Suppressive effects of an apoptotic mimicry prepared from jumbo-flying squid-skin phospholipids on the osteoclastogenesis in receptor activator of nuclear factor kappa B ligand/macrophage colony-stimulating factor-induced RAW 264.7 cells.

BACKGROUND: Liposomes containing docosahexaenoic acid (DHA) and phosphatidylserine were claimed to inhibit osteoclast formation and bone resorption in the inflammatory status. Herein, we proposed that an apoptotic mimicry (SQ liposome) prepared from squid-skin phospholipids can explore the suppressive osteoclastogenesis. METHODS: The intermolecular fatty-acid composition in the phospholipid of squid-skin extract was analyzed by GC-FID. The SQ liposome structure was characterized by size distribution and zeta potential (ζ). RAW 264.7 cell is used to study the effect of SQ liposomes on osteoclast differentiation. Secretion of prostaglandin E2 (PGE2) and transforming growth factor-ß (TGF-ß) from RAW 264.7 cells were assayed. Antiosteoclastogenesis effects were performed via the tartrate-resistant acid phosphatase (TRAP)-positive multinucleated cell (MNC) counting, bone resorption pit assay, and TRAP activity analysis. The specific gene expressions related to antiosteoclastogenesis were also detected. RESULTS: An apoptotic mimicry through the use of a single-layer liposome (SQ liposome) with phosphatidylserine exposure contains DHA (28.7%) and eicosapentaenoic acid (EPA, 11.8%). Co-treatment with receptor activator of nuclear factor kappa B ligand (RANKL)/macrophage colony-stimulating factor induced RAW 264.7-cell differentiation into mature osteoclasts, thus enhancing PGE2 and TGF-ß secretion. However, cotreatment with 1 mg/mL of SQ liposome restored (p < 0.05) the cell viabilities under the RANKL stress. Increased PGE2 levels was downregulated (p < 0.05) in cotreatments with 0.11 and 0.33 mg/mL of SQ liposome, but on the TGF-ß levels were not (p > 0.05) influenced in SQ liposome cotreatments. Cotreatments with 0.33-1 mg/mL of SQ liposome suppressed (p < 0.05) the osteoclast maturation (such as decreased MNCs and bone pit formation), inhibited TRAP activities, and downregulated the osteoclastogenesis-related gene expressions. CONCLUSION: In summary, current data support that a possible prevention of our prepared SQ liposomes which are rich in DHA and EPA on bone loss is through the suppression of osteoclastogenesis. Moreover, based on the results from this study an in vivo study warrants a further investigation.

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes.

OBJECTIVE: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes). MATERIALS AND METHODS: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement. RESULTS: A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care. CONCLUSIONS: The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value.