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

Diagnostic performance of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for detecting osteoarthritis and rheumatoid arthritis from blood serum

Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both conditions are highly heterogeneous, and their onset occurs insidiously with non-specific symptoms, so they are not always distinguishable from other arthritis during the initial stages. This makes early diagnosis difficult and resource-demanding in clinical environments. Here, we estimated its diagnostic performance in classifying ATR-FTIR spectra obtained from serum samples from OA patients, RA patients, and healthy controls. Altogether, 334 serum samples were obtained from 100 OA patients, 134 RA patients, and 100 healthy controls. The infrared spectral acquisition was performed on air-dried 1 µl of serum with a diamond-ATR-FTIR spectrometer. Machine learning models combining Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were trained to binary classify preprocessed ATR-FTIR spectra as follows: controls vs. OA, controls vs. RA, and OA vs. RA. For a separated test dataset and the validation dataset, the overall model performance was better in classifying OA and RA patients, followed by the RA and controls, and lastly, between OA and controls, with corresponding AUC-ROC values: 0.72 (0.05; standard deviation for 100 iterations), 0.67 (0.04; standard deviation for 100 iterations), and 0.61 (0.06; standard deviation for 100 iterations) (test dataset) and 0.87 (0.02; standard deviation for 100 iterations), 0.87 (0.02; standard deviation for 100 iterations), 0.70 (0.07; standard deviation for 100 iterations) (validation dataset). In conclusion, this study reports robust binary classifier models to differentiate the two most common arthritic diseases from blood serum, showing the potential of ATR-FTIR as an effective aid in arthritic disease classification.

Serum metabolic fingerprinting on Ag@AuNWs for traumatic brain injury diagnosis

Accurate and rapid diagnosis of traumatic brain injury (TBI) is very important for high quality medical services. Nonetheless, the current diagnostic platform still has challenges in the rapid and accurate analysis of clinical samples. Here, we prepared a highly stable, repeatable and sensitive gold-plated silver core-shell nanowire (Ag@AuNWs) for surface-enhanced Raman spectroscopy (SERS) metabolic fingerprint diagnosis of TBI. The core-shell structure significantly enhanced SERS intensity and enables the direct detection of 10μl serum within seconds. The principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares-DA (PLS-DA) are used to evaluate the classification effect of this technology on TBI, respectively. The diagnosis accuracy rate of PCA-LDA and PLS-DA is 73.3% and 86.7% for diagnosing TBI, respectively. Consequently, the PLS-DA model is the optimal selection for distinguishing between the TBI and sham groups. This research will facilitate the application-oriented creation of novel materials with tailored structural designs and the formulation of innovative precision medical protocols in the imminent future.

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.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

An auxiliary diagnostic technology and clinical efficacy evaluation in knee osteoarthritis based on serum surface-enhanced Raman spectroscopy

Knee osteoarthritis (KOA), a progressive joint disease, is a leading source of chronic pain and disability, and its diagnosis mainly depends on medical imaging findings and clinical symptoms. This study aimed to explore an auxiliary diagnostic technology and clinical efficacy evaluation in KOA based on surface-enhanced Raman scattering (SERS). Three sequential experiments were performed: 1) preliminary observation of the therapeutic effects of icariin (ICA); 2) using serum SERS spectra obtained from rat models belonging to sham group, KOA group and icariin treatment group, respectively, to analyze the KOA-related expression profiles; 3) employing partial least squares (PLS) and support vector machines (SVM) algorithms to establish KOA diagnosis model. Pathological changes verified the efficacy of icariin in KOA. Raman peak assignment combined with spectral difference analysis reflected the biochemical changes associated with KOA, including amino acid, carbohydrates and collagen. ICA intervention significantly reversed these changes, although full recovery could not be achieved. Based on PLS-SVM approach, the sensitivity, specificity and accuracy of 100%, 98.33% and 98.89%, respectively, were obtained for screening KOA. This work proves that SERS has great potential to be used as an auxiliary diagnostic technology for KOA, and is also helpful for the exploration of novel KOA treatment agent.

Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases

Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.

Flower-like Composite Material Delivery of Co-Packaged Lenvatinib and Bufalin Prevents the Migration and Invasion of Cholangiocarcinoma

The co-delivery of multiple drugs using nanocarriers has been recognized as a promising strategy for cancer treatment to enhance therapeutic efficacy. In this study, a monodisperse mesoporous silica nanoparticle (mSiO₂) is prepared and functionalized into high-efficiency loaded Lenvatinib and Bufalin for targeted delivery to Cholangiocarcinoma (CCA). mSiO₂ was synthesized on solid silica nanoparticles by oil–water interface method, and highly monodisperse mSiO₂ with uniform morphology was obtained. mSiO₂ was then sequentially modified by polyethylene glycol (PEG) and the targeting molecule folic acid (FA). mSiO₂-FA was designed as co-delivery system for Lenvatinib (Le) and Bufalin (Bu) to increase drug availability and highly target tumor cells. Compared with unfunctionalized mSiO₂, mSiO₂-FA can more efficiently enter human CCA cell lines (9810 cells) and enhance intracellular drug delivery. Moreover, drug-loaded mSiO₂-FA (Le/Bu@mSiO₂-FA) significantly inhibited the viability, migration and invasion of 9810 cells. In vivo, the nanocomplex significantly reduced the tumor load in CCA tumor-bearing mouse models compared to Le or Bu alone. The current work provides a useful strategy for highly targeted and multidrug-resistance reversal therapy for CCA.

Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.

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 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.

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 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 PO₄^3- 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.