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:

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention / development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["metabolic syndrome", "type 2 diabetes", "diseases", "obesity", "type-2 diabetes"], "BiomedicalOccupationOrDiscipline": ["genomics", "nutrition", "metabolomic", "Metabolomics", "pathologies", "metabolomics", "proteomics"], "BodySubstance": ["urine", "saliva", "blood", "biofluids"], "BodySystem": [], "Chemical": ["metabolites", "toxins"], "ClinicalAttribute": ["biomarkers", "Biomarkers"], "Eukaryote": ["human"], "Finding": ["Nutrition", "detect"], "Food": ["nutrients", "Diet", "food"], "HealthCareActivity": ["analytical techniques"], "InjuryOrPoisoning": [], "IntellectualProduct": ["bioinformatics tools", "statistical"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["population groups", "individuals"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["studies", "dietary patterns", "methodologies"], "SpatialConcept": [], "Virus": []}