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Background: Chiral-selective molecular interactions are considered crucial in numerous physiological processes. Chiral-selective analytical methods of biomolecules with sufficient sensitivity are of great interest in numerous applications. Several surface-enhanced Raman scattering (SERS)-based methods have recently been reported for chiral sensing of biomolecules. However, the lack of molecular-level understanding of SERS spectral changes of reporter and analyte molecules may mislead the development of chiral detection methods., Results: We report the chiral sensing of glucose (Glu) by SERS of L- and D-phenylalanine (Phe) with colloidal gold nanoparticles (AuNPs) synthesized by borohydride ions. The Phe SERS showed drastic spectral changes only when Glu of the same chirality as Phe was added, which also showed strong dependence on Glu concentration. The increase of δ(COO - ) and decrease of ν s (COO - ) modes in Phe SERS, exclusively observed with the chiral-selective bimolecular interactions of chirally matching Glu, are understood as modified surface adsorption geometry of the carboxylate group. Quantitative spectral analysis for the Glu concentration of a specific chirality showed the detection limit down to 2 × 10 -9 - 2 × 10 -7  M levels depending on the existence of the opposite enantiomer of Glu., Significance: In this study, we demonstrated that the Phe SERS on AuNPs can be utilized in the chiral sensing of Glu molecules with quantitative concentration analysis. The bimolecular interactions of surface-adsorbed Phe and chirally matching Glu are suggested for the chiral recognition of Phe SERS. These results imply that a molecule-level understanding is indispensable for developing SERS-based chiral sensing methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating timely intervention, curbing the propagation of antibiotic resistance, and enhancing patient outcomes., Results: This study introduces the utilization of surface-enhanced Raman spectroscopy (SERS) in tandem with machine learning (ML) for the sensitive detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and without preliminary purification or isolation the created fragments were captured by functional SERS substrates. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Method validation was performed using separately measured spectra, which did not overlap with the database used for ML training. To check the efficiency of recognising the target fragment, control experiments involved bacterial plasmids containing different resistance genes, the use of inappropriate enzymes, or the absence of plasmid., Significance: SERS-ML allowed express detection of bacterial plasmids containing a characteristic gene fragment up to the 10 -7 concentration of the initial plasmid, despite the complex composition of the biological sample, including the presence of interfering plasmids. Our approach offers a promising alternative to existing methods for monitoring antibiotic-resistant bacteria, characterized by its simplicity, low detection limit, and the potential for rapid and straightforward analysis., Competing Interests: Declaration of competing interest X The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: In the context of modern agriculture, the proliferation of chemical use calls for enhanced pesticide detection to safeguard food quality and public health. The development of accurate testing methodologies is imperative to mitigate the environmental impact of pesticides and ensure the integrity of ecosystems, thereby reflecting the pressing need for advancements in agricultural safety protocols. Therefore, the development of highly sensitive monitoring technology for detecting pesticide residues in agricultural products is necessary for safeguarding human health, ensuring food safety, and maintaining environmental sustainability., Results: Herein, a controllable surface charge on single tungsten atom-modified gold nanoparticles was used to create an electrostatic force with positively charged pesticide residues. Moreover, hydrogen bonds formed by single-atom sites can induce analyte-adsorbed nanoparticle aggregation, and the sizes of single-tungsten-atom-decorated AuNPs can maintain a gap between each other, resulting in improved SERS detection sensitivity through analyte enrichment at gold nanoparticle hotspots. In terms of the detection limits for pesticide residue analysis, we can effectively achieve an ultrahigh sensitivity of 0.1 ppb for acetamiprid, paraquat and carbendazim, which is among the best SERS sensitivities at the state of the art. For apple sample analysis, our work demonstrated good reproductivity (RSD<6 %) and a strong linear relationship (R 2  ≥ 0.97) for 4 pesticide residues after optimizing the pretreatment process, which proves the enormous potential in quantitative analysis., Significance: Single-atom sites hotspot are firstly successfully achieved and uniformly dispersed between Au nanoparticle, which can effectively increase the sensitivity, keep stability of the Raman scattering signals and possess a significant improvement beyond that of undecorated hotspots when applied in pesticide residue detection. This method can be employed as a universal strategy to capture pesticide residues at hotspots for SERS detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Spatially offset Raman scattering (SORS) line-mapping was explored as a versatile tool to examine accuracy variations in compositional analyses of tablets with different particle sizes. SORS spectra collected near the laser irradiation were less representative of tablet composition due to the limited spectroscopic sampling volume, while the signal-to-noise (S/N) ratios of corresponding spectra were higher. On the other hand, SORS spectra at longer offset distances were better representative of tablet composition, while their S/N ratios were decreased considerably. Therefore, the use of only a certain portion of sliced (line-mapped) spectra balanced with the sample representation and S/N ratio could be advantageous to enhance accuracy. Moreover, a group of optimal slice spectra is expected to vary when the particle size of the tablet changes since the characteristics of internal photon propagation also would change. For the overall examination, SORS spectra of 30 Anaprox tablets (composed of 4 constituents including naproxen sodium) with 2 particle sizes (88.4 ± 11.8 µm and 118.9 ± 38.8 µm) were analyzed, and the concentrations of three components in these tablets were determined. A total of 6 cases (3 components and 2 particle sizes) were examined. When the average optimal slice spectra were employed in each case, the errors were lower compared to those using the average of all slice spectra. The demonstrated scheme was versatile to study the offset distance-dependent accuracy variations according to particle size and target component., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The molecule of 2-Biphenyl Carboxylic Acid (2BCA), which contains peculiar features, was explored making use of density functional theory (DFT) and experimental approaches in the area of quantum computational research. The optimised structure, atomic charges, vibrational frequencies, electrical properties, electrostatic potential surface (ESP), natural bond orbital analysis and potential energy surface (PES) were obtained applying the B3LYP approach with the 6-311++ G (d,p) basis set.. The 2BCA molecule was examined for possible conformers using a PES scan. The methods applied for spectral analyses included FT-IR, FT-RAMAN, NMR, and UV-Vis results. Vibrational frequencies for all typical modes of vibration were found using the Potential Energy Distribution (PED) data. The UV-Vis spectrum was simulated using the TD-DFT technique, which is also seen empirically. The Gauge-Invariant Atomic Orbital (GIAO) approach was employed to model and study the 13 C and 1 H NMR spectra of the 2BCA molecule in a CDCL3 solution. The spectra were then exploited experimentally to establish their chemical shifts. To predict the donor and acceptor interaction, the NBO analysis was used. The electrostatic potential surface was employed to anticipate the locations of nucleophilic and electrophilic sites. Hirshfeld surfaces and their related fingerprint plots are exploited for the investigation of intermolecular interactions. Reduced Density Gradient (RDG) helps to measure and illustrate electron correlation effects, offering precise insights into chemical bonding, reactivity, and the electronic structure of 2BCA. According to Lipinski and Veber's drug similarity criteria, 2BCA exhibits the typical physicochemical and pharmacokinetic properties that make it a potential oral pharmaceutical candidate. According to the findings of a molecular docking study, the 2BCA molecule has promise as a treatment agent for the Nipah virus (PDB ID: 6 EB9), which causes severe respiratory and neurological symptoms in humans., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this study, electroporation-surface-enhanced Raman scattering (SERS) was applied to rapidly measure intracellular pH. The generation of a sensitive SERS probe for measuring pH in the range of 6.0-8.0 was