic delocalization when moving to either basic or acidic environments, whereas uracil shows no significant change in acidic environment but increases its electronic delocalization in basic environment.
- 39Lin, H.; Zhou, J.; Wu, Q.; Hung, T. M.; Chen, W.; Yu, Y.; Chang, J. T.; Pan, J.; Qiu, S.; Chen, R. Human blood test based on surface-enhanced Raman spectroscopy technology using different excitation light for nasopharyngeal cancer detection. IET Nanobiotechnol. 2019, 13 (9), 942– 945, DOI: 10.1049/iet-nbt.2019.0221Google ScholarThere is no corresponding record for this reference.
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Abstract
Figure 1
Figure 1. Workflow of the plasma sample treatment protocol used in this study.
Figure 2
Figure 2. Calibration curves prepared using the total population method for three replicate experiments. (A–C) Averaged SERS spectra for all concentrations and corresponding SERS intensity calibration curves beneath. Each spectrum shown is an average of 9030 spectra (1806 spectra from each concentration replicate). Each black data point represents the difference in SERS intensities at 792 and 723 cm–1 for each concentration replicate. Linear regression lines were calculated using the average of all concentration replicates (red data points). The regression line, equation, and correlation coefficient for each replicate experiment are shown in red. Spectra were offset for clarity.
Figure 3
Figure 3. Calibration curves prepared using the Qi sample method for three replicate experiments. (A–C) Averaged SERS spectra for all concentrations and corresponding SERS intensity calibration curves beneath. Each spectrum shown is an average of 100 spectra (20 spectra from each replicate corresponding to the highest 792 cm–1 Qi). Each black data point represents the difference in SERS intensities at 792 and 723 cm–1 for each concentration replicate. Linear regression lines were calculated using the average of all concentration replicates (red data points). The regression line, equation, and correlation coefficient for each replicate experiment are shown in red. Spectra were offset for clarity.
Figure 4
Figure 4. Calibration curves prepared using the CDF method for three replicate experiments. (A–C) Model CDFs of each FTC concentration and corresponding calibration curves beneath. The CDFs were constructed based on the Qi of the 792 cm–1 spectral region. A 4th order polynomial was fitted to each CDF in the probability range of 0.6–0.9. The Σ ΔQCDF was calculated for each concentration (see eq 2) and plotted as a function of the logarithm of FTC concentration. The Σ ΔQCDF values of the model CDFs (red data points) were used for linear regression. Black data points represent the Σ ΔQCDF of concentration replicates. Each data point in the calibration curves was increased by the absolute value of the smallest data point, ensuring all values are positive and maintain the same intervals between points. The regression line, equation, and correlation coefficient for each replicate experiment are shown in red.
Figure 5
Figure 5. PCA of the matrix blank (blue) and 78 ng/mL (red) SERS spectra used for calibrations in the (A–C) Qi sample method and (D–F) total population method. The Python library sklearn was used for PCA. (35) The spectra were preprocessed by first truncating the spectral region (585.48 to 1710.01 cm–1) followed by applying an improved asymmetrically reweighted penalized least-squares (IarPLS) background correction algorithm (36,37) (see Figure S10). The first two PC scores were plotted against each other, and the explained variance ratios for each PC are shown on corresponding axes. A 95% confidence ellipsoid of each group is shown. A detailed description of the PCA workflow is described in Figure S11.
Figure 6
Figure 6. SERS spectra of aqueous 1250 ng/mL FTC (blue), plasma containing 1250 ng/mL of FTC (black), and nonspiked plasma (red). Relevant peaks from each spectrum are noted by a vertical line with their wavenumber written at the top in the color corresponding to the sample type. Each spectrum shown is an average of 25 spectra, each acquired using 15 mW of laser power and 800 ms integration time.
