Collection and determination of nucleotide metabolites in neonatal and adult saliva by high performance liquid chromatography with tandem mass spectrometry

Saliva contains a number of biochemical components which may be useful for diagnosis/monitoring of metabolic disorders, and as markers of cancer or heart disease. Saliva collection is attractive as a non-invasive sampling method for infants and elderly patients. We present a method suitable for saliva collection from neonates. We have applied this technique for the determination of salivary nucleotide metabolites. Saliva was collected from 10 healthy neonates using washed cotton swabs, and directly from 10 adults. Two methods for saliva extraction from oral swabs were evaluated. The analytes were then separated using high performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS). The limits of detection for 14 purine/pyrimidine metabolites were variable, ranging from 0.01 to 1.0 mu M. Recovery of hydrophobic purine/pyrimidine metabolites from cotton tips was consistently high using water/acetonitrile extraction (92.7-111%) compared with water extraction alone. The concentrations of these metabolites were significantly higher in neonatal saliva than in adults. Preliminary ranges for nucleotide metabolites in neonatal and adult saliva are reported. Hypoxanthine and xanthine were grossly raised in neonates (49.3 +/- 25.4; 30.9 +/- 19.5 mu M respectively) compared to adults (4.3 +/- 3.3; 4.6 +/- 4.5 mu M); nucleosides were also markedly raised in neonates. This study focuses on three essential details: contamination of oral swabs during manufacturing and how to overcome this; weighing swabs to accurately measure small saliva volumes; and methods for extracting saliva metabolites of interest from cotton swabs. A method is described for determining nucleotide metabolites using HPLC with photo-diode array or MS/MS. The advantages of utilising saliva are highlighted. Nucleotide metabolites were not simply in equilibrium with plasma, but may be actively secreted into saliva, and this process is more active in neonates than adults. (C) 2013 Elsevier B.V. All rights reserved.

High-yeld RNA-extraction method for saliva

BACKGROUND: The use of salivary diagnostics is increasing because of its noninvasiveness, ease of sampling, and the relatively low risk of contracting infectious organisms. Saliva has been used as a biological fluid to identify and validate RNA targets in head and neck cancer patients. The goal of this study was to develop a robust, easy, and cost-effective method for isolating high yields of total RNA from saliva for downstream expression studies. METHODS: Oral whole saliva (200 mu L) was collected from healthy controls (n = 6) and from patients with head and neck cancer (n = 8). The method developed in-house used QIAzol lysis reagent (Qiagen) to extract RNA from saliva (both cell-free supernatants and cell pellets), followed by isopropyl alcohol precipitation, cDNA synthesis, and real-time PCR analyses for the genes encoding beta-actin ("housekeeping" gene) and histatin (a salivary gland-specific gene). RESULTS: The in-house QIAzol lysis reagent produced a high yield of total RNA (0.89 -7.1 mu g) from saliva (cell-free saliva and cell pellet) after DNase treatment. The ratio of the absorbance measured at 260 nm to that at 280 nm ranged from 1.6 to 1.9. The commercial kit produced a 10-fold lower RNA yield. Using our method with the QIAzol lysis reagent, we were also able to isolate RNA from archived saliva samples that had been stored without RNase inhibitors at -80 degrees C for >2 years. CONCLUSIONS: Our in-house QIAzol method is robust, is simple, provides RNA at high yields, and can be implemented to allow saliva transcriptomic studies to be translated into a clinical setting.

Saliva as an emerging biofluid for clinical diagnosis and applications of MEMS/NEMS in salivary diagnostics

Saliva as a biological fluid is gaining wider acceptance for diagnosing diseases. The growing interest in saliva as a biological fluid is due to its noninvasiveness, ease of use, cost-effectiveness, and multiple sample collection possibilities as well as minimal risk to health care professionals of contracting infectious organisms such as HIV and Hep B. However, the clinical translation of saliva is hampered by our lack of understanding of the biomolecular transportation from blood into saliva, the diurnal variations of biomolecules present in saliva, and relatively low levels of analytes (100th to a 1000th fold less than in blood). We provide information on the current status of salivary research, salivary diagnostics empowered by nanotechnology, and future prospects in this emerging field of saliva diagnostics.

