UV-Vis quantification of the iron content in iteratively steam and HCl purified single-walled carbon nanotubes.

As-produced carbon nanotubes contain impurities which can dominate the properties of the material and are thus undesired. Herein we present a multi-step purification treatment that combines the use of steam and hydrochloric acid in an iterative manner. This allows the reduction of the iron content down to 0.2 wt. % in samples of single-walled carbon nanotubes (SWCNTs). Remarkably, Raman spectroscopy analysis reveals that this purification strategy does not introduce structural defects into the SWCNTs' backbone. To complete the study, we also report on a simplified approach for the quantitative assessment of iron using UV-Vis spectroscopy. The amount of metal in SWCNTs is assessed by dissolving in HCl the residue obtained after the complete combustion of the sample. This leads to the creation of hexaaquairon(III) chloride which allows the determination of the amount of iron, from the catalyst, by UV-Vis spectroscopy. The main advantage of the proposed strategy is that it does not require the use of additional complexing agents.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Molecular Design Using Selected Concentration Effects in Optically Activated Fluorescent Matrices.

Molecular physics plays a pivotal role in various fields, including medicine, pharmaceuticals, and broader industrial applications. This study aims to enhance the methods for producing specific optically active materials with distinct spectroscopic properties at the molecular level, which are crucial for these sectors, while prioritizing human safety in both production and application. Forensic science, a significant socio-economic field, often employs hazardous substances in analyzing friction ridges on porous surfaces, posing safety concerns. In response, we formulated novel, non-toxic procedures for examining paper evidence, particularly thermal papers. Our laboratory model utilizes a polyvinyl alcohol polymer as a rigid matrix to emulate the thermal paper's environment, enabling precise control over the spectroscopic characteristics of 1,8-diazafluoro-9-one (DFO). We identified and analyzed the cyclodimer 1,8-diazafluoren-9-one (DAK DFO), which is a non-toxic and biocompatible alternative for revealing forensic marks. The reagents used to preserve fingerprints were optimized for their effectiveness and stability. Using stationary absorption and emission spectroscopy, along with time-resolved emission studies, we verified the spectroscopic attributes of the new structures under deliberate aggregation conditions. Raman spectroscopy and quantum mechanical computations substantiated the cyclodimer's configuration. The investigation provides robust scientific endorsement for the novel compound and its structural diversity, influenced by the solvatochromic sensitivity of the DFO precursor. Our approach to monitoring aggregation processes signifies a substantial shift in synthetic research paradigms, leveraging simple chemistry to yield an innovative contribution to forensic science methodologies.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer.

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm-1 (FTIR) and at 2944 cm-1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm-1, 2974 cm-1 (FTIR) and 1714 cm-1, 2817 cm-1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.

A sandwich-structured multifunctional platform based on self-assembled Ti3C2Tx@Au NPs films, antibiotics, and silent region SERS probe for the capture, determination, and drug resistance analysis of Gram-positive bacteria.

A multifunctional surface-enhanced Raman scattering (SERS) platform integrating sensitive detection and drug resistance analysis was developed for Gram-positive bacteria. The substrate was based on self-assembled Ti3C2Tx@Au NPs films and capture molecule phytic acid (IP6) to achieve specific capture of Gram-positive bacteria and different bacteria were analyzed by fingerprint signal. It had advantages of good stability and homogeneity (RSD = 8.88%). The detection limit (LOD) was 102 CFU/mL for Staphylococcus aureus and 103 CFU/mL for MRSA, respectively. A sandwich structure was formed on the capture substrate by signal labels prepared by antibiotics (penicillin G and vancomycin) and non-interference SERS probe molecules (4-mercaptobenzonitrile (2223 cm-1) and 2-amino-4-cyanopyridine (2240 cm-1)) to improve sensitivity. The LOD of Au NPs@4-MBN@PG to S. aureus and Au NPs@AMCP@Van to MRSA and S. aureus were all improved to 10 CFU/mL, with a wide dynamic linear range from 108 to 10 CFU/mL (R2 ≥ 0.992). The SERS platform can analyze the drug resistance of drug-resistant bacteria. Au NPs@4-MBN@PG was added to the substrate and captured MRSA to compare the SERS spectra of 4-MBN. The intensity inhomogeneity of 4-MBN at the same concentrations of MRSA and the nonlinearity at the different concentrations of MRSA revealed that MRSA was resistant to PG. Finally, the SERS platform achieved the determination of MRSA in blood. Therefore, this SERS platform has great significance for the determination and analysis of Gram-positive bacteria.

