Micromachined sensor for lactate monitoring in saliva

A miniaturised sensor for continuous lactate measurement in saliva was developed and tested. The sensor was built using silicon microfabrication technologies. The size of the chip is 5.5 mmx6.4 mmx0.7 mm and features a working, a counter and an Iridium reference electrode. The chip has a cavity whose floor is perforated by fine pores. The cavity contains the enzyme lactate oxidase (LOD), which is immobilised in an agarose gel. Prior to the amperometric detection of the reaction product hydrogen peroxide at the working electrode, the analyte lactate has to pass the pores to reach the cavity with the lactate oxidase by diffusion. To test the silicon sensor, capillary blood and saliva samples were obtained during standardised ergometer tests. Salivary lactate concentrations were determined with the sensor and compared to photometrically derived data from a lab-automate. In addition the saliva data were compared to standard capillary blood lactate concentrations measured with a pocket photometer. Lactate concentration versus load graphs were plotted and compared visually showing very similar progressions. The novel approach enables a location independent, permanent real-time measurement of the lactate concentration during exercise.     

Systematic evaluation of candidate blood markers for detecting ovarian cancer

Background

Epithelial ovarian cancer is a significant cause of mortality both in the United States and worldwide, due largely to the high proportion of cases that present at a late stage, when survival is extremely poor. Early detection of epithelial ovarian cancer, and of the serous subtype in particular, is a promising strategy for saving lives. The low prevalence of ovarian cancer makes the development of an adequately sensitive and specific test based on blood markers very challenging. We evaluated the performance of a set of candidate blood markers and combinations of these markers in detecting serous ovarian cancer.

Methods and Findings

We selected 14 candidate blood markers of serous ovarian cancer for which assays were available to measure their levels in serum or plasma, based on our analysis of global gene expression data and on literature searches. We evaluated the performance of these candidate markers individually and in combination by measuring them in overlapping sets of serum (or plasma) samples from women with clinically detectable ovarian cancer and women without ovarian cancer. Based on sensitivity at high specificity, we determined that 4 of the 14 candidate markers-MUC16, WFDC2, MSLN and MMP7-warrant further evaluation in precious serum specimens collected months to years prior to clinical diagnosis to assess their utility in early detection. We also reported differences in the performance of these candidate blood markers across histological types of epithelial ovarian cancer.

Conclusions

By systematically analyzing the performance of candidate blood markers of ovarian cancer in distinguishing women with clinically apparent ovarian cancer from women without ovarian cancer, we identified a set of serum markers with adequate performance to warrant testing for their ability to identify ovarian cancer months to years prior to clinical diagnosis. We argued for the importance of sensitivity at high specificity and of magnitude of difference in marker levels between cases and controls as performance metrics and demonstrated the importance of stratifying analyses by histological type of ovarian cancer. Also, we discussed the limitations of studies (like this one) that use samples obtained from symptomatic women to assess potential utility in detection of disease months to years prior to clinical detection.     

STR markers for detecting heterogeneity in Indian population

Short tandem repeats are highly polymorphic sequences of nucleotides, which are abundant in eukaryotic genome. They form approximately 3% of the total human genome and occur on average in every 10, 000 nucleotides. Due to their small dimension, low mutation, and high level of polymorphism, these markers are intensely used as important genetic markers for mapping studies, disease diagnosis, and human identity testing. In the present study allelic distribution of four autosomal short tandem repeat markers (D21S2055, D21S11, D21S1435 and D21S1411) has been analyzed in Indian population. For determination of heterogeneity and their allelic frequency QF-PCR analysis have been done. All the loci were found highly polymorphic. Marker D21S1411 was the most informative (93.6%) and D21S1435 (70.1%) was the least informative marker in Indian population.     

