Broken Bonds: Understanding and Addressing the Needs of Children With Incarcerated Parents

Describes the shared characteristics of children with parents in prison, reviews current research on the emotional and behavioral challenges they face, and discusses what charities, practitioners, and policy makers can do to address those challenges

Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized management and prevention of cancer.Comment: 5 figs, related papers, visit lab homepage: http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 201

Comparative transcriptomics of multidrug-resistant Acinetobacter baumannii in response to antibiotic treatments

Abstract Multidrug-resistant Acinetobacter baumannii, a major hospital-acquired pathogen, is a serious health threat and poses a great challenge to healthcare providers. Although there have been many genomic studies on the evolution and antibiotic resistance of this species, there have been very limited transcriptome studies on its responses to antibiotics. We conducted a comparative transcriptomic study on 12 strains with different growth rates and antibiotic resistance profiles, including 3 fast-growing pan-drug-resistant strains, under separate treatment with 3 antibiotics, namely amikacin, imipenem, and meropenem. We performed deep sequencing using a strand-specific RNA-sequencing protocol, and used de novo transcriptome assembly to analyze gene expression in the form of polycistronic transcripts. Our results indicated that genes associated with transposable elements generally showed higher levels of expression under antibiotic-treated conditions, and many of these transposon-associated genes have previously been linked to drug resistance. Using co-expressed transposon genes as markers, we further identified and experimentally validated two novel genes of which overexpression conferred significant increases in amikacin resistance. To the best of our knowledge, this study represents the first comparative transcriptomic analysis of multidrug-resistant A. baumannii under different antibiotic treatments, and revealed a new relationship between transposons and antibiotic resistance

Partitioning of Poly(amidoamine) Dendrimers between n-Octanol and Water

Dendritic nanomaterials are emerging as key building blocks for a variety of nanoscale materials and technologies. Poly(amidoamine) (PAMAM) dendrimers were the first class of dendritic nanomaterials to be commercialized. Despite numerous investigations, the environmental fate, transport, and toxicity of PAMAM dendrimers is still not well understood. As a first step toward the characterization of the environmental behavior of dendrimers in aquatic systems, we measured the octanol−water partition coefficients (logK_(ow)) of a homologous series of PAMAM dendrimers as a function of dendrimer generation (size), terminal group and core chemistry. We find that the logKow of PAMAM dendrimers depend primarily on their size and terminal group chemistry. For G1-G5 PAMAM dendrimers with terminal NH_2 groups, the negative values of their logK_(ow) indicate that they prefer to remain in the water phase. Conversely, the formation of stable emulsions at the octanol−water (O/W) interface in the presence of G6-NH_2 and G8-NH_2 PAMAM dendrimers suggest they prefer to partition at the O/W interface. In all cases, published studies of the cytotoxicity of Gx-NH_2 PAMAM dendrimers show they strongly interact with the lipid bilayers of cells. These results suggest that the logKow of a PAMAM dendrimer may not be a good predictor of its affinity with natural organic media such as the lipid bilayers of cell membranes

Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis.

ObjectiveTo determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were treated with rituximab. Patients were grouped into responders and non-responders according to the American College of Rheumatology improvement criteria, at a 20% level at 6 months. A Bruker Avance 700 MHz spectrometer and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer were used to acquire (1)H-NMR and ultra high pressure liquid chromatography (UPLC)-MS/MS spectra, respectively, of serum samples before and after rituximab therapy. Data processing and statistical analysis were performed in MATLAB. 14 patients were characterised as responders, and 9 patients were considered non-responders. 7 polar metabolites (phenylalanine, 2-hydroxyvalerate, succinate, choline, glycine, acetoacetate and tyrosine) and 15 lipid species were different between responders and non-responders at baseline. Phosphatidylethanolamines, phosphatidyserines and phosphatidylglycerols were downregulated in responders. An opposite trend was observed in phosphatidylinositols. At 6 months, 5 polar metabolites (succinate, taurine, lactate, pyruvate and aspartate) and 37 lipids were different between groups. The relationship between serum metabolic profiles and clinical response to rituximab suggests that (1)H-NMR and UPLC-MS/MS may be promising tools for predicting response to rituximab

Complex Agent Networks explaining the HIV epidemic among homosexual men in Amsterdam

Simulating the evolution of the Human Immunodeficiency Virus (HIV) epidemic requires a detailed description of the population network, especially for small populations in which individuals can be represented in detail and accuracy. In this paper, we introduce the concept of a Complex Agent Network(CAN) to model the HIV epidemics by combining agent-based modelling and complex networks, in which agents represent individuals that have sexual interactions. The applicability of CANs is demonstrated by constructing and executing a detailed HIV epidemic model for men who have sex with men (MSM) in Amsterdam, including a distinction between steady and casual relationships. We focus on MSM contacts because they play an important role in HIV epidemics and have been tracked in Amsterdam for a long time. Our experiments show good correspondence between the historical data of the Amsterdam cohort and the simulation results.Comment: 21 pages, 4 figures, Mathematics and Computers in Simulation, added reference