Multiple myeloma (MM) is a fatal disease that affects plasma cells. Patients with MM have 1 or more osteolytic lesions in their bone tissues, where insulin-like growth factors (IGFs; IGF-I and IGF-II) are mainly stored. The role of bone-derived IGFs in the development of MM has not been extensively studied because reliable animal models are lacking. We established an animal model using a human MM cell line, RPMI8226, in nonobese diabetic/severecombined immunodeficient (NOD/SCID) mice implanted with human adult bone (HAB) fragments. Treatment with an antihuman IGF-neutralizing monoclonal antibody, KM1468, inhibited the IGF-I-stimulated phosphorylation of type-I IGF receptors (IGF-IR) in RPMI8226 cells and the activation of the downstream PI3-K/Akt signaling pathway in vitro. KM1468 inhibited IGF-Imediated RPMI8226 cell growth in a dose-dependent manner. In the NOD/SCID-HAB model, treatment with KM1468 significantly inhibited the growth of RPMI8226 cells (p < 0.02). These results indicated that the growth of MM cells was predominantly stimulated not by serum-derived IGFs, but by bone-derived IGFs. Furthermore, the targeting of bone-derived IGFs, using a neutralizing antibody, may offer a new therapeutic strategy for MM. ' 2005 Wiley-Liss, Inc.

Microvessel density in biopsy specimens was closely correlated with local control of HPC treated by radiotherapy.

The discovery of pulsars is of great significance in the field of physics and astronomy. As the astronomical equipment produces a large number of pulsar data, an algorithm for automatically identifying pulsars becomes urgent. We propose a deep learning framework for pulsar recognition. In response to the extreme imbalance between positive and negative examples and the hard negative sample issue presented in the High Time Resolution Universe Medlat Training Data, there are two coping strategies in our framework: the smart under-sampling and the improved loss function. We also apply the early-fusion strategy to integrate features obtained from different attributes before classification to improve the performance. To our best knowledge, this is the first study that integrates these strategies and techniques in pulsar recognition. The experiment results show that our framework outperforms previous works with respect to either the training time or F1 score. We can not only speed up the training time by 10 × compared with the state-of-the-art work, but also get a competitive result in terms of F1 score.