Proinflammatory factors in saliva as possible markers for periodontal disease

Studies have indicated that host inflammatory proteins, enzymes and indicators of bone metabolism present in saliva differ in different types of periodontal disease. However, the number of markers analyzed was limited and the effect of edentulousness was not examined. We measured the concentration of host inflammatory proteins: C-reactive protein (CRP), C3 and C4 complement components, alpha-2-macroglobulin (alpha-2M) and tumor-necrosis factor (TNF) in unstimulated saliva of 14 periodontally healthy (PH), 9 edentulous persons (EP), 10 patients with chronic periodontitis (CP) and 18 with aggressive periodontitis (AgP). TNF was below the level of detection in all samples except one. Edentulous persons and patients with CP had significantly reduced concentrations of CRP, C3 and alpha-2M. Edentulous persons and AgP patients had lower C4 concentrations. We can conclude that edentulous persons and CP patients have reduced salivary concentrations of host inflammatory proteins. These findings suggest that a reduction in host responsiveness might play a role in the pathogenesis of CP.     

A model-based statistic for detecting molecular markers associated with complex survival patterns in early-stage cancer

ABSTRACT: BACKGROUND: : In early-stage of cancer, primary treatment can be considered as effective at eliminating the tumor for a non-negligible proportion of patients whereas for the others it leads to a lower tumor burden and thereby potentially prolonged survival. In this mixed population of patients, it is of great interest to detect complex differences in survival distributions associated with molecular markers that potentially activate latent downstream pathways implicated in tumor progression. METHOD: : We propose a novel model-based score test designed for identifying molecular markers with complex effects on survival in early-stage cancer. From a biological point of view, the proposed score test allows to detect complex changes in the survival distributions linked to either the tumor burden or its dynamic growth. RESULTS: : Simulation results show that the proposed statistic is powerful at identifying departure from the null hypothesis of no survival difference. The practical use of the proposed statistic is exemplified by analyzing the prognostic impact of Kras mutation in early-stage of lung adenocarcinomas. This analysis leads to the conclusion that Kras mutation has a significant negative prognostic impact on survival. Moreover, it emphasizes that the complex role of Kras mutation on survival would have been overlooked by considering results from the classical logrank test. CONCLUSION: With the growing number of biological markers to be tested in early-stage cancer, the proposed score test statistic is a powerful tool for detecting molecular markers associated with complex survival patterns.     

Multiplexed electrical detection of cancer markers with nanowire sensor arrays

We describe highly sensitive, label-free, multiplexed electrical detection of cancer markers using silicon-nanowire field-effect devices in which distinct nanowires and surface receptors are incorporated into arrays. Protein markers were routinely detected at femtomolar concentrations with high selectivity, and simultaneous incorporation of control nanowires enabled discrimination against false positives. Nanowire arrays allowed highly selective and sensitive multiplexed detection of prostate specific antigen (PSA), PSA-alpha1-antichymotrypsin, carcinoembryonic antigen and mucin-1, including detection to at least 0.9 pg/ml in undiluted serum samples. In addition, nucleic acid receptors enabled real-time assays of the binding, activity and small-molecule inhibition of telomerase using unamplified extracts from as few as ten tumor cells. The capability for multiplexed real-time monitoring of protein markers and telomerase activity with high sensitivity and selectivity in clinically relevant samples opens up substantial possibilities for diagnosis and treatment of cancer and other complex diseases.     

Exosomal surface protein markers in diagnosis of colorectal cancer

Colorectal cancer (CRC) is one of the most common primary malignancies. Early stages of the disease are asymptomatic in the majority of cases, leading to late detection and high mortality. Available noninvasive diagnostic techniques are limited in sensitivity and specificity, and designing new ones is still a pressing problem. Exosomes are membrane-derived microvesicles secreted into human biological fluids and provide a novel way to assess the course of an oncology disease. The review describes the repertoire of exosomal surface biomarkers found in the blood of CRC patients and the prospects of employing multiplexed tests for exosomal markers in early noninvasive diagnosis of cancer.     

Optical sensor revealed abnormal nuclease spatial activity on cancer cell membrane

Nucleases are important enzymes that cleave nucleic acids and play critical roles in DNA repair, immune defense and potentially in cancer invasion. However, their spatial dynamics at subcellular level is much less studied. Here, we developed a surface‐tethered nuclease sensor (SNS) which directly converts membrane‐bound nuclease (MN) activity to fluorescent signal, therefore, mapping MN activity on cell adhesion sites with high resolution and sensitivity. With SNS, we studied MN activity on the ventral membrane of cancer cells, where MN activity initially occurs in punctate regions and advances in a coral‐shaped pattern. In six tested cell‐lines, the MN activity levels in cancer cells are significantly higher than those in non‐cancer cells. We then tested SNS as a sensitive approach to detect cancer cells at single cell level. Single breast cancer cells were successfully detected from thousands of adherent non‐cancer cells and from millions of non‐adherent blood cells.      

