lacZ transduced human breast cancer xenografts as an in vivo model for the study of invasion and metastasis

A number of human cancer cell lines have been described as being invasive and metastatic in immune incompetent animals. However, it is difficult to assess metastatic spread of a subcutaneously injected or inoculated cell line, since an exact detection of all microfoci of human tumour cells in the animals by usual histological procedures would require extensive sectioning of the whole animal. To overcome this problem, we transduced human breast cancer cells with a replication-defective Moloney murine leukaemia retroviral vector (M-MuLV) containing both neo(R) (neomycin resistance) and lacZ genes. The resulting cell lines were selected for antibiotic (G418) resistance, and cell-sorted for lacZ expression. lacZ continued to be expressed in cultured cells for at least 20 passages without further G418 selection. The lacE gene codes for β-D-galactosidase, and cells expressing this gene stain blue with the chromogenic substrate X-gal. The lacZ-expressing cells retained the pre-transduction ability to traverse Matrigel in vitro, to form subcutaneous tumours in nude mice, and to grow invasively with the formation of metastases. X-gal staining showed high specificity, staining the tumour cells but not the surrounding mouse tissue on either whole tissue blocks or histological sections. The staining procedure was highly sensitive, allowing detection of microfoci of human cancer cells, and quantitative estimation of the metastatic capacity of the cells. These results indicate that lacZ transduction of human tumour cells is a powerful means of studying human cancer cell invasion and metastases in vivo.

BM18 : a novel androgen-dependent human prostate cancer xenograft model derived from a bone metastasis

BACKGROUND Androgen-dependent prostate cancer (PrCa) xenograft models are required to study PrCa biology in the clinically relevant in vivo environment. METHODS Human PrCa tissue from a femoral bone metastasis biopsy (BM18) was grown and passaged subcutaneously through male severe combined immune-deficient (SCID) mice. Human mitochondria (hMt), prostate specific antigen (PSA), androgen receptor (AR), cytokeratin-18 (CK-18), pan-cytokeratin, and high molecular weight-cytokeratin (HMW-CK) were assessed using immunohistochemistry (IHC). Surgical castration was performed to examine androgen dependence. Serum was collected pre- and post-castration for monitoring of PSA levels. RESULTS: BM18 stained positively for hMt, PSA, AR, CK-18, pan keratin, and negatively for HMW-CK, consistent with the staining observed in the original patient material. Androgen-deprivation induced tumor regression in 10/10 castrated male SCID mice. Serum PSA levels positively correlated with BM18 tumor size. CONCLUSIONS BM18 expresses PSA and AR, and rapidly regresses in response to androgen withdrawal. This provides a new clinically significant PrCa model for the study of androgen-dependent growth.

Enhancing paramedics procedural skills using a cadaveric model

Background Paramedic education has evolved in recent times from vocational post-employment to tertiary pre-employment supplemented by clinical placement. Simulation is advocated as a means of transferring learned skills to clinical practice. Sole reliance of simulation learning using mannequin-based models may not be sufficient to prepare students for variance in human anatomy. In 2012, we trialled the use of fresh frozen human cadavers to supplement undergraduate paramedic procedural skill training. The purpose of this study is to evaluate whether cadaveric training is an effective adjunct to mannequin simulation and clinical placement. Methods A multi-method approach was adopted. The first step involved a Delphi methodology to formulate and validate the evaluation instrument. The instrument comprised of knowledge-based MCQs, Likert for self-evaluation of procedural skills and behaviours, and open answer. The second step involved a pre-post evaluation of the 2013 cadaveric training. Results One hundred and fourteen students attended the workshop and 96 evaluations were included in the analysis, representing a return rate of 84%. There was statistically significant improved anatomical knowledge after the workshop. Students' self-rated confidence in performing procedural skills on real patients improved significantly after the workshop: inserting laryngeal mask (MD 0.667), oropharyngeal (MD 0.198) and nasopharyngeal (MD 0.600) airways, performing Bag-Valve-Mask (MD 0.379), double (MD 0.344) and triple (MD 0.326,) airway manoeuvre, doing 12-lead electrocardiography (MD 0.729), using McGrath(R) laryngoscope (MD 0.726), using McGrath(R) forceps to remove foreign body (MD 0.632), attempting thoracocentesis (MD 1.240), and putting on a traction splint (MD 0.865). The students commented that the workshop provided context to their theoretical knowledge and that they gained an appreciation of the differences in normal tissue variation. Following engagement in/ completion of the workshop, students were more aware of their own clinical and non-clinical competencies. Conclusions The paramedic profession has evolved beyond patient transport with minimal intervention to providing comprehensive both emergency and non-emergency medical care. With limited availability of clinical placements for undergraduate paramedic training, there is an increasing demand on universities to provide suitable alternatives. Our findings suggested that cadaveric training using fresh frozen cadavers provides an effective adjunct to simulated learning and clinical placements.

