The effect of formulation on radioiodide thyroid uptake in the hyperthyroid cat

This investigation was designed to compare in vitro dissolution profiles from sodium iodide capsules with radioiodide thyroid uptake in hyperthyroid cats using sodium iodide capsules prepared with a formulation exhibiting a complete release of radioiodide (I-123) in vitro and a formulation with an incomplete release of radioiodide. In vitro dissolution profiles for I-123 sodium iodide capsules with two different formulations were determined using the USP XXIII dissolution test. The two formulations studied in vitro were sodium phosphate dibasic powder with 1% magnesium stearate and calcium phosphate dibasic powder with 3% magnesium stearate. By 20 min after initiation of the dissolution test, over 95% of the I-123 was released from capsules of sodium phosphate dibasic powder. The capsules of calcium phosphate dibasic powder reached 75% at 65 min, with no further release occurring thereafter. There was a statistically significant difference in the dissolution profiles of the two formulations. The thyroid uptake of I-123 from capsules exhibiting complete release and incomplete release of radioiodide was determined in hyperthyroid cats. At 4 hr, the mean percentage thyroid uptake value for sodium phosphate dibasic powder with 1% magnesium stearate (complete release formulation) was 12.0% compared to 9.4% for calcium phosphate dibasic powder with 3% magnesium stearate (incomplete release formulation); at 24 hr, the values were 34.4% compared to 23.7%. The data suggest that the incomplete dissolution profile observed in vitro may correlate with a reduction in the bioavailability of the radioiodide in vivo. However, using the Wilcoxon signed rank test, statistically significant differences did not occur between the complete release formulation and incomplete release formulation at either 4 hr or 24 hr (p > .05). The results of the in vivo study with five hyperthyroid cats were not conclusive due to the variability in response between individual cats.     

Favorable influence of ssDNA-functionalized SWCNT on the navigation pattern of C. elegans

Microsystem Technologies - Single-Walled Carbon Nanotubes (SWCNTs) are widely used as potential carriers in drug delivery systems. The objective of this work was to observe the effects of pristine,...     

Predicting job stress using data from the Position Analysis Questionnaire.

Investigated whether any consistent relationship exists between the behavioral characteristics of different jobs and the levels of various stresses experienced by groups of employees in those jobs. Behavioral characteristics of jobs were assessed with data from the Position Analysis Questionnaire (PAQ) data bank. Correlational and regression analyses were conducted to determine the relationship between job dimension scores derived from the PAQ and 18 indices of job stress obtained from 3 archival sources (with job numbers of 92, 31, and 92, respectively). PAQ and stress data were matched using job titles and codes from the Dictionary of Occupational Titles. Results show a strong relationship between PAQ scores and stress data. (31 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model

Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an intensive care unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verify the model's ability to capture long-term observed glucose-insulin dynamics in hyperglycemic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.     

Modeling the glucose regulatory system in extreme preterm infants

Background

Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.

Methods

A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4 h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N = 25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.

Results

The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.

Conclusions

The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.     

Layered tetrahedral meshing of thin-walled solids for plastic injection molding FEM

This paper describes a method for creating a well-shaped, layered tetrahedral mesh of a thin-walled solid by adapting the surface triangle sizes to the estimated wall thickness. The primary target application of the method is the finite element analysis of plastic injection molding, in which a layered mesh improves the accuracy of the solution. The edge lengths of the surface triangles must be proportional to the thickness of the domain to create well-shaped tetrahedrons; when the edge lengths are too short or too long, the shape of the tetrahedron tends to become thin or flat. The proposed method creates such a layered tetrahedral mesh in three steps: (1) create a preliminary tetrahedral mesh of the target geometric domain and estimate thickness distribution over the domain; (2) create a non-uniform surface triangular mesh with edge length adapted to the estimated thickness, then create a single-layer tetrahedral mesh using the surface triangular mesh; and (3) subdivide tetrahedrons of the single-layer mesh into multiple layers by applying a subdivision template. The effectiveness of the layered tetrahedral mesh is verified by running some experimental finite element analyses of plastic injection molding.     

Nurturing health-related online support groups: Exploring the experiences of patient moderators

The aim of this study was to examine the views of moderators across a diverse and geographically broad range of online support groups about their moderator experiences and to explore both the personal benefits as well as challenges involved. Thirty-three patient moderators completed an online questionnaire which included a series of open-ended questions. Thematic analysis identified three themes: emergence, empowerment, nurturing. Several moderators declared their own diagnosis and for some, being able to share personal insights motivated them to establish the group and in turn offered validation. They felt empowered by helping others and learned more about the condition through accessing the “communal brain”. Some felt the group aided patients’ access to health services and their ability to communicate with health professionals while others worried about them becoming over-dependent. Moderators described needing to nurture their group to ensure it offered a safe space for members. Clear rules of engagement, trust, organisation skills, compassion and kindness were considered essential. Patient moderated online support groups can be successfully developed and facilitated and can be empowering for both the group member and moderator alike.     

Self-efficacy and work-related performance: The integral role of individual differences.

The present study estimated the unique contribution of self-efficacy to work-related performance controlling for personality (the Big 5 traits), intelligence or general mental ability, and job or task experience. Results, based on a meta-analysis of the relevant literatures, revealed that overall, across all studies and moderator conditions, the contribution of self-efficacy relative to purportedly more distal variables is relatively small. Within moderator categories, there were several cases in which self-efficacy made unique contributions to work-related performance. For example, self-efficacy predicted performance in jobs or tasks of low complexity but not those of medium or high complexity, and self-efficacy predicted performance for task but not job performance. Overall, results suggest that the predictive validity of self-efficacy is attenuated in the presence of individual differences, though this attenuation does depend on the context. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

Punctuated simplification of man-made objects

We present a simplification algorithm for manifold polygonal meshes of plane-dominant models. Models of this type are likely to appear in man-made environments. While traditional simplification algorithms focus on generality and smooth meshes, the approach presented here considers a specific class of man-made models. By detecting and classifying edge loops on the mesh and providing a guided series of binary mesh partitions, our approach generates a series of simplified models, each of which better respects the semantics of these kinds of models than conventional approaches do. A guiding principle is to eliminate simplifications that do not make sense in constructed environments. This, coupled with the concept of “punctuated simplification”, leads to an approach that is both efficient and delivers high visual quality. Comparative results are given.