Rationale: Prostacyclin analogs, used to treat idiopathic pulmonary arterial hypertension (IPAH), are assumed to work through prostacyclin (IP) receptors linked to cyclic AMP (cAMP) generation, although the potential to signal through peroxisome proliferatoractivated receptor-g (PPARg) exists. Objectives: IP receptor and PPARg expression may be depressed in IPAH. We wished to determine if pathways remain functional and if analogs continue to inhibit smooth muscle proliferation. Methods: We used Western blotting to determine IP receptor expression in peripheral pulmonary arterial smooth muscle cells (PASMCs) from normal and IPAH lungs and immunohistochemistry to evaluate IP receptor and PPARg expression in distal arteries. Measurements and Main Results: Cell proliferation and cAMP assays assessed analog responses in human and mouse PASMCs and HEK-293 cells. Proliferative rates of IPAH cells were greater than normal human PASMCs. IP receptor protein levels were lower in PASMCs from patients with IPAH, but treprostinil reduced replication and treprostinil-induced cAMP elevation appeared normal. Responses to prostacyclin analogs were largely dependent on the IP receptor and cAMP in normal PASMCs, although in IP 2/2 receptor cells analogs inhibited growth in a cAMP-independent, PPARg-dependent manner. In IPAH cells, antiproliferative responses to analogs were insensitive to IP receptor or adenylyl cyclase antagonists but were potentiated by a PPARg agonist and inhibited (z 60%) by the PPARg antagonist GW9662. This coincided with increased PPARg expression in the medial layer of acinar arteries. Conclusions: The antiproliferative effects of prostacyclin analogs are preserved in IPAH despite IP receptor down-regulation and abnormal coupling. PPARg may represent a previously unrecognized pathway by which these agents inhibit smooth muscle proliferation.

We investigated the effects of prostacyclin analogs and isoform-selective phosphodiesterase (PDE) inhibitors, alone and in combination, on pulmonary vascular remodeling in vitro and in vivo. Vascular smooth muscle cells (VSMC) isolated from pulmonary (proximal and distal) and systemic circulations demonstrated subtle variations in expression of PDE isoform mRNA. However, using biochemical assays, we found PDE3 and PDE4 isoforms to be responsible for the majority of cAMP hydrolysis in all VSMC. In growth assays, the prostacyclin analogs cicaprost and iloprost inhibited mitogen-induced proliferation of VSMC in a cAMP-dependent manner. In addition, isoform-selective antagonists of PDEs 1, 3, or 4 inhibited VSMC proliferation, an effect that synergized with the effect of prostacyclin analogs. The inhibitory effects were greater in cells isolated from pulmonary circulation. In an in situ perfused rat lung preparation, administration of prostacyclin analogs or the PDE inhibitors vinpocetine (PDE1), cilostamide (PDE3), or rolipram (PDE4), but not EHNA (PDE2), attenuated acute hypoxic vasoconstriction (HPV). Combinations of agents led to a greater reduction in HPV. Furthermore, during exposure to hypoxia for 13 days, Wistar rats were treated with iloprost, rolipram, cilostamide, or combinations of these agents. Compared with normoxic controls, hypoxic animals developed pulmonary hypertension and distal pulmonary artery muscularization. These parameters were attenuated by iloprost+cilostamide, iloprost+rolipram, and cilostamide+rolipram but were not significantly affected by single agents. Together, these findings provide a greater understanding of the role of cAMP PDEs in VSMC proliferation and provide rationale for combined use of prostacylcin analogs plus PDE3/4 inhibitors in treatment of pulmonary vascular remodeling.