In situ monitoring of loading and elution processes of cryoprotectants at the single-cell level with Raman spectroscopy.

BACKGROUND: The analysis of cell membrane permeability plays a crucial role in improving the procedures of cell cryopreservation, which will affect the specific parameter settings in loading, removal and cooling processes. However, existing studies have mostly focused on deriving permeability parameters through osmotic theoretical models and cell volume response analysis, and there is still a lack of the direct experimental evidence and analysis at the single-cell level regarding the migration of cryoprotectants. RESULTS: In this work, a side perfusion microfluidics chips combined with Raman spectroscopy system was built to monitor in situ the Raman spectroscopy of extracellular and intracellular solution during loading and elution process with different cryoprotectant solution systems (single and dual component). And it was found that loading a high concentration cryoprotectant solution system through a single elution cycle may result in significant residual protective agent, which can be mitigated by employing a multi-component formula but multiple elution operations are still necessary. Furthermore, the collected spectral signals were marked and analyzed to was perform preliminary relative quantitative analysis. The results showed that the intracellular concentration changes can be accurately quantified by the Raman spectrum and are closely related to the extracellular solution concentration changes. SIGNIFICANCE AND NOVELTY: By using the method of small flow perfusion (≤20 µL/min) in the side microfluidic chip after the gravity sedimentation of cells, the continuous loading and elution process of different cryoprotectants on chip and the spectral acquisition can be realized. The intracellular and extracellular concentrations can be quantified in situ based on the ratio of spectral peak intensities. These results indicate that spectroscopic analysis can be used to effectively monitor intracellular cryoprotectant residues.

Kidney stone growth through the lens of Raman mapping.

Bulk composition of kidney stones, often analyzed with infrared spectroscopy, plays an essential role in determining the course of treatment for kidney stone disease. Though bulk analysis of kidney stones can hint at the general causes of stone formation, it is necessary to understand kidney stone microstructure to further advance potential treatments that rely on in vivo dissolution of stones rather than surgery. The utility of Raman microscopy is demonstrated for the purpose of studying kidney stone microstructure with chemical maps at ≤ 1 µm scales collected for calcium oxalate, calcium phosphate, uric acid, and struvite stones. Observed microstructures are discussed with respect to kidney stone growth and dissolution with emphasis placed on < 5 µm features that would be difficult to identify using alternative techniques including micro computed tomography. These features include thin concentric rings of calcium oxalate monohydrate within uric acid stones and increased frequency of calcium oxalate crystals within regions of elongated crystal growth in a brushite stone. We relate these observations to potential concerns of clinical significance including dissolution of uric acid by raising urine pH and the higher rates of brushite stone recurrence compared to other non-infectious kidney stones.

Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate.

BACKGROUND: Bacterial spores are the main potential hazard in medium- and high-temperature sterilized meat products, and their germination and subsequent reproduction and metabolism can lead to food spoilage. Moreover, the spores of some species pose a health and safety threat to consumers. The rapid detection, prevention, and control of bacterial spores has always been a scientific problem and a major challenge for the medium and high-temperature meat industry. Early and sensitive identification of spores in meat products is a decisive factor in contributing to consumer health and safety. RESULTS: In this study, we developed a novel and stable Ag@AuNP array substrate by using a two-step synthesis approach and a liquid-interface self-assembly method that can directly detect bacterial spores in actual meat product samples without the need for additional in vitro bacterial culture. The results indicate that the Ag@AuNP array substrate exhibits high reproducibility and Raman enhancement effects (1.35 × 105). The differentiation in the Surface enhanced Raman scattering (SERS) spectra of five bacterial spores primarily arises from proteins in the spore coat and inner membrane, peptidoglycan of cortex, and Ca2⁺-DPA within the spore core. The correct recognition rate of linear discriminant analysis for spores in the meat product matrix can reach 100 %. The average recovery accuracy of the SERS quantitative model was at around 101.77 %, and the limit of detection can reach below 10 CFU/mL. SIGNIFICANCE: It provides a promising technological strategy for the characteristic substance analysis and timely monitoring of spores in meat products.

