Synthesis, characterization, interactions with the DNA duplex dodecamer d(5'-CGCGAATTCGCG-3')2 and cytotoxicity of binuclear η6-arene-Ru(II) complexes.

The novel binuclear η6-arene-Ru(II) complexes with the general formula {[(η6-cym)Ru(L)]2(µ-BL)}(PF6)4, and their corresponding water soluble {[(η6-cym)Ru(L)]2(µ-BL)}Cl4, where cym = p-cymene, L = 2,2'-bipyridine (bpy) and 1,10-phenanthroline (phen), BL = 4,4'-bipyridine (BL-1), 1,2-bis(4-pyridyl)ethane (BL-2) and 1,3-bis(4-pyridyl)propane (BL-3), were synthesized and characterized. The structure of {[(η6-cym)Ru(phen)]2(µ-BL-1)}(PF6)4 was determined by X-ray single crystal methods. The interaction of {[(η6-cym)Ru(phen)]2(µ-BL-i)}Cl4 (i = 1, 2, 3; (4), (5) and (6) correspondingly) with the DNA duplex d(5'-CGCGAATTCGCG-3')2 was studied by means of NMR techniques and fluorescence titrations. The results show that complex (4) binds with a Kb = 12.133 × 103 M-1 through both intercalation and groove binding, while (5) and (6) are groove binders (Kb = 2.333 × 103 M-1 and Kb = 3.336 × 103 M-1 correspondingly). Comparison with the mononuclear complex [(η6-cym)Ru(phen)(py)]2+ reveals that it binds to the d(5'-CGCGAATTCGCG-3')2 with a Kb value two orders of magnitude lower than (4) (Kb = 0.158 × 103 M-1), indicating that for the binuclear complexes both ruthenium moieties participate in the binding. The complexes were found to be cytotoxic against the A2780 and A2780 res. cancer cell line with a selectivity index (SI) in the range of 3.0-5.9.

Health in All Policy Making Utilizing Big Data.

INTRODUCTION: Health in all Policies (HiAP) is a valuable method for effective Healthcare policy development. Big data analysis can be useful to both individuals and clinicians so that the full potential of big data is employed. AIM: The present paper deals with Health in All Policies, and how the use of Big Data can lead and support the development of new policies. METHODS: To this end, in the context of the CrowdHEALTH project, data from heterogeneous sources will be exploited and the Policy Development Toolkit (PDT) model will be used. In order to facilitate new insights to healthcare by exploiting all available data sources. RESULTS: In the case study that is being proposed, the NOHS Story Board (inpatient and outpatient health care) utilizing data from reimbursement of disease-related groups (DRGs), as well as medical costs for outpatient data, will be analyzed by the PDT. CONCLUSION: PDT seems promising as an efficient decision support system for policymakers to align with HiAP as it offers Causal Analysis by calculating the total cost (expenses) per ICD-10, Forecasting Information by measuring the clinical effectiveness of reimbursement cost per medical condition, per gender and per age for outpatient healthcare, and Risk Stratification by investigating Screening Parameters, Indexes (Indicators) and other factors related to healthcare management. Thus, PDT could also support HiAP by helping policymakers to tailor various policies according to their needs, such as reduction of healthcare cost, improvement of clinical effectiveness and restriction of fraud.