Although Raman spectroscopy has demonstrated excellent performance for the identification of isolated bacterial cells, its application to spatially resolved analysis of complex polymicrobial communities remains a major analytical challenge. Here, we present a transferable supervised Raman imaging workflow that integrates spontaneous Raman spectroscopy, confocal Raman microscopy, and PCA-LDA chemometric analysis for the label-free identification of clinically relevant oral bacteria. The proposed framework uses Raman spectra acquired from isolated bacterial cells to train a supervised classifier that is directly transferred to hyperspectral Raman images, enabling quantitative pixel-wise bacterial identification without fluorescent labels or molecular probes. The workflow was validated using four representative oral bacterial species (Streptococcus oralis, Actinomyces naeslundii, Fusobacterium nucleatum, and Porphyromonas gingivalis), spanning the ecological transition from oral health to periodontal disease. The PCA-LDA classifier achieved 99.8% accuracy for binary classification, maintained an overall accuracy of 93.8% after extension to four bacterial species, and successfully identified P. gingivalis at bacterial ratios as low as 1:6000 within mixed populations. Importantly, the classifier, trained exclusively on planktonic single-cell Raman spectra, was directly transferred to Raman images of mixed bacterial communities and increasingly complex biofilm models without retraining, achieving classification accuracies of approximately 98%. The Raman-based classification was independently validated by morphology-based segmentation and Gram staining. These results establish a scalable and transferable analytical framework for quantitative Raman imaging of polymicrobial communities. By combining supervised machine learning with hyperspectral Raman microscopy, the proposed workflow provides a versatile strategy for label-free bacterial identification and spatial mapping, representing an important step toward Raman-based precision microbiology and future optical diagnostics of oral infectious diseases.

Engineered nanoparticles (NPs) are widely used in nanomedicine for precise disease diagnosis and therapy. Among the various types of NPs, inorganic nanoparticles stand out due to their tunable size, high stability, and unique physicochemical properties. This work investigates porous biosilica nanoparticles derived from diatomite (DNPs), produced through ultrasound treatment and size-selective filtration to obtain particles in the 100-400 nm range. The porous architecture of DNPs enables efficient drug loading, and these nanoparticles can be made photoluminescent for imaging applications. Previous studies have demonstrated their biocompatibility and cellular uptake in several cancer cell lines. Moreover, surface functionalization significantly enhances drug delivery efficiency and improves pharmacological performance. A hybrid nanosystem combining DNPs with gold nanoparticles has been developed for Raman imaging and drug delivery, together with ligand-based targeting strategies to enhance therapeutic efficacy in mouse models.

Hydrogel-based flexible plasmonic devices represent a cutting-edge technology for real-time monitoring of food safety, particularly for pesticide detection. This study presents a cost-effective, portable, and sensitive method to detect dimethoate (DMT), a hazardous organophosphorus pesticide, at concentrations below the maximum residue limit (MRL) of 0.01 ppm on olives. By integrating surface-enhanced Raman scattering (SERS) with gold nanoparticles (AuNPs) embedded in polyethylene glycol diacrylate (PEGDA) hydrogels, detection at parts-per-billion (ppb) levels is achieved. The hydrogel matrix enhances sensitivity and reproducibility by forming AuNP dimers, whose concentration increases with DMT levels, boosting signal output. The system uses UV polymerization of a pre-polymer solution, enabling direct application onto olive surfaces and achieving a detection limit of 3 ppb with a signal enhancement of ∼106. Tests with fungicides (RidoMil, Glyphosate) confirmed excellent selectivity for DMT. Validated on-site with a portable Raman spectrometer, this method is adaptable for detecting sulfur-containing pesticides, offering a practical solution for food safety.

