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.

[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.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy.

BACKGROUND: The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers. OBJECTIVES: To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC). METHODS: SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC). RESULTS: There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively. CONCLUSIONS: Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Nasopharyngeal carcinoma cell screening based on nuclear targeting Surface-Enhanced Raman Scattering (SERS) detection.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a malignant epithelial carcinoma arising from the nasopharyngeal mucosal lining. Diagnosis of NPC at early stage can improve the outcome of patients and facilitate reduction in cancer mortality. The most significant change between cancer cells and normal cells is the variation of cell nucleus. Therefore, accurately detecting the biochemical changes in nucleus between cancer cells and normal cells has great potential to explore diagnostic molecular markers for NPC. Highly sensitive surface-enhanced Raman scattering (SERS) could reflect the biochemical changes in the process of cell cancerization at the molecular level. However, rapid nuclear targeting SERS detection remains a challenge. RESULTS: A novel and accurate nuclear-targeting SERS detection method based on electroporation was proposed. With the assistance of electric pulses, nuclear-targeting nanoprobes were rapidly introduced into different NPC cells (including CNE1, CNE2, C666 cell lines) and normal nasopharyngeal epithelial cells (NP69 cell line), respectively. Under the action of nuclear localization signaling peptides (NLS), the nanoprobes entering cells were located to the nucleus, providing high-quality nuclear SERS signals. Hematoxylin and eosin (H&E) staining and in situ cell SERS imaging confirmed the excellent nuclear targeting performance of the nanoprobes developed in this study. The comparison of SERS signals indicated that there were subtle differences in the biochemical components between NPC cells and normal nasopharyngeal cells. Furthermore, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples, and high sensitivity, specificity, and accuracy were obtained in the screening of NPC cells from normal nasopharyngeal epithelial cells. SIGNIFICANCE: To the best of our knowledge, this is the first study that employing nuclear-targeting SERS testing to screen nasopharyngeal carcinoma cells. Based on the electroporation technology, nanoprobes can be rapidly introduced into living cells for intracellular biochemical detection. Nuclear-targeting SERS detection can analyze the biochemical changes in the nucleus of cancer cells at the molecular level, which has great potential for early cancer screening and cytotoxicity analysis of anticancer drugs.

Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion.

The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

A highly sensitive SERS substrate of porous membrane-Ag NPs for kidney cancer detection.

BACKGROUND: The substrate employed in surface-enhanced Raman spectroscopy (SERS) constitutes an essential element in the cancer detection methodology. In this research, we introduce a three-dimensional (3D) structured SERS substrate that integrates a porous membrane with silver nanoparticles to enhance SERS spectral signals through the utilization of the aggregation effect of silver nanoparticles. This enhancement is crucial because accurate detection results strongly depend on the intensity of specific peaks in Raman spectroscopy. A highly sensitive SERS substrate can significantly improve the accuracy of detection results. RESULTS: We collected 66 plasma samples from individuals with kidney cancer and control individuals, including both bladder cancer patients and healthy individuals. Then, we utilized substrates with and without porous membranes to acquire the SERS spectra of the samples, enabling us to evaluate the enhancement effect of our SERS substrate. The spectral analysis demonstrated enhanced peak intensities in the experimental group (with porous substrate) compared to the control group (without porous substrate). The uniformity and reproducibility of the SERS substrate are also significantly enhanced, which is very helpful for improving the accuracy of detection results. Additionally, the Principal Component Analysis-Linear Discriminant Analysis algorithm (PCA-LDA) was employed to classify the SERS spectra of both groups. In the experimental group, the classification accuracy was 98.5 % for kidney cancer, and 83.3 % for kidney and bladder cancer. Compared to the control group, it improved by 3 % and 12.6 % respectively. SIGNIFICANT: This indicates that our 3D structured SERS substrate combined with multivariate statistical algorithms PCA-LDA can not only improve the accuracy of SERS detection technology in single cancer detection, but also has great potential in multiple cancer detection. This 3D structured SERS substrate is expected to become a new auxiliary means for cancer detection.

Investigation of free fatty acid associated recombinant membrane receptor protein expression in HEK293 cells using Raman spectroscopy, calcium imaging, and atomic force microscopy.

G-protein-coupled receptor 120 (GPR120) is a previously orphaned G-protein-coupled receptor that apparently functions as a sensor for dietary fat in the gustatory and digestive systems. In this study, a cDNA sequence encoding a doxycycline (Dox)-inducible mature peptide of GPR120 was inserted into an expression vector and transfected in HEK293 cells. We measured Raman spectra of single HEK293 cells as well as GPR120-expressing HEK293-GPR120 cells at a 48 h period following the additions of Dox at several concentrations. We found that the spectral intensity of HEK293-GPR120 cells is dependent upon the dose of Dox, which correlates with the accumulation of GPR120 protein in the cells. However, the amount of the fatty acid activated changes in intracellular calcium (Ca(2+)) as measured by ratiometric calcium imaging was not correlated with Dox concentration. Principal components analysis (PCA) of Raman spectra reveals that the spectra from different treatments of HEK293-GPR120 cells form distinct, completely separated clusters with the receiver operating characteristic (ROC) area of 1, while those spectra for the HEK293 cells form small overlap clusters with the ROC area of 0.836. It was also found that expression of GPR120 altered the physiochemical and biomechanical properties of the parental cell membrane surface, which was quantitated by atomic force microscopy (AFM). These findings demonstrate that the combination of Raman spectroscopy, calcium imaging, and AFM may provide new tools in noninvasive and quantitative monitoring of membrane receptor expression induced alterations in the biophysical and signaling properties of single living cells.

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.

[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.

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.

Study on degranulation of mast cells under C48/80 treatment by electroporation-assisted and ultrasound-assisted surface-enhanced Raman spectrascopy.

Both electroporation-assisted and ultrasound-assisted delivery methods can rapidly deliver nanoparticles into living cells for surface-enhanced Raman scattering (SERS) detection, but these two methods have never been compared. In this study, electroporation-assisted SERS and ultrasound-assisted SERS were employed to detect the biochemical changes of degranulated mast cells induced by mast cell stimulator (C48/80). The results showed that the cell damage of electroporation based on controllable electric pulse was smaller than that of ultrasound based on cavitation. Transmission electron microscope images of cells indicated that the nanoparticles delivered by electroporation were mainly distributed in the cytoplasm, while ultrasound could transport nanoparticles to the cytoplasm and nucleus. Therefore, electroporation-assisted SERS mainly detects the biochemical information of cytoplasm, while ultrasound-assisted SERS gets more spectral signals of nucleic acid. Both methods can obtain high quality SERS signal of cells. With drug treatment, the SERS peak intensity of 733 cm-1 attributed to phosphatidylserine decreased significantly, which may be due to the activation of mast cell degranulation pathway stimulated by C48/80 agonist, resulting in a large amount of intracellular serine being used to synthesize tryptase, while the production of phosphatidylserine decreased. Further, based on principal component analysis and linear discriminant analysis (PCA-LDA approach), ultrasound-assisted SERS could achieve better sensitivity, specificity and accuracy in the discrimination and identification of drug-treated degranulated mast cells than electroporation assisted SERS. This exploratory work is helpful to realize the real-time dynamic SERS detection of intracellular biochemical components, and it also has great potential in intracellular SERS analysis, such as the cytotoxicity assay of anti-tumor drugs or cancer cell screening.

Label-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancers.

Surface-enhanced Raman spectroscopy (SERS) is considered as an ultrasensitive, non-invasive as well as rapid detection technology for cancer diagnosis. In this study, we developed a novel blood serum analysis strategy using coffee ring effect-assisted label-free SERS for different types of cancer screening. Additionally, the pretreated Ag nanoparticles (Ag NPs) were mixed with the serum from liver cancer patients (n = 40), prostate cancer patients (n = 32) and healthy volunteers (n = 30) for SERS measurement. The droplets of Ag NPs-serum mixture formed the coffee ring on the peripheral after air-drying, and thus extremely enhancing Raman signal and ensuring the stability and reliability of SERS detection. Partial least square (PLS) and support vector machine (SVM) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying, yielding the high diagnostic accuracy of 98.04% for normal group and two types of cancers simultaneously distinguishing. More importantly, for the unknown testing set, an ideal diagnostic accuracy of 100% could be achieved by PLS-SVM algorithm for differentiating cancers from the normal group. The results from this exploratory work demonstrate that serum SERS detection combined with PLS-SVM diagnostic algorithm and coffee ring effect has great potential for the noninvasive and label-free detection of cancer.

Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm.

In this study, we mainly aimed to investigate the diagnostic potential of surface-enhanced Raman spectroscopy for bladder cancer and kidney cancer which are the most common cancers of the urinary system, and evaluate the classification ability of three statistical algorithms: principal component analysis-linear discriminate analysis (PCA-LDA), partial least square-random forest (PLS-RF), and partial least square-support vector machine (PLS-SVM). The plasma of 26 bladder cancer patients, 38 kidney cancer patients and 39 normal subjects was mixed with the same volume of silver nanoparticles, respectively, and then high-quality SERS signal was obtained. The SERS spectra in the range of 400-1800 cm-1 were compared and analyzed. There were some significant differences in SERS peak intensity, which may reflect the changes in the content of some biomacromolecules in the plasma of cancer patients. Based on the three algorithms of PCA-LDA, PLS-RF and PLS-SVM, the classification accuracy of SERS spectra of plasma from cancer patients and normal subjects was 98.1%, 100% and 100%, respectively. In addition, the classification accuracy of the three diagnostic algorithms to classify the SERS spectra of bladder cancer and kidney cancer was 81.3%, 91.7%, and 98.4%, respectively. This exploratory work demonstrates that SERS combined with PLS-SVM algorithm has superior performance for clinical screening of bladder cancer and kidney cancer through peripheral plasma.

Quantitative and direct serum albumin detection by label-free SERS using tunable hydroxyapatite nanostructure for prostate cancer detection.

With the aid of good biocompatibility and stability with hydroxyapatite (HAp) in protein separation and adsorption fields, we developed a novel extraction-isolation albumin analysis method by relying on the specific adsorption capacity of HAp, combining with surface-enhanced Raman spectroscopy (SERS) for prostate cancer screening. Two different nanostructures of HAp particles, including the HAp flower and HAp sphere, were synthesized with a hydrothermal method, and the targeted binding and extraction abilities of serum albumin of these two HAp particles were compared. By changing the morphology of the nanostructure, the albumin-adsorption capacity of HAp varied significantly. Compared with spherical HAp particles, HAp flower particles have more albumin binding sites per unit area. Thus, the HAp flower displayed the superior capacity for adsorption-release of albumin, which was further employed for clinical prostate cancer screening. Based on the superior adsorption-extraction ability of albumin of HAp flower, serum albumin was adsorbed and extracted by HAp flower from serum samples of prostate cancer patients (n = 30) and healthy volunteers (n = 30), and mixed with silver colloids to perform SERS spectral analysis. The partial least square-support vector machines (PLS-SVM) model is used to analyze the obtained serum albumin SERS spectra and establish the diagnostic model, the diagnostic accuracy was up to 95.00% for differentiating the normal volunteer from prostate patient groups. The results demonstrate that the PLS-SVM model provides superior performance in the classification of a prostate cancer diagnosis. Due to the advantages of simplicity and rapidness, the HAp flower-adsorbed-released albumin combined with SERS was expected to become a promising tool for prostate cancer detection.

Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis.

The capabilities of using gold nanoparticle based surface-enhanced Raman spectroscopy (SERS) to obtain blood serum biochemical information for non-invasive colorectal cancer detection were presented in this paper. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Both empirical approach and multivariate statistical techniques, including principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and colorectal cancer serum. The empirical diagnostic algorithm based on the ratio of the SERS peak intensity at 725 cm(-1) for adenine to the peak intensity at 638 cm(-1) for tyrosine achieved a diagnostic sensitivity of 68.4% and specificity of 95.6%, whereas the diagnostic algorithms based on PCA-LDA yielded a diagnostic sensitivity of 97.4% and specificity of 100% for separating cancerous samples from normal samples. Receiver operating characteristic (ROC) curves further confirmed the effectiveness of the diagnostic algorithm based on PCA-LDA technique. The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.

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.