In the current paper, a new strategy for risk assessment of nanomaterials is described, which builds upon previous project outcomes and is developed within the FP7 NANoREG project. NANoREG has the aim to develop, for the long term, new testing strategies adapted to a high number of nanomaterials where many factors can affect their environmental and health impact. In the proposed risk assessment strategy, approaches for (Quantitative) Structure Activity Relationships ((Q)SARs), grouping and read-across are integrated and expanded to guide the user how to prioritise those nanomaterial applications that may lead to high risks for human health. Furthermore, those aspects of exposure, kinetics and hazard assessment that are most likely to be influenced by the nanospecific properties of the material under assessment are identified. These aspects are summarised in six elements, which play a key role in the strategy: exposure potential, dissolution, nanomaterial transformation, accumulation, genotoxicity and immunotoxicity. With the current approach it is possible to identify those situations where the use of nanospecific grouping, read-across and (Q)SAR tools is likely to become feasible in the future, and to point towards the generation of the type of data that is needed for scientific justification, which may lead to regulatory acceptance of nanospecific applications of these tools.

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Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.

El objetivo fue identificar las asociaciones entre apoyo social percibido, riesgo suicida y presencia de enfermedad crónica no transmisible en estudiantes universitarios. Utilizando un diseño de casos y controles, con 41 estudiantes universitarios por grupo. La mediana de edad fue de 22 años (R = 9), un 81,8% eran mujeres, el 26,8% de los sujetos tenía Asma y el 26,8% Hipotiroidismo, y el 63,6% pertenecía a la Facultad de Educación. No se comprobó la asociación entre enfermedad crónica, apoyo social, ni tampoco con riesgo suicida. Hay asociación indirecta entre riesgo suicida y apoyo social, no así cuando se evalúa el intento de suicidio previo. El nivel de riesgo suicida y apoyo social se asocian independiente de la presencia de enfermedad crónica. Esperamos que nuestros resultados permitan fomentar el apoyo social como una herramienta fundamental para la prevención de la suicidabilidad, especialmente en sujetos con enfermedad crónica.

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