Spectral analysis of human saliva for detection of lung cancer using surface-enhanced Raman spectroscopy

Nanopillar-Assisted SERS Chromatography

Practical implementation of surfaced enhanced Raman spectroscopy (SERS) sensing is hindered by complexity of real-life samples, which often requires long and costly pretreatment and purification. Here, we present a novel nanopillar-assisted SERS chromatography (NPC-SERS) method for simultaneous quantitation of target molecules and analysis of complex, multicomponent fluids, e.g., human urine spiked with a model drug paracetamol (PAR). Gold-coated silicon nanopillar (AuNP) SERS substrates and a centrifugal microfluidic platform are tactfully combined, which allows (i) a precise and fully automated sample manipulation and (ii) spatial separation of different molecular species on the AuNP substrate. The NPC-SERS technique provides a novel approach for wetting the stationary phase (AuNP) using the "wicking effect", and thus minimizes dilution of analytes. Separation of PAR and the main human urine components (urea, uric acid, and creatinine) has been demonstrated. Quantitative detection of PAR with ultrawide linear dynamic range (0-500 ppm) is achieved by analyzing the spreading profiles of PAR on the AuNP surface. NPC-SERS transforms SERS into a sensing technique with general applicability, facilitating rapid and quantitative detection of analytes in complex biofluids, such as saliva, blood, and urine.

Screening and staging for non-small cell lung cancer by serum laser Raman spectroscopy

OBJECTIVE

Lung cancer is the leading cause of cancer-related death worldwide. Current clinical screening methods to detect lung cancer are expensive and associated with many complications. Raman spectroscopy is a spectroscopic technique that offers a convenient method to gain molecular information about biological samples. In this study, we measured the serum Raman spectral intensity of healthy volunteers and patients with different stages of non-small cell lung cancer. The purpose of this study was to evaluate the application of serum laser Raman spectroscopy as a low cost alternative method in the screening and staging of non-small cell lung cancer (NSCLC).

METHODS

The Raman spectra of the sera of peripheral venous blood were measured with a LabRAM HR 800 confocal Micro Raman spectrometer for individuals from five groups including 14 healthy volunteers (control group), 23 patients with stage I NSCLC (stage I group), 24 patients with stage II NSCLC (stage II group), 19 patients with stage III NSCLC (stage III group), 11 patients with stage IV NSCLC (stage IV group). Each serum sample was measured 3 times at different spots and the average spectra represented the signal of Raman spectra in each case. The Raman spectrum signal data of the five groups were statistically analyzed by analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and cross-validation.

RESULTS

Raman spectral intensity was sequentially reduced in serum samples from control group, stage I group, stage II group and stage III/IV group. The strongest peak intensity was observed in the control group, and the weakest one was found in the stage III/IV group at bands of 848 cm^-1, 999 cm^-1, 1152 cm^-1, 1446 cm^-1 and 1658 cm^-1 (P < 0.05). Linear discriminant analysis showed that the sensitivity to identify healthy people, stage I, stage II, and stage III/IV NSCLC was 86%, 65%, 75%, and 87%, respectively; the specificity was 95%, 94%, 88%, and 93%, respectively; and the overall accuracy rate was 92% (71/77).

CONCLUSION

The laser Raman spectroscopy can effectively identify patients with stage I, stage II or stage III/IV Non-Small Cell Lung cancer using patient serum samples.

Human Saliva for Oral Precancer Detection: a Comparison of Fluorescence & Stokes Shift Spectroscopy

We report here a study on human saliva tested as a diagnostic medium for oral cancer detection on three groups: oral squamous cell carcinoma (OSCC), oral sub mucous fibrosis (OSMF; precancer), and healthy controls (normal). Measurements have been conducted using fluorescence spectroscopy with 350 nm excitation and Stokes shift (SS) spectroscopy (SSS) with 120 nm shift from a total of 99 saliva samples. For classification, principal component analysis (PCA) and linear discriminant analysis (LDA) have been applied on the data sets. Linear discriminant (LD) scores of fluorescence spectra are able to differentiate OSCC to normal, OSMF to normal and OSCC to OSMF with sensitivities 91%, 92%, 91% and specificities 97%, 100%, 94% respectively, while LD scores of SS spectra differentiate respective groups with sensitivities 100%, 94%, 94% and specificities 97%, 100%, 94%. Cross-validation on the datasets of PC scores during LDA illustrates that sensitivity and specificity of SSS data are less affected than those of fluorescence data. Saliva is thus seen as a potential non-invasive and simple diagnostic medium, with SS spectroscopy as a better diagnostic tool for oral precancer.