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- 8Zhang, Y.; Hong, H.; Cai, W. Imaging with Raman spectroscopy. Curr. Pharm. Biotechnol. 2010, 11 (6), 654– 661, DOI: 10.2174/1389201107922464838https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXht1CmsrnN&md5=d95c4534203495e1cb1aa97b109752c8Imaging with Raman spectroscopyZhang, Yin; Hong, Hao; Cai, WeiboCurrent Pharmaceutical Biotechnology (2010), 11 (6), 654-661CODEN: CPBUBP; ISSN:1389-2010. (Bentham Science Publishers Ltd.)A review. Raman spectroscopy, based on the inelastic scattering of a photon, has been widely used as an anal. tool in many research fields. Recently, Raman spectroscopy has also been explored for biomedical applications (e.g. cancer diagnosis) because it can provide detailed information on the chem. compn. of cells and tissues. For imaging applications, several variations of Raman spectroscopy have been developed to enhance its sensitivity. This review article will provide a brief summary of Raman spectroscopy-based imaging, which includes the use of coherent anti-Stokes Raman spectroscopy (CARS, primarily used for imaging the C-H bond in lipids), surface-enhanced Raman spectroscopy (SERS, for which a variety of nanoparticles can be used as contrast agents), and single-walled carbon nanotubes (SWNTs, with its intrinsic Raman signal). The superb multiplexing capability of SERS-based Raman imaging can be extremely powerful in future research where different agents can be attached to different Raman tags to enable the interrogation of multiple biol. events simultaneously in living subjects. The primary limitations of Raman imaging in humans are those also faced by other optical techniques, in particular limited tissue penetration. Over the last several years, Raman spectroscopy imaging has advanced significantly and many crit. proof-of-principle expts. have been successfully carried out. It is expected that imaging with Raman Spectroscopy will continue to be a dynamic research field over the next decade.
- 9Le Ru, E. C.; Blackie, E.; Meyer, M.; Etchegoin, P. G. Surface enhanced Raman scattering enhancement factors: a comprehensive study. J. Phys. Chem. C 2007, 111 (37), 13794– 13803, DOI: 10.1021/jp06879089https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXptlamu74%253D&md5=08762c5f5138a57f934fdb0b372efa5eSurface Enhanced Raman Scattering Enhancement Factors: A Comprehensive StudyLe Ru, E. C.; Blackie, E.; Meyer, M.; Etchegoin, P. G.Journal of Physical Chemistry C (2007), 111 (37), 13794-13803CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)This paper presents an in-depth study of Surface Enhanced Raman Scattering (SERS) enhancement factors (EFs) and cross sections, including several issues often overlooked. In particular, various possible rigorous definitions of the SERS EFs are introduced and discussed in the context of SERS applications, such as anal. chem. and single mol. SERS. These definitions highlight the importance of a careful characterization of the non-SERS cross sections of the probes under consideration. This aspect is illustrated by exptl. results for the non-SERS cross sections of representative SERS probes along with av. SERS EFs for the same probes. The accurate exptl. detn. of single mol. enhancement factors is tackled with 2 recently developed techniques, namely: bi-analyte SERS (BiASERS) and temp.-dependent SERS vibrational pumping. SERS EFs ≥107, as opposed to the figure of 1014 often claimed in the literature, are sufficient for the observation of single mol. SERS signals, with max. single mol. EFs typically on the order of ∼1010. A significant amt. of review material is included.
- 10Ali, A.; Nettey-Oppong, E. E.; Effah, E.; Yu, C. Y.; Muhammad, R.; Soomro, T. A.; Byun, K. M.; Choi, S. H. Miniaturized Raman instruments for SERS-based point-of-care testing on respiratory viruses. Biosensors 2022, 12 (8), 590, DOI: 10.3390/bios12080590There is no corresponding record for this reference.
- 11Leong, S. X.; Leong, Y. X.; Tan, E. X.; Sim, H. Y. F.; Koh, C. S. L.; Lee, Y. H.; Chong, C.; Ng, L. S.; Chen, J. R. T.; Pang, D. W. C. Noninvasive and point-of-care surface-enhanced Raman scattering (SERS)-based breathalyzer for mass screening of coronavirus disease 2019 (COVID-19) under 5 min. ACS Nano 2022, 16 (2), 2629– 2639, DOI: 10.1021/acsnano.1c0937111https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtFyns7k%253D&md5=186f011fee390d8389b1174c3b8badceNoninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 minLeong, Shi Xuan; Leong, Yong Xiang; Tan, Emily Xi; Sim, Howard Yi Fan; Koh, Charlynn Sher Lin; Lee, Yih Hong; Chong, Carice; Ng, Li Shiuan; Chen, Jaslyn Ru Ting; Pang, Desmond Wei Cheng; Nguyen, Lam Bang Thanh; Boong, Siew Kheng; Han, Xuemei; Kao, Ya-Chuan; Chua, Yi Heng; Phan-Quang, Gia Chuong; Phang, In Yee; Lee, Hiang Kwee; Abdad, Mohammad Yazid; Tan, Nguan Soon; Ling, Xing YiACS Nano (2022), 16 (2), 2629-2639CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile org. compds. presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible anal. protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple mol. receptors to establish a robust partial least-squares discriminant anal. model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through expt. and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.
