X
Xiaobo Ye
Publications - 4
Citations - 24
Xiaobo Ye is an academic researcher. The author has contributed to research in topics: Internal medicine & Prostate cancer. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.
Papers
More filters
Journal ArticleDOI
Effluent-free deep dyeing of cotton fabric with cacao husk extracts using the Taguchi optimization method
Md. Yousuf Hossain,Liang Yonghong,Md. Nahid Pervez,Xiaobo Ye,Dong Xiongwei,Mohammad Mahbubul Hassan,Yingjie Cai +6 more
TL;DR: In this paper, the authors used cacao husk extracts as a natural dye in the decamethylcyclopentasiloxane (D5) medium for the dyeing of cotton fabric, and subsequently, the dyed cotton was treated by a fixation treatment with a cationic dye-fixing agent in the D5 medium.
Journal ArticleDOI
MRI-measured periprostatic adipose tissue volume as a prognostic predictor in prostate cancer patients undergoing laparoscopic radical prostatectomy
TL;DR: Wang et al. as mentioned in this paper evaluated the association between the PPAT volume and the prognosis of PCa patients after LRP, and found that MRI-measured PPAT volumes is of significant prognostic value for PCA patients undergoing LRP.
Journal ArticleDOI
Skull metastasis is a poor prognostic factor for prostate cancer patients with bone metastasis: a retrospective study based on a Chinese population
TL;DR: In this article , the authors analyzed the prognostic value of skull metastasis (SM) for metastatic prostate cancer patients receiving androgen deprivation therapy (ADT) and found that SM was significantly correlated with more aggressive disease and indicated poor prognosis in patients with bone metastasis.
Journal ArticleDOI
MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population
TL;DR: Wang et al. as mentioned in this paper evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022 and established prediction models of all PCa and clinically significant PCa (csPCa) based on variables selected by multivariate logistic regression and prediction nomograms were constructed.