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: ["Protein", "Chemical"].

Please return the output as a JSON object of the format: {"Chemical" : ["Dexamethasone", ...], "Protein" : ["BRCA-1", ...]}. 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:

Pirfenidone inhibits TGF-beta expression in malignant glioma cells.
Due to its immunosuppressive properties, the cytokine transforming growth factor (TGF)-beta has become a promising target in the experimental treatment of human malignant gliomas. Here, we report that the antifibrotic drug 5-methyl-1-phenyl-2-(1H)-pyridone (pirfenidone, PFD) elicits growth-inhibitory effects and reduces TGF-beta2 protein levels in human glioma cell lines. This reduction in TGF-beta2 is biologically relevant since PFD treatment reduces the growth inhibition of TGF-beta-sensitive CCL-64 cells mediated by conditioned media of glioma cells. The downregulation of TGF-beta is mediated at multiple levels. PFD leads to a reduction of TGF-beta2 mRNA levels and of the mature TGF-beta2 protein due to decreased expression and direct inhibition of the TGF-beta pro-protein convertase furin. In addition, PFD reduces the protein levels of the matrix metalloproteinase (MMP)-11, a TGF-beta target gene and furin substrate involved in carcinogenesis. These data define PFD or PFD-related agents as promising agents for human cancers associated with enhanced TGF-beta activity.
{"Chemical": ["PFD", "pirfenidone", "Pirfenidone", "5-methyl-1-phenyl-2-(1H)-pyridone"], "Protein": ["matrix metalloproteinase (MMP)-11", "TGF-beta", "furin", "TGF-beta2", "cytokine", "transforming growth factor (TGF)-beta", "pro-protein convertase"]}