Recent studies have explored characteristics of brain tumors by means of magnetic resonance spectroscopy (MRS) to increase diagnostic accuracy and improve understanding of tumor biology. In this study, a computer-based neural network was developed to combine MRS data (ratios of N-acetyl-aspartate, choline, and creatine) with 10 characteristics of tumor tissue obtained from magnetic resonance (MR) studies, as well as tumor size and the patient's age and sex, in hopes of further improving diagnostic accuracy. Data were obtained in 33 children presenting with posterior fossa tumors. The cases were analyzed by a neuroradiologist, who then predicted the tumor type from among three categories (primitive neuroectodermal tumor, astrocytoma, or ependymoma/other) based only on the data obtained via MR imaging. These predictions were compared with those made by neural networks that had analyzed different combinations of the data. The neuroradiologist correctly predicted the tumor type in 73% of the cases, whereas four neural networks using different datasets as inputs were 58 to 95% correct. The neural network that used only the three spectroscopy ratios had the least predictive ability. With the addition of data including MR imaging characteristics, age, sex, and tumor size, the network's accuracy improved to 72%, consistent with the predictions of the neuroradiologist who was using the same information. Use of only the analog data (leaving out information obtained from MR imaging), resulted in 88% accuracy. A network that used all of the data was able to identify 95% of the tumors correctly. It is concluded that a neural network provided with imaging data, spectroscopic data, and a limited amount of clinical information can predict pediatric posterior fossa tumor type with remarkable accuracy.

BackgroundThe majority of established telestroke services are based on “hub‐and‐spoke” models for providing acute clinical assessment and thrombolysis. We report results from the first year of the successful implementation of a locally based telemedicine network, without the need of 1 or more hub hospitals, across a largely rural landscape.Methods and ResultsFollowing a successful pilot phase that demonstrated safety and feasibility, the East of England telestroke project was rolled out across 7 regional hospitals, covering an area of 7500 square miles and a population of 5.6 million to enable out‐of‐hours access to thrombolysis. Between November 2010 and November 2011, 142 telemedicine consultations were recorded out‐of‐hours. Seventy‐four (52.11%) cases received thrombolysis. Median (IQR) onset‐to‐needle and door‐to‐needle times were 169 (141.5 to 201.5) minutes and 94 (72 to 113.5) minutes, respectively. Symptomatic hemorrhage rate was 7.3% and stroke mimic rate was 10.6%.ConclusionsWe demonstrate the safety and effectiveness of a horizontal networking approach for stroke telemedicine, which may be applicable to areas where traditional “hub‐and‐spoke” models may not be geographically feasible.

Background: Morbidity in COPD results from a combination of factors including hypoxia-induced pulmonary hypertension, in part due to pulmonary vascular remodelling. Animal studies suggest a role of angiotensin II and acute studies in man concur. Whether chronic angiotensin-II blockade is beneficial is unknown. We studied the effects of an angiotensin-II antagonist losartan, on haemodynamic variables, exercise capacity and symptoms.

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Recent studies have explored characteristics of brain tumors by means of magnetic resonance spectroscopy (MRS) to increase diagnostic accuracy and improve understanding of tumor biology. In this study, a computer-based neural network was developed to combine MRS data (ratios of N-acetyl-aspartate, choline, and creatine) with 10 characteristics of tumor tissue obtained from magnetic resonance (MR) studies, as well as tumor size and the patient's age and sex, in hopes of further improving diagnostic accuracy.Data were obtained in 33 children presenting with posterior fossa tumors. The cases were analyzed by a neuroradiologist, who then predicted the tumor type from among three categories (primitive neuroectodermal tumor, astrocytoma, or ependymoma/other) based only on the data obtained via MR imaging. These predictions were compared with those made by neural networks that had analyzed different combinations of the data. The neuroradiologist correctly predicted the tumor type in 73% of the cases, whereas four neural networks using different datasets as inputs were 58 to 95% correct. The neural network that used only the three spectroscopy ratios had the least predictive ability. With the addition of data including MR imaging characteristics, age, sex, and tumor size, the network's accuracy improved to 72%, consistent with the predictions of the neuroradiologist who was using the same information. Use of only the analog data (leaving out information obtained from MR imaging), resulted in 88% accuracy. A network that used all of the data was able to identify 95% of the tumors correctly. It is concluded that a neural network provided with imaging data, spectroscopic data, and a limited amount of clinical information can predict pediatric posterior fossa tumor type with remarkable accuracy.