accomplished through the conjugation of the pH-sensitive molecule 4-mercaptobenzoic acid (4-MBA) to the surface of gold nanoparticles (Au NPs) through its thiol functional group. This bioprobe was then rapidly introduced into nasopharyngeal carcinoma CNE-1 cells by electroporation, followed by SERS scanning and the fitting of intensity ratios of each detection point's Raman peaks at 1423 cm -1 and 1072 cm -1 , to create the pH distribution map of CNE-1 cells. The electroporation-SERS assay introduces pH bioprobes into a living cell in a very short time and disperses the nanoprobe throughout the cytoplasm, ultimately enabling rapid and comprehensive pH analysis of the entire cell. Our work demonstrates the potential of electroporation-SERS for the biochemical analysis of live cells., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The impact of microbial infections is increasing over time, and it is one of the major reasons for death in both developed and developing countries. colistin is considered as the antibiotic of last choice for infections brought by major multidrug-resistant (MDR), gram-negative bacteria such as Enterobacter species, Acinetobacter species, and Pseudomonas aeruginosa. Existing approaches to diagnose these resistant species are relatively slow and take up to 2 to 3 days. In this work, we propose a novel interdisciplinary method based on Raman spectroscopy and heavy water to identify colistin-resistant microbes. Our hypothesis is based on the fact that resistant bacteria will be metabolically active in the culture medium containing antibiotics and heavy water, and these bacteria will take up deuterium instead of hydrogen to newly synthesized lipids and proteins. This effect will generate a 'C - D' bond-specific Raman spectral marker. Successful identification of this band in the spectral profile can confirm the presence of colistin-resistant bacteria. We have validated the efficacy of this approach in identifying colistin-resistant bacteria spiked in artificial urine and have compared sensitivity at different bacterial concentrations. Overall findings suggest that heavy water can potentially serve as a suitable Raman probe for identifying metabolically active colistin-resistant bacteria via urine under clinically implementable time and can be used in clinical settings after validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the current biopharmaceutical scenario, constant bioprocess monitoring is crucial for the quality and integrity of final products. Thus, process analytical techniques, such as those based on Raman spectroscopy, have been used as multiparameter tracking methods in pharma bioprocesses, which can be combined with chemometric tools, like Partial Least Squares (PLS) and Artificial Neural Networks (ANN). In some cases, applying spectra pre-processing techniques before modeling can improve the accuracy of chemometric model fittings to observed values. One of the biological applications of these techniques could have as a target the virus-like particles (VLP), a vaccine production platform for viral diseases. A disease that has drawn attention in recent years is Zika, with large-scale production sometimes challenging without an appropriate monitoring approach. This work aimed to define global models for Zika VLP upstream production monitoring with Raman considering different laser intensities (200 mW and 495 mW), sample clarification (with or without cells), spectra pre-processing approaches, and PLS and ANN modeling techniques. Six experiments were performed in a benchtop bioreactor to collect the Raman spectral and biochemical datasets for modeling calibration. The best models generated presented a mean absolute error and mean relative error respectively of 3.46 × 10 5  cell/mL and 35 % for viable cell density (Xv); 4.1 % and 5 % for cell viability (CV); 0.245 g/L and 3 % for glucose (Glc); 0.006 g/L and 18 % for lactate (Lac); 0.115 g/L and 26 % for glutamine (Gln); 0.132 g/L and 18 % for glutamate (Glu); 0.0029 g/L and 3 % for ammonium (NH 4 + ); and 0.0103 g/L and 2 % for potassium (K + ). Sample without conditioning (with cells) improved the models' adequacy, except for Glutamine. ANN better predicted CV, Gln, Glu, and K+, while Xv, Glc, Lac, and NH 4 + presented no statistical difference between the chemometric tools. For most of the assessed experimental parameters, there was no statistical need for spectra pre-filtering, for which the models based on the raw spectra were selected as the best ones. Laser intensity impacts quality model predictions in some parameters, Xv, Gln, and K + had a better performance with 200 mW of intensity (for PLS, ANN, and ANN, respectively), for CV the 495 mW laser intensity was better (for PLS), and for the other biochemical variables, the use of 200 or 495 mW did not impact model fitting adequacy., Competing Interests: Declaration of competing interest The authors declare that no relevant conflicts of interest concerning the content of this scientific paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The agricultural sector plays a pivotal role in driving the economy of many developing countries. Any dent in this economical structure may have a severe impact on a country's population. With rising climate change and increasing pollution, the agricultural sector is experiencing significant damage. Over time this cumulative damage will affect the integrity of food crops and create food security issues around the world. Therefore, an early warning system is needed to detect possible stress on food crops. Here we present a review of the recent developments in nanomaterial-based Surface Enhanced Raman Spectroscopy (SERS) substrates which could be utilized to monitor agricultural crop responses to natural and anthropogenic stress. Initially, our review delves into diverse and cost-effective strategies for fabricating SERS substrates, emphasizing their intelligent utilization across various agricultural scenarios. In the second phase of our review, we spotlight the specific application of SERS in addressing critical food security issues. By detecting nutrients, hormones, and effector molecules in plants, SERS provides valuable insights into plant health. Furthermore, our exploration extends to the detection of contaminants, chemicals, and foodborne pathogens within plants, showcasing the versatility of SERS in ensuring food safety. The cumulative knowledge derived from these discussions illustrates the transformative potential of SERS in bolstering the agricultural economy. By enhancing precision in nutrient management, monitoring plant health, and enabling rapid detection of harmful substances, SERS emerges as a pivotal tool in promoting sustainable and secure agricultural practices. Its integration into agricultural processes not only augments productivity but also establishes a robust defence against potential threats to crop yield and food quality. As SERS continues to evolve, its role in shaping the future of agriculture becomes increasingly pronounced, promising a paradigm shift in how we approach and address challenges in food production and safety., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Identifying the species and origin of adhesives in criminal investigations aids in narrowing inquiry scope and supporting case detection. This study introduces two advanced combined analytical techniques for distinguishing adhesive species, including attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with Raman spectroscopy, and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) together with multivariate statistical analysis. ATR-FTIR categorized seven adhesives into three groups based on the base materials, with further differentiation achieved via Raman spectra. Analysis of volatile components identified 79 volatile organic compounds (VOCs), with esters being the most concentrated. The fingerprint profile clearly illustrated the characteristic fingerprint sequence and unique marker compounds of each adhesive, effectively enabling their differentiation. Multivariate statistical analysis methods, including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), heatmap, and hierarchical cluster analysis (HCA), were utilized to visually interpret the classification of adhesives. This integrated analytical approach provides a comprehensive analysis of adhesive compositions, facilitating the diversification and precision of adhesive species identification, and broadening the scope for detecting and analyzing trace evidence in forensic science., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Confocal Raman Spectroscopy is recognised as a potent tool for molecular characterisation of biological specimens. There is a growing demand for In Vitro Permeation Tests (IVPT) in the pharmaceutical and cosmetic areas, increasingly conducted using Reconstructed Human Epidermis (RHE) skin models. In this study, chemical fixation of RHE in 10 % Neutral Buffered Formalin for 24 h has been examined for storing RHE samples at 4 °C for up to 21 days. Confocal Raman Spectroscopy (CRS), combined with Principal Components Analysis, revealed the molecular-level effects of fixation, notably in protein and lipid conformation within the stratum corneum and