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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Quantitative detection of crystal violet using a surface-enhanced Raman scattering based on a flower-like HAp/Ag nanocomposite.

Herein, we proposed a simple one-pot sol-thermal strategy to prepare a highly sensitive and reproducible SERS substrate. The silver-doped hydroxyapatite nanocomposite (HAp/Ag) could suppress the oxidation of silver nanoparticles, which endow the SERS substrate with good stability and reproducibility. Due to the strong interaction between the HAp/Ag substrate and the analytes, a stronger Raman signal generated during the process of SERS detection. In particular, the HAp/Ag substrate enabled the determination of rhodamine 6G (R6G) and crystal violet (CV), and the limits of detection (LOD) were low at 10-6 M and 10-5 M, respectively. In addition, the HAp/Ag substrate could be used for the quantitative analysis of CV in wastewater with a good linear relationship between 10-2 and 10-5 M. In this context, the HAp/Ag substrate combines the superior properties of both Ag NPs and HAp particles, providing a potential method for monitoring the environment and building a convenient SERS platform to detect pollutants in wastewater.

Label-free determination of liver cancer stages using surface-enhanced Raman scattering coupled with preferential adsorption of hydroxyapatite microspheres.

Here, we explored a label-free albumin targeted analysis method by utilizing hydroxyapatite (HAp) to adsorb-release serum albumin, in conjunction with surface-enhanced Raman scattering (SERS) for screening liver cancer (LC) at different tumor (T) stages. Excitingly, albumin can be preferentially adsorbed by HAp as compared with other serum proteins. Moreover, we developed a novel strategy using a high concentration of PO43- solution as the albumin-release agent. This method overcomes the shortcomings of the traditional purification technology of serum albumin, which requires acid to release protein, and ensures that the structure and properties of albumin are not damaged. The SERS spectra of serum albumin obtained from three sample groups were analyzed to verify the feasibility of this new method: healthy volunteers (n = 35), LC patients with T1 stage (n = 25) and LC patients with T2-T4 stage (n = 23). Furthermore, principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to classify the early T (T1) stage LC vs. normal group and advanced T (T2-T4) stage LC vs. normal group, yielding high diagnostic accuracies of 90.00% and 96.55%, respectively, which showed a 10% improvement in diagnostic accuracy for the early stage detection of cancer as compared with previous studies. The results of this exploratory work demonstrated that HAp-adsorbed-released serum albumin combined with SERS analysis has great potential for label-free, noninvasive and sensitive detection of different T stages of liver cancer.

Label-free diagnosis of breast cancer based on serum protein purification assisted surface-enhanced Raman spectroscopy.

Serum protein is generally used to assess the severity of disease, as well as cancer progression and prognosis. Herein, a simple and rapid serum proteins analysis method combined with surface-enhanced Raman spectroscopy (SERS) technology was applied for breast cancer detection. The cellulose acetate membrane (CA) was employed to extract human serum proteins from 30 breast cancer patients and 45 healthy volunteers and then extracted proteins were mixed with silver nanoparticles for SERS measurement. Additionally, we also mainly assessed the use of different ratios of proteins-silver nanoparticles (Ag NPs) mixture to generate maximum SERS signal for clinical samples detection. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-support vector machines (PLS-SVM) were used to analyze the obtained serum protein SERS spectra and establish the diagnostic model. The results demonstrate that the PLS-SVM model provides superior performance in the classification of breast cancer diagnosis compared with PCA-LDA. This exploratory work demonstrates that the label-free SERS analysis technique combined with CA membrane purified serum proteins has great potential for breast cancer diagnosis.

High spatio-temporal resolution measurement of A1 R and A2A R interactions combined with Iem-spFRET and E-FRET methods.