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

Surface-Area Enhanced Raman Spectroscopy of DNA in Porous Silica: A Quantitative and Reproducible Alternative to Plasmonic-Based SERS.

Despite the success of surface-enhanced Raman spectroscopy (SERS) for detecting DNA immobilized on plasmonic metal surfaces, its quantitative response is limited by the rapid falloff of enhancement with distance from the metal surface and variations in sensitivity that depend on orientation and proximity to plasmonic "hot spots". In this work, we assess an alternative approach for enhancing detection by immobilizing DNA on the interior surfaces of porous silica particles. These substrates provide over a 1000-fold greater surface area for detection compared to a planar support. The porous silica substrate is a purely dielectric material with randomly oriented internal surfaces, where scattering is independent of proximity and orientation of oligonucleotides relative to the silica surface. We characterize the quantitative response of Raman scattering from DNA in porous silica particles with sequences used in previous SERS investigations of DNA for comparison. The results show that Raman scattering of DNA in porous silica is independent of distance of nucleotides from the silica surface, allowing detection of longer DNA strands with constant sensitivity. The surface area enhancement within particles is reproducible (<4% particle-to-particle variation) owing to the uniform internal pore structure and surface chemistry of the silica support. DNA immobilization with a bis-thiosuccinimide linker provides a Raman-active internal standard for quantitative interpretation of Raman scattering results. Despite the high (30 mM) concentrations of immobilized DNA within porous silica particles, they can be used to measure nanomolar binding affinities of target molecules to DNA by equilibrating a very small number of particles with a sufficiently large volume of low-concentration solution of target molecules.

Relationships between conformational and vibrational features of tryptophan characteristic Raman markers.

Tryptophan (Trp) residue provides characteristic vibrational markers to the middle wavenumber spectral region of the Raman spectra recorded from peptides and proteins. In this report, we were particularly interested in eight Trp Raman markers, referred to as Wi (i = 1,…,8). All responsible for pronounced Raman lines, these markers originate from indole moiety, a bicyclic conjugated segment involved in the Trp structure. Numerous investigations have previously attempted to relate the variations observed in the spectral features of these markers to the environmental changes of Trp residues. To emphasize the most important points we can mention (i) the variations in the Raman profile of W4 (∼1360 cm-1) and W5 (∼1340 cm-1), frequently observed as a doublet with variable intensity ratio. These two markers were thought to result from a Fermi-resonance effect between certain planar and nonplanar modes; (ii) the changes observed in the wavenumbers and relative intensities of W4, W7 (∼880 cm-1) and W8 (∼760 cm-1) were supposed to be related to the accessibility of Trp to surrounding water molecules; and (iii) the wavenumber fluctuations of W3 (∼1550 cm-1), taken as a Trp side chain orientational marker. However, some ambiguities still exist regarding the interpretation of these markers, needing further clarification. Herein, upon a joint experimental and theoretical analysis based on a multiconformational approach, attention was paid to the relationships between structural and vibrational features of three indole-containing compounds with increasing structural complexity, i.e., skatole (3-methylindole), tryptophan, and tripeptide Gly-Trp-Gly. This study clearly shows that the existing assignments given to certain Trp Raman markers should be reconsidered, especially those based on the Fermi-resonance origin of W4-W5 (∼1360-1340 cm-1) doublet, as well as the purely environmental dependence of W7 and W8 markers.

Interface-constrained catalytic hairpin assembly permits highly sensitive SERS signaling of miRNA.

