Outcomes of pediatric patients with therapy-related myeloid neoplasms

Long-term outcomes after allogeneic hematopoietic cell transplantation (HCT) for therapy-related myeloid neoplasms (tMNs) are dismal. There are few multicenter studies defining prognostic factors in pediatric patients with tMNs. We have accumulated the largest cohort of pediatric patients who have undergone HCT for a tMN to perform a multivariate analysis defining factors predictive of long-term survival. Sixty-eight percent of the 401 patients underwent HCT using a myeloablative conditioning (MAC) regimen, but there were no statistically significant differences in the overall survival (OS), event-free survival (EFS), or cumulative incidence of relapse and non-relapse mortality based on the conditioning intensity. Among the recipients of MAC regimens, 38.4% of deaths were from treatment-related causes, especially acute graft versus host disease (GVHD) and end-organ failure, as compared to only 20.9% of deaths in the reduced-intensity conditioning (RIC) cohort. Exposure to total body irradiation (TBI) during conditioning and experiencing grade III/IV acute GVHD was associated with worse OS. In addition, a diagnosis of therapy-related myelodysplastic syndrome and having a structurally complex karyotype at tMN diagnosis were associated with worse EFS. Reduced-toxicity (but not reduced-intensity) regimens might help to decrease relapse while limiting mortality associated with TBI-based HCT conditioning in pediatric patients with tMNs

Observations and modelling of ion cyclotron emission observed in JET plasmas using a sub-harmonic arc detection system during ion cyclotron resonance heating

Peer reviewe

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox

Thoracic aortic disease in tuberous sclerosis complex: molecular pathogenesis and potential therapies in Tsc2+/− mice

Tuberous sclerosis complex (TSC) is a genetic disorder with pleiotropic manifestations caused by heterozygous mutations in either TSC1 or TSC2. One of the less investigated complications of TSC is the formation of aneurysms of the descending aorta, which are characterized on pathologic examination by smooth muscle cell (SMC) proliferation in the aortic media. SMCs were explanted from Tsc2+/− mice to investigate the pathogenesis of aortic aneurysms caused by TSC2 mutations. Tsc2+/− SMCs demonstrated increased phosphorylation of mammalian target of rapamycin (mTOR), S6 and p70S6K and increased proliferation rates compared with wild-type (WT) SMCs. Tsc2+/− SMCs also had reduced expression of SMC contractile proteins compared with WT SMCs. An inhibitor of mTOR signaling, rapamycin, decreased SMC proliferation and increased contractile protein expression in the Tsc2+/− SMCs to levels similar to WT SMCs. Exposure to α-elastin fragments also decreased proliferation of Tsc2+/− SMCs and increased levels of p27kip1, but failed to increase expression of contractile proteins. In response to artery injury using a carotid artery ligation model, Tsc2+/− mice significantly increased neointima formation compared with the control mice, and the neointima formation was inhibited by treatment with rapamycin. These results demonstrate that Tsc2 haploinsufficiency in SMCs increases proliferation and decreases contractile protein expression and suggest that the increased proliferative potential of the mutant cells may be suppressed in vivo by interaction with elastin. These findings provide insights into the molecular pathogenesis of aortic disease in TSC patients and identify a potential therapeutic target for treatment of this complication of the disease