Endoplasmic reticulum (ER) stress is becoming recognized as an important contributing factor in various diseases, including diabetes mellitus. Prolonged ER stress can cause β-cell apoptosis; however, the underlying mechanism(s) that contribute to this process are not well understood. Early reports suggested that arachidonic acid metabolites and a Ca2+-independent phospholipase A2 (iPLA2) activity play a role in β-cell apoptosis. The PLA2 family of enzymes catalyse the hydrolysis of the sn-2 substituent (i.e. arachidonic acid) of membrane phospholipids. In light of our findings that the pancreatic islet β-cells are enriched in arachidonate-containing phospholipids and express the group VIA iPLA2β, we considered the possibility that iPLA2β participates in ER stress-induced β-cell apoptosis. Our work revealed a novel mechanism, involving ceramide generation and triggering of mitochondrial abnormalities, by which iPLA2β participates in the β-cell apoptosis process. Here, we review our evidence linking ER stress, β-cell apoptosis and iPLA2β. Continued studies in this area will increase our understanding of the contribution of iPLA2β to the evolution of diabetes mellitus and will further our knowledge of factors that influence β-cell health in diabetes mellitus and identify potential targets for future therapeutic interventions to prevent β-cell death.

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Purpose: This work aims at reducing the uncertainty in proton stopping power (SP) estimation by a novel combination of a linear, separable basis vector model (BVM) for stopping power calculation (Med Phys 43:600) and a statistical, model‐based dual‐energy CT (DECT) image reconstruction algorithm (TMI 35:685). The method was applied to experimental data. Methods: BVM assumes the photon attenuation coefficients, electron densities, and mean excitation energies (I‐values) of unknown materials can be approximated by a combination of the corresponding quantities of two reference materials. The DECT projection data for a phantom with 5 different known materials was collected on a Philips Brilliance scanner using two scans at 90 kVp and 140 kVp. The line integral alternating minimization (LIAM) algorithm was used to recover the two BVM coefficient images using the measured source spectra. The proton stopping powers are then estimated from the Bethe‐Bloch equation using electron densities and I‐values derived from the BVM coefficients. The proton stopping powers and proton ranges for the phantom materials estimated via our BVM based DECT method are compared to ICRU reference values and a post‐processing DECT analysis (Yang PMB 55:1343) applied to vendorreconstructed images using the Torikoshi parametric fit model (tPFM). Results: For the phantom materials, the average stopping power estimations for 175 MeV protons derived from our method are within 1% of the ICRU reference values (except for Teflon with a 1.48% error), with an average standard deviation of 0.46% over pixels. The resultant proton ranges agree with the reference values within 2 mm. Conclusion: Our principled DECT iterative reconstruction algorithm, incorporating optimal beam hardening and scatter corrections, in conjunction with a simple linear BVM model, achieves more accurate and robust proton stopping power maps than the post‐processing, nonlinear tPFM based DECT analysis applied to conventional reconstructions of low and high energy scans. Funding Support: NIH R01CA 75371; NCI grant R01 CA 149305.