Exosomes are extracellular vesicles (EVs) that have attracted attention because of their important biological roles in intercellular communication and transportation of various biomolecules, including proteins and genetic materials. However, due to difficulties in their selective capture and detection, further application of exosomes remains challenging. To detect EVs, we fabricated a liposomal biosensor based on polydiacetylene (PDA), a conjugate polymer that has been widely used in sensing applications derived from its unique optical properties. To confer selectivity and sensitivity to the sensory material, antibodies targeting CD63, a membrane protein exclusively found in exosomes, were attached to the PDA liposomes and phospholipid molecules were incorporated into the PDA vesicles. Signal analysis derived from PDA liposomes for exosome detection and quantification was performed by observing colorimetric changes triggered by the ligand–receptor interaction of PDA vesicles. Visual, UV–visible, and fluorescence spectroscopic methods were used to obtain signals from the PDA lipid immunosensor, which achieved a detection limit of 3 × 108 vesicles/mL, the minimum concentration that can be used in practical applications. The strategies used in the system have the potential to expand into the field of dealing with exosomes.

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Immunohistochemical staining for DeltaNp63 is a powerful marker for squamous differentiation and useful in exclusion of glandular and neuroendocrine differentiation in uterine cervical cancers, but not always in endometrial cancers.

Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated by transforming growth factor β (TGF-β) and peroxisome proliferator-activated receptor gamma (PPARγ). Here, we developed an adverse outcome pathway (AOP) to better define the linkage of PPARγ antagonism to the adverse outcome of pulmonary fibrosis. We then conducted a systematic analysis to identify potential chemicals involved in this AOP, using the ToxCast database and deep learning artificial neural network models. We identified chemicals bearing a potential inhalation hazard and exposure hazards from the database that could be related to this AOP. For chemicals that were not present in the ToxCast database, multilayer perceptron models were developed based on the ToxCast assays related to the AOP. The reactivity of ToxCast untested chemicals was then predicted using these deep learning models. Both approaches identified a set of chemicals that could be used to validate the AOP. This study suggests that chemicals categorized using an existing database such as ToxCast can be used to validate an AOP and that deep learning approaches can be used to characterize a range of potential active chemicals for an AOP of interest.

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