Cryo-EM Structure of Functional BK Channels in Lipid Bilayers

It has already widely been shown that the extension of the urban space takes a polycentric shape: suburban employment sub-centres emerge. The objective in this study is to measure employment concentrations inside and around urban agglomerations and to determine operational tools (methods and data) that lead to the highlighting of these sub-centers. Advantages and limits of the methods are discussed. Our cases studies are Antwerp and Brussels. The results obtained by several methodologies are compared (shift and share analysis, cluster analysis, kernel interpolation and local autocorrelation) for three different types of employment data (social security, population census and regional development statistics) and at three different scales of analysis (individual postal address, neighbourhood and commune). The main difficulties encountered are: (1) the spatial and temporal heterogeneity of the data, and (2) the non-uniqueness of the methodology for discriminating sub-centers. Our conclusion is that there is an emerging polycentric structure in Brussels and Antwerp, but sub-centers are still difficult to put forward. The combination of several different methods and databases is necessary to get insight in the polycentric structure

Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network

Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data

Localization of Secondary Metabolites in Marine Invertebrates: Contribution of MALDI MSI for the Study of Saponins in Cuvierian Tubules of H. forskali

BACKGROUND: Several species of sea cucumbers of the family Holothuriidae possess a particular mechanical defense system called the Cuvierian tubules (Ct). It is also a chemical defense system as triterpene glycosides (saponins) appear to be particularly concentrated in Ct. In the present study, the precise localization of saponins in the Ct of Holothuria forskali is investigated. Classical histochemical labeling using lectin was firstly performed but did not generate any conclusive results. Thus, MALDI mass spectrometry Imaging (MALDI-MSI) was directly applied and completed by statistical multivariate tests. A comparison between the tubules of relaxed and stressed animals was realized. RESULTS: These analyses allowed the detection of three groups of ions, corresponding to the isomeric saponins of the tubules. Saponins detected at m/z 1287 and 1303 were the most abundant and were apparently localized in the connective tissue of the tubules of both relaxed and stressed individuals. Saponins at m/z 1125 and 1141 were detected in lower amount and were present in tissues of relaxed animals. Finally, saponin ions at 1433, 1449, 1463 and 1479 were observed in some Ct of stressed holothuroids in the outer part of the connective tissue. The saponin group m/z 14xx seems therefore to be stress-specific and could originate from modifications of the saponins with m/z of 11xx. CONCLUSIONS: All the results taken together indicate a complex chemical defense mechanism with, for a single organ, different sets of saponins originating from different cell populations and presenting different responses to stress. The present study also reflects that MALDI-MSI is a valuable tool for chemical ecology studies in which specific chemical signalling molecules like allelochemicals or pheromones have to be tracked. This report represents one of the very first studies using these tools to provide a functional and ecological understanding of the role of natural products from marine invertebrates

Salivary gland tumors in transgenic mice with targeted PLAG1 proto-oncogene overexpression.

peer reviewedPleomorphic adenoma gene 1 (PLAG1) proto-oncogene overexpression is implicated in various human neoplasias, including salivary gland pleomorphic adenomas. To further assess the oncogenic capacity of PLAG1, two independent PLAG1 transgenic mouse strains were established, PTMS1 and PTMS2, in which activation of PLAG1 overexpression is Cre mediated. Crossbreeding of PTMS1 or PTMS2 mice with MMTV-Cre transgenic mice was done to target PLAG1 overexpression to salivary and mammary glands, in the P1-Mcre/P2-Mcre offspring. With a prevalence of 100% and 6%, respectively, P1-Mcre and P2-Mcre mice developed salivary gland tumors displaying various pleomorphic adenoma features. Moreover, histopathologic analysis of salivary glands of 1-week-old P1-Mcre mice pointed at early tumoral stages in epithelial structures. Malignant characteristics in the salivary gland tumors and frequent lung metastases were found in older tumor-bearing mice. PLAG1 overexpression was shown in all tumors, including early tumoral stages. The tumors revealed an up-regulation of the expression of two distinct, imprinted gene clusters (i.e., Igf2/H19 and Dlk1/Gtl2). With a latency period of about 1 year, 8% of the P2-Mcre mice developed mammary gland tumors displaying similar histopathologic features as the salivary gland tumors. In conclusion, our results establish the strong and apparently direct in vivo tumorigenic capacity of PLAG1 and indicate that the transgenic mice constitute a valuable model for pleomorphic salivary gland tumorigenesis and potentially for other glands as well

The role of stakeholder involvement in the evolving EU HTA process:Insights generated through the European Access Academy’s multi-stakeholder pre-convention questionnaire