Noninvasive detection of nasopharyngeal carcinoma based on saliva proteins using surface-enhanced Raman spectroscopy

The present study evaluated the capability of saliva analysis combining membrane protein purification with surface-enhanced Raman spectroscopy (SERS) for noninvasive detection of nasopharyngeal carcinoma (NPC). A rapid and convenient protein purification method based on cellulose acetate membrane was developed. A total of 659 high-quality SERS spectra were acquired from purified proteins extracted from the saliva samples of 170 patients with pathologically confirmed NPC and 71 healthy volunteers. Spectral analysis of those saliva protein SERS spectra revealed specific changes in some biochemical compositions, which were possibly associated with NPC transformation. Furthermore, principal component analysis combined with linear discriminant analysis (PCA-LDA) was utilized to analyze and classify the saliva protein SERS spectra from NPC and healthy subjects. Diagnostic sensitivity of 70.7%, specificity of 70.3%, and diagnostic accuracy of 70.5% could be achieved by PCA-LDA for NPC identification. These results show that this assay based on saliva protein SERS analysis holds promising potential for developing a rapid, noninvasive, and convenient clinical tool for NPC screening.

Detection of Pseudomonas aeruginosa Metabolite Pyocyanin in Water and Saliva by Employing the SERS Technique

Pyocyanin (PYO) is a metabolite specific for Pseudomonas aeruginosa. In the case of immunocompromised patients, it is currently considered a biomarker for life-threating Pseudomonas infections. In the frame of this study it is shown, that PYO can be detected in aqueous solution by employing surface-enhanced Raman spectroscopy (SERS) combined with a microfluidic platform. The achieved limit of detection is 0.5 μM. This is ~2 orders of magnitude below the concentration of PYO found in clinical samples. Furthermore, as proof of principle, the SERS detection of PYO in the saliva of three volunteers was also investigated. This body fluid can be collected in a non-invasive manner and is highly chemically complex, making the detection of the target molecule challenging. Nevertheless, PYO was successfully detected in two saliva samples down to 10 μM and in one sample at a concentration of 25 μM. This indicates that the molecules present in saliva do not inhibit the efficient adsorption of PYO on the surface of the employed SERS active substrates.

PCA-MLP SVM distinction of salivary Raman spectra of dengue fever infection

Dengue fever (DF) is a disease of major concern caused by flavivirus infection. Delayed diagnosis leads to severe stages, which could be deadly. Of recent, non-structural protein (NS1) has been acknowledged as a biomarker, alternative to immunoglobulins for early detection of dengue in blood. Further, non-invasive detection of NS1 in saliva makes the approach more appealing. However, since its concentration in saliva is less than blood, a sensitive and specific technique, Surface Enhanced Raman Spectroscopy (SERS), is employed. Our work here intends to define an optimal PCA-SVM (Principal Component Analysis-Support Vector Machine) with Multilayer Layer Perceptron (MLP) kernel model to distinct between positive and negative NS1 infected samples from salivary SERS spectra, which, to the best of our knowledge, has never been explored. Salivary samples of DF positive and negative subjects were collected, pre-processed and analyzed. PCA and SVM classifier were then used to differentiate the SERS analyzed spectra. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Its performance is also compared to our previous works on simulated NS1 salivary samples. It is found that the best PCA-SVM (MLP) model can be defined by 95 PCs from CPV criterion with P1 and P2 values of 0.01 and -0.2 respectively. A classification performance of [76.88%, 85.92%, 67.83%] is achieved.

