By interconnecting nanomachines and forming nanonetworks, the capacities of singlenanomachines are expected to be enhanced, as the ensuing information exchange will allow themto cooperate towards a common goal. Nowadays, systems normally use electromagnetic signals toencode, send and receive information, however, in a novel communication paradigm, moleculartransceivers, channel models or protocols use molecules. This article presents the current developmentsin nanomachines along with their future architecture to better understand nanonetworkscenarios in biomedical applications. Furthermore, to highlight the communication needs betweennanomachines, two applications for nanonetworks are also presented: i) a new networking paradigm,called the Internet of NanoThings, that allows nanoscale devices to interconnect with existingcommunication networks, and ii) Molecular Communication, where the propagation of chemicalcompounds like drug particles, carry out the information exchange.
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Biomedical discovery through the integrative biomedical knowledge hub (iBKH)
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Human induced pluripotent stem cell (hiPSC)-derived brain organoids can recapitulate the complex cytoarchitecture of the brain as well as the genetic and epigenetic footprint of human brain development. Although the brain organoids are able to mimic the structures and functions of brain in vitro, the 3D models have difficulty in integrating a complex vascular network that can provide the interaction with organoids. Here we report on a microfluidicbased three-dimensional, vascularized cortical organoid tissue construct consisting of 1) a perfused micro-vessel against an extracellular matrix (ECM), dynamic flow and membrane-free culture of the endothelial layer, 2) a sprouted vascular network using a combination of angiogenic factors, and 3) a vascularized hiPSCderived cortical organoid. We report on an optimization of density/stiffness of ECM to induce angiogenic sprouting and effect of angiogenic factors to trigger robust, rapid, and directional angiogenesis for concentration-driven and repetitive sprout formation. Vascularized network in the microfluidic device was further characterized in terms of morphology, directional alignment under perfusion, lumen formation, and permeability. HiPSCderived cortical organoid was generated, placed, and integrated into a vascularized network in the vascularized microfluidic device. We investigate how vascularized micro-vessels interact with cortical organoid. This paper further demonstrates the potential utility of a membrane-free vascularized cortical organoid in perfusion used to model Alzheimer’s disease and for toxicity screening of nerve agents.more » « less
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Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable. This can be a barrier to model uptake in important domains such as biomedicine.There has been recent work on general interpretable representation learning (Onoe and Durrett, 2020), but these domain-agnostic representations do not readily transfer to the important domain of biomedicine. In this paper, we create a new entity type system and train-ing set from a large corpus of biomedical texts by mapping entities to concepts in a medical ontology, and from these to Wikipedia pages whose categories are our types. From this map-ping we deriveBiomedical Interpretable Entity Representations(BIERs), in which dimensions correspond to fine-grained entity types, and values are predicted probabilities that a given entity is of the corresponding type. We propose a novel method that exploits BIER’s final sparse and intermediate dense representations to facilitate model and entity type debugging. We show that BIERs achieve strong performance in biomedical tasks including named entity disambiguation and entity linking, and we provide error analysis to highlight the utility of their interpretability, particularly in low-supervision settings. Finally, we provide our induced 68K biomedical type system, the corresponding 37 million triples of derived data used to train BIER models and our best per-forming model.more » « less
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Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism‐centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data‐driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH‐funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.isci.2023.106460
