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The sperm-associated antigen 5 (SPAG5) is an important protein in mitosis and cell cycle checkpoint regulation, with more attention as a novel oncogene in various cancers. High level of SPAG5 expression has been detected in our clinical gastric cancer (GC) samples and The Cancer Genome Atlas GC data. However, the bio-function and potential mechanism of SPAG5 in GC remain unclear. In this study, we investigated the role of SPAG5 in GC development and the correlation between SPAG5 and 5-fluorouracil (5-FU) treatment. SPAG5 expression was increased in GC samples compared with that in normal tissues (80.8% vs. 22.0%), which was apparently associated with a worse outcome. Biological experiments showed that knockdown of SPAG5 induced apoptosis and suppressed proliferation in cells and animal models. Downregulation of SPAG5 enhanced the sensitivity of 5-FU in GC cells. Gene microarray chip identified 856 upregulated and 787 downregulated genes in SPAG5 silencing cells. Furthermore, 12 significant genes, including CDKN1A, CDKN1B, EIF4E, MAPK1, and HSP90B1, belonged to the PI3K/AKT signaling pathway using ingenuity pathway analysis. Meanwhile, real-time PCR and Western blotting results showed that knockdown of SPAG5 inhibited PI3K/AKT signaling pathway. Collectively, SPAG5 promotes the growth of GC cells by regulating PI3K/AKT signaling pathway, which could be the promising target gene in GC therapy. [ABSTRACT FROM AUTHOR]

Competing Interests: The authors declare that they have no conflict of interest.

Introduction: Although the 9-minute mean withdrawal time (m-WT) is often reported to be associated with the optimal adenoma detection rate (ADR), no randomized trials of screening colonoscopy have confirmed the impact of a 9-minute m-WT on adenoma miss rate (AMR) and ADR., Methods: A multicenter tandem trial was conducted in 11 centers. Seven hundred thirty-three asymptomatic participants were randomized to receive segmental tandem screening colonoscopy with a 9-minute withdrawal, followed by a 6-minute withdrawal (9-minute-first group, 9MF, n = 366) or vice versa (6-minute-first group, 6MF, n = 367). The primary outcome was the lesion-level AMR., Results: The intention-to-treat analysis revealed that 9MF significantly reduced the lesion-level (14.5% vs 36.6%, P < 0.001) and participant-level AMR (10.9% vs 25.9%, P < 0.001), advanced adenoma miss rate (AAMR, 5.3% vs 46.9%, P = 0.002), multiple adenomas miss rate (20.7% vs 56.5%, P = 0.01), and high-risk adenomas miss rate (14.6% vs 39.5%, P = 0.01) of 6MF without compromising detection efficiency ( P = 0.79). In addition, a lower false-negative rate for adenomas ( P = 0.002) and high-risk adenomas ( P < 0.05), and a lower rate of shortening surveillance schedule ( P < 0.001) were also found in 9MF, accompanying with an improved ADR in the 9-minute vs 6-minute m-WT (42.3% vs 33.5%, P = 0.02). The independent inverse association between m-WT and AMR remained significant even after adjusting ADR, and meanwhile, 9-minute m-WT was identified as an independent protector for AMR and AAMR., Discussion: In addition to increasing ADR, 9-minute m-WT also significantly reduces the AMR and AAMR of screening colonoscopy without compromising detection efficiency., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Objective: To investigate an artificial intelligence (AI) automatic segmentation and modeling method for knee joints, aiming to improve the efficiency of knee joint modeling., Methods: Knee CT images of 3 volunteers were randomly selected. AI automatic segmentation and manual segmentation of images and modeling were performed in Mimics software. The AI-automated modeling time was recorded. The anatomical landmarks of the distal femur and proximal tibia were selected with reference to previous literature, and the indexes related to the surgical design were calculated. Pearson correlation coefficient ( r ) was used to judge the correlation of the modeling results of the two methods; the consistency of the modeling results of the two methods were analyzed by DICE coefficient., Results: The three-dimensional model of the knee joint was successfully constructed by both automatic modeling and manual modeling. The time required for AI to reconstruct each knee model was 10.45, 9.50, and 10.20 minutes, respectively, which was shorter than the manual modeling [(64.73±17.07) minutes] in the previous literature. Pearson correlation analysis showed that there was a strong correlation between the models generated by manual and automatic segmentation ( r =0.999, P <0.001). The DICE coefficients of the 3 knee models were 0.990, 0.996, and 0.944 for the femur and 0.943, 0.978, and 0.981 for the tibia, respectively, verifying a high degree of consistency between automatic modeling and manual modeling., Conclusion: The AI segmentation method in Mimics software can be used to quickly reconstruct a valid knee model.