TRIM52 promotes proliferation, invasion, and migration of gastric cancer cells by regulating Wnt/β-catenin pathway

Purpose: This study aimed to reveal the role and mechanism of tripartite motif-containing 52 (TRIM52) in gastric cancer (GC) progression.Methods: The Cancer Genome Atlas (TCGA) database was utilized to analyze TRIM52 expression in GC samples and para-carcinoma tissue samples, and the results were confirmed by quantitative realtime polymerase chain reaction. Cell counting kit-8 and colony formation assays were used to evaluate cell viability. Wound healing assay was utilized to analyze cell migration, while Transwell assay was utilized to evaluate cell invasion. TRIM52, proliferating cell nuclear antigen, matrix metalloproteinase-2, Wnt5a, β-catenin, and c-Myc protein levels were measured by western blot.Results: TRIM52 was expressed more in GC tissue samples and cells compared to normal tissues and cells (p < 0.001). Overexpression of TRIM52 promoted growth, migration, and invasion of HGC-27 cells, and silencing inhibited growth, migration, and invasion of HGC-27 cells (p < 0.001). In addition, TRIM52 overexpression increased Wnt5a, β-catenin, and c-Myc protein expression, and silencing decreased Wnt5a, β-catenin, and c-Myc protein expression (p < 0.001 or p < 0.01), indicating that TRIM52 activates Wnt/β-catenin signaling pathway.Conclusion: These findings reveal that TRIM52 facilitates GC cell proliferation, migration and invasion, but activates Wnt/β-catenin signaling

A Preliminary Exploration into the Performance of Severity Encoding Strategies for Deep Learning-Based Severity Stratification of COVID-19 Patients using Chest X-Rays on A Clinical Site Cohort

A critical step in the clinical workflow for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients is lung disease severity assessment, providing valuable information to aid in effective patient care and management as well as treatment planning. Given the difficulty of performing such assessments by health-care workers and the necessity of expert radiologists who are al-ready burdened by the significant load caused by the pandemic,one promising direction is the use of computer-aided decision sup-port systems powered by deep learning. An important design consideration in the building of deep neural networks for SARS-CoV-2disease severity assessment is in the way severity scores are en-coded, as it can have a big influence on both the training and inference aspects of the neural network. In this study, we explore the performance impact of different severity encoding strategies for deep learning-based severity stratification of COVID-19 patients using chest x-rays (CXRs) on a clinical site cohort collected from the Stony Brook University Hospital. More specifically, we study the impact of different quantized severity encoding schemes, different granularity in the severity encoding, as well as compare quantized encoding vs. continuous encoding vs. hybrid centroid weighted en-coding

A Preliminary Exploration into the Performance of Severity Encoding Strategies for Deep Learning-Based Severity Stratification of COVID-19 Patients using Chest X-Rays on A Clinical Site Cohort

A critical step in the clinical workflow for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients is lung disease severity assessment, providing valuable information to aid in effective patient care and management as well as treatment planning. Given the difficulty of performing such assessments by health-care workers and the necessity of expert radiologists who are al-ready burdened by the significant load caused by the pandemic,one promising direction is the use of computer-aided decision sup-port systems powered by deep learning. An important design consideration in the building of deep neural networks for SARS-CoV-2disease severity assessment is in the way severity scores are en-coded, as it can have a big influence on both the training and inference aspects of the neural network. In this study, we explore the performance impact of different severity encoding strategies for deep learning-based severity stratification of COVID-19 patients using chest x-rays (CXRs) on a clinical site cohort collected from the Stony Brook University Hospital. More specifically, we study the impact of different quantized severity encoding schemes, different granularity in the severity encoding, as well as compare quantized encoding vs. continuous encoding vs. hybrid centroid weighted en-coding

Shoot organogenesis and somatic embryogenesis from leaf and root explants of Scaevola sericea

Abstract An efficient regeneration system via shoot organogenesis and somatic embryogenesis from in vitro leaf and root explants was established for Scaevola sericea for the first time. The highest axillary shoot proliferation coefficient (4.8) was obtained on Murashige and Skoog (MS) medium supplemented with 1.0 mg/L 6-benzyladenine (BA) and 0.1 mg/L α-naphthaleneacetic acid (NAA) every 45 days. Young in vitro leaves and roots, which were used as explants, were cultured onto medium supplemented with different plant growth regulators. Our results showed that only cytokinins BA and thidiazuron (TDZ), could induce adventitious shoots and somatic embryos from leaf and root explants. The optimal medium to achieve this was MS medium supplemented with 2.5 mg/L BA and which induced most adventitious shoots (2.7) and somatic embryos (17.3) from leaf explants within 30 days. From root explants, 1.1 adventitious shoots and 7.6 somatic embryos could be induced on MS medium supplemented with 2.5 mg/L TDZ. Histological observation showed that both somatic embryos and adventitious shoots were originated from homogeneous parenchyma and the development of somatic embryos was visible. Maximum rooting percentage (99.0%) was achieved on half-strength MS medium supplemented with 2.5 mg/L NAA. Well-rooted plantlets, which were transplanted into a substrate of pure river sand, displayed a high survival percentage of 91.7% after transplanting for 45 days while the best substrate for plantlet growth was river sand: coral sand (1:1)