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Background Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons., Author summary Neurons form complex networks that play a critical role in the development and aging of the brain, as well as in health and disease. Understanding how these networks form and evolve over time can lead us to advances in neuronal and cognitive health. Previous studies have mainly used summary statistics or graph features recorded at different points in time to analyze neuronal networks. However, this approach ignores the temporal dependency of these features and may lead to discovering spurious patterns in the data. In order to thoroughly characterize neuronal networks, time dependencies must be explicitly modeled. We present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks.

Introduction: In the last decade, the inclusion of a functioning indicator in health care has been discussed on the international agenda. The strategies related to the implementation of these indicators are issues that involve health education, governance, and medical records. Objective: study aims to discuss the International Classification of Functioning, Disability and Health (ICF) potential as a useful tool to produce information on health care services. Method: As theoretical assumptions, the universal model of the ICF based on the biopsychosocial model was used. Results: When used as a health indicator, functioning data can measure the real effect of some health conditions in different life domains. Based on the reflections carried out and theoretical foundations accessed, the study shows that the implementation of functioning indicators in periodical population health surveys and protocols of clinical documentation regardless the level of health service would be relevant for Patient Care Planning. Note that the group of functioning indicators should be proposed in a universal language and, therefore, ICF represents the most comprehensive model. Conclusion: Information regarding health status can be useful to enable health care management. Furthermore, ICF are essential to improve the documentation service of the health system and also can be used in planning and monitoring health care. It can also be used to collect disability data in surveys ensuring comparison among different surveys. Resumo Introdução: Na última década, a inclusão de indicadores de funcionalidade na atenção à saúde tem sido discutida na agenda internacional. As estratégias relacionadas à implementação de indicadores de funcionalidade são uma questão que envolve educação, gestão e assistência à saúde. Objetivo: Este ensaio teórico tem como objetivo discutir o potencial da Classificação Internacional de Funcionalidade, Incapacidade e Saúde (CIF) como ferramenta útil na produção de informação para os serviços de saúde. Método: Utilizamos como pressupostos teóricos o modelo universal, a CIF. Nosso pressuposto baseia-se no modelo biopsicossocial do binômio saúde-doença. Resultados: Indicadores de funcionalidade podem medir o real impacto das condições de saúde em diferentes domínios da vida. A partir das reflexões realizadas e alicerces teóricos acessados, percebe-se que a implementação de indicadores de funcionalidade nos inquéritos populacionais periódicos em saúde e protocolos de registro para informação clínica, independentemente do nível de serviço de saúde, seria de grande interesse. Vale ressaltar que o grupo de indicadores de funcionalidade deve ser proposto em uma linguagem universal e, portanto, a CIF representa o modelo mais abrangente. Conclusão: O indicador de funcionalidade pode ser útil para o gerenciamento da saúde. As informações sobre funcionalidade são essenciais para melhorar o planejamento e a qualidade dos serviços de saúde e podem ser usadas no planejamento e monitoramento dos cuidados de saúde; podem ser coletadas em pesquisas de saúde da população por ferramentas específicas; e poderiam ser produzidas usando a CIF como uma estrutura na coleta diária de informações sobre saúde. Resumen Introducción: En la última década, la inclusión de indicadores de funcionamiento en la atención de salud se ha discutido en la agenda internacional. Las estrategias relacionadas con la implementación de los indicadores de funcionamiento son un tema que involucra educación, gestión y atención médica. Objetivo: Este ensayo teórico tiene como objetivo discutir el potencial de Clasificación Internacional del Funcionamiento, de la Discapacidad y de la Salud (CIF) como una herramienta útil para producir información en los servicios de atención médica. Método: Utilizamos como suposiciones teóricas el modelo universal de la CIF con base en el modelo biopsicosocial del binomio salud-enfermedad. Resultados: Los indicadores de funcionamiento pueden medir el impacto real de las condiciones de salud en diferentes dominios de la vida. Con base en las reflexiones realizadas y los fundamentos teóricos, se observa que sería esencial la implementación de indicadores de funcionamiento en las encuestas periódicas de salud de la población y en los protocolos de registro para la información clínica, independiente del nivel del servicio de salud. Vale la pena mencionar que el grupo de indicadores de funcionamiento debe proponerse en un lenguaje universal y, por lo tanto, CIF representa el modelo más completo. Conclusión: El indicador de funcionamiento puede ser útil para la gestión de la salud. Las informaciones sobre funcionamiento son esenciales para mejorar la planificación y la calidad de los servicios de salud y se puede utilizarlas para planificar y monitorear los cuidados en salud; y se puede recopilarlas en encuestas de salud de la población mediante herramientas específicas; y podría recopilarse utilizando la CIF como herramienta en la recopilación diaria de información sobre salud.

