MS

thesisAnalysis of organic acids in urine is a valuable tool in the diagnosis of the inborn errors of metabolism known as organic acidurias. This test is commonly ordered in newborns with symptoms such as lethargy, failure to thrive, hepatic failure, and suspected familial disorders. A drawback of published methods is the overwhelming amount of data to examine for each patient, prior to the final laboratory report. Physicians will wait as long as two weeks for these time critical results. The goal of this research was to develop and export system to automate the process of screening for metabolic disorders of urine organic acids. The Xaminer® pattern recognition software (ThermoFinnigan, San Jose, CA) was adapted to predict and identify patterns of urine organic acid disorders. The gas chromatography-mass spectrometry (GC-MS) full scan spectra of organic acids were used to build the pattern match library and train the software to recognized methylmalonic aciduria (MMA) and associated vitamin B12 deficiency, as well as, a subset of fatty acid oxidation defects (FAOD), including medium chain acyl-CoA dehydrogenase (MCAD) deficiency. Patient data files were de-identified and reprocessed using the expert system. The expert system results were compared to the original laboratory findings. From a total of 2573 samples, the original laboratory findings were 20 positives for MMA and 29 positives for FAOD. The Xaminer software identified 17 of the 20 MMA positives, plus 4 additional candidate samples that matched the search pattern criteria. The software found 26 of the 29 FAOD positives. Five additional samplers found to be candidates for FAOD. Software analysis time averaged less than 10 seconds per sample. This expert system can use pattern recognition of full scan GC-MS data to aid in patient screening for MMA and fatty acid oxidation disorders. The performance of Xaminer shows promise for refining or expanding the reference library to include other metabolic disorders as well

Toward Partisan Politics in a Professional Association: Utility of the Candidates Poll

The purpose of this paper is to describe the initial efforts of one local chapter, The Puget Sound Chapter; to engage in partisan politics by the conduct of a poll of candidates for election to the Washington State Legislature in 1974. Properly speaking, the Chapter endorsed no candidates, merely rated them from weak to outstanding on their agreement with NASW policies on relevant programs and their social welfare attitudes. Thus, it is a mild form of partisan politics that will be considered. The paper will analyze the social and organizational context in which the candidates\u27 poll occurred, and then report on the advantages and shortcomings of the poll as a technique for the assessment of political candidates. Finally, there will be a brief commentary on the functions of the professional association in the politicalization of the activities of organized social workers

An Unsplit, Cell-Centered Godunov Method for Ideal MHD

We present a second-order Godunov algorithm for multidimensional, ideal MHD. Our algorithm is based on the unsplit formulation of Colella (J. Comput. Phys. vol. 87, 1990), with all of the primary dependent variables centered at the same location. To properly represent the divergence-free condition of the magnetic fields, we apply a discrete projection to the intermediate values of the field at cell faces, and apply a filter to the primary dependent variables at the end of each time step. We test the method against a suite of linear and nonlinear tests to ascertain accuracy and stability of the scheme under a variety of conditions. The test suite includes rotated planar linear waves, MHD shock tube problems, low-beta flux tubes, and a magnetized rotor problem. For all of these cases, we observe that the algorithm is second-order accurate for smooth solutions, converges to the correct weak solution for problems involving shocks, and exhibits no evidence of instability or loss of accuracy due to the possible presence of non-solenoidal fields.Comment: 37 Pages, 9 Figures, submitted to Journal of Computational Physic

Computational Feature of Selection and Classification of RET Phenotypic Severity

pre-printAlthough many reported mutations in the RET oncogene have been directly associated with hereditary thyroid carcinoma, other mutations are labelled as uncertain gene variants because they have not been clearly associated with a clinical phenotype. The process of determining the severity of a mutation is costly and time consuming. Informatics tools and methods may aid to bridge this genotype-phenotype gap. Towards this goal, machine-learning classification algorithms were evaluated for their ability to distinguish benign and pathogenic RET gene variants as characterized by differences in values of physicochemical properties of the residue present in the wild type and the one in the mutated sequence. Representative algorithms were chosen from different categories of machine learning classification techniques, including rules, bayes, and regression, nearest neighbour, support vector machines and trees. Machine-learning models were then compared to well-established techniques used for mutation severity prediction. Machine-learning classification can be used to accurately predict RET mutation status using primary sequence information only. Existing algorithms that are based on sequence homology (ortholog conservation) or protein structural data are not necessarily superior

Identification of histoplasma -specific peptides in human urine

pre-printHistoplasmosis is a severe dimorphic fungus infection, which is often difficult to diagnose due to similarity in symptoms to other diseases and lack of specific diagnostic tests. Urine samples from histoplasma-antigen-positive patients and appropriate controls were prepared using various sample preparation strategies including immunoenrichment, ultrafiltration, high-abundant protein depletion, deglycosylation, reverse-phase fractions, and digest using various enzymes. Samples were then analyzed by nanospray tandem mass spectrometry. Accurate mass TOF scans underwent molecular feature extraction and statistical analysis for unique disease makers, and acquired MS/MS data were searched against known human and histoplasma proteins. In human urine, some 52 peptides from 37 Histoplasma proteins were identified with high confidence. This is the first report of identification of a large number of Histoplasma-specific peptides from immunoassay-positive patient samples using tandem mass spectrometry and bioinformatics techniques. These findings may lead to novel diagnostic markers for histoplasmosis in human urine

Radon as an anthropogenic indoor air pollutant as exemplified by radium dial watches and other uranium- and radium- containing artefacts

Radon is generally regarded as a naturally occurring radiological hazard but we report here measurements of significant, hazardous radon concentrations that arise from man-made sources, e.g. radium-dial watches. This study is an examination and assessment of health risks from radium and uranium found in historical artefacts, and the radon that emanates from them. This includes radium-dial watches, the main focus, plus clocks, aircraft instruments, and ornaments and artefacts made of uranium glass / uranium-glazed. Such objects were very popular in the 1930s and 1940s and are still readily available today. A collection of 30 radium-dial pocket and wrist watches was measured and shown to be capable of giving rise to radon concentrations two orders of magnitude greater than the UK Domestic Action Level of 200 Bq•m-3 in unventilated or poorly ventilated rooms. Furthermore, individual watches are capable of giving rise to radon concentrations in excess of the UK Domestic Action Level. We also highlight a gap in remediation protocols, which are focused on preventing radon entering buildings from outside, with regard to internally-generated radon hazards. Radon as arising from man-made objects such as radium-dial watches should be considered appropriately in radon protocols and guidelines

Think Pink! a Pink Sticker Alert System for Women with Psychological Distress or Vulnerability During Pregnancy

The importance of good clinical communication to women during pregnancy and birth is clear. Poor communication in labour is associated with general dissatisfaction, more complaints and a range of perinatal mental health problems including posttraumatic stress disorder (PTSD) and postnatal depression. To communicate effectively, maternity staff needs information about which women are vulnerable and require extra support. To address this, we implemented and evaluated a pink sticker communication system to alert midwifery and obstetric staff to potential psychological difficulties experienced by some women. Evaluation showed this system was viewed positively by women and midwifery staff. Audit of referrals to the perinatal psychology service during this period suggests no woman with a pink sticker developed birth trauma as a direct result of perceived poor care. In addition, the proportion of referrals to perinatal psychology for birth trauma significantly reduced during this period