The rapid and precise monitoring of peripheral blood miRNA levels holds paramount importance for disease diagnosis and treatment monitoring. In this study, we propose an innovative research strategy that combines the catalytic hairpin assembly reaction with SERS signal congregation and enhancement. This combination can significantly enhance the stability of SERS detection, enabling stable and efficient detection of miRNA. Specifically, our paper-based SERS detection platform incorporates a streptavidin-modified substrate, biotin-labeled catalytic hairpin assembly reaction probes, 4-ATP, and primer-co-modified gold nanoparticles. In the presence of miRNA, the 4-ATP and primer-co-modified gold nanoparticles can specifically recognize the miRNA and interact with the biotin-labeled CHA probes to initiate an interfacial catalytic hairpin assembly reaction. This enzyme-free high-efficiency catalytic process can accumulate a large amount of biotin on the gold nanoparticles, which then bind to the streptavidin on the substrate with the assistance of the driving liquid, forming red gold nanoparticle stripes. These provide a multitude of hotspots for SERS, enabling enhanced signal detection. This innovative design achieves a low detection limit of 3.47 fM while maintaining excellent stability and repeatability. This conceptually innovative detection platform offers new technological possibilities and solutions for clinical miRNA detection.

Optical biosensors for diagnosis of COVID-19: nanomaterial-enabled particle strategies for post pandemic era.

The COVID-19 pandemic underlines the need for effective strategies for controlling virus spread and ensuring sensitive detection of SARS-CoV-2. This review presents the potential of nanomaterial-enabled optical biosensors for rapid and low-cost detection of SARS-CoV-2 biomarkers, demonstrating a comprehensive analysis including colorimetric, fluorescence, surface-enhanced Raman scattering, and surface plasmon resonance detection methods. Nanomaterials including metal-based nanomaterials, metal-organic frame-based nanoparticles, nanorods, nanoporous materials, nanoshell materials, and magnetic nanoparticles employed in the production of optical biosensors are presented in detail. This review also discusses the detection principles, fabrication methods, nanomaterial synthesis, and their applications for the detection of SARS-CoV-2 in four categories: antibody-based, antigen-based, nucleic acid-based, and aptamer-based biosensors. This critical review includes reports published in the literature between the years 2021 and 2024. In addition, the review offers critical insights into optical nanobiosensors for the diagnosis of COVID-19. The integration of artificial intelligence and machine learning technologies with optical nanomaterial-enabled biosensors is proposed to improve the efficiency of optical diagnostic systems for future pandemic scenarios.

Temperature-Caused Changes in Raman Pattern and Protein Profiles of Winter Triticale (x Triticosecale, Wittm.) Field-Grown Seedlings.

Climate change, which causes periods with relatively high temperatures in winter in Poland, can lead to a shortening or interruption of the cold hardening of crops. Previous research indicates that cold acclimation is of key importance in the process of acquiring cereal tolerance to stress factors. The objective of this work was to verify the hypothesis that both natural temperature fluctuations and the plant genotype influence the content of metabolites as well as proteins, including antioxidant enzymes and photosystem proteins. The research material involved four winter triticale genotypes, differing in their tolerance to stress under controlled conditions. The values of chlorophyll a fluorescence parameters and antioxidant activity were measured in their seedlings. Subsequently, the contribution of selected proteins was verified using specific antibodies. In parallel, the profiling of the contents of chlorophylls, carotenoids, phenolic compounds, and proteins was carried out by Raman spectroscopy. The obtained results indicate that a better PSII performance along with a higher photosystem II proteins content and thioredoxin reductase abundance were accompanied by a higher antioxidant activity in the field-grown triticale seedlings. The Raman studies showed that the cold hardening led to a variation in photosynthetic dyes and an increase in the phenolic to carotenoids ratio in all DH lines.

Multiplex detection of bacterial pathogens by PCR/SERS assay.