A1 R-A2A R heterodimers regulate striatal glutamatergic neurotransmission. However, few researches about kinetics have been reported. Here, we combined Iem-spFRET and E-FRET to investigate the kinetics of A1 R and A2A R interaction. Iem-spFRET obtains the energy transfer efficiency of the whole cell. E-FRET gets energy transfer efficiency with high spatial resolution, whereas, it was prone to biases because background was easily selected due to manual operation. To study the interaction with high spatio-temporal resolution, Iem-spFRET was used to correct the deviation of E-FRET. In this paper, A1 R and A2A R interaction was monitored, and the changes of FRET efficiency of the whole or/and partial cell membrane were described. The results showed that activation of A1 R or A2A R leads to rapid aggregation, inhibition of A1 R or A2A R leads to slow segregation, and the interaction is reversible. These results demonstrated that combination of Iem-spFRET and E-FRET could measure A1 R and A2A R interaction with high spatio-temporal resolution.

A microsphere nanoparticle based-serum albumin targeted adsorption coupled with surface-enhanced Raman scattering for breast cancer detection.

The serum albumin level is inseparable associated with survival in patients with breast cancer, and simultaneously serve as a good indicator of prognosis of cancer. Here, we proposed a novel extraction-isolation analysis method of albumin for breast cancer detection utilizing hydroxyapatite particles (HAp) to targeted adsorb albumin from serum relying on its specific adsorption capacity. An ideal protein-release reagent was used for isolating albumin from the surface of HAp, and meanwhile ensuring that the structure and property of albumin was not suffered damage. The surface-enhanced Raman scattering (SERS) signal of extracted albumin was obtained, and partial least squares (PLS) and linear discriminant analysis (LDA) analysis approach were employed to analyze SERS spectra data, with the aim to assess the capability of HAp method for identifying breast cancer, yielding an ideal diagnostic accuracy of 98.6%, demonstrating promising potential as a non-invasive and sensitive nanotechnology for breast cancer screening.

Label-free Raman spectroscopy: A potential tool for early diagnosis of diabetic keratopathy.

Diabetes has become a major public health problem worldwide, and the incidence of diabetes has been increasing progressively. Diabetes is prone to cause various complications, among which diabetic keratopathy (DK) emphasizes the significant impact on the cornea. The current diagnosis of DK lacks biochemical markers that can be used for early and non-invasive screening and detection. In contrast, in this study, Raman spectroscopy, which demonstrates non-destructive, label-free features, especially the unique advantage of providing molecular fingerprint information for target substances, were utilized to interrogate the intrinsic information of the corneal tissues from normal and diabetic mouse models, respectively. Visually, the Raman spectral response derived from the biochemical components and biochemical differences between the two groups were compared. Moreover, multivariate analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out for advanced statistical analysis. PCA yields a diagnostic results of 57.4% sensitivity, 89.2% specificity, 74.8% accuracy between the diabetic group and control group; Moreover, PLS-DA was employed to enhance the diagnostic ability, showing 76.1% sensitivity, 86.1% specificity, and 87.6% accuracy between the diabetic group and control group. Our proof-of-concept results show the potential of Raman spectroscopy-based techniques to help explore the underlying pathogenesis of DK disease and thus be further expanded for potential applications in the early screening of diabetic diseases.

Surface-enhanced Raman scattering analysis of serum albumin via adsorption-exfoliation on hydroxyapatite nanoparticles for noninvasive cancers screening.

Combining serum albumin via adsorption-exfoliation on hydroxyapatite particles (HAp) with surface-enhanced Raman scattering (SERS), we developed a novel quantitative analysis of albumin method from blood serum for cancers screening applications. The quantitatively analysis obtained by our HAp method had a good linear relationship from 1 to 10 g/dL, and the lower limit of detection was less than the albumin prognostic factor for disease (3.5 g/dL). Serum albumin was adsorbed and exfoliated by HAp from serum samples of liver cancer patients, breast cancer patients and healthy volunteers and mixed with silver colloids to perform SERS spectral analysis. Based on the PLS-SVM algorithm, the diagnostic accuracies of liver cancer patients and breast cancer patients were 100% and 96.68%, respectively. Moreover, this algorithm successfully predicted the unidentified subjects with a diagnostic accuracy of 93.75%. This exploratory work demonstrated that HAp-adsorbed-exfoliated serum proteins combined with SERS spectroscopy has great potential for cancer screening.