viable epidermis. IVPT by means of high-performance liquid chromatography, using caffeine as a model compound, showed minimal impact of formalin fixation on the cumulative amount, flux, and permeability coefficient after 12 h. While the biochemical architecture is altered, the function of the model as a barrier to maintain rate-limiting diffusion of active molecules within skin layers remains intact. This study opens avenues for enhanced flexibility and utility in skin model research, promising insights into mitigating the limited shelf life of RHE models by preserving performance in fixed samples for up to 21 days., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and β-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R 2  > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu 2 O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Computer-aided vibrational spectroscopy detection technology has achieved promising results in the field of early disease diagnosis. Yet limited by factors such as the number of actual samples and the cost of spectral acquisition in clinical medicine, the data available for model training are insufficient, and the amount of data varies greatly between different diseases, which constrain the performance optimization and enhancement of the diagnostic model. In this study, vibrational spectroscopy data of three common diseases are selected as research objects, and experimental research is conducted around the class imbalance situation that exists in medical data. When dealing with the challenge of class imbalance in medical vibrational spectroscopy research, it no longer relies on some kind of independent and single method, but considers the combined effect of multiple strategies. SVM, K-Nearest Neighbor (KNN), and Decision Tree (DT) are used as baseline comparison models on Raman spectroscopy medical datasets with different imbalance rates. The performance of the three strategies, Ensemble Learning, Feature Extraction, and Resampling, is verified on the class imbalance dataset by G-mean and AUC metrics, respectively. The results show that all the above three methods mitigate the negative impact caused by unbalanced learning. Based on this, we propose a hybrid ensemble classifier (HEC) that integrates resampling, feature extraction, and ensemble learning to verify the effectiveness of the hybrid learning strategy in solving the class imbalance problem. The G-mean and AUC values of the HEC method are 82.7 % and 83.12 % for the HBV dataset, is 2.02 % and 1.98 % higher than the optimal strategy; 83.62 % and 83.76 % for the HCV dataset, is 9.79 % and 8.47 % higher than the optimal strategy; while for the thyroid dysfunction dataset are 77.56 % and 77.85 %, is 6.92 % and 6.36 % higher than that of the optimal strategy, respectively. The experimental results show that the G-mean and AUC metrics of the HEC method are higher than those of the baseline classifier as well as the optimal combination using separate strategies. It can be seen that the HEC method can effectively counteract the unfavorable effects of imbalance learning and is expected to be applied to a wider range of imbalance scenarios., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 10 6 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 10 6 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Phosphorylated peptides are instrumental in studying protein phosphorylation events. In the present study, Raman optical activity (ROA) is employed to elucidate the structure of a dipeptide, L-alanyl-L-glutamine (L-Ala-L-Gln) and its two differently alkylated N-phosphorylated derivatives. Theoretical simulations were conducted to aid the interpretation of peptide conformation variations upon phosphorylation, and of the measured Raman and ROA spectra. Induced circularly polarized luminescence (CPL) was also recorded in solution, in the presence of a simple europium aqua ion. As the spectra are peptide specific, this type of stereochemical analysis is expected to aid identification of the phosphorylation sites also in other peptides and possibly proteins., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10 -6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R 2 ) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The extent of nanoplastic pollution has raised severe environmental and health concerns. While the means for microplastic detection are abundant, improved tools for nanoplastic detection are called-for. State-of-the-art microscopic techniques can detect nanoplastics down to tens of nanometers, however, only from small sample sizes (typically ∼10μl). In this work, we describe a method that enables sampling of 1 l of seawater by the means of correlative Raman- and SEM-techniques. This is achieved by adapting common microplastic sample purification protocols to suit the nanoplastic study. In addition, we decorate a membrane filter with SERS-property to amplify the Raman signals. Together, the purification method combined with the use of the SERS-activated-membrane-filter enables identification and imaging of individual nanoplastic particles from significantly larger sample sizes than before. In the nanoscale the average recovery rate is 5 %. These results aim to provide useful tools for researchers in the fight against plastic pollution., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Arto Hiltunen reports financial support was provided by Weisell-foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Bamboo holds significant potential as a substitute for plastics. Stress relaxation is a critical property during the fixation stage of plastic formation for bamboo products intended to replace plastic. However, research on this topic has been limited. In this study, a custom-built mechanical loading platform coupled with Raman spectroscopy was acquired during stress relaxation of bamboo under different humidity levels. Results reveal variations in stress relaxation patterns based on node position, radial location, and humidity, along with changes in cellulose molecules in response to these factors. Specifically, stress relaxation modulus in bamboo decreases from the outer to the inner part, increases in the presence of node structures, and decreases with increasing humidity. The shifts in Raman spectral bands exhibit significant changes in response to variations in stress and humidity. The elongation of cellulose chain bands at 1097 cm -1 in the Raman spectrum shows an increasing trend during stress relaxation, with larger band shifts observed at lower humidity. Simulations of cellulose molecular stretching show a stronger stress response at lower moisture content, with a maximum stress of 0.85 GPa for cellulose at 3.3 % moisture content, compared to 0.79 GPa at 7.8 % moisture content. These findings suggest a novel approach for the molecular investigation of materials during stress relaxation, which is crucial for the plasticity processing of bamboo., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The rise of multidrug-resistant bacteria (MDRB) has made bacterial infection one of the biggest health threats, causing numerous antibiotics to fail. Real-time monitoring of bacterial disease treatment efficacy at the infection site is required. Herein, we report a versatile Raman tag 3,3'-diethylthiatricarbocyanine iodide (DTTC)-conjugated star-shaped Au-MoS 2 @hyaluronic acid (AMD@HA) nanocomposite as a surface-enhanced Raman scattering (SERS) nanoprobe for quick bacterial identification and in-situ eradication. Localized surface plasmon resonance (LSPR) from the hybrid metallic nanostructure makes AMD@HA highly responsive to the near-infrared laser, enabling it to demonstrate a photothermal (PTT) effect, increased SERS activity, and peroxidase-like catalytic reaction to release reactive oxygen species. The tail vein injection of AMD@HA nanoprobes is invasive, however SERS imaging for bacterial identification is non-invasive and sensitive, making it an efficient residual bacteria monitoring method. The detection limit for methicillin-resistant Staphylococcus aureus (MRSA) is as low as 10 2 CFU·mL -1 , and the substrates allow for taking 120 s to acquire a Raman image of 1600 (40 × 40) pixels. In mouse models of MRSA-induced wound infection and skin abscess, the combination of AMD@HA-mediated PTT and catalytic therapy demonstrates a synergistic effect in promoting wound healing through rapid sterilization. This SERS-guided therapeutic approach exhibits little toxicity and does not cause considerable collateral damage, offering a highly promising intervention for treating diseases caused by MDRB. STATEMENT OF SIGNIFICANCE: This research introduces a SERS nanoprobe, AMD@HA, for the rapid identification and eradication of multidrug-resistant bacteria (MDRB), a critical health threat. The nanoprobe leverages localized surface plasmon resonance for photothermal therapy and enhanced Raman signals, offering a sensitive, non-invasive diagnostic tool. With a low detection limit for MRSA and a synergistic therapeutic effect in mouse models, our approach holds significant promise for treating MDRB-driven infections with minimal toxicity, advancing the field of antimicrobial strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.)