The emerging landscape of salivary diagnostics

Saliva contains a variety of biomolecules, including DNA, coding and noncoding RNA, proteins, metabolites and microbiota. The changes in the salivary levels of these molecular constituents can be used to develop markers for disease detection and risk assessment. Use of saliva as an early-detection tool is a promising approach because collection of saliva is easy and noninvasive. Here, we review recent developments in salivary diagnostics, accomplished using salivaomics approaches, including genomic, transcriptomic, proteomic, metabolomic and microbiomic technologies. Additionally, we illustrate the mechanisms of how diseases distal from the oral cavity can lead to the appearance of discriminatory biomarkers in saliva, and discuss the relevance of these markers for translational and clinical applications.

PCA criterion for SVM (MLP) classifier for flavivirus biomarker from salivary SERS spectra at febrile stage

Non-structural protein (NS1) has been conceded as one of the biomarkers for flavivirus that causes diseases with life threatening consequences. NS1 is an antigen that allows detection of the illness at febrile stage, mostly from blood samples currently. Our work here intends to define an optimum model for PCA-SVM with MLP kernel for classification of flavivirus biomarker, NS1 molecule, from SERS spectra of saliva, which to the best of our knowledge has never been explored. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Input vector to classifier determined by each PCA criterion is subjected to brute force tuning of MLP parameters for entirety. Its performance is also compared to our previous works where a Linear and RBF kernel are used. It is found that the best PCA-SVM (MLP) model can be defined by 5 PCs from Cattel's Scree test for PCA, together with P1 and P2 values of 0.1 and -0.2 respectively, with a classification performance of [96.9%, 93.8%, 100.0%].

High wavenumber Raman spectroscopy in the characterization of urinary metabolites of normal subjects, oral premalignant and malignant patients

Urine has emerged as one of the diagnostically potential bio fluids, as it has many metabolites. As the concentration and the physiochemical properties of the urinary metabolites may vary under pathological transformation, Raman spectroscopic characterization of urine has been exploited as a significant tool in identifying several diseased conditions, including cancers. In the present study, an attempt was made to study the high wavenumber (HWVN) Raman spectroscopic characterization of urine samples of normal subjects, oral premalignant and malignant patients. It is concluded that the urinary metabolites flavoproteins, tryptophan and phenylalanine are responsible for the observed spectral variations between the normal and abnormal groups. Principal component analysis-based linear discriminant analysis was carried out to verify the diagnostic potentiality of the present technique. The discriminant analysis performed across normal and oral premalignant subjects classifies 95.6% of the original and 94.9% of the cross-validated grouped cases correctly. In the second analysis performed across normal and oral malignant groups, the accuracy of the original and cross-validated grouped cases was 96.4% and 92.1% respectively. Similarly, the third analysis performed across three groups, normal, oral premalignant and malignant groups, classifies 93.3% and 91.2% of the original and cross-validated grouped cases correctly.

Salivary biomarkers in cancer detection

Cancer is the second most common cause of death in the USA. Its symptoms are often not specific and absent, until the tumors have already metastasized. Therefore, there is an urgent demand for developing rapid, highly accurate and noninvasive tools for cancer screening, early detection, diagnostics, staging and prognostics. Saliva as a multi-constituent oral fluid comprises secretions from the major and minor salivary glands, extensively supplied by blood. Molecules such as DNAs, RNAs, proteins, metabolites, and microbiota, present in blood, could be also found in saliva. Recently, salivary diagnostics has drawn significant attention for the detection of specific biomarkers, since the sample collection and processing are simple, cost-effective, and precise and do not cause patient discomfort. Here, we review recent salivary candidate biomarkers for systemic cancers by dividing them according to their origin into: genomic, transcriptomic, proteomic, metabolomic and microbial types.

Cancer Salivary Biomarkers for Tumours Distant to the Oral Cavity

The analysis of saliva as a diagnostic approach for systemic diseases was proposed just two decades ago, but recently great interest in the field has emerged because of its revolutionary potential as a liquid biopsy and its usefulness as a non-invasive sampling method. Multiple molecules isolated in saliva have been proposed as cancer biomarkers for diagnosis, prognosis, drug monitoring and pharmacogenetic studies. In this review, we focus on the current status of the salivary diagnostic biomarkers for different cancers distant to the oral cavity, noting their potential use in the clinic and their applicability in personalising cancer therapies.

