No abstract

Light has several advantages as the stimulus for a triggered drug release. Currently, the applications of phototriggered drug-release devices (PDDs) are largely limited by two factors: the limited tissue penetration and detrimental effects caused by excitation light (ultraviolet or visible light). To address this disadvantage, this study developed nanocomposites based on upconversion nanoparticles (UC), which could convert near-infrared light to ultraviolet–visible light and trigger drug release. By loading UC and doxorubicin (DOX) into photoresponsive copolymer PEG-NMAB-PLA (PNP), near-infrared responsive copolymer upconversion nanocomposites (PNP-DOX-UC) were constructed. We proved that PNP-DOX-UC showed the fast release and strong cytotoxicity under near-infrared irradiation in vitro. The therapeutic efficacy study indicated that PNP-DOX-UC+hv had the enhanced antitumor efficiency. In the study, UC becoming an internal ultraviolet–visible light source for near-infrared excitation develops an applicable and efficient approach to meet the requirements for UV/vis excitation, which is a major disadvantage in photosensitive materials developed for pharmaceutical and biomedical applications.

Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups.Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested.Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively.Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.