Reproducible and label-free biosensor for the selective extraction and rapid detection of proteins in biological fluids

Erythropoietin (EPO), a glycoprotein hormone of ∼34 kDa, is an important hematopoietic growth factor, mainly produced in the kidney and controls the number of red blood cells circulating in the blood stream. Sensitive and rapid recombinant human EPO (rHuEPO) detection tools that improve on the current laborious EPO detection techniques are in high demand for both clinical and sports industry. A sensitive aptamer-functionalized biosensor (aptasensor) has been developed by controlled growth of gold nanostructures (AuNS) over a gold substrate (pAu/AuNS). The aptasensor selectively binds to rHuEPO and, therefore, was used to extract and detect the drug from horse plasma by surface enhanced Raman spectroscopy (SERS). Due to the nanogap separation between the nanostructures, the high population and distribution of hot spots on the pAu/AuNS substrate surface, strong signal enhancement was acquired. By using wide area illumination (WAI) setting for the Raman detection, a low RSD of 4.92% over 150 SERS measurements was achieved. The significant reproducibility of the new biosensor addresses the serious problem of SERS signal inconsistency that hampers the use of the technique in the field. The WAI setting is compatible with handheld Raman devices. Therefore, the new aptasensor can be used for the selective extraction of rHuEPO from biological fluids and subsequently screened with handheld Raman spectrometer for SERS based in-field protein detection.

Active learning: A step towards automating medical concept extraction

Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

External knowledge and query strategies in active learning: A study in clinical information extraction

This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required. The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

Utility of web content blocks in content extraction

Currently we are facing an overburdening growth of the number of reliable information sources on the Internet. The quantity of information available to everyone via Internet is dramatically growing each year [15]. At the same time, temporal and cognitive resources of human users are not changing, therefore causing a phenomenon of information overload. World Wide Web is one of the main sources of information for decision makers (reference to my research). However our studies show that, at least in Poland, the decision makers see some important problems when turning to Internet as a source of decision information. One of the most common obstacles raised is distribution of relevant information among many sources, and therefore need to visit different Web sources in order to collect all important content and analyze it. A few research groups have recently turned to the problem of information extraction from the Web [13]. The most effort so far has been directed toward collecting data from dispersed databases accessible via web pages (related to as data extraction or information extraction from the Web) and towards understanding natural language texts by means of fact, entity, and association recognition (related to as information extraction). Data extraction efforts show some interesting results, however proper integration of web databases is still beyond us. Information extraction field has been recently very successful in retrieving information from natural language texts, however it is still lacking abilities to understand more complex information, requiring use of common sense knowledge, discourse analysis and disambiguation techniques.

Robust Web content extraction

We present an empirical evaluation and comparison of two content extraction methods in HTML: absolute XPath expressions and relative XPath expressions. We argue that the relative XPath expressions, although not widely used, should be used in preference to absolute XPath expressions in extracting content from human-created Web documents. Evaluation of robustness covers four thousand queries executed on several hundred webpages. We show that in referencing parts of real world dynamic HTML documents, relative XPath expressions are on average significantly more robust than absolute XPath ones.

Expression and characterization of digestive enzyme genes from hepatopancreatic transcripts from redclaw crayfish (Cherax quadricarinatus)

Redclaw crayfish, Cherax quadricarinatus, possess a number of biological and commercial attributes that make them ideal for commercial aquaculture. While some studies have investigated digestive enzyme activity and nutritional requirements of this species, little information exists about the expression of digestive enzyme genes and their role in regulating digestive capacity. The current study therefore sequenced and annotated a RNASeq library constructed from a redclaw hepatopancreas to identify genes involved in digestive enzyme production. We observed that most of the transcripts that were annotated as digestive enzyme genes are associated with carbohydrate metabolism, thus confirming that redclaw have an innate capacity to digest a range of carbohydrate substrates. While endoglucanases were the most abundant group of digestive enzymes found, a number of novel transcripts were also detected. Here, we provide the first report for the presence and expression of endo-b-mannanase in freshwater crayfish. This novel gene showed significant alignment with a GH5 family protein from marine Limnoriids, wood borers that do not possess symbiotic microbes in their gut system. Overall, the data generated here provide an important resource to better understand the suite of digestive enzymes in redclaw that are very useful to fully utilize the species’ digestive capacity and will assist development of specific artificial feeds.