- 12Cheng, S.; Gu, Z.; Zhou, L.; Hao, M.; An, H.; Song, K.; Wu, X.; Zhang, K.; Zhao, Z.; Dong, Y. Recent progress in intelligent wearable sensors for health monitoring and wound healing based on biofluids. Front. Bioeng. Biotechnol. 2021, 9, 765987, DOI: 10.3389/fbioe.2021.765987There is no corresponding record for this reference.
- 13Zhang, S.; Zhao, W.; Zeng, J.; He, Z.; Wang, X.; Zhu, Z.; Hu, R.; Liu, C.; Wang, Q. Wearable non-invasive glucose sensors based on metallic nanomaterials. Mater. Today Bio 2023, 20, 100638, DOI: 10.1016/j.mtbio.2023.10063813https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXotFKqsrY%253D&md5=ce18f2be60f61f209d088fb024f9d474Wearable non-invasive glucose sensors based on metallic nanomaterialsZhang, Sheng; Zhao, Wenjie; Zeng, Junyan; He, Zhaotao; Wang, Xiang; Zhu, Zehui; Hu, Runqing; Liu, Chen; Wang, QianqianMaterials Today Bio (2023), 20 (), 100638CODEN: MTBAC2; ISSN:2590-0064. (Elsevier Ltd.)The development of wearable non-invasive glucose sensors provides a convenient tech. means to monitor the glucose concn. of diabetes patients without discomfortability and risk of infection. Apart from enzymes as typical catalytic materials, the active catalytic materials of the glucose sensor are mainly composed of polymers, metals, alloys, metal compds., and various metals that can undergo catalytic oxidn. with glucose. Among them, metallic nanomaterials are the optimal materials applied in the field of wearable non-invasive glucose sensing due to good biocompatibility, large sp. surface area, high catalytic activity, and strong adsorption capacity. This review summarizes the metallic nanomaterials used in wearable non-invasive glucose sensors including zero-dimensional (0D), one-dimensional (1D), and two-dimensional (2D) monometallic nanomaterials, bimetallic nanomaterials, metal oxide nanomaterials, etc. Besides, the applications of wearable non-invasive biosensors based on these metallic nanomaterials towards glucose detection are summarized in detail and the development trend of the wearable non-invasive glucose sensors based on metallic nanomaterials is also outlook.
- 14Liu, G.; Wang, Z.; Sun, W.; Lin, X.; Wang, R.; Li, C.; Zong, L.; Fu, Z.; Liu, H.; Xu, S. Robust emission in near-infrared II of lanthanide nanoprobes conjugated with Au (LNPs-Au) for temperature sensing and controlled photothermal therapy. Chem. Eng. J. 2023, 452, 139504, DOI: 10.1016/j.cej.2022.13950414https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisFOlu7fN&md5=14e8ee44b02ce517a4a8518c850fd567Robust emission in near-infrared II of lanthanide nanoprobes conjugated with Au (LNPs-Au) for temperature sensing and controlled photothermal therapyLiu, Guofeng; Wang, Zhenxing; Sun, Wan; Lin, Xiaohui; Wang, Rui; Li, Chonghui; Zong, Li; Fu, Zuoling; Liu, Hanping; Xu, ShicaiChemical Engineering Journal (Amsterdam, Netherlands) (2023), 452 (Part_4), 139504CODEN: CMEJAJ; ISSN:1385-8947. (Elsevier B.V.)To address the poor activity and inaccurate in situ temp. measurements of composite photothermal probes, herein we report lanthanide nanoparticles-gold (LNPs-Au) nanoprobes for fluorescence temp. sensing and controlled photothermal therapy (PTT). The conjugation between LNPs and Au is done via electrostatic interactions. LNPs doped with rare earth cations (Yb3+, Ho3+, Er3+, and Ce3+) fluoresce strongly in the NIR-II region and their emission spectrum does not overlap with the absorption spectrum of Au. The intensities of LNPs-Au emission peaks at 1185 and 1560 nm highly depend on temp., which is utilized in this study for the non-contact temp. measurements in the biol. relevant range. Deep tissue penetration of NIR-II radiation is obsd., which is favorable for real-time temp. sensing. In addn., this radiation does not damage the normal tissue. Moreover, LNPs-Au nanoparticles reduce HeLa cell viability below 40% and adequately inhibited tumor tissue in mice upon 808-nm irradn. The results of this study will motivate the development of multifunctional photothermal probes for safer, controllable PTT of cancers with simultaneous temp. monitoring.
