A fluorinated chitosan-based QuEChERS method for simultaneous determination of 20 organophosphorus pesticide residues in ginseng using GC-MS/MS.

In this study, a new fluorinated methacrylamide (MACF) was synthesized and evaluated as an adsorbent in the dispersive solid-phase extraction for the effective determination and extraction of 20 organophosphorus pesticides (OPPs) from ginseng samples using the QuEChERS (quick, easy, cheap, effective, rugged, safe) method coupled with GC-MS/MS. The properties of MACF were characterized using Fourier-transform infrared spectroscopy, elemental analysis, and high-resolution 19 F NMR. MACF, chitosan, primary and secondary amine, octadecylsilane, graphitized carbon black, Z-Sep, Z-Sep+ , and EMR-Lipid were compared in terms of extraction efficiency. The best results were obtained when MACF was used. Matrix-matched calibration was employed for quantification. All the OPPs exhibited good linearity (r2 > 0.9969) with the concentration at their respective concentration ranges. The limits of detection were 1.5-3.0 µg/kg, and the limits of quantification were 5.0-10.0 µg/kg. The trueness of the 20 pesticides at four spiked levels ranged from 86.1 to 111.1%, and the relative standard deviation was less than 11.3%. The modified QuEChERS method using MACF as the adsorbent was sensitive, reliable, and cost-effective and could be used for the determination of 20 OPP residues in ginseng.

Ontology-based tools to expedite predictive model construction.

Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.

Developing a section labeler for clinical documents.

Natural language processing (NLP) technologies provide an opportunity to extract key patient data from free text documents within the electronic health record (EHR). We are developing a series of components from which to construct NLP pipelines. These pipelines typically begin with a component whose goal is to label sections within medical documents with codes indicating the anticipated semantics of their content. This Clinical Section Labeler prepares the document for further, focused information extraction. Below we describe the evaluation of six algorithms designed for use in a Clinical Section Labeler. These algorithms are trained with N-gram-based feature sets extracted from document sections and the document types. In the evaluation, 6 different Bayesian models were trained and used to assign one of 27 different topics to each section. A tree-augmented Bayesian network using the document type and N-grams derived from section headers proved most accurate in assigning individual sections appropriate section topics.

Direct competitive immunosorbent assay for detection of MEHP in human urine.

Di-(2-ethylhexyl) phthalate (DEHP) is the most commonly used plasticizer for flexible polyvinyl chloride (PVC), which is also known as one of the environmental endocrine disruptors with the reproductive, developmental and embryonic toxicity after entering human body. Mono-2-ethylhexyl phthalate (MEHP) is one of the most complicate metabolites from DEHP in vivo and responsible for many toxic effects of DEHP. In order to evaluate human exposure to DEHP, a direct competitive enzyme-linked immunosorbent (dcELISA) based on monoclonal antibody (mAb) was developed to detect MEHP. A hybridoma cell line 4B9 secreting mAb against MEHP was prepared, and the horseradish peroxidase (HRP) labeled antigen as a probe in the dcELISA was made. After optimization of ELISA reaction conditions, the standard curve with a linear range from 0.56 to 1000 ng mL(-1) and a detection limit of 0.39 ng mL(-1) was established. The cross-reactivities of anti-MEHP mAb to other ten phthalate esters were less than 5% except for mono-methylphthalate (MME). The average recoveries of MEHP from distilled water and negative human urine were both between 87.4% and 94.72% with coefficient of variation (CV) less than 5%. Here, the ELISA method on detecting MEHP was successfully established and applied to real urine sample analyses and the results were confirmed by HPLC. Furthermore, it was indicated that the immunoassay was reliable and suitable for monitoring MEHP.

An ontology-driven, diagnostic modeling system.

OBJECTIVES: To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. MATERIALS AND METHODS: We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. RESULTS: The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. DISCUSSION: Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. CONCLUSIONS: The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.

Identification of organic molecules from a structure database using proton and carbon NMR analysis results.

A compound is identified by matching its proton and/or carbon NMR spectra to NIH PubChem molecular structures. The matching process involves analyzing 1D proton, 1D carbon, DEPT, and/or HSQC spectra, and comparing the number of NMR resonances, detected proton and carbon shifts, likely number of methyl- and methoxy-groups, and an optionally specified molecular formula to predicted proton and carbon shifts of PubChem structures. A structure verification module rates the consistency between experimental spectral analysis results and a proposed structure (not limited to PubChem structures) and assigns observed shifts to the proposed structure. The spectral analysis, structure identification, and structure verification are largely automated in a software package and can be performed in minutes.

[Resolution of the epinephrine enantiomers derivatized with fluorescent chiral reagent by reversed-phase high performance liquid chromatography].

A new method was developed to separate epinephrine enantiomers derivatized with fluorescent chiral reagent, R-(-)/S-(+)-4-(N, N-dimethylaminosulfonyl)-7-(3-iso-thiocyanatopyrrolidino)-2, 1, 3-benzoxadiazole (DBD-PyNCS), by reversed-phase high performance liquid chromatography, on a Diamonsil C18 column(150 mm x 4.6 mm i.d., 5 microns), with a mobile phase of water-acetonitrile(72:28, volume ratio) and a fluorescent detector at the excitation wavelength of 460 nm and the emission wavelength of 550 nm. The column was eluted at 40 degrees C, with a flow rate of 1 mL/min. At first, let the fluorescent chiral reagent(36 mmol/L, 10 microL) react with epinephrine enantiomers(1 mmol/L, 10 microL), and then 10 microL 20% pyridine acetonitrile solution was added. The resulting mixture was stirred for 1 min and kept at 65 degrees C for 35 min and then a 10 microL aqueous of the diastereomeric derivatives was injected directly into the chromatograph. The diastereomeric compounds were efficiently separated. The Rs value was 2.6 with alpha of 1.07. The k's of L-epinephrine and D-epinephrine were 17.11 and 18.13 respectively. The retention times of L-epinephrine and D-epinephrine were 30.24 min and 31.94 min respectively. The linear range was 0.07 g/L -0.50 g/L (r = 0.9995). The RSD was 4.3%(n = 7). The detection limit was 12 g/L(S/N = 3).