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The high-throughput analysis of DNA methylation markers by label-free surface-enhanced Raman scattering (SERS) holds significant promise for advancing cancer detection. However, a deeper understanding of the factors governing DNA adsorption onto metal surfaces and the identification of reliable SERS bands indicative of DNA methylation levels are still needed. In this study, we evaluated the effects of several cations (Ca 2+ , Mg 2+ , Al 3+ , Be 2+ , Zn 2+ , Cu 2+ , Fe 2+ , and Na⁺) on the SERS signal of DNA and identified Ca 2+ as providing the highest enhancement. Thus, the addition of 5x10 -4 M Ca 2+ yielded optimal SERS signals for genomic DNA extracted from calf thymus and from six human cell lines, including both benign and malignant types with varying methylation levels. Notably, the SERS activation effect due to Ca 2+ could also be replicated by lowering the pH, suggesting that Ca 2+ increases the signal enhancement by generating surface Ag⁺ which favors the adsorption of DNA. A strong positive correlation (R = 0.94, p = 0.005) was observed between the intensity of the SERS band at 790 cm -1 and the level of 5-methylcytosine, establishing this band as a robust marker for DNA methylation. This finding was further validated by monitoring methylation levels in a 180 bp DNA sequence from the promoter region of the SEPT9 gene, an FDA-approved biomarker for colorectal cancer. Additionally, the use of SYBR Green fluorescence assays revealed that hypermethylated genomic DNA exhibits greater affinity for silver surfaces compared to lower methylated DNA. Collectively, these findings provide important theoretical insights and practical directions for the development of future nanoparticle-based cancer detection assays utilizing methylation markers.

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.

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 (<10 min) and simple protocol for obtaining highly SERS-active colloidal silver nanoparticles (AgNPs) with a mean diameter of 36 nm by photoconversion from AgCl precursor microparticles in the absence of any organic reducing or capping agent. The resulting AgNPs are already SERS-activated by the Cl − 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 + , Mg 2+ or Ca 2+ 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 + , Mg 2+ or Ca 2+ 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 + , Mg 2+ or Ca 2+ , which is a process that seems to prevail over the Raman enhancement due to nanoparticle aggregation.

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—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+ 10−4 M. Results: The addition of Ca(NO3)2 10−4 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 our recent studies we highlighted the role of adsorbed ions (adions) in turning on the surface-enhanced Raman scattering (SERS) effect in a specific mode for anionic and cationic analytes. In this work, we emphasize the role of Ag+, Ca2+, Pb2+ and Al3+ adions in the specific adsorption of anionic analytes such as the citrate capping agent and three organic acids. Our results suggest an adion-specific adsorption mechanism: the adsorption of anionic analytes is facilitated by positively charged adions such as Ag+, Ca2+, Pb2+ or Al3+, which provide adsorption sites specific for the anionic analytes. The turn-on of the SERS effect is explained in the context of the chemical mechanism of SERS. The adions form SERS-active sites on the silver surface enabling a charge transfer between the adsorbate and the silver surface. High-intensity SERS spectra of uric acid, salicylic acid and fumaric acid could be recorded at a concentration of 50 µM only after activation of the colloidal silver nanoparticles by Ca2+, Pb2+ or Al3+ (50 µM). The chemisorption of the three anionic species to the silver surface occurs competitively and is enhanced with the anions of higher affinities to the silver surface as indicated by the SERS spectra of corresponding mixed solutions.

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: Surface-enhanced Raman spectroscopy (SERS) analysis of urine is a promising liquid biopsy technique for cancer detection. However, its clinical translation is hindered by two major challenges that impact classification efficacy. First, the SERS signal of urine is confounded by fluctuations induced by physiological differences in urine composition such as pH and dilution. Second, the molecular origin of the SERS signal of urine is incompletely understood, limiting the interpretability of machine learning classifiers in terms of specific biochemical markers. Methods: In this pilot study, we analyzed urine samples from breast cancer patients (n = 18) and control subjects (n = 10) at three pH levels (5, 7, and 9). Additionally, we analyzed simulated urine mixtures consisting of uric acid, hypoxanthine, xanthine, and creatinine in physiological concentrations to explain the variation in the SERS spectra at different pH values. Results: Urine at pH 9 yielded the most detailed spectral features. The SERS spectral pattern under alkaline pH reflected greater contributions from hypoxanthine, uric acid, and creatinine, while xanthine contributions diminished due to competitive interactions at the SERS substrate surface. Normalizing SERS signals to the creatinine band at 1420 cm−1 effectively mitigated the confounding effects of urine dilution. Conclusions: Optimizing the pH to 9 and normalizing to creatinine significantly enhances the interpretability and accuracy of SERS-based urine analysis for cancer detection. These findings offer important theoretical and practical advancements for the development of SERS-based liquid biopsy tools for cancer detection.

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.

Measurable residual disease (MRD) assessment has become a cornerstone in the management of acute myeloid leukemia (AML), offering critical prognostic information and guiding post-remission therapy. Conventional MRD detection methods, including multiparameter flow cytometry (MFC), quantitative PCR (qPCR), and next-generation sequencing (NGS), have demonstrated strong predictive value but are limited by technical complexity, marker specificity, and accessibility. This review explores the current landscape of MRD monitoring in AML, covering cytogenetic, immunophenotypic, and molecular approaches, with particular emphasis on the strengths and limitations of each. We further examine promising emerging technologies—namely DNA methylation profiling and surface-enhanced Raman scattering (SERS)—as non-invasive alternatives. DNA methylation-based assays capitalize on the epigenetic dysregulation characteristic of AML, while proof-of-concept studies indicate SERS as a promising alternative for cancer subtypes, stages or specific mutation detection by analyzing biofluids or extracted DNA from blood. Together, these developments hold the potential to overcome current diagnostic limitations, enabling more universal and precise MRD assessment. Ongoing research and validation will determine their future integration into standard clinical practice.

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.