Bacterial infections are a leading cause of death globally. The detection of DNA sequences correlated to the causative pathogen has become a vital tool in medical diagnostics. In practice, PCR-based assays for the simultaneous detection of multiple pathogens currently rely on probe-based quantitative strategies that require expensive equipment but have limited sensitivity or multiplexing capabilities. Hence, novel approaches to address the limitations of the current gold standard methods are still in high demand. In this study, we propose a simple multiplex PCR/SERS assay for the simultaneous detection of four bacterial pathogens, namely P. aeruginosa, S. aureus, S. epidermidis, and M. smegmatis. Wherein, specific primers for amplifying each target gDNA were applied, followed by applying SERS nanotags functionalized with complementary DNA probes and Raman reporters for specific identification of the target bacterial pathogens. The PCR/SERS assay showed high specificity and sensitivity for genotyping bacterial pathogen gDNA, whereby as few as 100 copies of the target gDNA could be detected. With high sensitivity and the convenience of standard PCR amplification, the proposed assay shows great potential for the sensitive detection of multiple pathogen infections to aid clinical decision-making.

In situ monitoring of the shikimate pathway: a combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging.

Sensing and visualization of metabolites and metabolic pathways in situ are significant requirements for tracking their spatiotemporal dynamics in a non-destructive manner. The shikimate pathway is an important cellular mechanism that leads to the de novo synthesis of many compounds containing aromatic rings of high importance such as phenylalanine, tyrosine, and tryptophan. In this work, we present a cost-effective and extraction-free method based on the principles of stable isotope-coupled Raman spectroscopy and hyperspectral Raman imaging to monitor and visualize the activity of the shikimate pathway. We also demonstrated the applicability of this approach for nascent aromatic amino acid localization and tracking turnover dynamics in both prokaryotic and eukaryotic model systems. This method can emerge as a promising tool for both qualitative and semi-quantitative in situ metabolomics, contributing to a better understanding of aromatic ring-containing metabolite dynamics across various organisms.

Biochemical changes in lipid and protein metabolism caused by mannose-Raman spectroscopy studies.

Biochemical analysis of human normal bronchial cells (BEpiC) and human cancer lung cells (A549) has been performed by using Raman spectroscopy and Raman imaging. Our approach provides a biochemical compositional mapping of the main cell components: nucleus, mitochondria, lipid droplets, endoplasmic reticulum, cytoplasm and cell membrane. We proved that Raman spectroscopy and Raman imaging can distinguish successfully BEpiC and A549 cells. In this study, we have focused on the role of mannose in cancer development. It has been shown that changes in the concentration of mannose can regulate some metabolic processes in cells. Presented results suggest lipids and proteins can be considered as Raman biomarkers during lung cancer progression. Analysis obtained for bands 1444 cm-1, and 2854 cm-1 characteristic for lipids and derivatives proved that the addition of mannose reduced levels of these compounds. Results obtained for protein compounds based on bands 858 cm-1, 1004 cm-1 and 1584 cm-1 proved that the addition of mannose increases the values of protein in BEpiC cells and blocks protein glycolisation in A549 cells. Noticing Raman spectral changes in BEpiC and A549 cells supplemented with mannose can help to understand the mechanism of sugar metabolism during cancer development and could play in the future an important role in clinical treatment.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Advances in uremic toxin detection and monitoring in the management of chronic kidney disease progression to end-stage renal disease.

Patients with end-stage kidney disease (ESKD) rely on dialysis to remove toxins and stay alive. However, hemodialysis alone is insufficient to completely remove all/major uremic toxins, resulting in the accumulation of specific toxins over time. The complexity of uremic toxins and their varying clearance rates across different dialysis modalities poses significant challenges, and innovative approaches such as microfluidics, biomarker discovery, and point-of-care testing are being investigated. This review explores recent advances in the qualitative and quantitative analysis of uremic toxins and highlights the use of innovative methods, particularly label-mediated and label-free surface-enhanced Raman spectroscopy, primarily for qualitative detection. The ability to analyze uremic toxins can optimize hemodialysis settings for more efficient toxin removal. Integration of multiple omics disciplines will also help identify biomarkers and understand the pathogenesis of ESKD, provide deeper understanding of uremic toxin profiling, and offer insights for improving hemodialysis programs. This review also highlights the importance of early detection and improved understanding of chronic kidney disease to improve patient outcomes.