Quantitatively benchmarking similarities and differences between the in vivo central nervous system and in vitro neuronal cultures can qualify discrepancies in functional responses and establish the utility of in vitro platforms. In this work, extracellular electrophysiology responses of cortical neurons in awake, freely-moving animals were compared to in vitro cultures of dissociated cortical neurons. After exposure to two well-characterized drugs, atropine and ketamine, a number of key points were observed: (1) significant differences in spontaneous firing activity for in vivo and in vitro systems, (2) similar response trends in single-unit spiking activity after exposure to atropine, and (3) greater sensitivity to the effects of ketamine in vitro. While in vitro cultures of dissociated cortical neurons may be appropriate for many types of pharmacological studies, we demonstrate that for some drugs, such as ketamine, this system may not fully capture the responses observed in vivo. Understanding the functionality associated with neuronal cultures will enhance the relevance of electrophysiology data sets and more accurately frame their conclusions. Comparing in vivo and in vitro rodent systems will provide the critical framework necessary for developing and interpreting in vitro systems using human cells that strive to more closely recapitulate human in vivo function and response.

The brain’s extracellular matrix (ECM) is a macromolecular network composed of glycosaminoglycans, proteoglycans, glycoproteins, and fibrous proteins. In vitro studies often use purified ECM proteins for cell culture coatings, however these may not represent the molecular complexity and heterogeneity of the brain’s ECM. To address this, we compared neural network activity (over 30 days in vitro) from primary neurons co-cultured with glia grown on ECM coatings from decellularized brain tissue (bECM) or MaxGel, a non-tissue-specific ECM. Cells were grown on a multi-electrode array (MEA) to enable noninvasive long-term interrogation of neuronal networks. In general, the presence of ECM accelerated the formation of networks without affecting the inherent network properties. However, specific features of network activity were dependent on the type of ECM: bECM enhanced network activity over a greater region of the MEA whereas MaxGel increased network burst rate associated with robust synaptophysin expression. These differences in network activity were not attributable to cellular composition, glial proliferation, or astrocyte phenotypes, which remained constant across experimental conditions. Collectively, the addition of ECM to neuronal cultures represents a reliable method to accelerate the development of mature neuronal networks, providing a means to enhance throughput for routine evaluation of neurotoxins and novel therapeutics.

The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. We demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.AbbreviationsEMRelectronic medical recordCMMcomposite mixture modelKPNCKaiser Permanente Northern CaliforniaBICBayesian information criterionAICAkaike information criterionPAMpartitioning around medoidsMICEmultivariate imputation using chained equations

In vitro brain-on-a-chip platforms hold promise in many areas including: drug discovery, evaluating effects of toxicants and pathogens, and disease modelling. A more accurate recapitulation of the intricate organization of the brain in vivo may require a complex in vitro system including organization of multiple neuronal cell types in an anatomically-relevant manner. Most approaches for compartmentalizing or segregating multiple cell types on microfabricated substrates use either permanent physical surface features or chemical surface functionalization. This study describes a removable insert that successfully deposits neurons from different brain areas onto discrete regions of a microelectrode array (MEA) surface, achieving a separation distance of 100 μm. The regional seeding area on the substrate is significantly smaller than current platforms using comparable placement methods. The non-permanent barrier between cell populations allows the cells to remain localized and attach to the substrate while the insert is in place and interact with neighboring regions after removal. The insert was used to simultaneously seed primary rodent hippocampal and cortical neurons onto MEAs. These cells retained their morphology, viability, and function after seeding through the cell insert through 28 days in vitro (DIV). Co-cultures of the two neuron types developed processes and formed integrated networks between the different MEA regions. Electrophysiological data demonstrated characteristic bursting features and waveform shapes that were consistent for each neuron type in both mono- and co-culture. Additionally, hippocampal cells co-cultured with cortical neurons showed an increase in within-burst firing rate (p = 0.013) and percent spikes in bursts (p = 0.002), changes that imply communication exists between the two cell types in co-culture. The cell seeding insert described in this work is a simple but effective method of separating distinct neuronal populations on microfabricated devices, and offers a unique approach to developing the types of complex in vitro cellular environments required for anatomically-relevant brain-on-a-chip devices.

International audience; The nine countries sharing the Amazon forest accounted for 89% of all malaria cases reported in the Americas in 2008. Remote sensing can help identify the environmental determinants of malaria transmission and their temporo-spatial evolution. Seventeen studies characterizing land cover or land use features, and relating them to malaria in the Amazon subregion, were identified. These were reviewed in order to improve the understanding of the land cover/use class roles in malaria transmission. The indicators affecting the transmission risk were summarized in terms of temporal components, landscape fragmentation and anthropic pressure. This review helps to define a framework for future studies aiming to characterize and monitor malaria.