Smartphone-based low-cost and rapid quantitative detection of urinary creatinine with the Tyndall effect.

This research aims to develop a robust and quantitative method for measuring creatinine levels by harnessing the enhanced Tyndall effect (TE) phenomenon. The envisioned sensing assay is designed for practical deployment in resource-limited settings or homes, where access to advanced laboratory facilities is limited. Its primary objective is to enable regular and convenient monitoring of renal healthcare, particularly in cases involving elevated creatinine levels. The creatinine sensing strategy is achieved based on the aggregation of gold nanoparticles (AuNPs) triggered via the direct crosslinking reaction between creatinine and AuNPs, where an inexpensive laser pointer was used as a handheld light source and a smartphone as a portable device to record the TE phenomenon enhanced by the creatinine-induced aggregation of AuNPs. After evaluation and optimization of parameters such as AuNP concentrations and TE measurement time, the subsequent proof-of-concept experiments demonstrated that the average gray value change of TE images was linearly related to the logarithm of creatinine concentrations in the range of 1-50 µM, with a limit of detection of 0.084 µM. Meanwhile, our proposed creatinine sensing platform exhibited highly selective detection in complex matrix environments. Our approach offers a straightforward, cost-effective, and portable means of creatinine detection, presenting an encouraging signal readout mechanism suitable for point-of-care (POC) applications. The utilization of this assay as a POC solution exhibits potential for expediting timely interventions and enhancing healthcare outcomes among individuals with renal health issues.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

Silver nanocube coupling with a nanoporous silver film for dual-molecule recognition based ultrasensitive SERS detection of dopamine.

Dopamine (DA) is one of the catecholamine neurotransmitters used for the treatment of neural disorders. In this study, a novel sensor based on surface-enhanced Raman scattering (SERS) with dual molecule-recognition for ultrasensitive detection of DA was presented, with a limit of detection (LOD) of 40 fM, without any pretreatment of clinical samples. To realize the sensitive and selective detection of DA in complex samples, the nanoporous silver film (AgNF) surfaces were functionalized with mercaptopropionic acid (MPA) to accurately capture DA, while silver nanocubes (AgNCs) were modified with 4-mercaptobenzene boronic acid (4-MPBA) as a Raman reporter for the quantitative detection of DA. The nanogaps between AgNCs and the AgNF led to the generation of an abundance of hot spots for the SERS signal and thus effectively improved the sensitivity of DA detection. Measurements of DA concentrations in clinical body fluids such as human serum and urine samples are also demonstrated, showing excellent performance for DA detection in a complex environment. Our results demonstrate the promising potential for the ultrasensitive detection of DA for the potential diagnosis of DA-related diseases.

Rapid, Label-free Optical Spectroscopy Platform for Diagnosis of Heparin-Induced Thrombocytopenia.

The use of surface-enhanced Raman spectroscopy (SERS) to determine spectral markers for the diagnosis of heparin-induced thrombocytopenia (HIT), a difficult-to-diagnose immune-related complication that often leads to limb ischemia and thromboembolism, is proposed. The ability to produce distinct molecular signatures without the addition of labels enables unbiased inquiry and makes SERS an attractive complementary diagnostic tool. A capillary-tube-derived SERS platform offers ultrasensitive, label-free measurement as well as efficient handling of blood serum samples. This shows excellent reproducibility, long-term stability and provides an alternative diagnostic rubric for the determination of HIT by leveraging machine-learning-based classification of the spectroscopic data. We envision that a portable Raman instrument could be combined with the capillary-tube-based SERS analytical tool for diagnosis of HIT in the clinical laboratory, without perturbing the existing diagnostic workflow.

Label-free optical sensor based on red blood cells laser tweezers Raman spectroscopy analysis for ABO blood typing.

The clinical significance of ABO blood typing extends beyond transfusion medicine and is demonstrated to be associated with susceptibility to various diseases, even including cancer. In this study, a home-made laser tweezers Raman spectroscopy (LTRS) system was applied to detect red blood cells (RBCs) with the aim to develop a label-free, simple and objective blood typing method for the first time. High-quality Raman spectra of RBCs in the fingerprint region of 420-1700 cm-1 can be obtained, meanwhile exciting blood typing results can be achieved, especially with an accuracy of 100% for identifying Type AB from other blood types with the use of multivariate statistical analysis based on principal component analysis (PCA) combined with linear discriminant analysis (LDA). This primary work demonstrates that the label-free RBCs LTRS analysis in conjunction with PCA-LDA diagnostic algorithms has great potential as a biosensor for ABO blood typing.

[Studies of Male Fertility with Raman Spectroscopy].

