WuXi AppTec (China)

Protein-rich byproducts from food processing serve as promising sources for the generation of bioactive peptides with health-promoting properties, including the regulation of osteoblast proliferation and bone formation. Defatted egg yolk powder, a nutrient-dense natural matrix, is rich in bioactive precursors. However, the high phosphorylation and compact tertiary structure of its constituent proteins hinder the efficient release of osteogenic peptides. Here, we developed a thermal-enzymatic approach to efficiently produce hydrolyzed egg yolk peptide (YPEP) with enhanced bone-anabolic activity. YPEP enhanced bone development in adolescent models by activating osteoblasts, driving longitudinal growth, and improving trabecular microstructure. Proteomic profiling identified an osteogenic peptide, namely FRTPPFGGF (FR9), which targets the epidermal growth factor receptor (EGFR). FR9 markedly enhanced the proliferation and differentiation of murine MC3T3-E1 preosteoblasts. These findings underscore the osteogenic potential of YPEP and suggest the use of FR9 as a promising functional ingredient for promoting bone development during adolescence.

This study analyzed the formulation process of existing testing strategies in the iterative version laboratory and identified significant limitations in the current methods: first, excessive reliance on impact analysis from upstream development, lacking independent verification dimensions; second, the presence of numerous repetitive testing links, leading to redundant human and time costs; third, a high degree of dependence on individual testing experience in strategy formulation, which makes it difficult to identify hidden defects and increases the risk of missed tests during execution. To address these issues, this paper proposes a method of a hybrid testing strategy, optimized through the collaboration of three core modules: The change impact analysis module inherits the original testing strategy framework and can cover and resolve most known defects; The source code (including rule base) comparison module, through in-depth code-level comparison, can detect and address known code issues and potential unknown defects; The data configuration verification module fully covers parameter/data configuration defects and advances the verification process to enable early exposure of problems. These three modules mutually verify and complement each other, making the testing strategy more accurate and comprehensive. It can not only achieve precise testing, resolve unknown hidden defects, but also reduce the rate of missed tests. Most importantly, this hybrid strategy adds security level labels to test cases for the first time, realizing dynamic optimal allocation of testing resources by prioritizing and determining the importance of cases. This not only saves human and time costs but also achieves dual improvements in testing efficiency and quality.

This study aims to develop a deep learning (DL) model based on conventional ultrasound images for differentiating pancreatic serous neoplasms (SCN) from mucinous cystic neoplasms (MCN). We seek to determine if such a model can surpass the diagnostic performance of routine sonographic evaluation and provide a valuable, cost-effective decision-support tool. Data from 459 patients with histopathologically confirmed SCN and MCN from center 1 (n = 345) and center 2 (n = 114) were retrospectively collected. Five DL models (EfficientNet-B3, Resnet-101, Densenet-121, Se-resnext-50, Inception-v3) were constructed for differential diagnosis. Simultaneously compare the diagnostic performance of DL model with that of junior radiologists (JR) and senior radiologists (SR), and determine whether DL can assist radiologists in making better diagnoses. The EfficientNet-B3 model demonstrated the optimal predictive performance, with an area under the receiver operating curve (AUC) of 0.904 (95% CI:0.814–0.973) in the internal validation set and 0.866 (95% CI:0.783–0.936) in the external test set. In the external test set, the EfficientNet-B3 model demonstrated significantly superior diagnostic performance compared to both JR in both F1-score and specificity (all P < 0.0125), while showing fully comparable performance to both SR in these precise metrics (all P > 0.0125). With the EfficientNet-B3 model assistance, both JR showed statistically significant improvements in F1-score and specificity (all P < 0.0125), whereas both SR exhibited no statistically significant changes in either performance metric (all P > 0.0125). The EfficientNet-B3 model demonstrated comparable diagnostic performance to SR while significantly enhancing the diagnostic accuracy of JR. A deep learning-based ultrasound model was developed to distinguish pancreatic serous cystic neoplasms from mucinous cystic neoplasms, outperforming junior radiologists and matching the diagnostic accuracy of senior radiologists. With model assistance, junior radiologists demonstrated significantly enhanced diagnostic performance.