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Pyrus calleryana fruit (bean pear) is processed into fruit wine and used in traditional Chinese medicine. The present study reported phytochemical constituents, antioxidant, anti-inflammatory, and enzyme inhibitory activities of P. calleryana fruit water extract (WE) and ethanol extract (EE). In the P. calleryana fruit WE and EE, 63 compounds were identified using UHPLC-Q-Orbitrap-MS analysis, including 23 phenols, 13 flavonoids, 14 terpenoids, and 13 other types of compounds. In the antioxidant activity, WE and EE showed marked free radical scavenging effects on both ABTS (2.33 ± 0.15 μg/mL and 2.23 ± 0.15 μg/mL, respectively) and DPPH (5.93 ± 0.55 μg/mL and 7.07 ± 0.23 μg/mL, respectively), especially, their scavenging effects on DPPH free radicals were superior or equivalent to that of BHT (7.47 ± 0.47 μg/mL). In LPS-induced RAW264.7 cells, P. calleryana fruit WE and EE remarkably inhibited the secretion of inflammatory factors, and the inhibitory effect of WE on the release of IL-6, NO, and PGE2 was superior or equivalent to that of EE. Interestingly, P. calleryana fruit WE and EE exhibited potent inhibition on α-glucosidase (0.60 ± 0.09 μg/mL and 0.48 ± 0.09 μg/mL, respectively) and tyrosinase (210.11 ± 2.59 μg/mL and 45.35 ± 0.96 μg/mL, respectively), which were superior to their respective positive controls acarbose (302.57 ± 22.09 μg/mL) and arbutin (243.07 ± 15.91 μg/mL). Our findings suggested that P. calleryana fruit WE and EE possess significant antioxidant, anti-inflammatory, α-glucosidase, and tyrosinase inhibitory properties. Thus, P. calleryana fruit has great potential for application in functional food products. [ABSTRACT FROM AUTHOR] more...

Stomach adenocarcinoma (STAD) is the most prevalent gastrointestinal malignancy and seriously threatens the life of the global population. Anoikis, a process of programmed cell death that occurs when cells detach from the extracellular matrix, is closely associated with tumor invasion and metastasis. In this study, we used the TCGA-STAD database to identify the expression patterns and prognostic relevance of anoikis-related genes (ARGs) in STAD. Functional enrichment analysis was used to explore the potential pathway. LASSO and Cox regression were used to construct anoikis-related prognostic signature. The anoikis risk score (ARS) incorporated 7 genes and stratified patients into highand low-risk subgroups by median value splitting. In addition, external validation was performed based on GSE66229, GSE15459, and GSE84437 cohorts. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with time-dependent AUC, calibration curves, and decision curve analysis (DCA). The prognostic signature indicated that the low-risk subgroup had better outcomes and significant correlations with tumor microenvironment, immune landscape, immunotherapy response, and drug sensitivity. In addition, single-cell analysis displayed the cell types, the subcellular localization of prognostic genes, and the cellular interaction to reveal the potential molecular communication mechanism of anoikis resistance. Finally, in vitro experiments confirmed the critical role of CRABP2 in STAD. The results indicated that CRABP2 knockdown inhibited gastric cancer cell proliferation, migration and invasion, and promoted apoptosis. In summary, ARS can serve as a biomarker for predicting survival outcomes in STAD patients, providing new tools for personalized treatment decisions for STAD patients. [ABSTRACT FROM AUTHOR] more...

Objective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis. Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images. From Dataset 1, we randomly selected 3,000 images and used the Labelimg tool to annotate the cancerous regions within the images. These images were then divided into training and validation sets in a 7:3 ratio. The remaining 223 images, along with Dataset 2, were ultimately used as the internal test set and external test set, respectively, to evaluate the model's segmentation effect. A series of optimizations were made to the original YOLOv5 algorithm, introducing the Atrous Spatial Pyramid Pooling (ASPP), Convolutional Block Attention Module (CBAM), Coordinate Attention (CA) for structural improvement, resulting in several optimized versions, namely YOLOv5s-ASPP, YOLOv5s-CBAM, YOLOv5s-CA, YOLOv5s-ASPP-CBAM, and YOLOv5s-ASPP-CA. The training and validation sets were input into the original YOLOv5s model, five optimized models, and the YOLOv8s model for 100 rounds of iterative training. The best weight file of the model with the best evaluation index in the six trained models was used for the final test of the test set. Results: After iterative training, the seven models can segment and recognize brain tumor magnetic resonance images. Their precision rates on the validation set are 92.5, 93.5, 91.2, 91.8, 89.6, 90.8, and 93.1%, respectively. The corresponding recall rates are 84, 85.3, 85.4, 84.7, 87.3, 85.4, and 91.9%. The best weight file of the model with the best evaluation index among the six trained models was tested on the test set, and the improved model significantly enhanced the image segmentation ability compared to the original model. Conclusion: Compared with the original YOLOv5s model, among the five improved models, the improved YOLOv5s-ASPP model significantly enhanced the segmentation ability of brain tumor magnetic resonance images, which is helpful in assisting clinical diagnosis and treatment planning. [ABSTRACT FROM AUTHOR] more...