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:

Mapping circulating serum miRNAs to their immune - related target mRNAs Evidence suggests that circulating serum microRNAs (miRNAs) might preferentially target immune - related mRNAs. If this were the case, we hypothesized that immune - related mRNAs would have more predicted serum miRNA binding sites than other mRNAs and, reciprocally, that serum miRNAs would have more immune - related mRNA targets than non - serum miRNAs. We developed a consensus target predictor using the random forest framework and calculated the number of predicted miRNA - mRNA interactions in various subsets of miRNAs (serum, non - serum) and mRNAs (immune related, nonimmune related). Immune - related mRNAs were predicted to be targeted by serum miRNA more than other mRNAs. Moreover, serum miRNAs were predicted to target many more immune - related mRNA targets than non - serum miRNAs; however, these two biases in immune - related mRNAs and serum miRNAs appear to be completely independent. Immune - related mRNAs have more miRNA binding sites in general, not just for serum miRNAs; likewise, serum miRNAs target many more mRNAs than non - serum miRNAs overall, regardless of whether they are immune related or not. Nevertheless, these two independent phenomena result in a significantly larger number of predicted serum miRNA - immune mRNA interactions than would be expected by chance.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["miRNA binding", "interactions"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": ["serum"], "BodySystem": [], "Chemical": ["miRNAs", "mRNA", "miRNA", "mRNAs", "microRNAs"], "ClinicalAttribute": [], "Eukaryote": [], "Finding": [], "Food": [], "HealthCareActivity": [], "InjuryOrPoisoning": [], "IntellectualProduct": ["subsets"], "MedicalDevice": [], "Organization": [], "PopulationGroup": [], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": [], "SpatialConcept": ["sites"], "Virus": []}