Non-invasive and label-free detection of oral squamous cell carcinoma using saliva surface-enhanced Raman spectroscopy and multivariate analysis

Reported here is the application of silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS) as a label-free, non-invasive technique for detection of oral squamous cell cancer (OSCC) using saliva and desquamated oral cells. A total of 180 SERS spectra were acquired from saliva and 120 SERS spectra from oral cells collected from normal healthy individuals and from confirmed oropharyngeal cancer patients. Notable biochemical peaks in the SERS spectra were tentatively assigned to various components. Data were subjected to multivariate statistical techniques including principal component analysis, linear discriminate analysis (PCA-LDA) and logistic regression (LR) revealing a sensitivity of 89% and 68% and a diagnostic accuracy of 73% and 60% for saliva and oral cells, respectively. The results from this study demonstrate the potential of saliva and oral cell SERS combined with PCA-LDA or PCA-LR diagnostic algorithms as a promising clinical adjunct for the non-invasive detection of oral cancer.

Non-invasive detection of nasopharyngeal carcinoma using saliva surface-enhanced Raman spectroscopy

The present study evaluated the use of saliva surface-enhanced Raman spectroscopy (SERS) for the detection of non-invasive nasopharyngeal carcinoma (NPC). SERS measurements were taken from 62 saliva samples, of which 32 were from NPC patients and 30 from healthy volunteers. Notable biochemical Raman bands in the SERS spectra were tentatively assigned to various saliva components. The saliva SERS spectra obtained from the NPC patients and the healthy volunteers were also analyzed by multivariate statistical techniques based on principal component analysis and linear discriminant analysis (PCA-LDA). Significant differences were observed between the saliva SERS spectral intensities for NPC patients and healthy volunteers, particularly at 447, 496, 635, 729, 1134, 1270 and 1448 cm^-1, which primarily contained signals associated with proteins, nucleic acids, fatty acids, glycogen and collagen. The classification results based on the PCA-LDA method provided a relatively high diagnostic sensitivity of 86.7%, specificity of 81.3% and diagnostic accuracy of 83.9% for NPC identification. The results from the present study demonstrate that saliva SERS analysis used in conjunction with PCA-LDA diagnostic algorithms possesses a promising clinical application for the non-invasive detection of NPC.

Detecting Esophageal Cancer Using Surface-Enhanced Raman Spectroscopy (SERS) of Serum Coupled with Hierarchical Cluster Analysis and Principal Component Analysis

Serum samples taken from healthy individuals and pre- and post-operative esophageal cancer patients were analyzed using surface-enhanced Raman spectroscopy (SERS) to explore the feasibility of diagnosing esophageal cancer using the technique. The serum spectrum data were collected using a He-Ne laser of wavelength 632.8 nm. Differences in peaks assigned to nucleic acids, lipids, and proteins were found to be statistically significant between groups, which implies that corresponding serum alterations occur with the development of esophageal diseases. For quantitative analysis, the chemometric methods of hierarchical clustering analysis and principal component analysis were utilized on the obtained SERS spectra for classification with good results.

Sensing of p53 and EGFR Biomarkers Using High Efficiency SERS Substrates

In this paper we describe a method for the determination of protein concentration using Surface Enhanced Raman Resonance Scattering (SERRS) immunoassays. We use two different Raman active linkers, 4-aminothiophenol and 6-mercaptopurine, to bind to a high sensitivity SERS substrate and investigate the influence of varying concentrations of p53 and EGFR on the Raman spectra. Perturbations in the spectra are due to the influence of protein-antibody binding on Raman linker molecules and are attributed to small changes in localised mechanical stress, which are enhanced by SERRS. These influences are greatest for peaks due to the C-S functional group and the Full Width Half Maximum (FWHM) was found to be inversely proportional to protein concentration.