Saliva proteome research: current status and future outlook

Human saliva harbours proteins of clinical relevance and about 30% of blood proteins are also present in saliva. This highlights that saliva can be used for clinical applications just as urine or blood. However, the translation of salivary biomarker discoveries into clinical settings is hampered by the dynamics and complexity of the salivary proteome. This review focuses on the current status of technological developments and achievements relating to approaches for unravelling the human salivary proteome. We discuss the dynamics of the salivary proteome, as well as the importance of sample preparation and processing techniques and their influence on downstream protein applications; post-translational modifications of salivary proteome and protein: protein interactions. In addition, we describe possible enrichment strategies for discerning post-translational modifications of salivary proteins, the potential utility of selected-reaction-monitoring techniques for biomarker discovery and validation, limitations to proteomics and the biomarker challenge and future perspectives. In summary, we provide recommendations for practical saliva sampling, processing and storage conditions to increase the quality of future studies in an emerging field of saliva clinical proteomics. We propose that the advent of technologies allowing sensitive and high throughput proteome-wide analyses, coupled to well-controlled study design, will allow saliva to enter clinical practice as an alternative to blood-based methods due to its simplistic nature of sampling, non-invasiveness, easy of collection and multiple collections by untrained professionals and cost-effective advantages.

Quantification of D-dimer levels in human saliva

Background: Plasma D-dimer tests are currently used to exclude deep vein thrombosis and pulmonary embolism. Human saliva has numerous advantages over blood as a diagnostic sample. The aims of our study were to develop a reliable immunoassay to detect D-dimer levels in saliva, and to determine the correlation between salivary and blood D-dimer levels. Results/methodology: Saliva and blood samples were collected from 40 healthy volunteers. We developed a AlphaLISA((R)) immunoassay with acceptable analytical performances to quantify D-dimer levels in the samples. The median salivary D-dimer levels were 138.1 ng/ml (morning) and 140.7 ng/ml (afternoon), and the plasma levels were 75.0 ng/ml. Salivary D-dimer levels did not correlate with plasma levels (p = 0.61). Conclusion: For the first time, we have quantified D-dimer levels and found twofold increase in saliva (p < 0.05) than in plasma. Further studies are required to demonstrate the clinical relevance/utility of salivary D-dimer in patients with confirmed deep vein thrombosis and/or pulmonary embolism.

Profiling of immunoglobulins in resting and mechanically stimulated saliva

Background: Dysregulation of salivary immunoglobulins has been implicated in illnesses ranging from periodontal disease to HIV aids and malignant cancers. Despite these advances there is a lack of agreement among studies with regard to the salivary immunoglobulin levels in healthy controls. Methodology: Resting and mechanically stimulated saliva samples and matching serum samples were collected from healthy individuals (n = 33; 40-55 years of age; gender: 23 female, 10 male). A matrix-matched AlphaLISA((R)) assay was developed to determine the concentrations of IgG1 and IgG4 in serum and saliva samples. Conclusion: Clear relationships were observed in the flow rate and concentration of each immunoglobulin in the two types of saliva. This study affirms the need to establish and standardize collection methods before salivary IgGs are used for diagnostic purposes.