MicroRNAs (miRNAs) are small non-coding RNAs (18-22 nucleotides) that regulate gene expression and are associated with various diseases, including Laryngeal Cancer (LCa), which has a high mortality rate due to late diagnosis. Traditional methods for miRNA detection present several drawbacks (time-consuming steps, high cost and high false positive rate). Early-stage diagnosis and selective detection of miRNAs remain challenging. This study proposes a 3D flexible biosensor that combines nanofibers (NFs), gold nanoparticles (AuNPs), and an inverse molecular sentinel (iMS) for enzyme-free, SERS-based detection of miRNA-223-3p, evaluated as a potential LCa biomarker. The electrospun flexible nanofibers decorated with AuNPs enhance Raman signal. Selective detection of miRNA-223-3p is achieved by immobilizing an iMS-DNA probe labeled with a Raman reporter (Cyanine 3) on the AuNPs. The iMS distinctive stem-and-loop structure undergoes a conformational change upon interaction with the miRNA-223-3p, producing an "on to off" SERS signal. The proposed sensor demonstrated a linear detection range from 10 to 250 fM, with a limit of detection (LOD) of 19.50 +/- 0.05 fM. The sensor selectivity was confirmed by analyzing the SERS signal behaviour in the presence of both Noncomplementary miRNA and miRNA with three mismatched base pairs. This easily fabricable sensor requires no amplification and offers key advantages, including sensitivity, flexibility, and cost-effectiveness.

Lipid droplets (LDs) are key markers of cellular metabolism, often altered in cancer. While holotomography enables 3D, label-free imaging of LDs via refractive index, it relies on complex thresholding and lacks biochemical specificity. Here, polarization-sensitive holotomography (PS-HT), which leverages the intrinsic birefringence of LDs for high-contrast, selective identification with a fixed near-zero threshold is presented. Using prostate cell models (healthy PNT2 and cancer PC3), PS-HT is validated against fluorescence microscopy and holotomography, showing that it enables accurate quantification of birefringence, LD volume, dry mass, molecular organization, and spatial distribution. Cancer cells show significantly higher birefringence after glucose treatment, reflecting enhanced lipid accumulation. PS-HT, combined with principal component analysis, achieves near-perfect classification of cancer versus healthy cells, establishing it as a robust, label-free tool for studying lipid metabolism and cancer diagnostics.

The highly fibrotic microenvironment of pancreatic ductal adenocarcinoma (PDAC) poses significant challenges for effective treatment, particularly in drug delivery and tumor progression. Our study investigates the role of collagen dynamics in PDAC, revealing that TGF-β1 negatively regulates the expression of L1 cell adhesion molecule (L1CAM), leading to a more invasive tumor phenotype. We identify a subset of PDAC cells with low L1CAM expression (L1low) that actively influences collagen deposition and remodeling, as evidenced by the upregulation of collagen 17A1 (COL17A1) and matrix metalloproteinase 2 (MMP2), both associated with poor prognosis. In vivo studies demonstrate that L1low cells correlate with increased collagen deposition, reduced sensitivity to gemcitabine, and heightened liver metastasis. The secretion of COL17A1 and MMP2 by these cells enhances their migratory capabilities and contributes to the formation of a fibrotic stroma that facilitates tumor progression. This interaction underscores the critical role of collagen in shaping the tumor microenvironment and promoting aggressive tumor behavior. Notably, treatment with Tranilast significantly reduced collagen deposition and MMP2 levels while promoting L1CAM expression, suggesting a therapeutic avenue for counteracting the aggressive characteristics of L1low cells. By modulating collagen dynamics and enhancing drug delivery, Tranilast may improve treatment outcomes for patients with low L1CAM-expressing tumors. Understanding the mechanisms by which L1low cells contribute to collagen secretion and tumor aggressiveness is essential for developing effective interventions in pancreatic cancer.

The COVID-19 pandemic has emphasized the need for rapid, sensitive, and accessible molecular diagnostics. In this study, we present a label-free Surface-Enhanced Raman Spectroscopy (SERS) biosensor for the direct detection of SARS-CoV-2 RNA in biological fluids. The proposed sensor is based on a thiolated Peptide Nucleic Acid (PNA) probe immobilized on colloidal gold nanoparticles (AuNPs) deposited on functionalized glass substrates. A stable and selective hybridization with target sequences is provided by the intrinsic characteristics of PNA molecules, such as neutral backbone, high sequence affinity and enzymatic resistance. Whereas AuNPs enables strong signal enhancement and excellent reproducibility, without requiring complex nanofabrication techniques. Overall, the biosensor fabrication relies entirely on standard laboratory procedures and commercially available reagents, making it cost-effective and easily scalable. The detection of the target RNA occurs through label-free SERS, responsible for amplifying the vibrational fingerprint of nucleobases. Multivariate analysis through principal component analysis (PCA) and regression (PCR) further enhances spectral discrimination and detection sensitivity. The sensor exhibits a limit of detection of 110 pM, falling within the clinically relevant range of salivary SARS-CoV-2 RNA concentrations. Detection performance was assessed in both buffer and artificial saliva, demonstrating the potential of the platform for use with real biological samples. Moreover, the device demonstrates high selectivity, effectively distinguishing between fully matched, mismatched, and random sequences.This work highlights the potential of PNA-SERS biosensors for rapid, amplification-free viral RNA detection and offers a promising approach for point-of-care diagnostics in infectious diseases.

