BSD2000 Deep Hyperthermia Combined with Chemotherapy of PT regimen in Patients with Non-small Cell Lung Cancer

Background and objective The aim of this study is to determine the short-term efficacy, toxicity and the rate of life-quality improvement of BSD2000 deep hyperthermia combined with chemotherapy of PT regimen in patients with non-small cell lung cancer (NSCLC) by comparation with PT regimen alone. Methods Sixty patients with NSCLC were randomly divided into the treatment group and control group, with 30 each. The treatment group was treated with chemotherapy (paclitaxel:135mg/m2 ivdirp 3 h qd d1+cisplatin: 20 mg/m2 ivdirp qd d1-5) in combination with BSD2000 deep hyperthermia, and hyperthermia was positioned precisely and maintained for 60 min (2 times a cycle: d1, 4 after the end of chemotherapy within two hours). The control group was treated with chemotherapy alone. Treatment response in both groups were evaluated as well as side-effects after 3 cycles. By observing the results, comparing response rate, toxic side effects and quality of life improvement rate in two groups. Results The efficiency and the rate of life-quality improvement in the treatment group were 63.33%, 76.67% respectively, and 36.67%, 40.00% in the control group respectively. There were significant differences between two groups (P < 0.05). The main side-effects were myelosuppression and gastrointestinal reactions, no significant difference between two groups (P > 0.05). Conclusion BSD2000 deep hyperthermia combined with chemotherapy in patients with NSCLC can significantly increase the efficacy, response rate and quality of life improvement and without increasing sideeffects compared to chemotherapy alone

Space-Invariant Projection in Streaming Network Embedding

Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable. An exact threshold size of these new nodes, below which the node embedding space will be predicatively maintained, however, is rarely considered in either theory or experiment. From the view of matrix perturbation theory, a threshold of the maximum number of new nodes that keep the node embedding space approximately equivalent is analytically provided and empirically validated. It is therefore theoretically guaranteed that as the size of newly arriving nodes is below this threshold, embeddings of these new nodes can be quickly derived from embeddings of original nodes. A generation framework, Space-Invariant Projection (SIP), is accordingly proposed to enables arbitrary static MF-based embedding schemes to embed new nodes in dynamics networks fast. The time complexity of SIP is linear with the network size. By combining SIP with four state-of-the-art MF-based schemes, we show that SIP exhibits not only wide adaptability but also strong empirical performance in terms of efficiency and efficacy on the node classification task in three real datasets