Phylogenetic methodology for detecting protein interactions

Detecting protein-protein interactions and assigning proteins to functional complexes are key challenges of modern biology. The rise of genomics has lead to evidence that correlated patterns of presence/absence and/or fusing of proteins in any organism suggest these proteins interact. Unfortunately, methods based on such data work best with divergent genomes, whereas major sequencing efforts in vertebrates, for example, are yielding alignments of the same set of proteins sampled from the same set of taxa (species). Using vertebrate mitochondrial genomes to illustrate a novel method, we associate proteins based on vectors of their evolutionary tree edge (branch or internode) lengths. This approach is based on the expectation that molecular coevolution is greatest between proteins that interact in some way. Mitochondrial DNA-encoded proteins are associated into groups largely consistent with the complexes they come from. This association is apparently not due to the tree structure or mutation processes, leaving coevolution as the best explanation. We show that it is important that the tree used to derive the edge-length vector is estimated accurately in terms of both topology and edge lengths. Although more complex substitution models reduce systematic error, they also inflate stochastic error. This makes the use of less complex substitution models preferable in some circumstances. We describe a method to estimate correlations of pairwise evolutionary distances, which adjusts for non-independent correlations due to shared evolutionary history. Associations of proteins based on their edge-length vectors are visualized and assessed using a variety of hierarchical clustering and multidimensional scaling methods. New formula for estimating the fit of data to model, including the average percent standard deviation of distances on least squares trees, are presented. Use of edge-length vectors is compared and contrasted with correlated distance methods, correlated rates methods, and site-specific evidence of coevolution.     

Biosensors for cancer markers diagnosis

Point-of-care diagnostic devices present a viable option for the rapid and sensitive detection and analysis of cancer markers. With the growing number of cancer cases being diagnosed worldwide and the increased number of fatalities due to late disease detection, biosensors can play an important role in the early diagnosis of cancer. Molecular profiles of patients are being increasingly studied using new molecular tools such as genomic and proteomic techniques. These methods combined with bioinformatics tools are generating new data which is being employed in the elucidation of new disease biomarkers. As with many disease conditions finding specific and sensitive markers that are associated with only one type of the disease can be difficult. In addition to this, the level of the biomarkers in biological fluids can vary depending on different disease conditions and stages. A number of molecular markers are therefore usually evaluated for cancer diagnosis and these can include proteins, peptides, over/under expression of gene markers and gene mutations. This review provides an overview of the biosensor technology available today, areas which are currently being developed and researched for cancer markers diagnosis—and a consideration of future prospects for the technology.     

Fast statistical tests for detecting heterotachy in protein evolution

The w statistic introduced by Lockhart et al. (1998. A covariotide model explains apparent phylogenetic structure of oxygenic photosynthetic lineages. Mol Biol Evol. 15:1183-1188) is a simple and easily calculated statistic intended to detect heterotachy by comparing amino acid substitution patterns between two monophyletic groups of protein sequences. It is defined as the difference between the fraction of varied sites in both groups and the fraction of varied sites in each group. The w test has been used to distinguish a covarion process from equal rates and rates variation across sites processes. Using simulation we show that the w test is effective for small data sets and for data sets that have low substitution rates in the groups but can have difficulties when these conditions are not met. Using site entropy as a measure of variability of a sequence site, we modify the w statistic to a w' statistic by assigning as varied in one group those sites that are actually varied in both groups but have a large entropy difference. We show that the w' test has more power to detect two kinds of heterotachy processes (covarion and bivariate rate shifts) in large and variable data. We also show that a test of Pearson's correlation of the site entropies between two monophyletic groups can be used to detect heterotachy and has more power than the w' test. Furthermore, we demonstrate that there are settings where the correlation test as well as w and w' tests do not detect heterotachy signals in data simulated under a branch length mixture model. In such cases, it is sometimes possible to detect heterotachy through subselection of appropriate taxa. Finally, we discuss the abilities of the three statistical tests to detect a fourth mode of heterotachy: lineage-specific changes in proportion of variable sites.     