Cytochrome c oxidase (CcO) reduces O 2 , pumps protons in the mitochondrial respiratory chain, and is essential for oxygen consumption in the cell. The coiled-coil-helix-coiled-coil-helix domain-containing 2 (CHCHD2; also known as mitochondrial nuclear retrograde regulator 1 [MNRR1], Parkinson's disease 22 [PARK22] and aging-associated gene 10 protein [AAG10]) is a protein that binds to CcO from the intermembrane space and positively regulates the activity of CcO. Despite the importance of CHCHD2 in mitochondrial function, the mechanism of action of CHCHD2 and structural information regarding its binding to CcO remain unknown. Here, we utilized visible resonance Raman spectroscopy to investigate the structural changes around the hemes in CcO in the reduced and CO-bound states upon CHCHD2 binding. We found that CHCHD2 has a significant impact on the structure of CcO in the reduced state. Mapping of the heme peripheries that result in Raman spectral changes in the structure of CcO highlighted helices IX and X near the hemes as sites where CHCHD2 takes action. Part of helix IX is exposed in the intermembrane space, whereas helix X, located between both hemes, may play a key role in proton uptake to a proton-loading site in the reduced state for proton pumping. Taken together, our results suggested that CHCHD2 binds near helix IX and induces a structural change in helix X, accelerating proton uptake., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Spinal cord injury (SCI) is a debilitating neurological and pathological condition that results in significant impairments in motor, sensory, and autonomic functions. By integrating multispectral imaging (MSI) with Raman spectroscopy, a label-free optical methodology was developed for achieving a non-invasive in vivo understanding on the pathological features of SCI evolution. Under the guidance of captured the spectral imaging data cube with a rigid endoscope based MSI system, a special designed fiber probe passed through the instrumental channel for acquiring the finger-print spectral information from compression rat SCI models. After identifying the main visual features of injured spinal cord tissue in all Sham, 0-, 3- and 7-days post injury (0 DPI, 3 DPI, and 7 DPI) groups, the blood volume and oxygen content were visualized to describe hemorrhage, hypoxia and inflammatory state after acute injury. The averaged reflectance spectra, which were deduced from MSI data cubes, were utilized for describing oxygen saturation and hemoglobin concentration in living tissue. The results of Raman spectroscopy addressed complex compositional and conformational phenomena during SCI progression, correlated with the well-known event like neuronal apoptosis, hemorrhage, demyelination, and even the upregulation of chondroitin sulfate proteoglycans (CSPGs). A principal component analysis and linear discriminate algorithm (PCA-LDA) based discriminate model was introduced for categorizing spectral features in different injury stages, which was applicable for intraoperative interpretations on the complex pathological courses of SCI and therapeutic outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Glutathione (GSH) is a non-protein tripeptide thiol that plays a prominent role in oxidative stress defense. GSH concentration is particularly critical in the neonatal period, especially for premature newborns that face increased susceptibility to oxidative stress. Monitoring GSH levels provides valuable insights into newborn health, helping to tailor care to their specific needs. The aim of this study was the development of a sensor specifically targeted for its use in neonatology, enabling GSH determination in only 2 μL of whole blood. The newly developed sensing system simplifies sample processing, addressing a critical need in clinical applications. Unlike current methods that demand fast pre-processing of relatively large sample volumes, expensive equipment, and skilled personnel, the developed approach streamlines the analytical process. By using 2 μL of whole blood, a single syringe filter for sample treatment, a deuterated internal standard (IS) for signal normalization, and Surface Enhanced Raman Scattering (SERS) spectroscopy with a silver colloid substrate for GSH detection, the set-up's characteristics are compatible with point-of-care applications. The analytical procedure was validated and applied to diverse populations including healthy adults (N = 63) and newborns (N = 35), yielding GSH concentration values ranging from 0.6 to 1.8 and 0.8-2.1 mM, respectively. This new optical sensor offers a quick and cost-effective solution to support the assessment of GSH levels in newborns that can greatly benefit not only neonatal care, but also the study of adult populations for health monitoring., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Julia Kuligowski reports financial support was provided by Carlos III Health Institute. Abel Albiach-Delgado reports financial support was provided by Carlos III Health Institute. Jose Luis Moreno-Casillas reports financial support was provided by Spain Ministry of Science and Innovation. Julia Kuligowski reports financial support was provided by Government of Valencia. Guillermo Quintas reports financial support was provided by Spain Ministry of Science and Innovation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study describes a novel dual-mode immunosensor that combines electrochemical (EC) and surface-enhanced Raman scattering (SERS) techniques for the detection of prostate-specific antigen (PSA), a biomarker associated with prostate cancer. The sensor consists of a nanocomposite of gold nanoparticles (AuNPs) deposited on two-dimensional (2D) molybdenum disulfide (Au@MoS 2 ) modified on a working carbon electrode of a screen-printed electrode (SPE). Subsequently, the primary antibody (Ab1) is immobilized on the modified electrode, creating Ab1/Au@MoS 2 /SPE for specific recognition of the target PSA. In parallel, AuNPs are conjugated with a secondary antibody (Ab2) and a probe molecule, 3,3',5,5'-tetramethylbenzidine (TMB), leading nanotags (TMB/Ab2/AuNPs) formation exhibiting strong SERS and EC responses. Upon the presence of the target, sandwich immunocomplexes can be formed through antigen-antibody interactions (Ab1-PSA-Ab2). The differential pulse voltammetry (DPV) technique is employed for EC detection mode, while a handheld Raman spectrometer with a 785 nm excitation laser is utilized to collect SERS signals. The developed system demonstrates excellent selectivity and sensitivity, with low limits of detection (LODs) of 3.58 pg mL -1 and 4.83 pg mL -1 for EC and SERS sensing, respectively. Importantly, the dual-mode immunosensor proves effective quantifying PSA protein in human serum samples with good recovery. Given its high sensitivity and proficiency in analyzing biological samples, this proposed immunosensor holds promise as an alternative tool for the early diagnosis of cancers., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2024 Elsevier B.V. All rights reserved.)