Metastasis stands as the leading cause of mortality among colorectal cancer (CRC) patients. Galunisertib (LY2157299, LY) is a small molecule demonstrating promising anti-cancer effects by targeting the Transforming Growth Factor-beta (TGF-β) pathway. This route plays a pivotal role in initiating the epithelial-to-mesenchymal transition (EMT), a critical process for metastatic spread. Unfortunately, LY chronic treatment causes undesired effects. To mitigate these side effects, nanoscale drug delivery systems have emerged as a transformative approach in cancer treatment, enhancing drug effectiveness while minimizing toxicity. In this study, we introduce a hybrid nanosystem (DNP-AuNPs-LY@Gel) comprising porous diatomite nanoparticles decorated with plasmonic gold nanoparticles (AuNPs), encapsulating LY within a gelatin shell. This multifunctional nanosystem demonstrates efficient LY delivery, EMT reversal in CRC 2D and 3D cultures, and anti-cancer effects in vivo. Moreover, the nanosystem allowed the quantification with sub-femtogram resolution of the drug intracellularly released using surface-enhanced Raman spectroscopy (SERS). The release of LY is triggered by CRC cell acidic microenviroment. Real-time monitoring of drug release at the single-cell level is achieved by analyzing SERS signals of LY within CRC cells. The heightened efficacy of LY delivery through the DNP-AuNPs-LY@Gel complex offers a promising alternative strategy for reducing drug dosages and subsequent undesired effects.

Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.

The ability to make an accurate diagnosis at the time of treatment is crucial for many diseases. However, current standard diagnostic procedures can only be performed in specialised healthcare facilities. To bring diagnostic methods from a specialised laboratory to the point of treatment, many alternative methods have been proposed. One of them is surface-enhanced Raman scattering (SERS), which offers advantageous features such as high sensitivity in biotarget detection and higher accuracy. Here, we have developed an advanced SERS platform for the ultrasensitive, rapid and highly specific identification of tumour biomarkers in liquid biopsies. Our particular focus is on the detection of Thyroglobulin (Tg), the most important tumour biomarker for the diagnosis and prognosis of thyroid cancer. Specifically, SERS-active substrates fabricated by nanosphere lithography on chip or on tips of optical fiber (OF) were functionalized with Tg Capture antibodies. Gold nanoparticles were functionalized with Detection antibodies and conjugated with a Raman reporter. The sandwich assay platform was validated in the planar configuration and a detection limit of only 7 pg/ml was successfully achieved. The same approach has been successfully demonstrated on washout fluids from fine needle aspiration biopsies of cancer patients. Finally, the functionalization strategy was translated to the LOF-SERS platform and successfully used to detect Tg concentration. The proposed SERS-assisted immunoassay platform has proven to be highly versatile and can be used with both microfluidic chip POC devices and SERS-OF-based optrodes to perform sensitive, specific and rapid ex vivo assays for Tg detection in liquid intraoperative biopsies.

In this study, we report the results of two non-invasive optical methods, Raman microscopy (RM) and polarization-sensitive digital holographic imaging (PSDHI), for distinguishing prostate cancer cells from healthy ones. RM reveals cancer cells metabolize glucose faster, storing it as fatty acids and cholesteryl esters in lipid droplets (LDs). On the other hand, PSDHI shows significant morphological changes in LDs in glucose-incubated cancer cells, including number, volume, and refractive index. High birefringence in cancer LDs under perpendicular polarizations was observed, enabling fast discrimination with over 90% accuracy. PSDHI results align closely with Raman microscopy, suggesting its potential as a promising, high-speed technique for cancer screening purposes.