Optical colorimetric sensor strip for direct readout glucose measurement

A novel direct readout colorimetric optical glucose sensor strip was constructed based on a three-layer film, including a green-emitted CdTe/CdS quantum dots (QDs) layer as a stable color background, a red-fluorescent platinum-porphyrin oxygen-sensing layer and a glucose oxidase layer. The sensor achieved high resolution (up to 0.2 mmol L⁻¹) glucose determination with a detection range from 0 to 3.0 mmol L⁻¹. A “glucose ruler” which acts as a glucose standard colorimetric card was obtained. Glucose concentration could easily be directly readout using the “glucose ruler”, which made the glucose determination rapid, convenient and easy. The effects of pH, salinity and temperature were systematically investigated. The prepared sensor was finally applied for glucose sample analysis, compared with the “glucose ruler”, accurate results could be directly readout.     

Optimal clustering for detecting near-native conformations in protein docking

Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because long-range electrostatic and/or desolvation forces steer the proteins to a low free-energy attractor at the binding region. Something similar occurs in the docking of small molecules, although in this case shorter-range van der Waals forces play a more critical role. Based on the above, we have developed two different clustering strategies to predict docked conformations based on the clustering properties of a uniform sampling of low free-energy protein-protein and protein-small molecule complexes. We report on significant improvements in the automated prediction and discrimination of docked conformations by using the cluster size and consensus as a ranking criterion. We show that the success of clustering depends on identifying the appropriate clustering radius of the system. The clustering radius for protein-protein complexes is consistent with the range of the electrostatics and desolvation free energies (i.e., between 4 and 9 Angstroms); for protein-small molecule docking, the radius is set by van der Waals interactions (i.e., at approximately 2 Angstroms). Without any a priori information, a simple analysis of the histogram of distance separations between the set of docked conformations can evaluate the clustering properties of the data set. Clustering is observed when the histogram is bimodal. Data clustering is optimal if one chooses the clustering radius to be the minimum after the first peak of the bimodal distribution. We show that using this optimal radius further improves the discrimination of near-native complex structures.     

Bienzymatic amperometric sensor for protein assay in milk

A new bienzymatic amperometric sensor is proposed for the assay of the protein content of milk. The sensor is based on two enzymes: carboxypeptidase A and L-amino acid oxidase. The response characteristics obtained for this sensor (detection limit of 1.5 micromol/L, linear concentration range between 1.8 and 2.8 micromol/L), as well as high selectivity over possible interferences from milk, made it applicable as a detector in flow injection analysis (FIA). The response characteristics obtained in the non-equilibrium conditions (FIA system) are: detection limit of 1.5 micromol/L and linear concentration range between 2 and 3.5 micromol/L. Without FIA, the average recovery of proteins from milk and milk products is 99.06 +/- 0.07% and, by utilization of FIA, it increased to 99.73 +/- 0.03. The sensor proved a good reliability for the assay of proteins in milk and milk products.     

Statistical power for detecting genetic divergence—organelle versus nuclear markers

Statistical power is critical in conservation for detecting genetic differences in space or time from allele frequency data. Organelle and nuclear genetic markers have fundamentally different transmission dynamics; the potential effect of these differences on power to detect divergence have been speculated on but not investigated. We examine, analytically and with computer simulations, the relative performance of organelle and nuclear markers under basic, ideal situations. We conclude that claims of a generally higher resolving power of either marker type are not correct. The ratio R = F _ST,organelle/F _ST,nuclear varies between 1 and 4 during differentiation and this greatly affects the power relationship. When nuclear F _ST is associated with organelle differentiation four times higher, the power of the organelle marker is similar to two nuclear loci with the same allele frequency distribution. With large sample sizes (n ≥ 50) and several populations or many alleles per locus (≥5), the power difference may typically be disregarded when nuclear F _ST > 0.05. To correctly interpret observed patterns of genetic differentiation in practical situations, the expected F _STs and the statistical properties (i.e., power analysis) of the genetic markers used should be evaluated, taking the observed allele frequency distributions into consideration.