You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["toxicological"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": ["silver", "carbon nanotubes", "titanium dioxide"], "ClinicalAttribute": [], "Eukaryote": ["human"], "Finding": [], "Food": [], "HealthCareActivity": ["banding"], "InjuryOrPoisoning": [], "IntellectualProduct": ["Bayesian networks", "Bayesian Networks", "Method", "data sets"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["non-experts"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": [], "SpatialConcept": [], "Virus": []}