- 15Tian, M.; Wang, J.; Li, C.; Wang, Z.; Liu, G.; Lv, E.; Zhao, X.; Li, Z.; Cao, D.; Liu, H. Qualitative and quantitative detection of microcystin-LR based on SERS-FET dual-mode biosensor. Biosens. Bioelectron. 2022, 212, 114434, DOI: 10.1016/j.bios.2022.11443415https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhsFyrsbrI&md5=a0ae1f69e407d64bbf85353f80a71115Qualitative and quantitative detection of microcystin-LR based on SERS-FET dual-mode biosensorTian, Meng; Wang, Jihua; Li, Chonghui; Wang, Zhenxing; Liu, Guofeng; Lv, Enguang; Zhao, Xiaofei; Li, Zhen; Cao, Dongyan; Liu, Huilan; Zhang, Chao; Xu, Shicai; Man, BaoyuanBiosensors & Bioelectronics (2022), 212 (), 114434CODEN: BBIOE4; ISSN:0956-5663. (Elsevier B.V.)Microcystin-LR (MC-LR), a kind of hepatoxin produced by cyanobacteria blooms, can promote liver cancer through long-term exposure even at low concns. In this study, a novel biosensor based on surface-enhanced Raman scattering (SERS) and field effect transistor (FET) dual sensing mode was developed by using gold nanoparticles (AuNPs)/graphene composite as sensing material. Based on the SERS sensing mode, the Raman fingerprint spectrum of MC-LR was obtained through the specific combination of MC-LR aptamer and MC-LR. The SERS enhanced effect of the AuNPs was also verified by theor. simulation. By using FET sensing mode, the graphene field effect transistor (G-FET) biosensor resp. exhibited the detection limit as low as 0.62 aM and 0.91 aM in phosphate buffered saline (PBS) and human serum, and showed a good linear relationship in a wide range of 1 x 10-18 to 1 x 10-8 M in both solns. Meanwhile, the sensor was utilized for the detection of MC-LR in actual water samples, and the complex components in the water did not interfere with MC-LR detection, indicating a significant high specificity of the sensor. The SERS-FET dual-mode biosensor can provide more detection options and improve the reliability of measurement results, which may has a great application prospect in the field of water environment detection.
- 16Li, C.; Man, B.; Zhang, C.; Yu, J.; Liu, G.; Tian, M.; Li, Z.; Zhao, X.; Wang, Z.; Cui, W. Strong plasmon resonance coupling in micro-extraction SERS membrane for in situ detection of molecular aqueous solutions. Sens. Actuators, B 2024, 398, 134767, DOI: 10.1016/j.snb.2023.134767There is no corresponding record for this reference.
- 17Reese, T.; Suarez, C.; Premasiri, W. R.; Shaine, M. L.; Ingraham, H.; Brodeur, A. N.; Ziegler, L. D. Surface enhanced Raman scattering specificity for detection and identification of dried bloodstains. Forensic Sci. Int. 2021, 328, 111000, DOI: 10.1016/j.forsciint.2021.11100017https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXisV2lsr3F&md5=8eea8f7392d717aaf86ffd7dcc1a8cdfSurface enhanced Raman scattering specificity for detection and identification of dried bloodstainsReese, T.; Suarez, C.; Premasiri, W. R.; Shaine, M. L.; Ingraham, H.; Brodeur, A. N.; Ziegler, L. D.Forensic Science International (2021), 328 (), 111000CODEN: FSINDR; ISSN:0379-0738. (Elsevier Ltd.)Surface enhanced Raman spectroscopy (SERS) provides highly specific vibrational signatures identifying dried blood for a variety of forensic applications. SERS spectra on Au nanoparticle substrates excited at 785 nm are found to identify dried stains of human and nonhuman blood from seven animals, and distinguish stains due to menstrual and peripheral blood. In addn., the unique SERS bloodstain spectrum is distinct from the SERS spectra of thirty red-brown stains of potential household substances that could be visually mistaken for bloodstains and from food stains that have been shown to give pos. results with presumptive colorimetric blood tests. Finally, a SERS swab procedure has been developed and demonstrates that the substrates that a blood sample dried on does not offer any Raman or fluorescence interference for the SERS identification of dried blood. Such bloodstains on porous and nonporous materials are all identical and exclusively due to the heme moiety of Hb. Optimized selection of the extn. solvent is found to control the chem. compn. of mol. components appearing in the SERS spectrum of complex, multicomponent biol. mixts., such as body fluids.
