Background and purpose:How to predict the survival length for terminally cancer patients is very important, it will help families and physicians to make decisions. This study aimed to reveal the factors related to the survival time of terminally ill cancer patients who received palliative care in our hospital. Methods:The clinical data of 271 dead patients treated in the Department of Palliative Care, Fudan University Shanghai Cancer Center from Mar. 2007 to Mar. 2012 were analyzed. The Kaplan-Meier method and the log-rank test were used to determine the corresponding factors with survival. Cox regression model was used to examine the independent prognostic factors. Different survival length of groups divided by different prognostic indexes was compared by log-rank test. Results:Seven factors were found to be related with the survival according to univariate analysis. The related factors were Karnofsky performance score (P<0.001), dyspnea (P=0.037), delirium (P=0.015), high white blood cell count (P=0.012), low lymphocyte percentage (P=0.030), high lactate dehydrogenase (P<0.001) and low serum albumin (P=0.001). The multivariate analysis selected four independent factors:Karnofsky performance score<30, high lactate dehydrogenase, low serum albumin and delirium.Conclusion:The study shows the clinical survival prognostics with Chinese characteristics. The combination of the seven factors may be useful but more studies in this area deserve further investigated.

The prediction of survival time for advanced cancer patients undergoing palliative care has important clinical and social value. The prediction of survival time of advanced cancer patients includes clinical prediction and statistical prediction. Due to the late start of palliative medicine in China, it is particularly important to evaluate the widely used survival prediction tools in clinical practice. In this paper, we will review the common survival prediction tools of advanced cancer patients from the perspective of Western and Traditional Chinese Medicine,to provide reference for development and application of a survival prediction system in China.

Background and purpose:The quality of life and psychological status of the advanced cancer patients has been widely valued. This study aimed to analyze the psychological status of the advanced cancer patients and its influence factors.Methods:The patients who were hospitalized in the palliative care department of Fudan University Cancer Center from Sep. 2011 to Mar. 2013 were included in this study. Self-rating anxiety scale(SAS), self-rating depression scale (SDS), EORTC QLQ-C30, social support revalued scale and symptom checklist 90 (SCL-90) were recorded and analyzed.Results:Fifty-six patients were included in this study, 18 were depression and 24 were anxiety while 16 of them were depression together with anxiety. The patients were divided into 2 groups according to their psychological status. There’s no signiifcant difference between the age and the social support between the two groups, while the mental disorders group have lower scores on body function, role function, emotional function and social function. The mental disorders were positive correlated with the symptoms score while negative correlated with the quality of life.Conclusion:There’s a high ratio of mental disorders in advanced cancer patients, the symptom scores and quality of life are related to mental disorders. We should pay more attention to the patients who were suffered more from the illness or had lower QOL scores.

Objective: To investigate the correlation between sleep quality and the use of smart phone before bed in middle school students, and to provide a reference for relevant prevention and control. Methods: A total of 3 749 students from 4 middle school which were randomly selected by stratified cluster sampling method were assessed with selfdesigned questionnaire for the students’ general information,smart phone usage before bed,as well as the Epworth Sleepiness Scale(ESS),and Insomnia Severity Index(ISI). Results: A total of 63.17% of students in the seventh grade used mobile phones before going to bed. 67.04% in the eighth grade, 81.18% in the ninth grade, 83.54% in the first grade of high school and 80.11% in the second grade of high school. Mobile phones usage before bed gradually increased with grade. Students with ≥2 h smart phone usage before bed(group 2) had higher scores than those with <2 h smart phone usage before bed(group 1) in each item of ESS, except dozing during traffic jam(P<0.01). The group 2 had higher scores than group 1 in ESS and ISI scores(P<0.01). Conclusion Smart phone usage before bed in middle school students is very common. The risk of sleep disorders increased with the duration of smart phone usage before bed. Parental supervision should be involved in the intervention of smart phone usage before bed among middle school students.

Objective: To identify the avian influenza virus subtype from the avian and environmental samples using the Ion Torrent new-generation semiconductor sequencing technology and to establish a high-throughput sequencing method to identify unclassified influenza A virus. Methods: Virus RNA was extracted from the nine avian swab and environmental samples and real-time RT-PCR was carried out to detect universal fluA, H5N1, H7N9 and H9N2. The whole genome of influenza A virus was amplified by PathAmpFluA kit. Sequencing library was prepared using Next Fast DNA Fragmentation & Library Prep Set for Ion Torrent kit and high-throughput sequencing was done by Ion Torrent Personal Genome Machine(PGM). Data from the PGM was processed and quality evaluated using Ion TorrentSuite v3.0 software. Sequence assembly and influenza database blast were carried out by FluAtyping v4.0 and PathogenAnalyzer bioinformatics software to identify the influenza A virus subtype of these nine samples. Results: The results of real-time RT-PCR for universal fluA of these nine samples were positive but the results for H5N1, H7N9 and H9N2 were negative. Seven subtypes of influenza A virus were identified by high-throughput sequencing and bioinformatics analysis: six samples were H2N3, H5N6, H5N8, H7N1, H7N7, H11N3 subtype respectively and three samples were H6N6 subtype. Conclusions Avian influenza virus has many subtypes in the environment of Zhejiang province. Ion Torrent semiconductor sequencing technology is suitable for fast identification of unclassified influenza virus for avian influenza environment monitoring.