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: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. 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:

Sample solution constraints on motor-driven diagnostic nanodevices. The last decade has seen appreciable advancements in efforts towards increased portability of lab-on-a-chip devices by substituting microfluidics with molecular motor-based transportation. As of now, first proof-of-principle devices have analyzed protein mixtures of low complexity, such as target protein molecules in buffer solutions optimized for molecular motor performance. However, in a diagnostic work-up, lab-on-a-chip devices need to be compatible with complex biological samples. While it has been shown that such samples do not interfere with crucial steps in molecular diagnostics (for example antibody-antigen recognition), their effect on molecular motors is unknown. This critical and long overlooked issue is addressed here. In particular, we studied the effects of blood, cell lysates and solutions containing genomic DNA extracts on actomyosin and kinesin-microtubule-based transport, the two biomolecular motor systems that are most promising for lab-on-a-chip applications. We found that motor function is well preserved at defined dilutions of most of the investigated biological samples and demonstrated a molecular motor-driven label-free blood type test. Our results support the feasibility of molecular-motor driven nanodevices for diagnostic point-of-care applications and also demonstrate important constraints imposed by sample composition and device design that apply both to kinesin-microtubule and actomyosin driven applications.
{"Chemical": []}