- 18Shi, L.; Li, Y.; Li, Z. Early cancer detection by SERS spectroscopy and machine learning. Light: Sci. Appl. 2023, 12 (1), 234, DOI: 10.1038/s41377-023-01271-718https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXhvFarsrjI&md5=517e0219428ac674c35680f686862366Early cancer detection by SERS spectroscopy and machine learningShi, Lingyan; Li, Yajuan; Li, ZhiLight: Science & Applications (2023), 12 (1), 234CODEN: LSAIAZ; ISSN:2047-7538. (Nature Portfolio)Abstr.: A new approach for early detection of multiple cancers is presented by integrating SERS spectroscopy of serum mol. fingerprints and machine learning.
- 19Bonifacio, A.; Dalla Marta, S.; Spizzo, R.; Cervo, S.; Steffan, A.; Colombatti, A.; Sergo, V. Surface-enhanced Raman spectroscopy of blood plasma and serum using Ag and Au nanoparticles: a systematic study. Anal. Bioanal. Chem. 2014, 406 (9–10), 2355– 2365, DOI: 10.1007/s00216-014-7622-119https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhslSku78%253D&md5=655abe670985fc2e97778b18ecf4ae46Surface-enhanced Raman spectroscopy of blood plasma and serum using Ag and Au nanoparticles: a systematic studyBonifacio, Alois; Dalla Marta, Silvia; Spizzo, Riccardo; Cervo, Silvia; Steffan, Agostino; Colombatti, Alfonso; Sergo, ValterAnalytical and Bioanalytical Chemistry (2014), 406 (9-10), 2355-2365CODEN: ABCNBP; ISSN:1618-2642. (Springer)Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on biofluids in point-of-care or screening tests. In particular, label-free SERS spectra of blood serum and plasma, two biofluids widely used in diagnostics, could be used as a metabolic fingerprinting approach for biomarker discovery. This study aims at a systematic evaluation of SERS spectra of blood serum and plasma, using various Ag and Au aq. colloids, as SERS substrates, in combination with three excitation lasers of different wavelengths, ranging from the visible to the near-IR. The anal. of the SERS spectra collected from 20 healthy subjects under a variety of exptl. conditions revealed that intense and repeatable spectra are quickly obtained only if proteins are filtered out from samples, and an excitation in the near-IR is used in combination with Ag colloids. Moreover, common plasma anticoagulants such as EDTA and citrate are found to interfere with SERS spectra; accordingly, filtered serum or heparin plasma are the samples of choice, having identical SERS spectra. Most bands obsd. in SERS spectra of these biofluids are assigned to uric acid, a metabolite whose blood concn. depends on factors such as sex, age, therapeutic treatments, and various pathol. conditions, suggesting that, even when the right exptl. conditions are chosen, great care must be taken in designing studies with the purpose of developing diagnostic tests.
- 20Wang, Y.; Yu, C.; Ji, H.; Liu, Z.; Wang, X.; Ji, Y.; Sun, X.; Zhao, Y.; Qiu, X.; Zhang, T. Label-free therapeutic drug monitoring in human serum by the 3-step surface enhanced Raman spectroscopy and multivariate analysis. Chem. Eng. J. 2023, 452, 139588, DOI: 10.1016/j.cej.2022.13958820https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xis1WlsLnI&md5=16fd06fe1bca8ef5d13edc320a5ca85aLabel-free therapeutic drug monitoring in human serum by the 3-step surface enhanced Raman spectroscopy and multivariate analysisWang, Yunpeng; Yu, Cuiwei; Ji, Haoyu; Liu, Zhehan; Wang, Xiaotong; Ji, Yinghe; Sun, Xiaomeng; Zhao, Yue; Qiu, Xiaohong; Zhang, Ting; Li, Jing; Liu, Xin; Lv, Xinpeng; Cai, Benzhi; Zhao, Yingqi; Huang, Jian-An; Li, YangChemical Engineering Journal (Amsterdam, Netherlands) (2023), 452 (Part_4), 139588CODEN: CMEJAJ; ISSN:1385-8947. (Elsevier B.V.)Surface enhanced Raman spectroscopy (SERS) is widely used in drug mol. detection. However, SERS detections of drug mols. in serum with high sensitivity and reproducibility remains extremely challenging due to signal interference of complex constituents of serum. The latter presents a high SERS background noise that buries the signals of the drug mols. Here, we report a 3-step method to make SERS system of silver nanoparticle clusters to overcome the interference and achieve quant. SERS anal. of drugs in serum by 1 proteins removal from serum; 2 enhanced drug adsorption on the nanoparticles; and 3 background suppression by internal std. in nanoparticle aggregation. By careful selection of the aggregation agents and internal std., clear SERS peaks of the internal std. and six different drug analytes were obsd. for pesticide identification in human serum. Significantly, the SERS peak ratio of the internal std. and drug analytes has achieved univariate quant. monitoring of drug metab. in mice serum, which is in agreement with anal. by the multivariate curve resoln.-alternating least squares method. Our method shows great clin. application potential in therapeutic drug monitoring and personalized medicine.
