The aim of this study was to examine the efficacy and safety of duloxetine, a balanced and potent dual reuptake inhibitor of serotonin and norepinephrine, in the management of diabetic peripheral neuropathic pain. Serotonin and norepinephrine are thought to inhibit pain via descending pain pathways. In a 12-week, multicenter, double-blind study, 457 patients experiencing pain due to polyneuropathy caused by Type 1 or Type 2 diabetes mellitus were randomly assigned to treatment with duloxetine 20 mg/d (20 mg QD), 60 mg/d (60 mg QD), 120 mg/d (60 mg BID), or placebo. The diagnosis was confirmed by a score of at least 3 on the Michigan Neuropathy Screening Instrument. The primary efficacy measure was the weekly mean score of the 24-h Average Pain Score, which was rated on an 11-point (0-10) Likert scale (no pain to worst possible pain) and computed from diary scores between two site visits. Duloxetine 60 and 120 mg/d demonstrated statistically significant greater improvement compared with placebo on the 24-h Average Pain Score, beginning 1 week after randomization and continuing through the 12-week trial. Duloxetine also separated from placebo on nearly all the secondary measures including health-related outcome measures. Significantly more patients in all three active-treatment groups achieved a 50% reduction in the 24-h Average Pain Score compared with placebo. Duloxetine treatment was considered to be safe and well tolerated with less than 20 percent discontinuation due to adverse events. Duloxetine at 60 and 120 mg/d was safe and effective in the management of diabetic peripheral neuropathic pain.

This article considers the problem of modeling a class of nonstationary time series using piecewise autoregressive (AR) processes. The number and locations of the piecewise AR segments, as well as the orders of the respective AR processes, are assumed unknown. The minimum description length principle is applied to compare various segmented AR fits to the data. The goal is to find the "best" combination of the number of segments, the lengths of the segments, and the orders of the piecewise AR processes. Such a "best" combination is implicitly defined as the optimizer of an objective function, and a genetic algorithm is implemented to solve this difficult optimization problem. Numerical results from simulation experiments and real data analyses show that the procedure has excellent empirical properties. The segmentation of multivariate time series is also considered. Assuming that the true underlying model is a segmented autoregression, this procedure is shown to be consistent for estimating the location of the breaks.

SUMMARY Retinoblastomas develop due to the loss of the Rb protein, yet the cell type in which Rb suppresses retinoblastoma, and the cellular circuitry that underlies the need for Rb are undefined. Here, we show that retinoblastoma cells express markers of post-mitotic cone precursors, but not markers of other retinal cell types. We also demonstrate that human cone precursors prominently express MDM2 and N-Myc, that retinoblastoma cells require both of these proteins for proliferation and survival, and that MDM2 is specifically needed to suppress ARF-induced apoptosis in cultured retinoblastoma cells. Interestingly, retinoblastoma cell MDM2 expression was regulated by the cone-specific RXRγ transcription factor and a human-specific RXRγ consensus binding site, and proliferation required RXRγ as well as the cone-specific thyroid hormone receptor-β2. These findings provide support for a cone precursor origin of retinoblastoma and suggest that human cone-specific signaling circuitry sensitizes to the oncogenic effects of RB1 mutations.

Recall the data generating equation ( 1), and assume that U ∈ R n is an absolutely continuous random vector with a joint density f U (u), defined with respect to the Lebesgue measure on R n , continuous on its support U. We need the following assumptions.Assumption A.1. The function G has continuous partial derivatives with respect to all variables θ j , j = 1, . . . , p and u i , i = 1, . . . n.Assumption A.2. For each y and θ there is at most one u ∈ U so that y = G(u, θ). For the observed data y there is a θ and u ∈ U so that y = G(u, θ). Additionally, the determinant of the n × n Jacobian matrix det d du G(u, θ) = 0 for all θ ∈ Θ and u ∈ U. Assumption A.3. The n × p Jacobian matrix d dθ G(U , θ) is of rank p.For Part (iii) of Theorem 1 we will also need the following assumption.