Search

Electrocatalytic N

Purpose Magnetic resonance imaging (MRI) contrast agents are pharmaceuticals that enable a better visualization of internal body structures. In this study, we present the synthesis, MRI signal enhancement capabilities, in vitro as well as in vivo cytotoxicity results of gold-coated iron oxide nanoparticles (Fe3O4@AuNPs) as potential contrast agents. Methods Fe3O4@AuNPs were obtained by synthesizing iron oxide nanoparticles and gradually coating them with gold. The obtained Fe3O4@AuNPs were characterized by spectroscopies, transmission electron microscopy (TEM) and energy dispersive X-ray diffraction. The effect of the nanoparticles on the MRI signal was tested using a 7T Bruker PharmaScan system. Cytotoxicity tests were made in vitro on Fe3O4@AuNP-treated retinal pigment epithelium cells by WST-1 tests and in vivo by following histopathological changes in rats after injection of Fe3O4@AuNPs. Results Stable Fe3O4@AuNPs were successfully prepared following a simple and fast protocol (

We show that by modifying the chemical interface of silver nanoparticles (AgNPs) with halide ions, it is possible to tune the total decay rate of adsorbed excited molecules and the plasmon damping rate. Through single-molecule surface-enhanced Raman scattering and surface-enhanced fluorescence enhancement factors of crystal violet (CV) and rhodamine 6G (R6G), we show that I–-modified AgNPs (AgNPs@I) and Br–-modified AgNPs (AgNPs@Br) lead to an increase in the total decay rate of excited CV and R6G by a factor between ∼1.6–2.6, compared to Cl–-modified AgNPs (AgNPs@Cl). In addition, we found that the chemical interface damping, which characterizes the plasmon resonance decay into surface states, is stronger on AgNPs@I and AgNPs@Br when compared to AgNPs@Cl. These results point toward the formation of metal–halide surface complexes. These new interfacial states can accept electrons from both excited molecular orbitals and surface plasmon excitations, completely altering the electronic dynamics and reactivity of plasmonic interfaces.

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Despite recent promising advances in plasmonic chemistry and chemical reactions on the surface of metal nanostructures, it is still challenging to control the adsorption of molecules and the charge transfer processes occurring at the metal–molecule interface [1] . This study describes a simple method to control metal-molecule charge transfer processes by tuning the surface potential and, consequently, the Fermi energy of silver nanoparticles (AgNPs) in-situ.

Purpose Surface-enhanced Raman scattering (SERS) spectroscopy on serum and other biofluids for cancer diagnosis represents an emerging field, which has shown promising preliminary results in several types of malignancies. The purpose of this study was to demonstrate that SERS spectroscopy on serum can be employed for the differential diagnosis between five of the leading malignancies, ie, breast, colorectal, lung, ovarian and oral cancer. Patients and methods Serum samples were acquired from healthy volunteers (n=39) and from patients diagnosed with breast (n=42), colorectal (n=109), lung (n=33), oral (n=17), and ovarian cancer (n=13), comprising n=253 samples in total. SERS spectra were acquired using a 532 nm laser line as excitation source, while the SERS substrates were represented by Ag nanoparticles synthesized by reduction with hydroxylamine. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA). Results The sensitivity and specificity in discriminating between cancer patients and controls was 98% and 91%, respectively. Cancer samples were correctly assigned to their corresponding cancer types with an accuracy of 88% for oral cancer, 86% for colorectal cancer, 80% for ovarian cancer, 76% for breast cancer and 59% for lung cancer. Conclusion SERS on serum represents a promising strategy of diagnosing cancer which can discriminate between cancer patients and controls, as well as between cancer types such as breast, colorectal, lung ovarian and oral cancer.