- 21Lin, D.; Pan, J.; Huang, H.; Chen, G.; Qiu, S.; Shi, H.; Chen, W.; Yu, Y.; Feng, S.; Chen, R. Label-free blood plasma test based on surface-enhanced Raman scattering for tumor stages detection in nasopharyngeal cancer. Sci. Rep. 2014, 4 (1), 4751, DOI: 10.1038/srep0475121https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXovV2isrw%253D&md5=07a576168d2d0ab919a3315d2073d40fLabel-free blood plasma test based on surface-enhanced Raman scattering for tumor stages detection in nasopharyngeal cancerLin, Duo; Pan, Jianji; Huang, Hao; Chen, Guannan; Qiu, Sufang; Shi, Hong; Chen, Weiwei; Yu, Yun; Feng, Shangyuan; Chen, RongScientific Reports (2014), 4 (), 4751/1-4751/8CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)This study aims to evaluate the feasibility of a label-free nanobiosensor based on blood plasma surface-enhanced Raman spectroscopy (SERS) method for exploring variability of different tumor (T) stages in nasopharyngeal cancer (NPC). Au nanoparticles as the SERS-active nanostructures were directly mixed with human blood plasma to enhance the Raman scattering signals. High quality SERS spectra can be acquired from blood plasma samples belong to 60 healthy volunteers, 25 NPC patients with T1 stage and 75 NPC patients with T2-T4 stage. A diagnostic accuracy of 83.5% and 93.3%, resp., can be achieved for classification between early T (T1) stage cancer and normal; and advanced T (T2-T4) stage cancer and normal blood groups. This exploratory study demonstrates that the nanobiosensor based on SERS technique in conjunction with PCA-LDA has great potential as a clin. complement for different T stages detection in nasopharyngeal cancer.
- 22Gao, S.; Lin, Y.; Zhao, X.; Gao, J.; Xie, S.; Gong, W.; Yu, Y.; Lin, J. Label-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancers. Spectrochim. Acta, Part A 2022, 267, 120605, DOI: 10.1016/j.saa.2021.12060522https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXisFChu7bO&md5=1da5274ca180553f5157c1fa49b769adLabel-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancersGao, Siqi; Lin, Yamin; Zhao, Xin; Gao, Jiamin; Xie, Shusen; Gong, Wei; Yu, Yun; Lin, JuqiangSpectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (2022), 267 (Part_2), 120605CODEN: SAMCAS; ISSN:1386-1425. (Elsevier B.V.)Surface-enhanced Raman spectroscopy (SERS) is considered as an ultrasensitive, non-invasive as well as rapid detection technol. for cancer diagnosis. In this study, we developed a novel blood serum anal. strategy using coffee ring effect-assisted label-free SERS for different types of cancer screening. Addnl., the pretreated Ag nanoparticles (Ag NPs) were mixed with the serum from liver cancer patients (n = 40), prostate cancer patients (n = 32) and healthy volunteers (n = 30) for SERS measurement. The droplets of Ag NPs-serum mixt. formed the coffee ring on the peripheral after air-drying, and thus extremely enhancing Raman signal and ensuring the stability and reliability of SERS detection. Partial least square (PLS) and support vector machine (SVM) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying, yielding the high diagnostic accuracy of 98.04% for normal group and two types of cancers simultaneously distinguishing. More importantly, for the unknown testing set, an ideal diagnostic accuracy of 100% could be achieved by PLS-SVM algorithm for differentiating cancers from the normal group. The results from this exploratory work demonstrate that serum SERS detection combined with PLS-SVM diagnostic algorithm and coffee ring effect has great potential for the noninvasive and label-free detection of cancer.
- 23Peng, S.; Lu, D.; Zhang, B.; You, R.; Chen, J.; Xu, H.; Lu, Y. Machine learning–assisted internal standard calibration label-free SERS strategy for colon cancer detection. Anal. Bioanal. Chem. 2023, 415 (9), 1699– 1707, DOI: 10.1007/s00216-023-04566-1There is no corresponding record for this reference.