Raman spectroscopy which belongs to scattering spectroscopy obtained molecular vibrational and rotational information to achieve detection and analysis of molecular structure and corresponding changes through recording the frequency shift when light interacted with materials. Compared with routine biochemical analysis, Raman spectroscopy has the advantage of non-invasive, label-free and no sample requirement. Raman spectroscopy has been widely applied in biomedical field such as human tissue, organs, cells and human body fluids for disease diagnosis. This article mainly focuses on recent research advances of Raman spectroscopy in human semen. Firstly, Raman spectroscopy(including surface-enhanced Raman spectroscopy, SERS) employed in forensic science for semen analysis, and some related data processing methods were introduced, then Raman spectroscopy involved investigations of male fertility was highlighted, more specifically, the Raman-based qualitative and quantitative analysis which assist the objective detection and evaluation of male fertility. Furthermore, studies of single sperm cell based on micro-Raman system to characterize and evaluate sperm quality and the preliminarily obtained Raman biomarkers which indicate high-quality sperm cell were introduced. Finally, the potential development of Raman spectroscopy involved in reproduction and fertility field was also discussed.

Ultrasound-mediated method for rapid delivery of nano-particles into cells for intracellular surface-enhanced Raman spectroscopy and cancer cell screening.

Surface-enhanced Raman spectroscopy (SERS) is a powerful technology for providing finger-printing information of cells. A big challenge has been the long time duration and inefficient uptake of metal nano-particles into living cells as substrate for SERS analysis. Herein, a simple method (based on ultrasound) for the rapid transfer of silver nanoparticles (NPs) into living cells for intracellular SERS spectroscopy was presented. In this study, the ultrasound-mediated method for NP delivery overcame the shortcoming of 'passive uptake', and achieved quick acquisition of reproducible SERS spectra from living human nasopharyngeal carcinoma cell lines (C666 and CNE1) and normal nasopharyngeal cell line (NP69). Tentative assignment of the Raman bands in the measured SERS spectra showed cancer cell specific biomolecular differences, including significantly lower DNA concentrations and higher protein concentrations in cancerous nasopharyngeal cells as compared to those of normal cells. Combined with PCA-LDA multivariate analysis, ultrasound-mediated cell SERS spectroscopy differentiated the cancerous cells from the normal nasopharyngeal cells with high diagnostic accuracy (98.7%), demonstrating great potential for high-throughput cancer cell screening applications.

Phase and Texture Characterizations of Scar Collagen Second-Harmonic Generation Images Varied with Scar Duration.

This work developed a phase congruency algorithm combined with texture analysis to quantitatively characterize collagen morphology in second-harmonic generation (SHG) images from human scars. The extracted phase and texture parameters of the SHG images quantified collagen directionality, homogeneity, and coarseness in scars and varied with scar duration. Phase parameters showed an increasing tendency of the mean of phase congruency with scar duration, indicating that collagen fibers are better oriented over time. Texture parameters calculated from local difference local binary pattern (LD-LBP) and Haar wavelet transform, demonstrated that the LD-LBP variance decreased and the energy of all subimages increased with scar duration. It implied that collagen has a more regular pattern and becomes coarser with scar duration. In addition, the random forest regression was used to predict scar duration, demonstrating reliable performance of the extracted phase and texture parameters in characterizing collagen morphology in scar SHG images. Results indicate that the extracted parameters using the proposed method can be used as quantitative indicators to monitor scar progression with time and can help understand the mechanism of scar progression.

Machine learning-based exosome profiling of multi-receptor SERS sensors for differentiating adenocarcinoma in situ from early-stage invasive adenocarcinoma.

Exosomes, extracellular vesicles released by cells, hold potential as diagnostic markers for the early detection of lung cancer. Despite their clinical promise, current technologies lack rapid and effective means to discriminate between exosomes derived from adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC). This challenge arises from the intrinsic structural heterogeneity of exosomes, necessitating the development of advanced methodologies for precise differentiation. Here, we demonstrate a novel approach for plasma exosome detection utilizing multi-receptor surface-enhanced Raman spectroscopy (SERS) technology to differentiate between AIS and early-stage IAC. To accomplish this, we synthesized a stable and uniform two-dimensional SERS substrate (BC/Au NPs film) by fabricating gold nanoparticles onto bacterial cellulose. We then enhanced its capabilities by introducing multi-receptor SERS functionality via modifying the substrate with both low-specificity and physicochemical-selective molecules. Furthermore, by strategically combining all capturer-exosome SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of the exosome. Combining these features with partial least squares regression-discriminant analysis (PLS-DA) modeling significantly improved discriminatory accuracy, achieving 90% sensitivity and 95% specificity in distinguishing AIS from early-stage IAC. Our developed SERS sensor provides an effective method for early detection of lung cancer, thereby paving a new way for innovative advancements in diagnosing lung cancer.

Early diagnosis of thyroid-associated ophthalmopathy using label-free Raman spectroscopy and multivariate analysis.

