Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning.

Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC.

Enrichment desired quality chitosan fraction and advance yield by sequential static and static-dynamic supercritical CO2.

This study aimed to establish the sequential static and static-dynamic supercritical carbon dioxide (SDCO2) fractionation conditions to obtain a higher yield and desired chitosan with lower polydispersity index (PDI) and higher degree of deacetylation (DD). The yield increased with increasing DD of used chitosan and amount of cosolvent. The yield of acetic acid cosolvent was higher than those of malic and citric acid cosolvents. SDCO2, compared to static supercritical carbon dioxide, has higher yield. The yield of extracted chitosan was 5.82-14.70% by SDCO2/acetic acid, which increases with increasing pressure. The DD of fractionated chitosan increased from 66.1% to 70.81-85.33%, while the PDI decreased from 3.97 to 1.69-3.16. The molecular weight changed from 622kDa to 412-649kDa, which increased as density of supercritical carbon dioxide increases. Hence, higher DD and lower PDI extracted chitosan can be obtained through controlling the temperature and pressure of SDCO2.