Publications by authors named "Haoda Huang"

Background: Chemotherapy is an important therapeutic approach for non-small cell lung cancer (NSCLC). However, a successful long-term treatment can be prevented by the occurring of chemotherapy resistance frequently, and the molecular mechanisms of chemotherapy resistance in NSCLC remain unclear. In this study, abnormal expressions of miR-17 and miR-92 families are observed in cisplatin-resistant cells, suggesting that miR-17 and miR-92 families are involved in the regulation of cisplatin resistance in NSCLC.

Methods: miRNA microarray shows that miR-17 and miR-92 families are all down-regulated in cisplatin-resistant A549/DDP cells compared with cisplatin-sensitive A549 cells. The aim of this study is to investigate the regulatory functions of miR-17 and miR-92 families on the formation of cisplatin resistance and the predictive functions of them as biomarkers of platinum-based chemotherapy resistance in NSCLC.

Results: The low expressions of miR-17 and miR-92 families can maintain cisplatin resistance through the regulation of CDKN1A and RAD21. As a result of high expressions of CDKN1A and RAD21, the inhibition of DNA synthesis and the repair of DNA damage are achieved and these may be two major contributing factors to cisplatin resistance. Moreover, we demonstrate that the expressions of miR-17 and miR-92 families in NSCLC tissues are significantly associated with platinum-based chemotherapy response.

Conclusion: Our study indicates that miR-17 and miR-92 families play important roles in cisplatin resistance and can be used as potential biomarkers for better predicting the clinical response to platinum-based chemotherapy in NSCLC.

MicroRNAs (miRNAs) have been proven to play crucial roles in cancer, including tumor chemotherapy resistance and metastasis of non-small-cell lung cancer (NSCLC). TGFβ signal pathway abnormality is widely found in cancer and correlates with tumor proliferation, apoptosis and metastasis. Here, miR-17, 20a, 20b were detected down-regulated in A549/DDP cells (cisplatin resistance) compared with A549 cells (cisplatin sensitive). Over-expression of miR-17, 20a, 20b can not only decrease cisplatin-resistant but also reduce migration by inhibiting epithelial-to-mesenchymal transition (EMT) in A549/DDP cells. These functions of miR-17, 20a, 20b may be caused at least in part via inhibition of TGFβ signal pathway, as miR-17, 20a, 20b are shown to directly target and repress TGF-beta receptor 2 (TGFβR2) which is an important component of TGFβ signal pathway. Consequently, our study suggests that miRNA 17 family (including miR-17, 20a, 20b) can act as TGFβR2 suppressor for reversing cisplatin-resistant and suppressing metastasis in NSCLC.

Recent advances in laser scanning technology have made it possible to faithfully scan a real object with tiny geometric details, such as pores and wrinkles. However, a faithful digital model should not only capture static details of the real counterpart but also be able to reproduce the deformed versions of such details. In this paper, we develop a data-driven model that has two components; the first accommodates smooth large-scale deformations and the second captures high-resolution details. Large-scale deformations are based on a nonlinear mapping between sparse control points and bone transformations. A global mapping, however, would fail to synthesize realistic geometries from sparse examples, for highly deformable models with a large range of motion. The key is to train a collection of mappings defined over regions locally in both the geometry and the pose space. Deformable fine-scale details are generated from a second nonlinear mapping between the control points and per-vertex displacements. We apply our modeling scheme to scanned human hand models, scanned face models, face models reconstructed from multiview video sequences, and manually constructed dinosaur models. Experiments show that our deformation models, learned from extremely sparse training data, are effective and robust in synthesizing highly deformable models with rich fine features, for keyframe animation as well as performance-driven animation. We also compare our results with those obtained by alternative techniques.