Involvement of all relevant stakeholders will be of utmost importance for the success of the developing EU HTA harmonization process. A multi-step procedure was applied to develop a survey across stakeholders/collaborators within the EU HTA framework to assess their current level of involvement, determine their suggested future role, identify challenges to contribution, and highlight efficient ways to fulfilling their role. The ‘key’ stakeholder groups identified and covered by this research included: patients‘, clinicians‘, regulatory, and Health Technology Developer representatives. The survey was circulated to a wide expert audience including all relevant stakeholder groups in order to determine self-perception by the ‘key’ stakeholders regarding involvement in the HTA process (self-rating), and in a second, slightly modified version of the questionnaire, to determine the perception of ‘key’ stakeholder involvement by HTA bodies, payers, and policymakers (external rating). Predefined analyses were conducted on the submitted responses. Fifty-four responses were received (patients 9; clinicians: 8; regulators: 4; HTDs 14; HTA bodies: 7; Payers: 5; policymakers 3; others 4). The mean self-perceived involvement score was consistently lower for each of the ‘key’ stakeholder groups than the respective external ratings. Based on the qualitative insights generated in the survey, a RACI Chart (Responsible/Accountable/Consulted/Informed) was developed for each of the stakeholder groups to determine their roles and involvement in the current EU HTA process. Our findings suggest extensive effort and a distinct research agenda are required to ensure adequate involvement of the key stakeholder groups in the evolving EU HTA process.</p

The role of stakeholder involvement in the evolving EU HTA process: Insights generated through the European Access Academy's multi-stakeholder pre-convention questionnaire.

Involvement of all relevant stakeholders will be of utmost importance for the success of the developing EU HTA harmonization process. A multi-step procedure was applied to develop a survey across stakeholders/collaborators within the EU HTA framework to assess their current level of involvement, determine their suggested future role, identify challenges to contribution, and highlight efficient ways to fulfilling their role. The 'key' stakeholder groups identified and covered by this research included: patients', clinicians', regulatory, and Health Technology Developer representatives. The survey was circulated to a wide expert audience including all relevant stakeholder groups in order to determine self-perception by the 'key' stakeholders regarding involvement in the HTA process (self-rating), and in a second, slightly modified version of the questionnaire, to determine the perception of 'key' stakeholder involvement by HTA bodies, payers, and policymakers (external rating). Predefined analyses were conducted on the submitted responses. Fifty-four responses were received (patients 9; clinicians: 8; regulators: 4; HTDs 14; HTA bodies: 7; Payers: 5; policymakers 3; others 4). The mean self-perceived involvement score was consistently lower for each of the 'key' stakeholder groups than the respective external ratings. Based on the qualitative insights generated in the survey, a RACI Chart (Responsible/Accountable/Consulted/Informed) was developed for each of the stakeholder groups to determine their roles and involvement in the current EU HTA process. Our findings suggest extensive effort and a distinct research agenda are required to ensure adequate involvement of the key stakeholder groups in the evolving EU HTA process

2018 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1005/thumbnail.jp

Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network

Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.status: publishe

Real-world evidence gathering in oncology: The need for a biomedical big data insight-providing federated network

Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data

Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees

Objectives: Little research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide. Methods: Based on a retrospective observational registry covering 3090 patients with glioblastoma treated with temozolomide, we proposed the use of a two-step iterative exploratory learning process consisting of an initialization phase and a machine learning phase. For initialization, we defined a binary response variable as the target label using one-by-one nearest neighbor propensity score matching. Secondly, a classification tree algorithm was trained and validated for dividing individual patients into treatment response and non-response groups. Theorizing about treatment response was then done by evaluating the tree performance. Results: The classification tree model has an area under the curve (AUC) classification performance of 67% corresponding to a sensitivity of 0.69 and a specificity of 0.51. This result in predicting patient-level response was slightly better than the logistic regression model featuring an AUC of 64% (0.63 sensitivity and 0.54 specificity). The tree confirms confounding by age and discovers further age-related stratification with chemotherapy-treatment dependency, both not revealed in preceding clinical studies. The model lacked genetic information confounding treatment response. Conclusions: A classification tree was found to be suitable for understanding patient-level effectiveness for this glioblastoma-temozolomide case because of its high interpretability and capability to deal with covariate interdependencies, essential in a real-world environment. Possible improvements in the model's classification can be achieved by including genetic information and collecting primary data on treatment response. The model can be valuable in clinical practice for predicting personal treatment pathways.status: publishe