Intracellular surface-enhanced Raman scattering probes based on TAT peptide-conjugated Au nanostars for distinguishing the differentiation of lung resident mesenchymal stem cells

Lung resident mesenchymal stem cells (LR-MSCs) are important regulators of pathophysiological processes including tissue repair and fibrosis, inflammation, angiogenesis and tumor formation. Therefore, increasing attention has focused on the functional differentiation of LR-MSCs. However, the distinction between the undifferentiated and differentiated LR-MSCs, which are closely related and morphologically similar, is difficult to achieve by conventional methods. In this study, by employing the TAT Peptide-conjugated Au nanostars (AuNSs) as an intracellular probe, we developed a method for the identification of LR-MSC differentiation by surface-enhanced Raman scattering (SERS) spectroscopy. SERS spectra were analyzed using principal component analysis (PCA) that allowed unambiguous distinction of subtypes and monitoring of component changes during cellular differentiation. Furthermore, to ascertain whether co-culture with alveolar epithelial type II (ATII) cells and incubation with transform growth factor (TGF)-β were involved in regulating the differentiation of LR-MSCs, we investigated the protein expression levels of epithelial markers and fibroblastic markers on LR-MSCs. Our results demonstrated that co-culture with ATII cells or incubation with TGF-β could induce the differentiation of LR-MSCs as confirmed by SERS analysis, a method that is capable of noninvasive characterization of and distinction between subtypes of LR-MSCs during differentiation. We have provided a new tool that may facilitate stem cell research.

Surface-enhanced Raman spectroscopy of saliva proteins for the noninvasive differentiation of benign and malignant breast tumors

The capability of saliva protein analysis, based on membrane protein purification and surface-enhanced Raman spectroscopy (SERS), for detecting benign and malignant breast tumors is presented in this paper. A total of 97 SERS spectra from purified saliva proteins were acquired from samples obtained from three groups: 33 healthy subjects; 33 patients with benign breast tumors; and 31 patients with malignant breast tumors. Subtle but discernible changes in the mean SERS spectra of the three groups were observed. Tentative assignments of the saliva protein SERS spectra demonstrated that benign and malignant breast tumors led to several specific biomolecular changes of the saliva proteins. Multiclass partial least squares–discriminant analysis was utilized to analyze and classify the saliva protein SERS spectra from healthy subjects, benign breast tumor patients, and malignant breast tumor patients, yielding diagnostic sensitivities of 75.75%, 72.73%, and 74.19%, as well as specificities of 93.75%, 81.25%, and 86.36%, respectively. The results from this exploratory work demonstrate that saliva protein SERS analysis combined with partial least squares–discriminant analysis diagnostic algorithms has great potential for the noninvasive and label-free detection of breast cancer.

A preliminary Raman spectroscopic study of urine: diagnosis of breast cancer in animal models

Analysis of urine by Raman spectroscopy (RS) as an alternative screening and diagnostic tool for breast cancer..

Glycan-based diagnostic devices: current progress, challenges and perspectives

The development of glycan-based diagnostic devices is illustrated with recent examples from both carbohydrate recognition and device design aspects.

Raman spectroscopy analysis of the biochemical characteristics of molecules associated with the malignant transformation of gastric mucosa

Objective

The purpose of this study was to comparatively analyze the signature Raman spectra of genomic DNA, nuclei, and tissue of normal gastric mucosa and gastric cancer and to investigate the biochemical transformation of molecules associated with gastric mucosa malignancy.

Method

Genomic DNA, nuclei, and tissue from normal gastric mucosa and gastric cancer were analyzed by Raman spectroscopy.

Results

1) The Raman spectrum of gastric cancer genomic DNA showed that two peaks appeared, one at approximately 1090 cm^-1 with a higher intensity than the peak at 1050 cm^-1 in the spectrum. Characteristic peaks appeared at 950 cm^-1, 1010 cm^-1, and 1100-1600 cm^-1. 2) Using a hematoxylin and eosin (H&E)-stained section, the intensity of the characteristic peak of nucleic acids at 1085 cm^-1 was increased and shifted to 1088 cm^-1 in cancer cells. The relative intensity of the characteristic peaks of nucleoproteins at 755 cm^-1 and 1607 cm^-1 was significantly increased in cancer cells compared with normal cells. 3) Compared with normal tissues, the peak representing PO^2- symmetric stretching vibration shifted from 1088 cm^-1 to 1083 cm^-1 in cancer tissue, and the characteristic peak for collagen at 938 cm^-1 shifted to 944 cm^-1. In addition, an extra characteristic peak indicating C = C stretching vibration appeared at 1379 cm^-1 in the lipid spectrum in cancer tissue.