A pilot study to assess the appropriateness of prescribing from a collaborative pharmacist prescribing study in a surgical pre admission clinic

Background Current evidence to support non-medical prescribing is predominantly qualitative, with little evaluation of appropriateness. This study aims to evaluate the appropriateness of prescribing, and significance of omissions, from a doctor pharmacist collaborative prescribing model in an elective surgery pre admission clinic (PAC). Method A modified version of the Medication Appropriate Index (MAI) was developed, piloted and subsequently used by an expert panel, comprised of a surgeon, anaesthetist, clinical pharmacologist, pharmacist, resident medical officer (RMO) and clinical nurse. The tool was used to rate the appropriateness of prescribing of medications, and the significance of omissions in a 5% sample (N=19) of the total cohort from a randomised, controlled two arm trial of doctor-pharmacist collaborative prescribing. Results When reviewer assessments were combined, 32 out of 294 (10.9%) medications assessed for appropriateness in the control arm were classed as inappropriate, compared to 13 of 266 (4.9%) in the intervention arm. Out of 89 regular medications in the control arm, 25 (28%) were omitted from the medication charts, compared to 1 out of 55 (2%) in the intervention arm (p<0.001, fishers exact) On average, 52% of omissions in the control arm were judged to have potential for patient harm or ward inconvenience. Conclusion For the appropriateness of prescribing, overall results were similar between arms, as judged by individual panel members. Medication charts in the control arm contained significantly more omissions than in the intervention arm, a number of which were rated by the panel members as having the potential for patient harm or ward inconvenience.

Internet marketing and export market growth in Chile

Previous studies show that the Internet positively influences firms’ export activities from developed markets. However, the literature is vague as to whether the Internet has an impact on the export performance of firms from emerging markets. This study tests a conceptual model that includes the effect of Internet marketing capabilities on export market growth in an emerging market. Drawing on a cross-national sample of 204 export firms from a Latin American country (Chile), findings indicate that Internet marketing capabilities positively influence the availability of export information, which in turn impacts the development of business network relationships and export market growth.

Effects of selective oestrogen receptor modulators on proliferation in tissue cultures of pre- and postmenopausal human endometrium

We characterised the effects of selective oestrogen receptor modulators (SERM) in explant cultures of human endometrium tissue. Endometrium tissues were cultured for 24 h in Millicell-CM culture inserts in serum-free medium in the presence of vehicle,17 beta-estradiol (17 beta-E2,1 nM), oestrogen receptor (ER) antagonist ICI 164.384 (40 nM), and 4-OH-tamoxifen (40 nM), raloxifene (4 nM), lasofoxifene (4 nM)and acolbifene (4 nM). Protein expression of ER alpha, ER beta 1 and Ki-67 were evaluated by immunohistochemistry (IHC). The proliferative fraction was assessed by counting the number of Ki-67 positive cells. Nuclear staining of ER( and ER(1 was observed in the glandular epithelium and stroma of pre- and postmenopausal endometrium. ER(1 protein was also localized in the endothelial cells of blood vessels. Treating premenopausal endometrium tissue with 17 beta-E2 increased the fraction of Ki-67 positive cells (p < 0.001) by 55% in glands compared to the control. Raloxifene (4 nM) increased (p < 0.05) the Ki-67 positive fraction. All other SERMS did not affect proliferation in this model. Treating postmenopausal endometrium with 17(-E2 increased (p < 0.001) the fraction of Ki-67 positive cells by 250% in glands compared to the control. A similar effect was also seen for 4-OH-tamoxifen, whereas the rest of SERMs did not stimulate proliferation. We demonstrated that oestradiol increases the fraction of proliferating cells in short term explant cultures of postmenopausal endometrium. In addition, we were able to reveal the agonistic properties of 4-OH-tamoxifen and confirm that raloxifene and next-generation SERMs acolbifene and lasofoxifene were neutral on the human postmenopausal endometrium. (C) 2008 Elsevier Ltd. All rights reserved.

Generation and assessment of accuracy of Digital Elevation Model by using Pcigeomatica (10.3)

This study aims to assess the accuracy of Digital Elevation Model (DEM) which is generated by using Toutin’s model. Thus, Toutin’s model was run by using OrthoEngineSE of PCI Geomatics 10.3.Thealong-track stereoimages of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) sensor with 15 m resolution were used to produce DEM on an area with low and near Mean Sea Level (MSL) elevation in Johor Malaysia. Despite the satisfactory pre-processing results the visual assessment of the DEM generated from Toutin’s model showed that the DEM contained many outliers and incorrect values. The failure of Toutin’s model may mostly be due to the inaccuracy and insufficiency of ASTER ephemeris data for low terrains as well as huge water body in the stereo images.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Developing a three-dimensional in-vitro cell culture model for studying breast and prostate cancer using starPEG-heparin hydrogels