Heme-based sensor proteins are used by organisms to control signaling and physiological effects in response to their gaseous environment. Globin-coupled sensors (GCS) are oxygen-sensing proteins that are widely distributed in bacteria. These proteins consist of a heme globin domain linked by a middle domain to various output domains, including diguanylate cyclase domains, which are responsible for synthesizing c-di-GMP, a bacterial second messenger crucial for regulating biofilm formation. To understand the roles of heme pocket residues in controlling activity of the diguanylate cyclase domain, variants of the Pectobacterium carotovorum GCS (PccGCS) were characterized by enzyme kinetics and resonance Raman (rR) spectroscopy. Results of these studies have identified roles for hydrogen bonding and heme edge residues in modulating heme pocket conformation and flexibility. Better understanding of the ligand-dependent GCS signaling mechanism and the residues involved may allow for future development of methods to control O 2 -dependent c-di-GMP production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Avian influenza viruses (AIV) are capable of infecting a considerable proportion of the world's population each year, leading to severe epidemics with high rates of morbidity and mortality. The methods now used to diagnose influenza virus A include the Western blot test (WB), hemagglutination inhibition (HI), and enzyme-linked immunosorbent assays (ELISAs). But because of their labor-intensiveness, lengthy procedures, need for costly equipment, and inexperienced staff, these approaches are considered inappropriate. The present review elucidates the recent advancements in the field of avian influenza detection through the utilization of nanomaterials-based immunosensors between 2014 and 2024. The classification of detection techniques has been taken into account to provide a comprehensive overview of the literature. The review encompasses a detailed illustration of the commonly employed detection mechanisms in immunosensors, namely, colorimetry, fluorescence assay, surface plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), electrochemical detection, quartz crystal microbalance (QCM) piezoelectric, and field-effect transistor (FET). Furthermore, the challenges and future prospects for the immunosensors have been deliberated upon. The present review aims to enhance the understanding of immunosensors-based sensing platforms for virus detection and to stimulate the development of novel immunosensors by providing novel ideas and inspirations. Therefore, the aim of this paper is to provide an updated information about biosensors, as a recent detection technique of influenza with its details regarding the various types of biosensors, which can be used for this review., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Lignocellulosic biomass-based derivatives coupled with surface-enhanced Raman spectroscopy (SERS) technology have emerged as an appealing and indispensable tool in food safety and environmental monitoring for rapidly detecting trace contaminants like pesticide residues. The membrane material, serving as a substrate, ensures both sampling flexibility and test accuracy by directing the diffusion-adsorption process of the molecules. However, the existing membrane substrates, critical for the practical application of SERS, suffer from issues such as costly, intricate fabrication procedures, or restricted detection capabilities., Results: Herein, we present a flexible, transparent, and biodegradable cellulose acetate membrane with gold nanoparticles (AuNPs) uniformly embedded, fabricated using a simple scraping method. This membrane achieved a limit of detection (LOD) of thiram pesticide in water at 10 -8  g mL -1 . The unique optical transparency of the substrates allowed for in-situ detection on surfaces, with an LOD of thiram reaching 30 ng cm -2 ., Significance: Furthermore, SERS substrates made from corn stover-derived cellulose acetate enable the detection of various contaminants, highlighting their cost-effectiveness and eco-friendliness because of the abundance and low environmental impact of the raw materials., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Saccharomyces cerevisiae is the most common microbe used for the industrial production of bioethanol, and it encounters various stresses that inhibit cell growth and metabolism during fermentation. However, little is currently known about the physiological changes that occur in individual yeast cells during ethanol fermentation. Therefore, in this work, Raman spectroscopy and chemometric techniques were employed to monitor the metabolic changes of individual yeast cells at distinct stages during high gravity ethanol fermentation. Raman tweezers was used to acquire the Raman spectra of individual yeast cells. Multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis were employed to analyze the Raman spectra dataset. MCR-ALS extracted the spectra of proteins, phospholipids, and triacylglycerols and their relative contents in individual cells. Changes in intracellular biomolecules showed that yeast cells undergo three distinct physiological stages during fermentation. In addition, heterogeneity among yeast cells significantly increased in the late fermentation period, and different yeast cells may respond to ethanol stress via different mechanisms. Our findings suggest that the combination of Raman tweezers and chemometrics approaches allows for characterizing the dynamics of molecular components within individual cells. This approach can serve as a valuable tool in investigating the resistance mechanism and metabolic heterogeneity of yeast cells during ethanol fermentation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The evolution of analytical techniques has opened the possibilities of accurate analyte detection through a straightforward method and short acquisition time, leading towards their applicability to identify medical conditions. Surface-enhanced Raman spectroscopy (SERS) has long been proven effective for rapid detection and relies on SERS spectra that are unique to each specific analyte. However, the complexity of viruses poses challenges to SERS and hinders further progress in its practical applications. The principle of SERS revolves around the interaction among substrate, analyte, and Raman laser, but most studies only emphasize the substrate, especially label-free methods, and the synergy among these factors is often ignored. Therefore, issues related to reproducibility and consistency of results, which are crucial for medical diagnosis and are the main highlights of this review, can be understood and largely addressed when considering these interactions. Viruses are composed of multiple surface components and can be detected by label-free SERS, but the presence of non-target molecules in clinical samples interferes with the detection process. Appropriate spectral data processing workflow also plays an important role in the interpretation of results. Furthermore, integrating machine learning into data processing can account for changes brought about by the presence of non-target molecules when analyzing spectral features to accurately group the data, for example, whether the sample corresponds to a positive or negative patient, and whether a virus variant or multiple viruses are present in the sample. Subsequently, advances in interdisciplinary fields can bring SERS closer to practical applications., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jiunn-Der Liao reports financial support was provided by National Science Council. Jiunn-Der Liao reports a relationship with National Cheng Kung University that includes: employment., (Copyright © 2024 Elsevier B.V. All rights reserved.)