Conclusions

The position, intensity, and shape of peaks in the Raman spectra of DNA, nuclei, and tissue from gastric cancer were significantly different compared with those of normal cells. These results indicate that the DNA phosphate backbone becomes unstable in cancer cells and might be broken; the relative content of histones is increased and stable; the relative collagen content is reduced, facilitating cancer cell metastasis; and the relative content of unsaturated fatty acids is increased, increasing the mobility of the plasma membrane of cancer cells.

Bile analysis using high-throughput FTIR spectroscopy for the diagnosis of malignant biliary strictures: a pilot study in 57 patients

This study aimed at determining whether FTIR spectroscopy is able to distinguish bile samples from patients with and without malignant biliary strictures. Bile samples were collected in 19 patients with malignant biliary strictures and 38 with benign biliary diseases during endoscopic procedures. FTIR spectra were acquired on dried drops of whole bile, aqueous and organic phases obtained after lipid extraction. Data were analyzed by principal component analysis and by the support vector machine classification using a leave-n-out cross validation procedure. This was applied to the whole set of spectra and the mean and median spectra of each patient. By leaving one patient out, the classifier allowed discriminating patients with and without malignant biliary strictures with a sensitivity between 82% and 95% and a specificity between 85% and 100%. Using a randomized leave-n -out cross-validation with n = 2, 5 and 10 patients, the sensitivity decreased slightly by about 5 to 10% while the specificity remained stable, suggesting the robustness of the classifier. FTIR spectroscopy combined with chemometrics therefore shows potential to differentiate bile from patients with and without malignant biliary strictures. Although promising, the results of this pilot study cannot be generalized and needs to be confirmed in a larger population.

Glucose detection using SERS with multi-branched gold nanostructures in aqueous medium

Gold nanoparticles (AuNPs), multi-branched gold nanoparticles (MBGNs), and silica-coated MBGNs (MBGNs-silica) were studied for rhodamine B (RB) and α-glucose Raman detection at low concentration in water.

The development of surface-enhanced Raman scattering as a detection modality for portable in vitro diagnostics: progress and challenges

This perspective provides an overview of the diverse surface-enhanced Raman scattering (SERS)-based sensor platforms that have been developed for in vitro diagnostic applications. To provide focus, protein and nucleic acid detection assays based on the principle of extrinsic SERS sensing are emphasized, as well as their potential for translation to fully integrated point-of-care (POC) test platforms. The development of intrinsic SERS sensors, which are predicated on the direct detection of analytes by laser excitation, entails unique opportunities and challenges deserving of their own attention. As the robust sensing of disease pathogens and cancers in both clinical facilities and limited resource settings is the targeted objective of many next-generation biosensors, the majority of the research progress summarized here centers on SERS sensors developed for the rapid, sensitive and selective detection of disease-causing pathogens and biomarkers. In our effort to communicate a realistic assessment of the progress that has been made and the challenges that lie ahead, we avoid an overtly optimistic appraisal of the current status of SERS diagnostics that does not tacitly acknowledge the difficulties inherent in aligning SERS-based technologies alongside ELISA and PCR technologies as a complementary method for bioanalyte detection possessing unique advantages.

Human saliva-based quantitative monitoring of clarithromycin by flow injection chemiluminescence analysis: a pharmacokinetic study

Human saliva quantitative monitoring of clarithromycin (CLA) by chemiluminescence (CL) with flow injection analysis was proposed for the first time, which was based on the quenching effect of CLA on luminol-bovine serum albumin (BSA) CL system with a linear range from 7.5 × 10(-4) to 2.0 ng/ml. This proposed approach, offering a maximum sample throughput of 100 h(-1), was successfully applied to the quantitative monitoring of CLA levels in human saliva during 24 h after a single oral dose of 250 mg intake, with recoveries of 95.2 ∼ 109.0% and relative standard deviations lower than 6.5 % (N = 7). Results showed that CLA reached maximum concentration of 2.28 ± 0.02 μg/ml at approximately 3 h, and the total elimination ratio was 99.6 % in 24 h. The pharmacokinetic parameters including absorption rate constant (0.058 ± 0.006 h(-1)), elimination rate constant (0.149 ± 0.009 h(-1)) and elimination half-life time (4.66 ± 0.08 h) were obtained. A comparison of human saliva and urine monitoring was also given. The mechanism study of BSA-CLA interaction revealed the binding of CLA to BSA is an entropy driven and spontaneous process through hydrophobic interaction, with binding constant K BSA-CLA of 4.78 × 10(6) l/mol and the number of binding sites n of 0.82 by flow injection-chemiluminescence model. Molecular docking analysis further showed CLA might be in subdomain IIA of BSA, with K BSA-CLA of 6.82 × 10(5) l/mol and ΔG of -33.28 kJ/mol.