Expression profiling in spondyloarthropathy synovial biopsies highlights changes in expression of inflammatory genes in conjunction with tissue remodelling genes

Background: In the spondyloarthropathies, the underlying molecular and cellular pathways driving disease are poorly understood. By undertaking a study in knee synovial biopsies from spondyloarthropathy (SpA) and ankylosing spondylitis (AS) patients we aimed to elucidate dysregulated genes and pathways. Methods RNA was extracted from six SpA, two AS, three osteoarthritis (OA) and four normal control knee synovial biopsies. Whole genome expression profiling was undertaken using the Illumina DASL system, which assays 24000 cDNA probes. Differentially expressed candidate genes were then validated using quantitative PCR and immunohistochemistry. Results: Four hundred and sixteen differentially expressed genes were identified that clearly delineated between AS/SpA and control groups. Pathway analysis showed altered gene-expression in oxidoreductase activity, B-cell associated, matrix catabolic, and metabolic pathways. Altered «myogene» profiling was also identified. The inflammatory mediator, MMP3, was strongly upregulated (5-fold) in AS/SpA samples and the Wnt pathway inhibitors DKK3 (2.7-fold) and Kremen1 (1.5-fold) were downregulated. Conclusions: Altered expression profiling in SpA and AS samples demonstrates that disease pathogenesis is associated with both systemic inflammation as well as local tissue alterations that may underlie tissue damaging modelling and remodelling outcomes. This supports the hypothesis that initial systemic inflammation in spondyloarthropathies transfers to and persists in the local joint environment, and might subsequently mediate changes in genes directly involved in the destructive tissue remodelling.

Proteogenomics of selective susceptibility to endotoxin using circulating acute phase biomarkers and bioassay development in sheep: a review

Scientists have injected endotoxin into animals to investigate and understand various pathologies and novel therapies for several decades. Recent observations have shown that there is selective susceptibility to Escherichia coli lipopolysaccharide (LPS) endotoxin in sheep, despite having similar breed characteristics. The reason behind this difference is unknown, and has prompted studies aiming to explain the variation by proteogenomic characterisation of circulating acute phase biomarkers. It is hypothesised that genetic trait, biochemical, immunological and inflammation marker patterns contribute in defining and predicting mammalian response to LPS. This review discusses the effects of endotoxin and host responses, genetic basis of innate defences, activation of the acute phase response (APR) following experimental LPS challenge, and the current approaches employed in detecting novel biomarkers including acute phase proteins (APP) and micro-ribonucleic acids (miRNAs) in serum or plasma. miRNAs are novel targets for elucidating molecular mechanisms of disease because of their differential expression during pathological, and in healthy states. Changes in miRNA profiles during a disease challenge may be reflected in plasma. Studies show that gel-based two-dimensional electrophoresis (2-DE) coupled with either matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) or liquid chromatography-mass spectrometry (LC-MS/MS) are currently the most used methods for proteome characterisation. Further evidence suggests that proteomic investigations are preferentially shifting from 2-DE to non-gel based LC-MS/MS coupled with data extraction by sequential window acquisition of all theoretical fragment-ion spectra (SWATH) approaches that are able to identify a wider range of proteins. Enzyme-linked immunosorbent assay (ELISA), quantitative real-time polymerase chain reaction (qRT-PCR), and most recently proteomic methods have been used to quantify low abundance proteins such as cytokines. qRT-PCR and next generation sequencing (NGS) are used for the characterisation of miRNA. Proteogenomic approaches for detecting APP and novel miRNA profiling are essential in understanding the selective resistance to endotoxin in sheep. The results of these methods could help in understanding similar pathology in humans. It might also be helpful in the development of physiological and diagnostic screening assays for determining experimental inclusion and endpoints, and in clinical trials in future

A novel fluorescent probe involving a graphene quantum dot-enzyme hybrid system for the analysis of hydroquinone in the presence of toxic resorcinol and catechol