L-cysteine, an indispensable amino acid present in natural proteins, plays pivotal roles in various biological processes. Consequently, precise and selective monitoring of its concentrations is imperative. Herein, we propose a Surface-enhanced Raman Scattering (SERS) sensor for detecting L-cysteine based on the anti-aggregation of 4-mercaptobenzoic acid (4-MBA) and histidine (His) functionalized silver nanoparticles (Ag NPs). The presence of Hg 2+ ions can induce the aggregation of Ag NPs@His@4-MBA due to the unique nanostructures of Ag NPs@His@4-MBA, resulting in a robust SERS intensity of 4-MBA. However, in the presence of L-cysteine, the stronger affinity between L-cysteine and Hg 2+ reduces the concentration of free Hg 2+ , causing the dispersion of the aggregated functionalized Ag NPs and the reduction of the SERS signal intensity of 4-MBA. The developed SERS platform demonstrates excellent performance with a low detection limit of 5 nM (S/N = 3) and linear detection capabilities within the range of 0.01-100 μM for L-cysteine. Additionally, the method was successfully employed for the determination of L-cysteine in spiked serum samples, yielding recoveries ranging from 95.0 % to 108.1 % with relative standard deviations of less than 3.3 %. This study not only presents a novel approach for fabricating highly sensitive and specific SERS biosensors for biomolecule detection but also offers a significant strategy for the development and construction of SERS substrates using anti-aggregation design., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Biomarker testing plays a crucial role in the early detection of liver cancer. Herein, we developed a dual-signal amplification approach utilizing magnetic aggregation and a recognition competition strategy for the simultaneous detection of alpha-fetoprotein (AFP) and manganese superoxide dismutase (MnSOD) in serum. 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 were synthesized by functionalizing Raman signaling molecules and aptamer complementary chains onto the surface of gold nanoparticles (AuNPs). The detection complex Raman signal molecule@AuNPs@H-Fe 3 O 4 @cDNA was assembled by conjugating 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 with two nucleic acid aptamers (cDNA1 and cDNA2) modified with Fe 3 O 4 through partial base complementary pairing. The target protein exhibited specific binding with the aptamer, leading to the competitive displacement of 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 from the Fe 3 O 4 array surface, consequently resulting in a reduction of the Surface-Enhanced Raman Spectroscopy (SERS) signal. Through this approach, the limit of detection (LOD) for AFP and MnSOD in serum was achieved at remarkably low levels of 5.89 pg/mL and 6.23 pg/mL, respectively, with a reaction incubation period of only 5 min. Finally, the platform was utilized for the quantification of AFP and MnSOD in the serum of a nude mouse hepatocellular carcinoma model. The results obtained through SERS were consistent with those from enzyme-linked immunosorbent assay (ELISA), validating its accuracy. This methodology presents a novel approach for the swift and concurrent detection of proteins, holding significant clinical promise for the early diagnosis of hepatocellular carcinoma., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Ankylosing spondylitis (AS), Osteoarthritis (OA), and Sjögren's syndrome (SS) are three prevalent autoimmune diseases. If left untreated, which can lead to severe joint damage and greatly limit mobility. Once the disease worsens, patients may face the risk of long-term disability, and in severe cases, even life-threatening consequences., Result: In this study, the Raman spectral data of AS, OA, and SS are analyzed to auxiliary disease diagnosis. For the first time, the Euclidean distance(ED) upscaling technique was used for the conversation from one-dimensional(1D) disease spectral data to two-dimensional(2D) spectral images. A dual-attention mechanism network was then constructed to analyze these two-dimensional spectral maps for disease diagnosis. The results demonstrate that the dual-attention mechanism network achieves a diagnostic accuracy of 100 % when analyzing 2D ED spectrograms. Furthermore, a comparison and analysis with s-transforms(ST), short-time fourier transforms(STFT), recurrence maps(RP), markov transform field(MTF), and Gramian angle fields(GAF) highlight the significant advantage of the proposed method, as it significantly shortens the conversion time while supporting disease-assisted diagnosis. Mutual information(MI) was utilized for the first time to validate the 2D Raman spectrograms generated, including ED, ST, STFT, RP, MTF, and GAF spectrograms. This allowed for evaluation of the similarity between the original 1D spectral data and the generated 2D spectrograms., Significant: The results indicate that utilizing ED to transform 1D spectral data into 2D images, coupled with the application of convolutional neural network(CNN) for analyzing 2D ED Raman spectrograms, holds great promise as a valuable tool in assisting disease diagnosis. The research demonstrated that the 2D spectrogram created with ED closely resembles the original 1D spectral data. This indicates that ED effectively captures key features and important information from the original data, providing a strong descript., Competing Interests: Declaration of competing interest All the authors declare that there is no potential conflict of interest referring to this article., (Copyright © 2024. Published by Elsevier B.V.)

Over the last 50 years resonance Raman spectroscopy has become an invaluable tool for the exploration of chromophores in biological macromolecules. Among them, heme proteins and metal complexes have attracted considerable attention. This interest results from the fact that resonance Raman spectroscopy probes the vibrational dynamics of these chromophores without direct interference from the surrounding. However, the indirect influence via through-bond and through-space chromophore-protein interactions can be conveniently probed and analyzed. This review article illustrates this point by focusing on class 1 cytochrome c, a comparatively simple heme protein generally known as electron carrier in mitochondria. The article demonstrates how through selective excitation of resonance Raman active modes information about the ligation, the redox state and the spin state of the heme iron can be obtained from band positions in the Raman spectra. The investigation of intensities and depolarization ratios emerged as tools for the analysis of in-plane and out-of-plane deformations of the heme macrocycle. The article further shows how resonance Raman spectroscopy was used to characterize partially unfolded states of oxidized cytochrome c. Finally, it describes its use for exploring structural changes due to the protein's binding to anionic surfaces like cardiolipin containing membranes., Competing Interests: Declaration of competing interest The author does not declare any conflict of interest., (Copyright © 2024 The Author. Published by Elsevier Inc. All rights reserved.)