Intracellular SERS nanoprobes for distinction of different neuronal cell types

Distinction between closely related and morphologically similar cells is difficult by conventional methods especially without labeling. Using nuclear-targeted gold nanoparticles (AuNPs) as intracellular probes we demonstrate the ability to distinguish between progenitor and differentiated cell types in a human neuroblastoma cell line using surface-enhanced Raman spectroscopy (SERS). SERS spectra from the whole cell area as well as only the nucleus were analyzed using principal component analysis that allowed unambiguous distinction of the different cell types. SERS spectra from the nuclear region showed the developments during cellular differentiation by identifying an increase in DNA/RNA ratio and proteins transcribed. Our approach using nuclear-targeted AuNPs and SERS imaging provides label-free and noninvasive characterization that can play a vital role in identifying cell types in biomedical stem cell research.

Continuous, quantitative monitoring of roxithromycin in human saliva by flow injection chemiluminescence analysis

Human saliva quantitative monitoring of roxithromycin (ROX) at picomolar-level by flow injection (FI) chemiluminescence (CL) analysis is described for the first time, to our knowledge. Monitoring was based on the CL intensity from luminol-BSA reaction, which can be quenched in the presence of ROX, with the decreasing CL intensity linearly proportional to the logarithm of the ROX concentration, ranging from 0.6 to 1000 pmol·L(-1). The detection limit of the proposed method for the determination of ROX was as low as 0.2 pmol·L(-1) (3σ), and the relative standard deviations were less than 4.0% (n = 7). A complete analytical process, including sampling and washing for ROX determination, conducted at a flow rate of 2.0 mL·min(-1), was performed completely within 30 s, yielding a sample efficiency of 120 h(-1). The proposed method was successfully applied to the determination of ROX in human saliva and serum samples with recoveries from 90.9% to 110.1%. The continuous monitoring of ROX in human saliva after oral intake showed that the total elimination ratio was 87.1% during 24 h, and the pharmacokinetic parameters were 0.97 ± 0.18 h(-1) for the absorption rate constant K(a), 0.082 ± 0.010 h(-1) for the elimination rate constant K(e), and 8.56 ± 1.11 h for the elimination half-life time t(1/2). It was also found that ROX in human saliva and urine simultaneously reached the maximum at 2 h with the concentration correlate ratio of 0.97.

Salivary biomarker development using genomic, proteomic and metabolomic approaches

The use of saliva as a diagnostic sample provides a non-invasive, cost-efficient method of sample collection for disease screening without the need for highly trained professionals. Saliva collection is far more practical and safe compared with invasive methods of sample collection, because of the infection risk from contaminated needles during, for example, blood sampling. Furthermore, the use of saliva could increase the availability of accurate diagnostics for remote and impoverished regions. However, the development of salivary diagnostics has required technical innovation to allow stabilization and detection of analytes in the complex molecular mixture that is saliva. The recent development of cost-effective room temperature analyte stabilization methods, nucleic acid pre-amplification techniques and direct saliva transcriptomic analysis have allowed accurate detection and quantification of transcripts found in saliva. Novel protein stabilization methods have also facilitated improved proteomic analyses. Although candidate biomarkers have been discovered using epigenetic, transcriptomic, proteomic and metabolomic approaches, transcriptomic analyses have so far achieved the most progress in terms of sensitivity and specificity, and progress towards clinical implementation. Here, we review recent developments in salivary diagnostics that have been accomplished using genomic, transcriptomic, proteomic and metabolomic approaches.