Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.)

The Danish National Chronic Myeloid Neoplasia Registry

AIM: The Danish National Chronic Myeloid Neoplasia Registry (DCMR) is a population-based clinical quality database, introduced to evaluate diagnosis and treatment of patients with chronic myeloid malignancies. The aim is to monitor the clinical quality at the national, regional, and hospital departmental levels and serve as a platform for research. STUDY POPULATION: The DCMR has nationwide coverage and contains information on patients diagnosed at hematology departments from January 2010 onward, including patients with essential thrombocythemia, polycythemia vera, myelofibrosis, unclassifiable myeloproliferative neoplasms, chronic myelomonocytic leukemia, and chronic myeloid leukemia. MAIN VARIABLES: Data are collected using standardized registration forms (so far up to four forms per patient), which are consecutively filled out online at time of diagnosis, after 2-year and 5-year follow-ups, and at end of follow-up. The forms include variables that describe clinical/paraclinical assessments, treatment, disease progression, and survival – disease-specific variables – as well as variables that are identical for all chronic myeloid malignancies. DESCRIPTIVE DATA: By the end of 2014, the DCMR contained data on 2,690 patients with an inclusion rate of ∼500 patients each year. Since the registry was established, annual reports have shown consistently high national coverage and data completeness, ≥90% and ≥88%, respectively. CONCLUSION: The DCMR is a national database used for monitoring the quality of patient care in patients with chronic myeloid malignancies, but until validation has been conducted, the data must be used with caution. However, the DCMR is a valuable data source accessible to clinicians and researchers

Longitudinal Intra- and Inter-individual variation in T-cell subsets of HIV-infected and uninfected men participating in the LA Multi-Center AIDS Cohort Study.

To assess the intra-individual and inter-individuals biological variation and the effect of aging on lymphocyte T-cells subsets.We assessed lymphocyte phenotypes (CD3, CD4, and CD8 T-cells) in 89 HIV-1-infected and 88 uninfected white non-Hispanic men every 6 months, to examine the biological variation for those measurements, and the average change in lymphocyte phenotype over 34 years.The markers showed significant intra-individuality in HIV-infected and uninfected individuals with index of individuality of <1.4. No mean changes were seen over the 34 years, with the exception of percentage CD4T-cells in HIV-uninfected individuals.In the pre-HAART era, HIV-infected individuals experienced an increase in mean absolute CD3 T-cell numbers (11.21 cells/μL, P = 0.02) and absolute CD8 T-cell numbers (34.57 cell/μl, P < .001), and in the percentage of CD8 T-cells (1.45%, P < .001) per year and a significant decrease in mean absolute CD4 T-cell numbers (23.68 cells/μl, P < .001) and in the percentage of CD4 T-cells (1.49%, P < .001) per year.In the post-HAART era, no changes in mean levels were observed in absolute CD3 T-cell count (P = .15) or percentage (P = .99). Significant decreases were seen in mean count (8.56 cells/μl, P < .001) and percentage (0.59%, P < .001) of CD8 T-cells, and increases in mean absolute count (10.72 cells/μl, P < .001) and percentage (0.47%, P < .001) of CD4 T-cells.With the exception of CD4 (%), no average changes per year were seen in lymphocyte phenotype of HIV-uninfected men. The results of coefficients of variation of intra and inter-individuals of this study can be useful for HIV-1 infection monitoring and in addition the observation could be a useful guide for intra- and inter-individual coefficient variations, and establishing quality goal studies of different blood biomarkers in healthy and other diseases

Binimetinib inhibits MEK and is effective against neuroblastoma tumor cells with low NF1 expression.

BackgroundNovel therapies are needed for children with high-risk and relapsed neuroblastoma. We hypothesized that MAPK/ERK kinase (MEK) inhibition with the novel MEK1/2 inhibitor binimetinib would be effective in neuroblastoma preclinical models.MethodsLevels of total and phosphorylated MEK and extracellular signal-regulated kinase (ERK) were examined in primary neuroblastoma tumor samples and in neuroblastoma cell lines by Western blot. A panel of established neuroblastoma tumor cell lines was treated with increasing concentrations of binimetinib, and their viability was determined using MTT assays. Western blot analyses were performed to examine changes in total and phosphorylated MEK and ERK and to measure apoptosis in neuroblastoma tumor cells after binimetinib treatment. NF1 protein levels in neuroblastoma cell lines were determined using Western blot assays. Gene expression of NF1 and MEK1 was examined in relationship to neuroblastoma patient outcomes.ResultsBoth primary neuroblastoma tumor samples and cell lines showed detectable levels of total and phosphorylated MEK and ERK. IC50 values for cells sensitive to binimetinib ranged from 8 nM to 1.16 μM, while resistant cells did not demonstrate any significant reduction in cell viability with doses exceeding 15 μM. Sensitive cells showed higher endogenous expression of phosphorylated MEK and ERK. Gene expression of NF1, but not MEK1, correlated with patient outcomes in neuroblastoma, and NF1 protein expression also correlated with responses to binimetinib.ConclusionsNeuroblastoma tumor cells show a range of sensitivities to the novel MEK inhibitor binimetinib. In response to binimetinib, sensitive cells demonstrated complete loss of phosphorylated ERK, while resistant cells demonstrated either incomplete loss of ERK phosphorylation or minimal effects on MEK phosphorylation, suggesting alternative mechanisms of resistance. NF1 protein expression correlated with responses to binimetinib, supporting the use of NF1 as a biomarker to identify patients that may respond to MEK inhibition. MEK inhibition therefore represents a potential new therapeutic strategy for neuroblastoma

Exploring the relationship between the Engineering and Physical Sciences and the Health and Life Sciences by advanced bibliometric methods

We investigate the extent to which advances in the health and life sciences (HLS) are dependent on research in the engineering and physical sciences (EPS), particularly physics, chemistry, mathematics, and engineering. The analysis combines two different bibliometric approaches. The first approach to analyze the 'EPS-HLS interface' is based on term map visualizations of HLS research fields. We consider 16 clinical fields and five life science fields. On the basis of expert judgment, EPS research in these fields is studied by identifying EPS-related terms in the term maps. In the second approach, a large-scale citation-based network analysis is applied to publications from all fields of science. We work with about 22,000 clusters of publications, each representing a topic in the scientific literature. Citation relations are used to identify topics at the EPS-HLS interface. The two approaches complement each other. The advantages of working with textual data compensate for the limitations of working with citation relations and the other way around. An important advantage of working with textual data is in the in-depth qualitative insights it provides. Working with citation relations, on the other hand, yields many relevant quantitative statistics. We find that EPS research contributes to HLS developments mainly in the following five ways: new materials and their properties; chemical methods for analysis and molecular synthesis; imaging of parts of the body as well as of biomaterial surfaces; medical engineering mainly related to imaging, radiation therapy, signal processing technology, and other medical instrumentation; mathematical and statistical methods for data analysis. In our analysis, about 10% of all EPS and HLS publications are classified as being at the EPS-HLS interface. This percentage has remained more or less constant during the past decade