Raman spectroscopy is a type of vibrational spectroscopy based on the inelastic scattering of photons, which has attracted much attention due to its potential clinical application in rheumatology. In this review, we discuss the typical spectral features of cartilage, bone, synovial fluid, and pathologic crystal deposits, as well as methods of amplifying the Raman signal of biofluids such as drop-coating deposition Raman spectroscopy. Further, applications of Raman and drop-coating deposition Raman spectroscopy in osteoarthritis are described, highlighting the clinical potential of these methods. We also discuss the role of Raman and related techniques in analyzing pathologic crystals such as monosodium urate, calcium pyrophosphate dihydrate, and hydroxyapatite. The results presented in this review demonstrate that Raman spectroscopy has grown past the stage of proof-of-concept, especially in the case of pathologies involving crystal depositions such as gout and calcium pyrophosphate deposition disease , for which the method has been validated on large number of samples. As the medical community becomes more and more aware of Raman spectroscopy, it is envisioned that it will become a standard technique in the near future.

Surface-enhanced Raman scattering (SERS) is emerging as a novel strategy for biofluid analysis. In this review, we delineate four experimental SERS protocols that are frequently used for the profiling of biofluids: 1) liquid SERS for the detection of purine metabolites; 2) iodide-modified liquid SERS for the detection of proteins; 3) dried SERS for the detection of both purine metabolites and proteins; 4) resonant Raman for the detection of carotenoids. To explain the selectivity of each experimental SERS protocol, we introduce a heuristic model for the chemisorption of analytes mediated by adsorbed ions (adions) onto the SERS substrate. Next, we show that the promising results of SERS liquid biopsy stem from the fact that the concentration levels of purine metabolites, proteins and carotenoids are informative of the cellular turnover rate, inflammation, and oxidative stress, respectively. These processes are perturbed in virtually every disease, from cancer to autoimmune maladies. Finally, we review recent SERS liquid biopsy studies and discuss future steps that are required for translating SERS in the clinical setting.

This study highlights the potential of surface-enhanced Raman scattering (SERS) to differentiate between B-cell lymphoma (BCL), T-cell lymphoma (TCL), lymph node metastasis of melanoma (Met) and control (Ctr) samples based on the specific SERS signal of DNA extracted from lymph node tissue biopsy. Differences in the methylation profiles as well as the specific interaction of malignant and non-malignant DNA with the metal nanostructure are captured in specific variations of the band at 1005 cm−1, attributed to 5-methylcytosine and the band at 730 cm−1, attributed to adenine. Thus, using the area ratio of these two SERS marker bands as input for univariate classification, an area under the curve (AUC) of 0.70 was achieved in differentiating between malignant and non-malignant DNA. In addition, DNA from the BCL and TCL groups exhibited differences in the area of the SERS band at 730 cm−1, yielding an AUC of 0.84 in differentiating between these two lymphadenopathies. Lastly, using multivariate data analysis techniques, an overall accuracy of 94.7% was achieved in the differential diagnosis between the BCL, TCL, Met and Ctr groups. These results pave the way towards the implementation of SERS as a novel tool in the clinical setting for improving the diagnosis of malignant lymphadenopathy.

We highlight a new metal–molecule charge transfer process by tuning the Fermi energy of plasmonic silver nanoparticles (AgNPs) in situ. The strong adsorption of halide ions upshifts the Fermi level of AgNPs by up to ∼0.3 eV in the order Cl– < Br– < I–, favoring the spontaneous charge transfer to aligned molecular acceptor orbitals until charge neutrality across the interface is achieved. By carefully quantifying, experimentally and theoretically, the Fermi level upshift, we show for the first time that this effect is comparable in energy to different plasmonic effects such as the plasmoelectric effect or hot-carriers production. Moreover, by monitoring in situ the adsorption dynamic of halide ions in different AgNP–molecule systems, we show for the first time that the catalytic role of halide ions in plasmonic nanostructures depends on the surface affinity of halide ions compared to that of the target molecule.

SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61-89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.

As colorectal cancer (CRC) is one of the forms of cancer with the highest prevalence globally and with a high mortality, screening and early detection remains a major issue. Colonoscopy is still the gold standard for detecting premalignant lesions, but it is burdened by some complications. For instance, it is laborious, with some difficulties of acceptance for some patients, and is ultimately an imperfect standard, given that some premalignant lesions or incipient malignancies can be missed by colonoscopic evaluation. In this context, new non-invasive approaches such as surface-enhanced Raman spectroscopy (SERS) based liquid biopsy have gained ground in recent years, showing promising results in oncological pathology diagnosis. These new methods have enabled the detection of subtle molecular profile alterations prior to any macroscopic morphological changes, thus providing a useful tool for early CRC detection. In the present review, we provide a summary of published studies applying SERS in CRC detection, along with our personal experience in using SERS in the diagnosis of different oncological pathologies, including CRC.

Purpose Magnetic resonance imaging (MRI) contrast agents are pharmaceuticals that enable a better visualization of internal body structures. In this study, we present the synthesis, MRI signal enhancement capabilities, in vitro as well as in vivo cytotoxicity results of gold-coated iron oxide nanoparticles (Fe3O4@AuNPs) as potential contrast agents. Methods Fe3O4@AuNPs were obtained by synthesizing iron oxide nanoparticles and gradually coating them with gold. The obtained Fe3O4@AuNPs were characterized by spectroscopies, transmission electron microscopy (TEM) and energy dispersive X-ray diffraction. The effect of the nanoparticles on the MRI signal was tested using a 7T Bruker PharmaScan system. Cytotoxicity tests were made in vitro on Fe3O4@AuNP-treated retinal pigment epithelium cells by WST-1 tests and in vivo by following histopathological changes in rats after injection of Fe3O4@AuNPs. Results Stable Fe3O4@AuNPs were successfully prepared following a simple and fast protocol (

Chloride-capped silver nanoparticles (Cl-AgNPs) allow for high-intensity surface-enhanced Raman scattering (SERS) spectra of cationic molecules to be obtained (even at nanomolar concentration) and may also play a key role in understanding some fundamental principles behind SERS. In this study, we describe a fast (− ions chemisorbed onto the metal surface where the chloride concentration in the colloidal solution is 10−2 M. Consequently, the enhanced SERS spectra of cationic dyes (e.g., crystal violet or 9-aminoacridine) demonstrate the advantages of Cl-AgNPs compared to the as-synthesized AgNPs obtained by standard Ag+ reduction with hydroxylamine (hya-AgNPS) or citrate (cit-AgNPs). The results of SERS experiments on anionic and cationic test molecules comparing Cl-AgNPs, hya-AgNPs and cit-AgNPs colloids activated with different amounts of Cl− and/or cations such as Ag+, Mg2+ or Ca2+ can be explained within the understanding of the adatom model – the chemisorption of cationic analytes onto the metal surface is mediated by the Cl− ions, whereas ions like Ag+, Mg2+ or Ca2+ mediate the electronic coupling of anionic species to the silver metal surface. Moreover, the SERS effect is switched on only after the electronic coupling of the adsorbate to the silver surface at SERS-active sites. The experiments presented in this study highlight the SERS-activating role played by ions such as Cl−, Ag+, Mg2+ or Ca2+, which is a process that seems to prevail over the Raman enhancement due to nanoparticle aggregation.

The lack of an accurate point-of-care detection system for microalbuminuria represents an important unmet medical need that contributes to the morbidity and mortality of patients with kidney diseases. In this proof-of-concept study, we used SERS spectroscopy to detect urinary albumin concentrations in the normal-to-mildly increased albuminuria range, a strategy that could be useful for the early diagnosis of renal impairment due to uncontrolled hypertension, cardiovascular disease or diabetes. We analyzed 27 urine samples by SERS, using iodide-modified silver nanoparticles and we could discriminate between groups with high and low albumin concentrations with an overall accuracy of 89%, 93% and 89%, using principal component analysis-linear discriminant analysis and cut-off values of 3, 6 and 10 μg mL-1 for urinary albumin concentrations, respectively. We achieved a detection limit of 3 μg mL-1 for human serum albumin based on the 1002 cm-1 SERS band, attributed to the ring breathing vibration of phenylalanine. Our detection limit is similar to that of the immunoturbidimetric assays and around one order of magnitude below the detection limit of urinary dipsticks used to detect microalbuminuria. We used principal least squares regression for building a spectral model for quantifying albumin. Using an independent prediction set, the R2 and root mean squared error of prediction between predicted and reference values of human serum albumin concentrations were 0.982 and 2.82, respectively. Here, we show that direct SERS spectroscopy has the sensitivity required for detecting clinically relevant concentrations of urinary albumin, a strategy that could be used in the future for the point-of-care screening of microalbuminuria.

Stopped-flow UV-vis data allow for the first time direct spectroscopic detection of a ferryl species during the reaction of met myoglobin (Mb) with chlorite, analogous to what is observed in the reaction with peroxides. Ferryl is also observed in the reaction of oxy Mb+chlorite. A pathway involving Fe-O-O-ClO2 is explored by analogy with the Fe-O-O-NO and Fe-O-O-NO2 previously proposed as intermediates in the reactions of oxy globins with nitric oxide and nitrite, respectively. However, Fe-O-O-ClO2 is not detectable in these stopped-flow experiments and is in fact, unlike its nitrogenous congeners, predicted by density functional theory (DFT) to be impossible for a heme complex. Deoxy Mb reacts with chlorite faster than met - suggesting that, unlike with hydrogen peroxide (with which deoxy Mb reacts slower than met), binding of chlorite to the heme is not a rate-determining step (hence, most likely, an outer-sphere electron transfer mechanism); to correlate this, a Fe-O-Cl-O adduct was not observed experimentally for the met or for the deoxy reactions - even though prior DFT calculations suggest it to be feasible and detectable.

In this label-free surface-enhanced Raman scattering (SERS) study of genomic DNA, we demonstrate that the cancer-specific DNA methylation pattern translates into specific spectral differences. Thus, DNA extracted from an acute myeloid leukemia (AML) cell line presented a decreased intensity of the 1005 cm-1 band of 5-methylcytosine compared to normal DNA, in line with the well-described hypomethylation of cancer DNA. The unique methylation pattern of cancer DNA also influences the DNA adsorption geometry, resulting in higher adenine SERS intensities for cancer DNA. The possibility of detecting cancer DNA based on its SERS spectrum was validated on peripheral blood genomic DNA samples from n = 17 AML patients and n = 17 control samples, yielding an overall classification of 82% based on the 1005 cm-1 band of 5-methylcytosine. By demonstrating the potential of SERS in assessing the methylation status in the case of real-life DNA samples, the study paves the way for novel methods of diagnosing cancer. Graphical abstract.

In this preliminary study, we employed surface-enhanced Raman scattering (SERS) of saliva and serum samples for diagnosing Sjogren's syndrome (SjS), a systemic autoimmune disease characterized by dryness of the mouth and eyes. The saliva and serum samples from n = 29 patients with SjS and n = 21 controls were deproteinized with methanol and then the SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride. In the case of both saliva and serum, the SERS spectra were dominated by similar bands attributed to purine metabolites such as uric acid, xanthine, and hypoxanthine. Principal component analysis-linear discriminant analysis (PCA-LDA) models built from SERS spectra of saliva and serum yielded an overall classification accuracy of 94% and 98%, respectively. These results suggest that the SERS analysis of saliva and serum is able to capture the complex biochemical perturbations that accompany the onset of SjS, a strategy which could be translated in the future into a novel point-of-care diagnosis method. Graphical abstract.

Background: There is an ongoing research for breast cancer diagnostic tools that are cheaper, more accurate and more convenient than mammography. Methods: In this study, we employed surface-enhanced Raman scattering (SERS) for analysing urine from n = 53 breast cancer patients and n = 22 controls, with the aim of discriminating between the two groups using multivariate data analysis techniques such as principal component analysis&mdash, linear discriminant analysis (PCA-LDA). The SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride, which were additionally activated with Ca2+ 10&minus, 4 M. Results: The addition of Ca(NO3)2 10&minus, 4 M promoted the specific adsorption to the metal surface of the anionic purine metabolites such as uric acid, xanthine and hypoxanthine. Moreover, the SERS spectra of urine were acquired without any filtering or processing step for removing protein traces and other contaminants. Using PCA-LDA, the SERS spectra of urine from breast cancer patients were classified with a sensitivity of 81%, a specificity of 95% and an overall accuracy of 88%. Conclusion: The results of this preliminary study contribute to the translation of SERS in the clinical setting and highlight the potential of SERS as a novel screening strategy for breast cancer.

In this study, we combine the molecular structural information gained by SERS of saliva samples with the morphological data given by two-dimensional shear wave elastography (2D-SWE) (SuperSonic Imagine, Aixplorer) of parotid glands in the case of n = 31 patients with Sjogren's syndrome (SjS) and n = 22 controls, with the aim to discriminate between the two groups. The overall classification accuracy yielded by a hybrid principal component analysis-linear discriminant analysis (PCA-LDA) model based on both SERS and elastography (81%) was superior to that yielded by SERS spectra alone (75%) and elastography data alone (71%). This preliminary study is the first report on the use of 2D-SWE of parotid glands for the diagnosis of SjS as well as the first to describe the diagnosis of SjS based on the SERS spectra of dried saliva samples, the results suggesting that the strategy of combining the two methods could improve the diagnosis of SjS.

Early diagnosis based on screening is recognized as one of the most efficient ways of mitigating cancer-associated morbidity and mortality. Therefore, reliable but cost-effective methodologies are needed. By using a portable Raman spectrometer, a small and easily transportable instrument, the needs of modern diagnosis in terms of rapidity, ease of use and flexibility are met. In this study, we analyzed the diagnostic accuracy yielded by the surface-enhanced Raman scattering (SERS)-based profiling of serum, performed with a portable Raman device operating in a real-life hospital environment, in the case of 53 patients with gastrointestinal tumors and 25 control subjects. The SERS spectra of serum displayed intense bands attributed to carotenoids and purine metabolites such as uric acid, xanthine and hypoxanthine, with different intensities between the cancer and control groups. Based on principal component analysis-quadratic discriminant analysis (PCA-QDA), the cancer and control groups were classified with an accuracy of 76.92%. By combining SERS spectra with general inflammatory markers such as C-reactive protein levels, neutrophil counts, platelet counts and hemoglobin levels, the discrimination accuracy was increased to 83.33%. This study highlights the potential of SERS-based liquid biopsy for the point-of-care diagnosis of gastrointestinal tumors using a portable Raman device operating in a clinical setting.

In this preliminary study on synovial fluid (SF), knee osteoarthritis (OA) grading of n = 23 patients was accomplished by combining two methods: resonant Raman spectroscopy, and surface-enhanced Raman scattering (SERS) of native proteins acquired with iodide-modified silver nanoparticles and a laser emitting at 633 nm. Based on principal component analysis–linear discriminant analysis (PCA–LDA), the SERS spectra of proteins enabled the classification of low-grade and high-grade OA groups with an accuracy of 91%. Resonant Raman spectra of SF, recorded with laser excitation at 532 nm, exhibited carotenoid-associated bands that were less intense in the case of high-grade knee OA patients. Based on the resonant Raman spectra, the grading of OA patients was accomplished with an accuracy of 74%. Concatenating SERS and Raman spectral information increased the classification accuracy between the two groups to 100%. These results demonstrate the potential of Raman and SERS as a point-of-care method for aiding OA grading.

Aim: Previous studies regarding surface-enhanced Raman scattering (SERS) of serum have shown promising initial results in discriminating prostate cancer, a strategy which could complement standard tests such as the prostate-specific antigen (PSA). Materials & methods: SERS spectra of serum samples were combined with serum PSA levels to improve the discrimination accuracy between prostate cancer and nonmalignant pathologies in a cohort of 54 patients using principal component analysis-linear discriminant analysis (PCA-LDA). Results & discussion: Combining SERS spectra with serum PSA levels in a single PCA-LDA model could discriminate between the two groups with an overall accuracy of 94%, yielding better results than either method alone. Conclusion: These results highlight that combining SERS-based cancer screening with serum PSA levels represents a promising strategy for improving the accuracy of prostate cancer diagnosis.