Thyroid-associated ophthalmopathy (TAO) is the most common orbital disease in adults, with complex clinical manifestations and significant impacts on the life quality of patients. The current diagnosis of TAO lacks reliable biomarkers for early and non-invasive screening and detection, easily leading to poor prognosis. Therefore, it is essential to explore new methods for accurately predicting TAO development in its early stage. In this study, Raman spectroscopy, with non-destructive, label-free, and high-sensitivity characteristics, was used to analyze the differences in biochemical components of orbital adipocyte and tear samples between TAO and control groups. Furthermore, a multivariate analysis method (i.e., Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA)) was applied for data processing and analysis. Compared with controls, PCA-LDA yielded TAO diagnostic accuracies of 72.7% and 75.0% using orbital adipocytes and tears, respectively. Our proof-of-concept results suggest that Raman spectroscopy holds potential for exploring the underlying pathogenesis of TAO, and its potential application in early screening of other thyroid-associated diseases can be further expanded.

Blood plasma surface-enhanced Raman spectroscopy for non-invasive optical detection of cervical cancer.

Based on blood plasma surface-enhanced Raman spectroscopy (SERS) analysis, a simple and label-free blood test for non-invasive cervical cancer detection is presented in this paper. SERS measurements were performed on blood plasma samples from 60 cervical cancer patients and 50 healthy volunteers. Both the empirical approach and multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were employed to analyze and differentiate the obtained blood plasma SERS spectra. The empirical diagnostic algorithm based on the integration area of the SERS spectral bands (1310-1430 and 1560-1700 cm(-1)) achieved a diagnostic sensitivity of 70% and 83.3%, and a specificity of 76% and 78%, respectively, whereas the diagnostic algorithms based on PCA-LDA yielded a better diagnostic sensitivity of 96.7% and a specificity of 92% for separating cancerous samples from normal samples. This exploratory work demonstrates that a silver nanoparticle based SERS plasma analysis technique in conjunction with PCA-LDA has potential for improving cervical cancer detection and screening.

Shifted-excitation Raman difference spectroscopy for improving in vivo detection of nasopharyngeal carcinoma.

A strong fluorescence background is one of the common interference factors of Raman spectroscopic analysis in biological tissue. This study developed an endoscopic shifted-excitation Raman difference spectroscopy (SERDS) system for real-time in vivo detection of nasopharyngeal carcinoma (NPC) for the first time. Owing to the use of the SERDS method, the high-quality Raman signals of nasopharyngeal tissue could be well extracted and characterized from the complex raw spectra by removing the fluorescence interference signals. Significant spectral differences relating to proteins, phospholipids, glucose, and DNA were found between 42 NPC and 42 normal tissue sites. Using linear discriminant analysis, the diagnostic accuracy of SERDS for NPC detection was 100%, which was much higher than that of raw Raman spectroscopy (75.0%), showing the great potential of SERDS for improving the accurate in vivo detection of NPC.

Machine learning-assisted global DNA methylation fingerprint analysis for differentiating early-stage lung cancer from benign lung diseases.

DNA methylation plays a critical role in the development of human tumors. However, routine characterization of DNA methylation can be time-consuming and labor-intensive. We herein describe a sensitive, simple surface-enhanced Raman spectroscopy (SERS) approach for identifying the DNA methylation pattern in early-stage lung cancer (LC) patients. By comparing SERS spectra of methylated DNA bases or sequences with their counterparts, we identified a reliable spectral marker of cytosine methylation. To move toward clinical applications, we applied our SERS strategy to detect the methylation patterns of genomic DNA (gDNA) extracted from cell line models as well as formalin-fixed paraffin-embedded tissues of early-stage LC and benign lung diseases (BLD) patients. In a clinical cohort of 106 individuals, our results showed distinct methylation patterns in gDNA between early-stage LC (n = 65) and BLD patients (n = 41), suggesting cancer-induced DNA methylation alterations. Combined with partial least square discriminant analysis, early-stage LC and BLD patients were differentiated with an area under the curve (AUC) value of 0.85. We believe that the SERS profiling of DNA methylation alterations, together with machine learning could potentially offer a promising new route toward the early detection of LC.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

BACKGROUND/PURPOSE OF THE STUDY: There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. METHODS: Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. RESULTS: Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. CONCLUSIONS: Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

[Surface-enhanced Raman spectroscopic analysis of uric acid].

Based on Ag nanoparticles as the surface-enhanced Raman spectroscopy (SERS)-active nanostructure, the SERS of uric acid was presented in the paper. The absorption spectroscopies of uric acid and the mixture of silver colloids and uric acid were measured. The possible enhancing mechanism of the uric acid on silver colloid was speculated. The characteristic SERS bands of uric acid were tentatively assigned. The influence of absorption time and different ion on the SERS of uric acid were also discussed. The SERS spectral intensity changes linearly with the uric acid concentration, which indicated that the SERS might provide a new kind of direct and fast detecting method for the detection of uric acid. The detection limit of uric acid in silver sol is found to be 1 mg/L.