A novel saliva-based microRNA biomarker panel to detect head and neck cancers

Background MicroRNAs (miRNAs) are known to play an important role in cancer development by post-transcriptionally affecting the expression of critical genes. The aims of this study were two-fold: (i) to develop a robust method to isolate miRNAs from small volumes of saliva and (ii) to develop a panel of saliva-based diagnostic biomarkers for the detection of head and neck squamous cell carcinoma (HNSCC). Methods Five differentially expressed miRNAs were selected from miScript™ miRNA microarray data generated using saliva from five HNSCC patients and five healthy controls. Their differential expression was subsequently confirmed by RT-qPCR using saliva samples from healthy controls (n = 56) and HNSCC patients (n = 56). These samples were divided into two different cohorts, i.e., a first confirmatory cohort (n = 21) and a second independent validation cohort (n = 35), to narrow down the miRNA diagnostic panel to three miRNAs: miR-9, miR-134 and miR-191. This diagnostic panel was independently validated using HNSCC miRNA expression data from The Cancer Genome Atlas (TCGA), encompassing 334 tumours and 39 adjacent normal tissues. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capacity of the panel. Results On average 60 ng/μL miRNA was isolated from 200 μL of saliva. Overall a good correlation was observed between the microarray data and the RT-qPCR data. We found that miR-9 (P <0.0001), miR-134 (P <0.0001) and miR-191 (P <0.001) were differentially expressed between saliva from HNSCC patients and healthy controls, and that these miRNAs provided a good discriminative capacity with area under the curve (AUC) values of 0.85 (P <0.0001), 0.74 (P < 0.001) and 0.98 (P < 0.0001), respectively. In addition, we found that the salivary miRNA data showed a good correlation with the TCGA miRNA data, thereby providing an independent validation. Conclusions We show that we have developed a reliable method to isolate miRNAs from small volumes of saliva, and that the saliva-derived miRNAs miR-9, miR-134 and miR-191 may serve as novel biomarkers to reliably detect HNSCC. © 2014 International Society for Cellular Oncology.

Validation of an immunoassay to measure plasminogen-activator inhibitor-1 concentrations in human saliva

Introduction We have previously shown that the concentrations of D-dimer are significantly elevated in saliva compared with plasma. Saliva offers several advantages compared with blood analysis. We hypothesised that human saliva contains plasminogen activator inhibitor-1 (PAI-1) and that the concentrations are not affected by the time of saliva collection. The aim was to adopt and validate an immunoassay to quantify PAI-1 concentrations in saliva and to determine whether saliva collection time has an influence in the measurement. Materials and methods Two saliva samples (morning and afternoon) from the same day were collected from healthy subjects (N = 40) who have had no underlying heart conditions. A customized AlphaLISA® immunoassay (PerkinElmer®, MA, USA) was adopted and used to quantify PAI-1 concentrations. We validated the analytical performance of the customized immunoassay by calculating recovery of known amount of analyte spiked in saliva. Results: The recovery (95.03%), intra- (8.59%) and inter-assay (7.52%) variations were within the acceptable ranges. The median salivary PAI-1 concentrations were 394 pg/mL (interquartile ranges (IQR) 243.4-833.1 pg/mL) in the morning and 376 (129.1-615.4) pg/mL in the afternoon and the plasma concentration was 59,000 (24,000-110,000) pg/mL. Salivary PAI-1 did not correlate with plasma (P = 0.812). Conclusions The adopted immunoassay produced acceptable assay sensitivity and specificity. The data demonstrated that saliva contains PAI-1 and that its concentration is not affected by the time of saliva collection. There is no correlation between salivary and plasma PAI-1 concentrations. Further studies are required to demonstrate the utility of salivary PAI-1 in CVD risk factor studies.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks.

Time-expanded and heterodyned echolocation calls of the New Zealand long-tailed Chalinolobus tuberculatus and lesser short-tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time-expanded calls and power spectra generated for both time-expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time-expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time-expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Saliva diagnostics for oral diseases

Oral diseases, or stomatognathic diseases, denote the diseases of the mouth (“stoma”) and jaw (“gnath”). Dental caries and periodontal diseases have been traditionally considered as the most important global oral health burdens. It is important to note that in oral diagnostics, the greatest challenges are determining the clinical utility of potential biomarkers for screening (in asymptomatic people), predicting the early onset of disease (prognostic tests), and evaluating the disease activity and the efficacy of therapy through innovative diagnostic tests. An oral diagnostic test, in principle, should provide valuable information for differential diagnosis, localization of disease, and severity of infection. This information can then be incorporated by the physician when planning treatments and will provide means for assessing the effectiveness of therapy.