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The use of oil-in-water (O/W) emulsion has drawn increasing attention in the baking industry. Compared with some of the well-recognized functionalities, such as textural improvers and flavor carriers, its cryoprotective behavior in frozen dough has not been extensively investigated. Herein, this study reported a pea-protein (PP)-stabilized O/W emulsion with good freeze–thaw stability and evaluated its effectiveness as a high-performance dough cryoprotectant. Specifically, the emulsions were stabilized by 2, 3, and 4 wt% of PP (PP-2, -3, and -4, respectively) and incorporated into frozen doughs, whose cryoprotective effects were systematically evaluated in terms of dough storage stability and baking performance after 4 weeks of storage. Results showed that the frozen dough with PP-3 emulsion exhibited the most uniform water distribution and reduced content of freezable water as reflected by the results from differential scanning calorimetry and low-field nuclear magnetic resonance analyses. Moreover, the PP emulsion helped to maintain the integrity of the gluten network, thus enhancing the dough elasticity. Accordingly, the emulsion-added bread samples exhibited significantly improved loaf volume and textural properties (e.g., softness) and less baking loss. Our findings highlighted the potential of PP emulsion as a viable and high-performance dough cryoprotectant. [ABSTRACT FROM AUTHOR]

Background The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. Methods ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. Results The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P <0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P <0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P <0.001). Conclusions ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals. [ABSTRACT FROM AUTHOR]

Objectives The histopathologic diagnosis of colorectal sessile serrated lesions (SSLs) and hyperplastic polyps (HPs) is of low consistency among pathologists. This study aimed to develop and validate a deep learning (DL)–based logical anthropomorphic pathology diagnostic system (LA-SSLD) for the differential diagnosis of colorectal SSL and HP. Methods The diagnosis framework of the LA-SSLD system was constructed according to the current guidelines and consisted of 4 DL models. Deep convolutional neural network (DCNN) 1 was the mucosal layer segmentation model, DCNN 2 was the muscularis mucosa segmentation model, DCNN 3 was the glandular lumen segmentation model, and DCNN 4 was the glandular lumen classification (aberrant or regular) model. A total of 175 HP and 127 SSL sections were collected from Renmin Hospital of Wuhan University during November 2016 to November 2022. The performance of the LA-SSLD system was compared to 11 pathologists with different qualifications through the human-machine contest. Results The Dice scores of DCNNs 1, 2, and 3 were 93.66%, 58.38%, and 74.04%, respectively. The accuracy of DCNN 4 was 92.72%. In the human-machine contest, the accuracy, sensitivity, and specificity of the LA-SSLD system were 85.71%, 86.36%, and 85.00%, respectively. In comparison with experts (pathologist D: accuracy 83.33%, sensitivity 90.91%, specificity 75.00%; pathologist E: accuracy 85.71%, sensitivity 90.91%, specificity 80.00%), LA-SSLD achieved expert-level accuracy and outperformed all the senior and junior pathologists. Conclusions This study proposed a logical anthropomorphic diagnostic system for the differential diagnosis of colorectal SSL and HP. The diagnostic performance of the system is comparable to that of experts and has the potential to become a powerful diagnostic tool for SSL in the future. It is worth mentioning that a logical anthropomorphic system can achieve expert-level accuracy with fewer samples, providing potential ideas for the development of other artificial intelligence models. [ABSTRACT FROM AUTHOR]

Integration of separation and reaction, Ion exchange column, The copper()-hesperetin complex, Synthesis, Bioactivity. [Extracted from the article]