Ratiometric SERS quantitative analysis of tyrosinase activity based on gold-gold hybrid nanoparticles with Prussian blue as an internal standard.

Tyrosinase (TYR) is a polyphenol oxidase that regulates melanin biosynthesis. Abnormal levels of TYR have been confirmed closely associated with melanoma cancer and other diseases, making the establishment of highly sensitive and accurate quantitative detection of TYR is thus essential for fundamental research and clinical applications. Herein, we proposed a new strategy that combines surface-enhanced Raman scattering (SERS) with Dopamine (DA) and Prussian blue (PB) functionalized gold-gold hybrid nanoparticles for TYR detection. DA is oxidized to dopaquinone with the presence of TYR, leading to the consumption of DA in the reaction solution and the corresponding decrease in DA characteristic peak intensity at 1480 cm-1. Our SERS quantitative assay of TYR with "on-off" sensing strategy yields an excellent limit of detection (LOD) of 3 × 10-3 U mL-1 in a linear range of 10-3 to 100 U mL-1. In particular, the intensity ratio of 1480 cm-1 to 2121 cm-1 allows us to achieve remarkably reliable quantitative detection of TYR, with the 2121 cm-1 peak intensity of the standard internal (IS) molecule PB being used to correct SERS signal fluctuations. Furthermore, our proposed assay has been successfully demonstrated to quantify TYR spiked in human serum samples, with excellent percentage recovery of 90.0-110.6 %. The potential of our ratiometric SERS strategy for the performance evaluation of TYR inhibitors has also been verified. Our work is therefore expected to provide a new route for accurate and reliable monitoring of TYR activity in the complex biological environment.

Analysis of the interaction between A1 R and A2A R proteins in living cells based on FRET imaging and batch processing method.

The quantitative FRET analysis of living cells is a tedious and time-consuming task for freshman lacks technical training. In this study, FRET imaging and batch processing method were combined to analyze reagents-induced interactions of A1 R and A2A R on cell membranes. Results showed that the method had taken less time than if cell-by-cell was analyzed. The accuracy and repeatability of FRET efficiency values were likewise improved by removing the interference from anthropogenic factors. Then this method was applied to rapidly analyze acetaldehyde-induced interactions, which analyzed hundreds of single-cell trends by one operation, and the results revealed that interactions were consistently attenuated in LX-2 cells, and statistical differences appeared after 30 min. Combined with batch processing method, procedures of cells FRET analysis have been greatly simplified without additional technical work, which has broad prospects in large-scale analysis of cellar protein interaction.

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma.

Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

Micro-Raman Analysis of Sperm Cells on Glass Slide: Potential Label-Free Assessment of Sperm DNA toward Clinical Applications.

Routine assessment of sperm DNA integrity involves the time-consuming and complex process of staining sperm chromatin. Here, we report a Raman spectroscopy method combined with extended multiplicative signal correction (EMSC) for the extraction of characteristic fingerprints of DNA-intact and DNA-damaged sperm cells directly on glass slides. Raman results of sperm cell DNA integrity on glass substrates were validated one-to-one with clinical sperm cell staining. Although the overall Raman spectral pattern showed considerable similarity between DNA-damaged and DNA-intact sperm cells, differences in specific Raman spectral responses were observed. We then employed and compared multivariate statistical analysis based on principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. In comparison, the PLS-DA model showed relatively better results in terms of diagnostic sensitivity, specificity, and the classification rate between the sperm DNA damaged group and the DNA intact group. Our results demonstrate the potential of Raman based label-free DNA assessment of sperm cell on glass substrates as a simple method toward clinical applications.

A novel blood plasma analysis technique combining membrane electrophoresis with silver nanoparticle-based SERS spectroscopy for potential applications in noninvasive cancer detection.

Combining membrane electrophoresis with silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS), we have developed a novel method for blood plasma analysis for cancer detection applications. In this method, total serum proteins are isolated from blood plasma by membrane electrophoresis and mixed with silver nanoparticles to perform SERS spectral analysis. The obtained SERS spectra present information-rich, fingerprint-type signatures of the biochemical constituents of whole proteins. We evaluated the utility of this method by analyzing blood plasma samples from patients with gastric cancer (n=31) and healthy volunteers (n=33). Principal components analysis of the spectra revealed that the data points for the two groups form distinct, completely separated clusters with no overlap. The gastric cancer group can be unambiguously distinguished from the normal group in this initial test-that is, with both diagnostic sensitivity and specificity of 100%. These results are very promising for developing a label-free, noninvasive clinical tool for cancer detection and screening.