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:

Optimization of Early Response Monitoring and Prediction of Cancer Antiangiogenesis Therapy via Noninvasive PET Molecular Imaging Strategies of Multifactorial Bioparameters Objective: Antiangiogenesis therapy (AAT) has provided substantial benefits regarding improved outcomes and survival for suitable patients in clinical settings. Therefore, the early definition of therapeutic effects is urgently needed to guide cancer AAT. We aimed to optimize the early response monitoring and prediction of AAT efficacy, as indicated by the multi-targeted anti-angiogenic drug sunitinib in U87MG tumors, using noninvasive positron emission computed tomography (PET) molecular imaging strategies of multifactorial bioparameters. Methods: U87MG tumor mice were treated via intragastric injections of sunitinib (80 mg/kg) or vehicle for 7 consecutive days. Longitudinal MicroPET/CT scans with (18)F-FDG, (18)F-FMISO, (18)F-ML-10 and (18)F-Alfatide II were acquired to quantitatively measure metabolism, hypoxia, apoptosis and angiogenesis on days 0, 1, 3, 7 and 13 following therapy initiation. Tumor tissues from a dedicated group of mice were collected for immunohistochemical (IHC) analysis of key biomarkers (Glut-1, CA-IX, TUNEL, ανβ3 and CD31) at the time points of PET imaging. The tumor sizes and mouse weights were measured throughout the study. The tumor uptake (ID%/gmax), the ratios of the tumor / muscle (T / M) for each probe, and the tumor growth ratios (TGR) were calculated and used for statistical analyses of the differences and correlations. Results: Sunitinib successfully inhibited U87MG tumor growth with significant differences in the tumor size from day 9 after sunitinib treatment compared with the control group (P < 0.01). The uptakes of (18)F-FMISO (reduced hypoxia), (18)F-ML-10 (increased apoptosis) and (18)F-Alfatide II (decreased angiogenesis) in the tumor lesions significantly changed during the early stage (days 1 to 3) of sunitinib treatment; however, the uptake of (18)F-FDG (increased glucose metabolism) was significantly different during the late stage. The PET imaging data of each probe were all confirmed via ex vivo IHC of the relevant biomarkers. Notably, the PET imaging of (18)F-Alfatide II and (18)F-FMISO was significantly correlated (all P < 0.05) with TGR, whereas the imaging of (18)F-FDG and (18)F-ML-10 was not significantly correlated with TGR. Conclusion: Based on the tumor uptake of the PET probes and their correlations with MVD and TGR, (18)F-Alfatide II PET may not only monitor the early response but also precisely predict the therapeutic efficacy of the multi-targeted, anti-angiogenic drug sunitinib in U87MG tumors. In conclusion, it is feasible to optimize the early response monitoring and efficacy prediction of cancer AAT using noninvasive PET molecular imaging strategies of multifactorial bioparameters, such as angiogenesis imaging with (18)F-Alfatide II, which represents an RGD -based probe.
{"AnatomicalStructure": ["M", "Tumor tissues", "muscle"], "Bacterium": [], "BiologicFunction": ["U87MG tumors", "uptakes", "cancer", "apoptosis", "T", "Cancer", "tumor", "glucose metabolism", "metabolism", "hypoxia", "U87MG tumor", "uptake", "angiogenesis"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": ["(18)F-FMISO,", "anti-angiogenic drug", "sunitinib", "CD31", "CA-IX", "(18)F-FMISO", "vehicle", "biomarkers", "(18)F-ML-10", "Glut-1", "(18)F-FDG", "(18)F-Alfatide II", "Sunitinib", "RGD", "\u03b1\u03bd\u03b23"], "ClinicalAttribute": ["therapeutic effects"], "Eukaryote": ["mouse", "mice"], "Finding": ["multifactorial", "lesions", "Multifactorial"], "Food": [], "HealthCareActivity": ["PET imaging", "Antiangiogenesis Therapy", "injections", "positron emission computed tomography (PET) molecular imaging", "MicroPET/CT scans", "response monitoring", "response", "imaging", "PET Molecular Imaging", "PET", "Antiangiogenesis therapy", "PET molecular imaging", "treatment", "therapy", "AAT", "Response Monitoring"], "InjuryOrPoisoning": [], "IntellectualProduct": ["Noninvasive", "noninvasive"], "MedicalDevice": [], "Organization": [], "PopulationGroup": [], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": [], "SpatialConcept": ["tumor sizes", "tumor size", "Longitudinal", "intragastric"], "Virus": []}