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Your search keyword '"Hao, Hua"' showing total 344 results
Start Over Author "Hao, Hua" Publisher elsevier bv
344 results on '"Hao, Hua"'

44. Magnetic-assisted laparoscopic liver transplantation in swine

Author

Feng, Zhe, primary, Wang, Shan-Pei, additional, Wang, Hao-Hua, additional, Lu, Qiang, additional, Qiao, Wei, additional, Wang, Kai-Ling, additional, Ding, Hong-Fan, additional, Wang, Yue, additional, Wang, Rong-Feng, additional, Shi, Ai-Hua, additional, Ren, Bing-Yi, additional, Jiang, Yu-Nan, additional, He, Bin, additional, Yu, Jia-Wei, additional, Wu, Rong-Qian, additional, and Lv, Yi, additional

Published
2022
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49. A panel of eight autophagy-related long non-coding RNAs is a good predictive parameter for clear cell renal cell carcinoma

Author

Yiming Tang, Yi-Hui Pan, Yunlin Ye, Lei Tan, Yong Huang, Wei Chen, Junhang Luo, Jiazheng Cao, Pengju Li, Yu-Hang Chen, Zhenhua Chen, Jinhuan Wei, Zike Qin, Jun Lu, Hao-Hua Yao, and Chengpeng Gui

Subjects
0106 biological sciences, Oncology, medicine.medical_specialty, Biology, 01 natural sciences, 03 medical and health sciences, Postoperative risk, Renal cell carcinoma, Internal medicine, Autophagy, Biomarkers, Tumor, Genetics, medicine, Humans, Carcinoma, Renal Cell, 030304 developmental biology, 0303 health sciences, Prognostic signature, medicine.disease, Kidney Neoplasms, Long non-coding RNA, Blot, Clear cell renal cell carcinoma, RNA, Long Noncoding, Risk assessment, 010606 plant biology & botany
Abstract

Clear-cell renal cell carcinoma (ccRCC) carries a variable prognosis. Prognostic biomarkers can stratify patients according to risk, and can provide crucial information for clinical decision-making. We screened for an autophagy-related long non-coding lncRNA (lncRNA) signature to improve postoperative risk stratification in The Cancer Genome Atlas (TCGA) database. We confirmed this model in ICGC and SYSU cohorts as a significant and independent prognostic signature. Western blotting, autophagic-flux assay and transmission electron microscopy were used to verify that regulation of expression of 8 lncRNAs related to autophagy affected changes in autophagic flow in vitro. Our data suggest that 8-lncRNA signature related to autophagy is a promising prognostic tool in predicting the survival of patients with ccRCC. Combination of this signature with clinical and pathologic parameters could aid accurate risk assessment to guide clinical management, and this 8-lncRNAs signature related to autophagy may serve as a therapeutic target.

Published
2021

50. SUVmax of 18FDG PET/CT Predicts Histological Grade of Lung Adenocarcinoma

Author

Chun Xie, Maomei Ruan, Cheng Chang, Bei Lei, Hao-Hua Teng, Xiaoyan Sun, Yun-Hai Yang, Lihua Wang, Wenhui Xie, Liu Liu, and Tian-Xiang Chen

Subjects
PET-CT, medicine.medical_specialty, Lung, business.industry, Standardized uptake value, 18fdg pet ct, medicine.disease, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, 030220 oncology & carcinogenesis, medicine, Adenocarcinoma, Treatment strategy, Radiology, Nuclear Medicine and imaging, Histological grades, Radiology, business, Pathological
Abstract

Objectives The relationship between the 18FDG PET-CT maximum standard uptake value (SUVmax) and the type of lung adenocarcinoma is still not established. The aim of this study was to investigate the relationship between SUVmax value and histological grade and pathological subtype of lung adenocarcinoma, and to determine the optimum SUVmax cutoffs for distinguishing different histological grades. Materials and Methods The data of 618 lung adenocarcinoma patients were retrospectively analyzed. The relationship between SUVmax measured on preoperative 18FDG-PET-CT and the histological grade and pathological subtype was examined. The Kruskal-Wallis test was used to compare differences among groups, and the Bonferroni-Dunn test for pairwise comparison among groups. ROC analysis was applied to determine the optimal cut-off values for distinguishing different groups. In addition, the cut-off value was verified in an independent cohort of 85 consecutive lung adenocarcinoma cases. Results The SUVmax was significantly different between the low, intermediate, and high-grade groups(p Conclusion SUVmax can be used to predict pathological subtype and histological grade of lung adenocarcinoma. Thus, 18FDG PET-CT can serve as a noninvasive tool for precise diagnosis and help in the preoperative formulation of patient-specific treatment strategies.

Published
2021

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