Aromatase inhibitors (AIs) show promising features as drugs to treat estrogen-responsive breast cancer as they block aromatase activity, the key enzyme in estrogen synthesis. The current AIs approved by the Food and Drug Administration for breast cancer treatment present severe adverse effects. For these reasons, it is important to develop of new AIs that are more specific and sensitive. In this paper, we report the synthesis and the characterization of new nonsteroidal aromatase AIs containing triazoles moieties for the treatment of hormone-dependent breast cancer in post-menopausal women. A new series of 1,2,3-triazole based molecules were successfully synthetized and their chemical structures were determined from the spectral data (FT-IR, 13C NMR, 1H NMR, mass spectroscopy) and micro-analytical data. Additionally, the physical properties of the newly synthesized derivatives were reported. The novel compounds were also tested for their anticancer activity in both breast cancer (MCF7 and T-47D) and normal breast (MCF 10A) cell lines, evaluating their effect on cell proliferation, migration, and invasion. The results revealed that the compounds exhibited promising and specific anti-cancer action.

Assessing HER2 expression in breast cancer cells holds significant diagnostic and prognostic importance. Traditional methods like immunohistochemistry and in situ hybridization suffer from low sensitivity and misclassification rates. In this frame, techniques such as vibrational microscopies can ensure, together with low costs and analytical speed, both high accuracy and precision. Herein, we propose a combined Raman and SERS approach for characterizing 4 breast cancer cell lines and normal cells with varying HER2 expression levels. We show that Raman spectroscopy offers a promising alternative, providing unique molecular fingerprints for cell types based on their biochemical signatures. Its non-invasive nature and ability to detect subtle changes in cellular metabolism make it ideal for cancer cell analysis. Coupled with machine learning techniques like PCA and LDA, Raman spectroscopy can classify different breast cancer subcategories accurately. Surface Enhanced Raman Scattering (SERS) further enhances sensitivity, allowing the detection of single molecules like HER2 receptors. Overall, our results enable fast screening of cancer subpopulation in terms of HER2 concentration and macromolecule cell content. Integration of Raman spectroscopy with SERS offers precise identification and opens avenues for personalized therapies.

BACKGROUND:Breast cancer (BC) is a heterogeneous neoplasm characterized by several subtypes. One of the most aggressive with high metastasis rates presents overexpression of the human epidermal growth factor receptor 2 (HER2). A quantitative evaluation of HER2 levels is essential for a correct diagnosis, selection of the most appropriate therapeutic strategy and monitoring the response to therapy.RESULTS:In this paper, we propose the synergistic use of SERS and Raman technologies for the identification of HER2 expressing cells and its accurate assessment. To this end, we selected SKBR3 and MDA-MB-468 breast cancer cell lines, which have the highest and lowest HER2 expression, respectively, and MCF10A, a non-tumorigenic cell line from normal breast epithelium for comparison. The combined approach provides a quantitative estimate of HER2 expression and visualization of its distribution on the membrane at single cell level, clearly identifying cancer cells. Moreover, it provides a more comprehensive picture of the investigated cells disclosing a metabolic signature represented by an elevated content of proteins and aromatic amino acids. We further support these data by silencing the HER2 gene in SKBR3 cells, using the RNA interference technology, generating stable clones further analysed with the same combined methodology. Significant changes in HER2 expression are detected at single cell level before and after HER2 silencing and the HER2 status correlates with variations of fatty acids and downstream signalling molecule contents in the context of the general metabolic rewiring occurring in cancer cells. Specifically, HER2 silencing does reduce the growth ability but not the lipid metabolism that, instead, increases, suggesting that higher fatty acids biosynthesis and metabolism can occur independently of the proliferating potential tied to HER2 overexpression.CONCLUSIONS:Our results clearly demonstrate the efficacy of the combined SERS and Raman approach to definitely pose a correct diagnosis, further supported by the data obtained by the HER2 gene silencing. Furthermore, they pave the way to a new approach to monitor the efficacy of pharmacologic treatments with the aim to tailor personalized therapies and optimize patients' outcome.