Self-driven microfluidic systems have attracted significant attention and demonstrated great potential in the field of point-of-care (POC) testing due to their device simplicity, low power consumption, increased portability, and reduced sample consumption. To develop POC detection chips with diverse characteristics that meet different requirements, there is a strong demand for feasible strategies that enable easy operation and reduce processing time. Here, a one-step processing approach using femtosecond laser direct writing technology was proposed to fabricate a capillary-actuated POC microfluidic chip. The driving force of the chip is highly dependent on its surface wettability, which can be easily adjusted by changing the laser processing parameters. This POC microfluidic chip allowed for the detection of intracellular H 2 O 2 through a catalytic reaction system that incorporated 5-aminosalicylic acid -sensitized colloidal TiO 2 nanoparticles and horse radish peroxidase, with integrating semiconductor-based surface-enhanced Raman scattering (SERS) quantitative technique. The concentration of H 2 O 2 was determined by the SERS signal of the catalytic products in the microfluidic chip, resulting in rapid detection with minimal sample consumption. Our method provides a simple, feasible, and alternative strategy for POC testing of H 2 O 2 , with a linear range of 10 -2 ∼10 -6  M and a limit of detection of 0.55 μM. This approach was successfully applied to rapid detection of intracellular H 2 O 2 in MCF-7 breast cancer cells with high sensitivity and minimal sample consumption. Additionally, this study not only demonstrates the exceptional advantages of femtosecond laser processing technology in fabricating diverse microfluidic chips for various applications, but also presents an efficient POC testing strategy for detecting cell signaling molecules., Competing Interests: Declaration of competing interest This manuscript has not been published previously, that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Recently, solution-based surface-enhanced Raman scattering (SERS) detection technique has been widely recognized due to its cost-effectiveness, simplicity, and ease of use. However, solution-based SERS is limited for practical applications mainly because of the weak adsorption affinity of the target biomolecules to the surface of plasmonic nanoparticles. Herein, we developed a highly sensitive solution-based SERS sensing platform based on mercaptopropionic acid (MPA)-capped silver-coated gold nanostars (SGNS@MPA), which allows efficient enrichment on the nanostars surface for improved detection of an analyte: creatinine, a potential biomarker of chronic kidney disease (CKD). The SGNS@MPA exhibited high enrichment ability towards creatinine molecules in alkaline medium (pH-9) through multiple hydrogen bonding interaction, which causes aggregation of the nanoparticles and enhances the SERS signal of creatinine. The detection limit for creatinine was achieved at 0.1 nM, with a limit of detection (LOD) value of 14.6 pM. As a proof-of-concept demonstration, we conducted the first quantitative detection of creatinine in noninvasive human fluids, such as saliva and sweat, under separation-free conditions. We achieved a detection limit of up to 1 nM for both saliva and sweat, with LOD values as low as 0.136 nM for saliva and 0.266 nM for sweat. Overall, our molecular enrichment strategy offers a new way to improve the solution-based SERS detection technique for real-world practical applications in point-of-care settings and low-resource settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The COVID-19 pandemic presents global challenges, notably with co-infections in respiratory tract involving SARS-CoV-2 variants and influenza strains. Detecting multiple viruses simultaneously is crucial for accurate diagnosis, effective tracking infectious sources, and containment of the epidemic. This study uses a label-free surface-enhanced Raman spectroscopy (SERS) method using Au NPs/pZrO 2 (250) and FIB-made Au NRs (100) to detect dual viruses, including SARS-CoV-2 Delta variant (D) and influenza A (A) or B (B) virus. Results demonstrate distinct peaks facilitating virus differentiation, especially between D and A or B, with clear disparities between substrates; specific peaks at 950 and 1337 cm -1 are pivotal for discerning viruses using Au NPs/pZrO 2 (250), while those at 1050, 1394, and 1450 cm -1 and 1033, 1165, 1337, and 1378 cm -1 are key for validation using Au NRs (100). Differences in substrate surface morphology and spatial disposition of accommodating viruses significantly influence hotspot formation and Raman signal amplification efficiency, thereby affecting the ability to distinguish various viruses. Furthermore, both substrates offer insights, even in the presence of oxymetazoline hydrochloride (an interfering substance), with practical implications in viral diagnosis. The customized design and reproducibility underscore efficient Raman signal amplification, even in challenging environments, highlighting potential for widespread virus detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Herein, the surface-enhanced Raman scattering (SERS) platform was combined with an azo coupling reaction and an aluminum alloy covered with a hydrophobic layer of praseodymium oxide and stearic acid complexes for the detection of histamine. The praseodymium oxide on aluminum alloy was successfully synthesized by the rare-earth-salt-solution boiling bath method and modified by stearic acid. Its surface exhibits a water contact angle (WCA) of 125.0°. Through the azo derivatization reaction with 3-amino-5-mercapto-1,2,4-triazole (AMTA) diazonium salts, histamine can be converted into the derivatization product with higher Raman activity. The mixture of the derivatization product and β-cyclodextrin-modified Ag nanoparticles (β-CD-AgNPs) were dropped onto the surface of an aluminum alloy covered with a hydrophobic layer of praseodymium oxide and stearic acid complexes, and dried for SERS measurement. The intensity ratio between the SERS peaks at 1246 cm -1 and 1104 cm -1 (I 1246 /I 1104 ) of the derivatization product was used for the quantification of histamine. Under the selected conditions, the limit of detection (LOD) and the limit of quantification (LOQ) for this method were 7.2 nM (S/N = 3) and 24 nM (S/N = 10), respectively. The relative standard deviation (RSD) of this method for the determination of 1 μM histamine was 6.1 % (n = 20). The method was also successfully used for the determination of histamine in fish samples with recoveries ranging from 92 % to 111 %. The present method is simple, sensitive, reliable, and may provide a new approach for preparing the composite hydrophobic layer that can enhance SERS signals through hydrophobic condensation effect. Meanwhile, it may have a promising future in the determination of small molecular compounds containing an imidazole ring., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The spread of the SARS-CoV-2 virus has had an unprecedented impact, both by posing a serious risk to human health and by amplifying the burden on the global economy. The rapid identification of the SARS-CoV-2 virus has been crucial to preventing and controlling the spread of SARS-CoV-2 infections. In this study, we propose a multilayered plasmonic nanotrap (MPNT) device for the rapid identification of single particles of SARS-CoV-2 virus in ultra-high sensitivity by surface-enhanced Raman scattering (SERS). The MPNT device is composed of arrays of concentric cylindrical cavities with Ag/SiO 2 /Ag multilayers deposited on the top and at the bottom. By varying the diameter of the cylinders and the thickness of the multilayers, the resonant optical absorption and local electric field were optimized. The SERS enhancement factors of the proposed device are of the order of 10 8 , which enable the rapid identification of SARS-CoV-2 N protein in concentrations as low as 1.25 × 10 -15 －12.5 × 10 -15  g mL -1 within 1 min. The developed MPNT SERS device provides a label-free and rapid detection platform for SARS-CoV-2 virus. The general nature of the device makes it equally suitable to detect other infectious viruses., Competing Interests: Declaration of competing interest The authors declared no potential conflicts of interest with respect to the research, author-ship, and/or publication of this article., (Copyright © 2024. Published by Elsevier B.V.)

