The Vista of Information Communication Technology in the Ageing Society: A Perspective from Elderly’s Basic Needs

This study aimed to explore&nbsp;the&nbsp;elderly&lsquo;s&nbsp;basic&nbsp;needs&nbsp;which are indispensable for developing&nbsp;age-friendly&nbsp;Information&nbsp;Communication&nbsp;Technology&nbsp;(ICT) products. Seventeen interviewees who were&nbsp;aged&nbsp;from&nbsp;54&ndash;90&nbsp;yrs participated&nbsp;in&nbsp;the&nbsp;online/offline interviews. By qualitative and quantitative analysis, we showed&nbsp;the&nbsp;implication and contributory factors&nbsp;of&nbsp;the&nbsp;elderly&#39;s&nbsp;basic&nbsp;needs&nbsp;of&nbsp;&quot;Health&quot; and &quot;Longevity.&quot; Physical (normal sensory function, no disease, etc.), psychological (inner peace, good mood, etc.) and environmental factors (companionship, etc.) all play important roles for elderly health and longevity. Among them, psychological factors are&nbsp;the&nbsp;most important, rather than physical factors. We found&nbsp;the&nbsp;typical problems&nbsp;in&nbsp;using and accepting ICT products, such as Technophobia and Learnability.&nbsp;The&nbsp;results suggested ICT products would play a more vital role&nbsp;in&nbsp;a future&nbsp;aging&nbsp;society.&nbsp;The&nbsp;age-friendly design is&nbsp;needed&nbsp;according to psychological and physical&nbsp;aging&nbsp;features and based on&nbsp;the&nbsp;elderly&rsquo;s&nbsp;basic&nbsp;needs.</p

MDM2 phenotypic and genotypic profiling, respective to TP53 genetic status, in diffuse large B-cell lymphoma patients treated with rituximab-CHOP immunochemotherapy: a report from the International DLBCL Rituximab-CHOP Consortium Program

Contains fulltext : 125629.pdf (publisher's version ) (Closed access)MDM2 is a key negative regulator of the tumor suppressor p53, however, the prognostic significance of MDM2 overexpression in diffuse large B-cell lymphoma (DLBCL) has not been defined convincingly. In a p53 genetically-defined large cohort of de novo DLBCL patients treated with rituximab, cyclophosphamide, hydroxydaunorubicin, vincristine, and prednisone (R-CHOP) chemotherapy, we assessed MDM2 and p53 expression by immunohistochemistry (n = 478), MDM2 gene amplification by fluorescence in situ hybridization (n = 364), and a single nucleotide polymorphism in the MDM2 promoter, SNP309, by SNP genotyping assay (n = 108). Our results show that MDM2 overexpression, unlike p53 overexpression, is not a significant prognostic factor in overall DLBCL. Both MDM2 and p53 overexpression do not predict for an adverse clinical outcome in patients with wild-type p53 but predicts for significantly poorer survival in patients with mutated p53. Variable p53 activities may ultimately determine the survival differences, as suggested by the gene expression profiling analysis. MDM2 amplification was observed in 3 of 364 (0.8%) patients with high MDM2 expression. The presence of SNP309 did not correlate with MDM2 expression and survival. This study indicates that evaluation of MDM2 and p53 expression correlating with TP53 genetic status is essential to assess their prognostic significance and is important for designing therapeutic strategies that target the MDM2-p53 interaction

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users