Sulfur dioxide (SO2) and sulfites are well-known additives in winemaking due to their preservative properties. Although they can prevent oxidation and inhibit microbial growth, they pose health risks and require limitations on their use. Consequently, the total level of SO2 is regulated and several quantification strategies have been proposed. The approved detection methods require the extraction of SO2 by heating and/or acid treatment. Then, iodine or acid/base titrations are conducted for the detection of liberated SO2. Although these methods can provide sensitive detection of SO2, they are complex, time-consuming, and require sample preparation steps and skilled operators. Thus, to overcome these disadvantages, an easy-to-use method, involving simple sample preparation steps, and offering high sensitivity and selectivity, is desirable. Herein, we introduce a SERS-based strategy for SO2 detection in liquids using hydrogel nanocomposites. The hydrogels are prepared by poly (ethylene glycol) diacrylate (PEGDA) in the presence of gold nanoparticles (AuNPs), acting as the SERS substrate. The use of hydrogels ensures a homogenous signal distribution and an efficient collection of SO2, and drying the hydrogels enhances and stabilizes the obtained SO2 signal. The detection strategy is based on the pH-dependent dissociation of SO2. By adjusting the pH value of wine to 10 through simple dilutions, SO2 can be directly detected in wine, down to 0.4 ppm, well below the regulatory limits. The proposed method allows for sensitive, direct, cost-effective detection of SO2 by eliminating the loss of the gaseous form of the sample and avoids titration-based detection methods.

Background: Metastasis-initiating cells are key players in progression, resistance, and relapse of colorectal cancer (CRC), by leveraging the regulatory relationship between Transforming Growth Factor-beta (TGF-(3) signaling and anti-L1 cell adhesion molecule (L1CAM). Methods: This study introduces a novel strategy for CRC targeted therapy and imaging based on the use of a hybrid nanosystem made of gold nanoparticles-covered porous biosilica further modified with the (L1CAM) antibody. Results: The nanosystem intracellularly delivers galunisertib (LY), a TGF-(3 inhibitor, aiming to inhibit epithelial-mesenchymal transition (EMT), a process pivotal for metastasis. Anti-L1CAM antibody-functionalized nanoparticles (NPs) target tumor-initiating cells expressing L1CAM, inhibiting cancer growth. The number of antibody molecules conjugated to the single NP is precisely quantified, revealing a high surface coverage that facilitates the tumor targeting. The therapeutic efficacy of the nanosystem is investigated in organoid-like cultures of CRC cells and in vivo mouse models, showing a significant reduction in tumor growth. The spatial distribution of NPs within CRC tumors from mice is investigated using a label-free optical approach based on Raman micro- spectroscopy. Conclusion: This research highlights the multifunctional capabilities of engineered biosilica NPs, which offer new insights in targeted CRC therapy and imaging, improving patient outcomes and paving the way for personalized therapies.

In recent decades, driven by the needs of industry and medicine, researchers have been investigating how to remove carefully from the main flow microscopic particles or clusters of them. Among all the approaches proposed, crossflow filtration is one of the most attractive as it provides a non-destructive, label-free and in-flow sorting method. In general, the separation performance shows capture and separation efficiencies ranging from 70% up to 100%. However, the maximum flow rate achievable (µL/min) is still orders of magnitude away from those suitable for clinical or industrial applications mainly due to the low stiffness of the materials typically used. In this work, we propose an innovative hydrodynamic-crossflow hybrid filter geometry, buried in a fused silica substrate by means of the femtosecond laser irradiation followed by chemical etching technique. The material high stiffness combined with the accuracy of our manufacturing technique allows the 3D fabrication of non-deformable channels with higher aspect ratio posts, while keeping the overall device dimensions compact. The filter performance has been validated through experiments with both Newtonian (water-based solution of microbeads) and non-Newtonian fluids (blood), achieving separation efficiencies of up to 94% and large particles recovery rates of 100%, even at very high flow rates (mL/h).

In this study, we utilized polarization-sensitive holotomography and Raman imaging to analyze prostate healthy (PNT2) and cancer (PC3) cells treated with glucose. After 48-hour incubation, distinct morphological differences were observed in cancer cells, including changes in volume, number, and refractive index of lipid droplets (LDs). Raman imaging confirmed the glucose uptake of these LDs. Cancer cells exhibited larger and more numerous LDs, with higher mean refractive-index and birefringence, compared to healthy cells. The study achieved over 90% accuracy in discriminating between cell types, highlighting its potential in cancer diagnostics. The research contributes to biomedical spectroscopy, offering a valuable tool for understanding cancer cell morphology and enabling early and precise cancer detection.