http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Roger A. Zwahlen,Alexander T. H. Tang,Wai Keung Leung,Su Keng Tan 대한악안면성형재건외과학회 2022 Maxillofacial Plastic Reconstructive Surgery Vol.44 No.-
Background: The established recommendations and guidelines regarding ideal measurements for an attractive face are mostly based on data gathered among Caucasian population. The aim of this study was to examine the relationship between perception of 3-dimensional facial attractiveness and golden ratio, neoclassical canons, ‘ideal’ ratios and ‘ideal’ angles in Hong Kong Chinese. Methods: Thirty 3-D photographs (15 males and 15 females) were shown to 101 laypersons and 60 patients seeking orthognathic treatment. The photographs were rated based on a 100 mm visual analogue scale (VAS) from 0 (very unattractive) to 100 (very attractive). Results: More than half of the measurements (42/77) in females and thirty-two measurements in males were found to be significantly different from the ideal target value (p < 0.05) upon the comparison of the attractive faces with golden ratio, neoclassical canons, ‘ideal’ ratios and ‘ideal’ angles. Meanwhile, correlation tests between VAS scores and the parameters detected significant results (p < 0.05) in only six ratios, eight angles, one neoclassical canon and one proportion. Conclusions: Despite several renowned ‘ideal’ parameters of attractive faces that have been recommended in the literature, only a few of them were found to be significantly correlated with attractive faces in Hong Kong Chinese.
Li, Yue,Jiang, Zhao-Zhao,Chen, Hai-Xu,Leung, Wai-Keung,Sung, Joseph J.Y.,Ma, Wei-Jun Korean Society for Biochemistry and Molecular Biol 2005 Journal of biochemistry and molecular biology Vol.38 No.4
HOX11 encodes a homeodomain-containing transcription factor which directs the development of the spleen during embryogenesis. While HOX11 expression is normally silenced through an unknown mechanism in all tissues by adulthood, the deregulation of HOX11 expression is associated with leukemia, such as T-cell acute lymphoblastic leukemia. The elucidation of regulatory elements contributing to the molecular mechanism underlying the regulation of HOX11 gene expression is of great importance. Previous reports of HOX11 regulatory elements mainly focused on the 5'-flanking region of HOX11 on the chromosome related to transcriptional control. To expand the search of putative cis-elements involved in HOX11 regulation at the post-transcriptional level, we analyzed HOX11 mRNA 3'-untranslated region (3'UTR) and found an AU-rich region. To characterize this AU-rich region, in vitro analysis of HOX11 mRNA 3'UTR was performed with human RNA-binding protein HuR, which interacts with AU-rich element (ARE) existing in the 3'UTR of many growth factors' and cytokines' mRNAs. Our results showed that the HOX11 mRNA 3'UTR can specifically bind with human HuR protein in vitro. This specific binding could be competed effectively by typical ARE containing RNA. After the deletion of the AU-rich region present in the HOX11 mRNA 3'UTR, the interaction of HOX11 mRNA 3'UTR with HuR protein was abolished. These findings suggest that HOX11 mRNA 3'UTR contains cis-acting element which shares similarity in the action pattern with RE-HuR interactions and may involve in the post-transcriptional regulation of the HOX11 gene.
Ben Man Fei Cheung,Kin Sang Lau,Victor Ho Fun Lee,To Wai Leung,Feng-Ming Spring Kong,Mai Yee Luk,Kwok Keung Yuen 대한방사선종양학회 2021 Radiation Oncology Journal Vol.39 No.4
Purpose: Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the ablative treatment of thoracic tumors. This study aims to identify predictors of treatment responses in patients affected by early stage non-small cell lung cancer (NSCLC) or pulmonary oligo-metastases treated with SBRT and to develop an accurate machine learning model to predict radiological response to SBRT.Materials and Methods: Computed tomography (CT) images of 85 tumors (stage I–II NSCLC and pulmonary oligo-metastases) from 69 patients treated with SBRT were analyzed. Gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) or partial response (PR) were defined as responders. One hundred ten radiomic features were extracted using PyRadiomics module based on the GTV. The association of features with response to SBRT was evaluated. A model using support vector machine (SVM) was then trained to predict response based solely on the extracted radiomics features. Receiver operating characteristic curves were constructed to evaluate model performance of the identified radiomic predictors.Results: Sixty-nine patients receiving thoracic SBRT from 2008 to 2018 were retrospectively enrolled. Skewness and root mean squared were identified as radiomic predictors of response to SBRT. The SVM machine learning model developed had an accuracy of 74.8%. The area under curves for CR, PR, and non-responder prediction were 0.86 (95% confidence interval [CI], 0.794–0.921), 0.946 (95% CI, 0.873–0.978), and 0.857 (95% CI, 0.789–0.915), respectively.Conclusion: Radiomic analysis of pre-treatment CT scan is a promising tool that can predict tumor response to SBRT.