Verification of point-of-care analysers for C-reactive protein, lipid studies and glycated haemoglobin.

Due to increased convenience and faster test results, interest in point-of-care testing (PoCT) has grown significantly. Though PoCT may improve the speed and convenience of testing, the devices need to be fit for their intended purpose. Our aim was to verify the performance of Roche cobas b 101 and Abbott Afinion 2 for C-reactive protein (CRP), lipid studies and glycated haemoglobin (HbA1c), and Siemens Atellica DCA for HbA1c. For all PoCT analysers and measurands, accuracy was assessed by method comparison with central laboratory analysers. Passing-Bablok linear regression was performed, and Bland-Altman plots were generated. The proportion of samples within the Royal College of Pathologists of Australasia Quality Assurance Programs Analytical Performance Specifications (RCPAQAP APS) was assessed. Within-run and between-day imprecision was assessed and compared with manufacturer claims and biological variation or clinical guidelines for desirable imprecision. For CRP, both evaluated PoCT analysers had all samples within the RCPAQAP APS and had optimal imprecision according to biological variation. For lipid studies, the Roche cobas b 101 had most samples within the RCPAQAP APS, with two of 22 cholesterol, one of 22 high-density-lipoprotein-cholesterol (HDL-C) and zero of 22 triglyceride comparisons outside the RCPAQAP APS. The Abbott Afinion 2 had a positive bias with all three measured parameters, although the effect was more limited in the calculated parameters cholesterol:HDL-C ratio, non-HDL-C and low-density-lipoprotein-cholesterol (LDL-C). For HbA1c, all analysers had acceptable imprecision for monitoring with coefficient of variation (CV) <3% and minimal bias at the treatment target (HbA1c 53 mmol/mol or 7.0%). However, significant biases were apparent at higher or lower HbA1c for all analysers. All evaluated analysers were fit for purpose for CRP and for serial monitoring of HbA1c, although bias in some analysers was present at extremes of HbA1c. For lipid studies, the Roche cobas b 101 had fewer results outside the RCPAQAP allowable limits, and better precision. The Abbott Afinion 2 had a positive bias on both the cholesterol and HDL-C, but there is limited clinical impact when calculating cholesterol:HDL-C, LDL-C and non-HDL-C.

New Tools in Education: Development and Learning Effectiveness of a Computer Application for Use in a University Biology Curriculum.

In recent years student exposure to computer applications has increased at an unprecedented rate. Yet the use of these promising technologies in education remains in its infancy. The growing practice of 'gamification' offers today's educators the means of conveying their lessons in a more engaging way, by utilising computer game mechanics. However, many of these learning tools have not been empirically evaluated. This research investigated the development of a desktop computer application, to replace an existing learning resource, a video, currently used by over 700 life sciences students a year in one of the top 100 universities of the world. The interactive game presents the same essential information as the video, on key anatomical features of mammalian skulls, and provides student self-testing. Results from a two-treatment, pre- and post-intervention experimental design suggest the new product is better for providing both knowledge acquisition and a positive learning experience. Nevertheless, the results are unlikely to be statistically significant. Insights from the findings are discussed and directions for future research are given.

Are more complicated tumour control probability models better?

Mathematical models for the tumour control probability (TCP) are used to estimate the expected success of radiation treatment protocols of cancer. There are several TCP models in the literature, from the simplest (Poissonian TCP) to the well-advanced stochastic birth-death processes. Simple and complex models often make the same predictions. Hence, here, we present a systematic study where we compare six of these TCP models: the Poisson TCP, the Zaider-Minerbo TCP, a Monte Carlo TCP and their corresponding cell cycle (two-compartment) models. Several clinical non-uniform treatment protocols for prostate cancer are employed to evaluate these models. These include fractionated external beam radiotherapies, and high and low dose rate brachytherapies. We find that in realistic treatment scenarios, all one-compartment models and all two-compartment models give basically the same results. A difference occurs between one-compartment and two-compartment models due to reduced radiosensitivity of quiescent cells.We find that care must be taken for the right choice of parameters, such as the radiosensitivities α and ß and the hazard function h. Typically, different hazard functions are used for fractionated treatment (fractionated survival fraction) and for brachytherapies (Lea-Catcheside protraction factor). We were able to combine these two approaches into one 'effective' hazard function. Based on our results, we can recommend the use of the Poissonian TCP for everyday treatment planning. More complicated models should only be used when absolutely necessary.

From cell population models to tumor control probability: including cell cycle effects.

BACKGROUND: Classical expressions for the tumor control probability (TCP) are based on models for the survival fraction of cancer cells after radiation treatment. We focus on the derivation of expressions for TCP from dynamic cell population models. In particular, we derive a TCP formula for a generalized cell population model that includes the cell cycle by considering a compartment of actively proliferating cells and a compartment of quiescent cells, with the quiescent cells being less sensitive to radiation than the actively proliferating cells. METHODS: We generalize previously derived TCP formulas of Zaider and Minerbo and of Dawson and Hillen to derive a TCP formula from our cell population model. We then use six prostate cancer treatment protocols as a case study to show how our TCP formula works and how the cell cycle affects the tumor treatment. RESULTS: The TCP formulas of Zaider-Minerbo and of Dawson-Hillen are special cases of the TCP formula presented here. The former one represents the case with no quiescent cells while the latter one assumes that all newly born cells enter a quiescent cell phase before becoming active. From our case study, we observe that inclusion of the cell cycle lowers the TCP. CONCLUSION: The cell cycle can be understood as the sequestration of cells in the quiescent compartment, where they are less sensitive to radiation. We suggest that our model can be used in combination with synchronization methods to optimize treatment timing.