Advances in the field of nanooncology

Nanooncology, the application of nanobiotechnology to the management of cancer, is currently the most important chapter of nanomedicine. Nanobiotechnology has refined and extended the limits of molecular diagnosis of cancer, for example, through the use of gold nanoparticles and quantum dots. Nanobiotechnology has also improved the discovery of cancer biomarkers, one such example being the sensitive detection of multiple protein biomarkers by nanobiosensors. Magnetic nanoparticles can capture circulating tumor cells in the bloodstream followed by rapid photoacoustic detection. Nanoparticles enable targeted drug delivery in cancer that increases efficacy and decreases adverse effects through reducing the dosage of anticancer drugs administered. Nanoparticulate anticancer drugs can cross some of the biological barriers and achieve therapeutic concentrations in tumor and spare the surrounding normal tissues from toxic effects. Nanoparticle constructs facilitate the delivery of various forms of energy for noninvasive thermal destruction of surgically inaccessible malignant tumors. Nanoparticle-based optical imaging of tumors as well as contrast agents to enhance detection of tumors by magnetic resonance imaging can be combined with delivery of therapeutic agents for cancer. Monoclonal antibody nanoparticle complexes are under investigation for diagnosis as well as targeted delivery of cancer therapy. Nanoparticle-based chemotherapeutic agents are already on the market, and several are in clinical trials. Personalization of cancer therapies is based on a better understanding of the disease at the molecular level, which is facilitated by nanobiotechnology. Nanobiotechnology will facilitate the combination of diagnostics with therapeutics, which is an important feature of a personalized medicine approach to cancer

Deep Brain Stimulation of Nucleus Accumbens Region in Alcoholism Affects Reward Processing

The influence of bilateral deep brain stimulation (DBS) of the nucleus nucleus (NAcc) on the processing of reward in a gambling paradigm was investigated using H2[15O]-PET (positron emission tomography) in a 38-year-old man treated for severe alcohol addiction. Behavioral data analysis revealed a less risky, more careful choice behavior under active DBS compared to DBS switched off. PET showed win- and loss-related activations in the paracingulate cortex, temporal poles, precuneus and hippocampus under active DBS, brain areas that have been implicated in action monitoring and behavioral control. Except for the temporal pole these activations were not seen when DBS was deactivated. These findings suggest that DBS of the NAcc may act partially by improving behavioral control

Inter-organizational governance and trilateral trust building: a case study of crowdsourcing-based open innovation in China

In a case study of a Chinese crowdsourcing intermediary, we explore the impact of inter-organizational governance on trilateral trust-building. We show that formal control and relational governance mechanisms are essential for swift and knowledge-based trust in R&D crowdsourcing. The case also indicates that Chinese businesses continue to use guanxi (informal personal connections) as a relational and contingent mechanism to maintain affect-based trust, but guanxi is shown to inhibit the growth of Internet-based crowdsourcing for open innovation in China

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs