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:

LDL particle number and size and cardiovascular risk: anything new under the sun? We provide here an up-to-date perspective on the potential use of LDL particle number and size as complementary risk factors to predict and manage cardiovascular disease (CVD) risk in the clinical realm. Studies show that a significant proportion of the population has discordant LDL particle number and cholesterol indices [non-HDL cholesterol (HDL-C)]. Data also show that risk prediction may be improved when using information on LDL particle number in patients with discordant particle number and cholesterol data. Yet, most of the current CVD guidelines conclude that LDL particle number is not superior to cholesterol indices, including non-HDL-C concentrations, in predicting CVD risk. LDL particle size, on the other hand, has not been independently associated with CVD risk after adjustment for other risk factors such as LDL cholesterol, triglycerides, and HDL-C and that routine use of information pertaining to particle size to determine and manage patients' risk is not yet justified. Additional studies are required to settle the debate on which of cholesterol indices and LDL particle number is the best predictor of CVD risk, and if such measures should be integrated in clinical practice.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["CVD", "cardiovascular disease"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": ["HDL-C", "non-HDL-C", "non-HDL cholesterol", "LDL cholesterol", "cholesterol", "triglycerides", "LDL"], "ClinicalAttribute": [], "Eukaryote": [], "Finding": ["risk factors"], "Food": [], "HealthCareActivity": ["clinical practice"], "InjuryOrPoisoning": [], "IntellectualProduct": ["indices", "guidelines"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["population"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["studies", "Studies"], "SpatialConcept": ["cardiovascular"], "Virus": []}