- 24Schinazi, R. F.; Patel, D.; Ehteshami, M. The best backbone for HIV prevention, treatment, and elimination: Emtricitabine+tenofovir. Antivir. Ther. 2022, 27 (2), 13596535211067599, DOI: 10.1177/13596535211067599There is no corresponding record for this reference.
- 25Muller, J. T.; Al Khalili, Y. Emtricitabine. In StatPearls [Internet]; StatPearls Publishing, 2023.There is no corresponding record for this reference.
- 26Ciccullo, A.; Baldin, G.; Putaggio, C.; Di Giambenedetto, S.; Borghetti, A. Comparative safety review of recommended, first-line single-tablet regimens in patients with HIV. Expert Opin. Drug Saf. 2021, 20 (11), 1317– 1332, DOI: 10.1080/14740338.2021.1931115There is no corresponding record for this reference.
- 27Butler, M. R.; Hrncirova, J.; Clark, M.; Dutta, S.; Cooper, J. B. Quantification of antiviral drug tenofovir (TFV) by surface-enhanced Raman spectroscopy (SERS) using cumulative distribution functions (CDFs). ACS Omega 2024, 9 (1), 1310– 1319, DOI: 10.1021/acsomega.3c07641There is no corresponding record for this reference.
- 28Butler, M. R.; Hrncirova, J.; Jacot, T. A.; Dutta, S.; Clark, M. R.; Doncel, G. F.; Cooper, J. B. Detection and quantification of antiviral drug tenofovir using silver nanoparticles and surface enhanced Raman spectroscopy (SERS) with spatially resolved hotspot selection. Front. Nanotechnol. 2023, 5, 1270474, DOI: 10.3389/fnano.2023.1270474There is no corresponding record for this reference.
- 29Hrncirova, J.; Butler, M. R.; Dutta, S.; Clark, M. R.; Cooper, J. B. Cumulative distribution function and spatially resolved surface-enhanced Raman spectroscopy for the quantitative analysis of emtricitabine. Appl. Spectrosc. Pract. 2024, 2 (1), 1– 11, DOI: 10.1177/27551857241235972There is no corresponding record for this reference.
- 30Leopold, N.; Lendl, B. A new method for fast preparation of highly surface-enhanced Raman scattering (SERS) active silver colloids at room temperature by reduction of silver nitrate with hydroxylamine hydrochloride. J. Phys. Chem. B 2003, 107 (24), 5723– 5727, DOI: 10.1021/jp027460u30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXjvFSntr0%253D&md5=08cf57bc48c7a5cd1b537bb873e31d7cA New Method for Fast Preparation of Highly Surface-Enhanced Raman Scattering (SERS) Active Silver Colloids at Room Temperature by Reduction of Silver Nitrate with Hydroxylamine HydrochlorideLeopold, Nicolae; Lendl, BernhardJournal of Physical Chemistry B (2003), 107 (24), 5723-5727CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)A very effective and simple way to produce silver colloids for surface-enhanced Raman scattering (SERS) is reported. Redn. of silver nitrate with hydroxylamine hydrochloride at alk. pH and at room temp. yields highly sensitive SERS colloids within a short time. The so-produced colloids can be used for SERS spectroscopy immediately after prepn. The overall procedure is fast, simple, and characterized by a high prepn. success rate. Changing the mixing order and rate of the two involved solns., silver nitrate and hydroxylamine hydrochloride contg. sodium hydroxide, one can control the size and dispersion of the produced colloids. The obtained colloids have been characterized by UV-vis spectroscopy, transmission electron microscopy, and SERS using a 1064 nm laser line on a Fourier transform and a 785 nm laser line on a dispersive Raman spectrometer. The SERS enhancement factor of the hydroxylamine-reduced silver colloids was tested using crystal violet, rhodamine 6G, methylene blue, and 9-aminoacridine. It was found that for both excitation lines sensitivities comparable to those achievable with a Lee-Meisel silver colloid were obtained thus rendering the new colloid advantageous because of its significantly simpler and faster synthesis.
- 31Darragh-Hickey, C.; Flowers, K. C.; Shipman, A. R.; Allen, G. T.; Kaur, S.; Shipman, K. E. Investigative algorithms for disorders affecting plasma chloride: a narrative review. J. Lab. Precis. Med. 2022, 7, 22, DOI: 10.21037/jlpm-22-7There is no corresponding record for this reference.
- 32Chang, T. M. S. Chapter 45─Nanodimension biodegradable polymeric membrane artificial red blood cells. In Blood Substitutes; Winslow, R. M., Ed.; Academic Press, 2006; pp 523– 531.There is no corresponding record for this reference.