Introduction Hydrogels prepared from poly(ethylene glycol) (PEG) and maleimide-functionalized heparin provide a potential matrix for use in developing three dimensional (3D) models. We have previously demonstrated that these hydrogels support the cultivation of human umbilical vein endothelial cells (HUVECs) (1). We extend this body of work to study the ability to create an extracellular matrix (ECM)-like model to study breast and prostate cancer cell growth in 3D. Also, we investigate the ability to produce a tri-culture mimicking tumour angiogenesis with cancer spheroids, HUVECs and mesenchymal stem cells (MSC). Materials and Methods The breast cancer cell lines, MCF-7 and MDA-MB-231, and prostate cancer cell lines, LNCaP and PC3, were seeded into starPEG-heparin hydrogels and grown for 14 Days to analyse the effects of varying hydrogel stiffness on spheroid development. Resulting hydrogel constructs were analyzed via Alamar Blue assays, light microscopy, and immunofluorescence staining for cytokeratin 8/18, Ki67 and E-Cadherin. Cancer cell lines were then pre-grown in hydrogels for 5-7 days and then re-seeded into starPEG-heparin hydrogels functionalised with RGD, SDF-1, bFGF and VEGF as spheroids with HUVECs and MSC and grown for 14 days as a tri-culture in Endothelial Cell Growth Medium (ECGM; Promocell). Cell lines were also seeded as a single cell suspension into the functionalised tri-culture system. Cultures were fixed in 4% paraformaldehyde and analysed via immunostaining for Von Willebrand Factor and CD31, as well as the above mentioned markers, and observed using confocal microscopy. Results Cultures prepared in MMP-cleavable starPEG-heparin hydrogels display spheroid formation in contrast to adherent growth on tissue culture plastic. Small differences were visualised in cancer spheroid growth between different gel stiffness across the range of cell lines. Cancer cell lines were able to be co-cultivated with HUVECs and MSC. HUVEC tube formation and cancer line spheroid formation occured after 3-4 days. Interaction was visualised between tumours and HUVECs via confocal microscopy. Slightly increased interaction was seen between cancer tumours and micro-vascular tubes when seeded as single cells compared with the pre-formed spheroid approach. Further studies intend to utilise cytokine gradients to further optimise the ECM environment of in situ tumour angiogenesis. Discussion and Conclusions Our results confirm the suitability of hydrogels constructed from starPEG-heparin for HUVECs and MSC co-cultivation with cancer cell lines to study cell-cell and cell-matrix interactions in a 3D environment. This represents a step forward in the development of 3D culture models to study the pathomechanisms of breast and prostate cancer. References 1. Tsurkan MV, Chwalek K, Prokoph S, Zieris A, Levental KR, Freudenberg U, Werner C. Advanced Materials. 25, 2606-10, 2013. Disclosures The authors declare no conflicts of interest

Injury narrative text classification using factorization model

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.

A statistical model for combustion resonance from a DI diesel engine with applications

Introduced in this paper is a Bayesian model for isolating the resonant frequency from combustion chamber resonance. The model shown in this paper focused on characterising the initial rise in the resonant frequency to investigate the rise of in-cylinder bulk temperature associated with combustion. By resolving the model parameters, it is possible to determine: the start of pre-mixed combustion, the start of diffusion combustion, the initial resonant frequency, the resonant frequency as a function of crank angle, the in-cylinder bulk temperature as a function of crank angle and the trapped mass as a function of crank angle. The Bayesian method allows for individual cycles to be examined without cycle-averaging|allowing inter-cycle variability studies. Results are shown for a turbo-charged, common-rail compression ignition engine run at 2000 rpm and full load.

Developing knowledge and leadership in pre-service teacher education systems

Pre-service teacher education institutions are large and complex organizations, which are notoriously difficult to change. One factor is that many change efforts focus largely on individual pre-service teacher educators altering their practice. We report here on our experience using a model for effecting change, which views pre-service teacher education institutions and educators as a part of a much broader system. We identified numerous possibilities for, and constraints on, embedding change, but focus only on two in this paper: participants’ knowledge of change strategies and their leadership capacities. As a result of our study findings and researcher reflections, we argue that being a leader in an academic area within pre-service teacher education does not equate to leadership knowledge or skills to initiate and enact systems-wide change. Furthermore, such leadership capacities must be explicitly developed if education for sustainability is to become embedded in pre-service teacher education.

Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

Pre-experimental familiarization increases hippocampal activity for both targets and lures in recognition memory: An fMRI study

In the present study, items pre-exposed in a familiarization series were included in a list discrimination task to manipulate memory strength. At test, participants were required to discriminate strong targets and strong lures from weak targets and new lures. This resulted in a concordant pattern of increased "old" responses to strong targets and lures. Model estimates attributed this pattern to either equivalent increases in memory strength across the two types of items (unequal variance signal detection model) or equivalent increases in both familiarity and recollection (dual process signal detection [DPSD] model). Hippocampal activity associated with strong targets and lures showed equivalent increases compared with missed items. This remained the case when analyses were restricted to high-confidence responses considered by the DPSD model to reflect predominantly recollection. A similar pattern of activity was observed in parahippocampal cortex for high-confidence responses. The present results are incompatible with "noncriterial" or "false" recollection being reflected solely in inflated DPSD familiarity estimates and support a positive correlation between hippocampal activity and memory strength irrespective of the accuracy of list discrimination, consistent with the unequal variance signal detection model account.