Surface antigen profiles of leukocytes and melanoma cells in lymph node metastases are associated with survival in AJCC stage III melanoma patients

There is an urgent need to identify more accurate prognostic biomarkers in melanoma patients, particularly in those with metastatic disease. This study aimed to identify melanoma and leukocyte surface antigens predictive of survival in a prospective series of AJCC stage IIIb/c melanoma patients (n = 29). Live cell suspensions were prepared from melanoma metastases within lymph nodes (LN). The suspensions were immuno-magnetically separated into CD45+ (leukocyte) and CD45− (non-hematopoietic, enriched melanoma cell) fractions. Surface antigens on CD45− and CD45+ cell populations were profiled using DotScan™ microarrays (Medsaic Pty. Ltd.) and showed differential abundance levels for 52 and 78 antigens respectively. Associations of the surface profiles with clinicopathologic and outcome data (median follow-up 35.4 months post LN resection) were sought using univariate (log-rank test) and multivariate (Wald’s test; modelled with patient’s age, gender and AJCC staging at LN recurrence) survival models. CD9 (p = 0.036), CD39 (p = 0.004) and CD55 (p = 0.005) on CD45+ leukocytes were independently associated with distant metastasis-free survival using multivariate analysis. Leukocytes with high CD39 levels were also significantly associated with increased overall survival (OS) in multivariate analysis (p = 0.016). LNs containing leukocytes expressing CD11b (p = 0.025), CD49d (p = 0.043) and CD79b (p = 0.044) were associated with reduced OS on univariate analysis. For enriched melanoma cells (CD45− cell populations), 11 surface antigens were significantly correlated with the disease-free interval (DFI) between diagnosis of culprit primary melanoma and LN metastasis resection. Nine antigens on CD45+ leukocytes also correlated with DFI. Following validation in independent datasets, surface markers identified here should enable more accurate determination of prognosis in stage III melanoma patients and provide better risk stratification of patients entering clinical trials

Origin of the hot gas in low-mass protostars, Herschel-PACS spectroscopy of HH 46

Aims. “Water In Star-forming regions with Herschel” (WISH) is a Herschel key programme aimed at understanding the physical and chemical structure of young stellar objects (YSOs) with a focus on water and related species. Methods. The low-mass protostar HH 46 was observed with the Photodetector Array Camera and Spectrometer (PACS) on the Herschel Space Observatory to measure emission in H2O, CO, OH, [O i], and [C ii] lines located between 63 and 186 μm. The excitation and spatial distribution of emission can disentangle the different heating mechanisms of YSOs, with better spatial resolution and sensitivity than previously possible. Results. Far-IR line emission is detected at the position of the protostar and along the outflow axis. The OH emission is concentrated at the central position, CO emission is bright at the central position and along the outflow, and H2O emission is concentrated in the outflow. In addition, [O i] emission is seen in low-velocity gas, assumed to be related to the envelope, and is also seen shifted up to 170 km s−1 in both the red- and blue-shifted jets. Envelope models are constructed based on previous observational constraints. They indicate that passive heating of a spherical envelope by the protostellar luminosity cannot explain the high-excitation molecular gas detected with PACS, including CO lines with upper levels at >2500 K above the ground state. Instead, warm CO and H2O emission is probably produced in the walls of an outflow-carved cavity in the envelope, which are heated by UV photons and non-dissociative C-type shocks. The bright OH and [Oi] emission is attributed to J-type shocks in dense gas close to the protostar. In the scenario described here, the combined cooling by far-IR lines within the central spatial pixel is estimated to be 2 × 10−2 L, with 60–80% attributed to J- and C-type shocks produced by interactions between the jet and the envelope

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.New methods for child psychiatric diagnosis and treatment outcome evaluatio

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa