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Aquacobalamin binds hydrogen peroxide reversibly to form a cobalt(III) hydroperoxo adduct with a 0.25 mM dissociation constant, as evidenced by UV-vis absorption spectroscopy and corroborated by NMR, Raman spectroscopy, stopped-flow UV-vis measurements, and density functional theory calculations.

The ideal photothermal therapy agent should exhibit a high energy conversion capacity, but it should not induce any cytotoxic effects unless it is exposed to electromagnetic radiation. We present data on biocompatibility and photothermal conversion capacity of colloidal gold nanoparticle assemblies (NPAs), obtained by hydroxylamine hydrochloride reduction at room temperature. The cytotoxic profile of NPAs with a mean diameter of 20 and 120 nm, respectively, was assessed using 3-[4,5-dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay, demonstrating tolerable cytotoxic effect on A549 human lung adenocarcinoma cell line at gold concentrations up to 150 μg gold/mL. Insights regarding the oxidative stress were gained using peroxide-sensitive fluorescent probes, which showed no additional oxidative stress due to NPA exposure. Using infrared thermal imaging, the photothermal conversion was evidenced, the effect being more prominent for the 120-nm diameter NPAs irradiated with the 785-nm laser. The photothermal capabilities of NPAs were highlighted also by Raman imaging studies, showing intracellular carbon by-products due to the thermal damage. Intracellular carbon deposits overlapping with the nanoparticle-rich sites were identified. These results highlight that NPAs are a versatile platform with possible applications in nanoparticle-based photothermal therapy.

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 (

Background and aim. Osteoarthritis (OA) is the most common joint condition and the leading cause of pain and disability in elderly patients. Currently, there is no biomarker available for the early diagnosis of OA, and limited data is available regarding the molecular basis of progression for OA. For this reason, this study aimed to identify the metabolomic profile of early and late OA using high-performance liquid chromatography coupled with untargeted mass spectrometry (LC-MS). Methods. 31 patients with knee OA and joint effusion were enrolled. Based on Kellgren/Laurence scale, 12 patients were classified as early OA (eOA) and 19 as late OA (lOA). The synovial fluid (SF) was collected and characterized by untargeted LC-MS. Only the metabolites identified in more than 25% of each group were kept for further analysis. Principal component analysis (PCA) enabled the unsupervised clustering of the eOA and lOA groups. Further, for classification, the best three principal components (PCs) were used as input for two machine learning algorithms (random forest and naïve Bayes), which were trained to discriminate between the eOA and lOA groups. Results. 43 metabolites were identified in both eOA and lOA, but after selecting the metabolites present in at least 25% of the patients in each group, the metabolomics analysis yielded a panel of only nine metabolites: four metabolites related to phospholipids (phosphatidylcholine 20:0/18:2 and 18:0/20:2, sphingomyelin, and ceramide), three metabolites belonging to purine metabolites (inosine 5'-phosphate, adenosine thiamine diphosphate, and diadenosine 5',5'-diphosphate), one metabolite was a gonadal steroid hormone (estrone 3-sulfate), and one metabolite represented by heme, with all but ceramide (d18:1/20:0) being enriched in the lOA group. By using as features the best three PCs (PC2, PC8 and PC9), random forest and naïve Bayes machine learning algorithms yielded a classification accuracy of 0.81 and 0.78, respectively. Conclusion. Our LC-MS analysis of SF from patients with eOA and lOA indicates stage-dependent differences, lOA being associated with a perturbed metabolome of phospholipids, purine metabolites, gonadal steroid hormones (estrone 3-sulfate) and a heme molecule. Specific questions need to be answered regarding the biosynthesis and function of these metabolites in osteoarthritic joints, with the aim of developing new relevant biomarkers and therapeutic strategies.

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.

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.

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.

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.

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.