- 33Hrncirova, J.; Butler, M. R.; Clark, M. R.; Doncel, G. F.; Cooper, J. B. A new approach for discriminating spatially acquired SERS spectra using antiretroviral drug emtricitabine as a test sample. J. Raman Spectrosc. 2024, 55, 1129– 1138, DOI: 10.1002/jrs.6721There is no corresponding record for this reference.
- 34Zhang, Y.; Zhao, C.; Tian, G.; Lu, C.; Li, Y.; He, L.; Xiao, H.; Zheng, J. Simultaneous characterization of chemical structures and bioactivities of citrus-derived components using SERS barcodes. Food Chem. 2018, 240, 743– 750, DOI: 10.1016/j.foodchem.2017.07.10334https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtlSltLfF&md5=9a321578e802cef6059d2f3e15e03a1bSimultaneous characterization of chemical structures and bioactivities of citrus-derived components using SERS barcodesZhang, Ye; Zhao, Chengying; Tian, Guifang; Lu, Chang; Li, Yuzhi; He, Lili; Xiao, Hang; Zheng, JinkaiFood Chemistry (2018), 240 (), 743-750CODEN: FOCHDJ; ISSN:0308-8146. (Elsevier Ltd.)Rapid detection of bioactive components in food has attracted great attention. Herein, we report a novel method through surface-enhanced Raman spectroscopy (SERS) spectra based barcodes for simultaneous characterization of chem. structures and bioactivities of nine citrus components for the first time. SERS barcodes were successfully used to characterize and discriminate all the components with high sensitivity down to 40-60 ng. Importantly, SERS barcodes exhibited the 'identity' characteristics. Beyond the mol. structure information, bioactivity information can also be read from the barcodes according to the bioactivity assay and structure-activity relationship. Hence, a simple and intuitive SERS barcoding approach used for simultaneous characterization of chem. structures and bioactivities was established. With a large database of barcodes, there is high potential that the SERS barcoding approach could be further developed to be a rapid, simple and effective foodomics-like approach for bioactive compd. identification from a complex food matrix.
- 35Fabian, P. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825– 2830There is no corresponding record for this reference.
- 36Ye, J.; Tian, Z.; Wei, H.; Li, Y. Baseline correction method based on improved asymmetrically reweighted penalized least squares for the Raman spectrum. Appl. Opt. 2020, 59 (34), 10933– 10943, DOI: 10.1364/AO.404863There is no corresponding record for this reference.
- 37Erb, D. pybaselines: A Python library of algorithms for the baseline correction of experimental data. In Zenodo , 2022.There is no corresponding record for this reference.
- 38Billinghurst, B. E.; Oladepo, S. A.; Loppnow, G. R. pH-Dependent UV resonance Raman spectra of cytosine and uracil. J. Phys. Chem. B 2009, 113 (20), 7392– 7397, DOI: 10.1021/jp811327w38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXltVChtLo%253D&md5=390453b5fca0695f36062b5f332f5cdfpH-Dependent UV Resonance Raman Spectra of Cytosine and UracilBillinghurst, Brant E.; Oladepo, Sulayman A.; Loppnow, Glen R.Journal of Physical Chemistry B (2009), 113 (20), 7392-7397CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)Cytosine is a nucleobase found in both DNA and RNA, while uracil is found only in RNA. Uracil has abstractable protons at N3 and N1. Cytosine has only one abstractable proton at N1 but can also accept a proton at N3. The pKa values of these protons are well-known, but the effect of the change in protonation on the rest of the mol. is not well understood and is very important in base stacking, base pairing, and protein-nucleic acid interactions. In this paper, UV resonance Raman (UVRR) spectroscopy is used to probe the structures of both cytosine and uracil at varying pH to det. the structural changes that take place. The results show that cytosine has increased electronic delocalization when moving to either basic or acidic environments, whereas uracil shows no significant change in acidic environment but increases its electronic delocalization in basic environment.
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Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.4c06162.
DLS of nanoparticles, UV–vis of nanoparticles, image of aluminum well plate, schematic of Qi calculation process, process diagram of CDF calculation workflow, calibration curves calculated using the total population method after subtracting the matrix blank SERS spectrum, calibration curves calculated using the Qi sample method after subtracting the matrix blank SERS spectrum, unfitted CDFs of the entire probability range (0–1), CDF method analysis using the SERS intensity at 792 cm–1 to construct CDFs, preprocessed SERS spectra used for PCA, overview of PCA workflow, histograms of the Qi distribution of all data sets, chemical structure of FTC, peak assignments of an aqueous FTC SERS spectrum (PDF)
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