Bacteria infections pose a serious threat to public health, and it is urgent to develop facile and accurate detection methods. To meet the important need, a potable and high-sensitive surface enhanced Raman scattering (SERS) biosensor based on aptamer recognition and catalytic hairpin assembly (CHA) signal amplification was proposed for point-of-care detection of Staphylococcus aureus (S. aureus). The SERS biosensor contains three parts: recognition probes, SERS sensing chip, and SERS tags. The feasibility of the strategy was verified by gel electrophoresis, and the one-step test route was optimized. The bacteria SERS biosensor has a good linear relationship ranging from 10 to 10 7  CFU mL -1 with high sensitivity low to 5 CFU mL -1 , and shows excellent specificity, uniformity, and repeatability on S. aureus identification and enumeration, which can distinguish S. aureus from other bacteria. The SERS biosensor shows a good recovery rate (95.73 %-109.65 %) for testing S. aureus spiked in milk, and has good practicability for detecting S. aureus infected mouse wound, which provides a facile and reliable approach for detection of trace bacteria in the real samples., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Accurate and sensitive determination of human immunoglobulin G (HIgG) level is critical for diagnosis and treatment of various diseases, including rheumatoid arthritis, humoral immunodeficiencies, and infectious disease. In this study, versatile tri-signal probes were developed by preparing CdS@SiO 2 nanorods that integrate photoluminescence (PL), multi-phonon resonant Raman scattering (MRRS) and infrared absorption (IRA) properties. Through the coating of multiple CdS nanoparticles as cores within SiO 2 shells, the PL and MRRS properties of CdS were improved, resulting in a significantly lowered limit of detection (LOD), with the lowest LOD of 12.37 ag mL -1 . Integration with the distinctive IRA property of SiO 2 shells widened the detection range towards higher concentrations, establishing a final linear range of 50 ag mL -1 to 10 μg mL -1 . The remarkable consistency among the three signals highlighted the robust internal verification capability for accurate detection. This approach enhances flexibility in selecting detection methodologies to suit diverse scenarios, facilitating HIgG detection. The tri-signal nanoprobes also exhibited excellent detection selectivity, specificity and repeatability. This study presents a fresh idea for developing high-performance detection strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The high efficient surface-enhanced Raman scatterring (SERS) methods to detect thiacloprid and imidacloprid were established using ZIF-8-wrapped Ag nanoparticles (AgNPs) modified with β-cyclodextrin (β-CD). The substrate of ZIF-8/β-CD@AgNPs was characterized by ultraviolet visible spectra (UV-vis), thermogravimetric analysis (TGA), X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). The interaction between the substrate and thiacloprid/imidacloprid was also explored. The optimum measurement conditions were obtained by response surface model based on single-factor experiments. Enhancement factors (EFs) of thiacloprid and imidacloprid were respectively 2.29 × 10 6 and 2.60 × 10 6 . A good linearity between the scattering intensity and the concentration of thiacloprid/imidacloprid within 3-1000 nmol L -1 /6-400 nmol L -1 was established. The interference experiments indicated that the methods had good selectivity. The SERS methods were successfully applied to detect thiacloprid and imidacloprid in several vegetables samples. The recoveries ranged from 95.5 % to 105 % (n = 5). The detection limits (LODs) (S/N = 3) for thiacloprid and imidacloprid were 1.50 and 0.83 nmol L -1 , respectively., Competing Interests: Declaration of competing interest We would like to submit the enclosed manuscript entitled “ZIF-8-wrapped AgNPs modified with β-cyclodextrin for sensitive detection of thiacloprid and imidacloprid by SERS technology”, which we wish to be considered for publication in “Talanta”. No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part., (Copyright © 2024 Elsevier B.V. All rights reserved.)

There is an ongoing effort in the US illicit drug market to make new psychoactive compounds more potent and addictive. Due to continuous chemical modifications, many fentanyl analogs are developed and mixed with more traditional illicit drugs, such as cocaine and heroin. Detecting fentanyl and fentanyl analogs in these illicit drug mixtures has become more crucial because of the increased potency and associated health risks. Most confirmatory procedures require time-consuming and expensive, highly sophisticated laboratory equipment and experimental procedures, which can delay critical information that might save a victim or find a suspect. In this study, we propose miniaturizing and accelerating this process by combining surface-enhanced Raman spectroscopy (SERS) analysis and paper spray mass spectrometry (PS-MS). For this aim, dual-purposed paper substrates were developed through soaking in Au/Ag nanostars suspensions. These novel, in-house prepared paper SERS substrates showed stability for up to four weeks with and without the presence of drug compounds. Fentanyl analogs with similar SERS spectra were differentiated by coupling with PS-MS. The limit of detection (LOD) for fentanyl on the paper substrates is 34 μg/mL and 0.32 μg/mL for SERS and PS-MS, respectively. Fentanyl and fentanyl analogs show selective SERS enhancement that helped to detect trace amounts of these opioids in heroin and cocaine street samples. In short, we propose the combination of SERS/PS-MS by using modified paper substrates to develop cost-effective, sensitive, rapid, portable, reliable, and reproducible methods to detect illicit drugs, especially trace amounts of fentanyl and fentanyl analogs in illicit drug mixtures. The combination of these two category A techniques allows for the identification of illicit drugs according to the SWGDRUG guidelines., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

SERS (Surface Enhanced Raman Spectroscopy) is a new Raman spectroscopy which relies on Surface Plasmon Resonance (SPR) of metal nanoparticles. We have applied colloidal silver and gold nanoparticles as amplifier agents to enhance nucleotide Raman signals. It is observed that without these enhancing agents, it is impossible to investigate nucleotide spectrum due to weak Raman signals. Interaction mechanism of Melphalan, an anticancer drug with four nucleotides (Adenine, Cytosine, Guanine, Thymine) was investigated using SERS to detect and identify changes due to alkylating process in Raman spectra. After incubating Melphalan drug with nucleotides for 24 h at 37 °C, some changes occurred in SERS spectrum and interpretation of SERS spectra revealed the influence of the alkyl substitution on peaks and Raman shifts. After incubation of Melphalan with each nucleotide, intensity of relevant SERS signals assigned to Amid III group of Cytosine and Amid I of Thymine decreased significantly, confirming alkylating taking place. In this study, we also investigated the effect of nanoparticles type on nucleotide spectrum. We could not obtain useful information in the cases of guanine nucleotide. The SERS spectrum of Cytosine as an example of nucleotides in aqueous solution compared to solid state and results demonstrated that in solid state better signals were obtained than in liquid state., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Over the years, osteosarcoma therapy has had a significative improvement with the use of a multidrug regime strategy, increasing the survival rates from less than 20 % to circa 70 %. Different types of development of new antineoplastic agents are critical to achieve irreversible damage to cancer cells, while preserving the integrity of their healthy counterparts. In the present study, complexes with two and three Pd(II) centres linked by the biogenic polyamines: spermine (Pd 2 SpmCl 4 ) and spermidine (Pd 3 Spd 2 Cl 6 ) were tested against non-malignant (osteoblasts, HOb) and cancer (osteosarcoma, MG-63) human cell lines. Either alone or in combination according to the EURAMOS-1 protocol, they were used versus cisplatin as a drug reference. By evaluating the cytotoxic effects of both therapeutic approaches (single and drug combination) in HOb and MG-63 cell lines, the selective anti-tumoral potential is assessed. To understand the different treatments at a molecular level, Synchrotron Radiation Fourier Transform Infrared and Raman microspectroscopies were applied. Principal component analysis and hierarchical cluster analysis are applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug-to-cell impact. The main changes were observed for the B-DNA native conformation to either Z-DNA (higher in the presence of polynuclear complexes) or A-DNA (preferably after cisplatin exposure). Additionally, a higher effect upon variation in proteins content was detected in drug combination when compared to single drug administration proving the efficacy of the EURAMOS-1 protocol with the new drugs tested., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific "metabolic fingerprints" of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are "in-house" protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm -1 , primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)