An efficient method for the analysis of hydroquinone at trace levels in water samples has been developed in the form of a fluorescent probe based on graphene quantum dots (GQDs). The analytical variable, fluorescence quenching, was generated from the formation of benzoquinone intermediates, which formed during the catalytic oxidation of hydroquinone by horseradish peroxidase (HRP). In general, the reaction mechanism involved hydroquinone, as an electron acceptor, which affected the surface state of GQDs via an electron transfer effect. The water-soluble GQDs were directly prepared by the pyrolysis of citric acid and with the use of the mentioned hybrid enzyme system, the detection limit for hydroquinone was as low as 8.4 × 10−8 M. Furthermore, this analysis was almost unaffected by other phenol and quinine compounds, such as phenol, resorcinol and other quinines, and therefore, the developed GQD method produced satisfactory results for the analysis of hydroquinone in several different lake water samples.

Determination of tricyclazole in water using solid phase extraction and liquid chromatography

A method for determination of tricyclazole in water using solid phase extraction and high performance liquid chromatography (HPLC) with UV detection at 230nm and a mobile phase of acetonitrile:water (20:80, v/v) was developed. A performance comparison between two types of solid phase sorbents, the C18 sorbent of Supelclean ENVI-18 cartridge and the styrene-divinyl benzene copolymer sorbent of Sep-Pak PS2-Plus cartridge was conducted. The Sep-Pak PS2-Plus cartridges were found more suitable for extracting tricyclazole from water samples than the Supelclean ENVI-18 cartridges. For this cartridge, both methanol and ethyl acetate produced good results. The method was validated with good linearity and with a limit of detection of 0.008gL-1 for a 500-fold concentration through the SPE procedure. The recoveries of the method were stable at 80% and the precision was from 1.1-6.0% within the range of fortified concentrations. The validated method was also applied to measure the concentrations of tricyclazole in real paddy water.

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Serum immunoglobulin G directed against porcine trypsin in the serum of cystic fibrosis children receiving porcine pancreatic enzyme supplements

Cystic fibrosis (CF) patients require pancreatic enzyme replacement therapy to correct pancreatic insufficiency. These enzymes are derived from porcine pancreas and are known to be antigenic. To determine the possible clinical consequences, a specific ELISA was developed to detect IgG antibody directed against porcine trypsin (PTAb) in the sera of CF patients. The assay was used to evaluate the occurrence of PTAb in a cross sectional study of 103 CF patients in relation to the introduction of porcine enzyme therapy, clinical status and genotype. Antibodies against porcine trypsin were detected in the sera of 63% of patients unrelated to the age of commencement or the duration of enzyme therapy. No differences were observed in the clinical status of CF patients who had developed PTAb (n = 65) and those who had no detectable PTAb (n = 38) as determined from: the current prescribed dose of porcine pancreatic enzyme capsules; Z scores for height and weight; and respiratory function tests. It is suggested that the PTAb commonly found in the sera of CF patients are of doubtful clinical significance but the prospect of PTAb contributing to immune complex disease should be examined further.

Analysis of word embeddings and sequence features for clinical information extraction

This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

The use of a synthetic prostaglandin e1 analogue (misoprostol) as an adjunct to pancreatic enzyme replacement in cystic fibrosis

Eleven cystic fibrosis children (mean age, 9.6 years) were chosen at random to participate in a study to observe the effects of concurrently stimulating gastric/duodenal bicarbonate secretion and inhibiting gastric acid secretion, using a methylated prostaglandin E1 analogue in patients with pancreatic insufficiency and taking pancreatic enzymes. Percentage fat absorption in 3-day stool collections were calculated before and after commencing therapy with misoprostol, 400 μg/day in divided doses. We found a significant reduction in fat output (14.7 ± 11.7 versus 7.5 ± 3.5 g/day, p < 0.05) in the study group as a whole and a significant reduction in steatorrhoeic level as a percentage of fat intake in all of the patients with abnormal base-line collections (23.1% versus 9.2% p < 0.002). We conclude that misoprostol should be considered in cystic fibrosis patients with steatorrhoea as a means of improving nutrient absorption. © 1988 Informa UK Ltd All rights reserved: reproduction in whole or part not permitted.