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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Immune-modulating therapy is a promising therapy for patients with cholangiocarcinoma (CCA). Microsatellite instability (MSI) might be a favorable predictor for treatment response, but comprehensive data on the prevalence of MSI in CCA are missing. The aim of the current study was to determine the prevalence of MSI in a German tertiary care hospital. Formalin-fixed paraffin-embedded tissue samples, obtained in the study period from 2007 to 2015 from patients with CCA undergoing surgical resection with curative intention at Johann Wolfgang Goethe University hospital, were examined. All samples were investigated immunohistochemically for the presence of MSI (expression of MLH1, PMS2, MSH2, and MSH6) as well as by pentaplex polymerase chain reaction for five quasimonomorphic mononucleotide repeats (BAT-25, BAT-26, NR-21, NR-22, and NR-24). In total, 102 patients were included, presenting intrahepatic (n = 35, 34.3%), perihilar (n = 42, 41.2%), and distal CCA (n = 25, 24.5%). In the immunohistochemical analysis, no loss of expression of DNA repair enzymes was observed. In the PCR-based analysis, one out of 102 patients was found to be MSI-high and one out of 102 was found to be MSI-low. Thus, MSI seems to appear rarely in CCA in Germany. This should be considered when planning immune-modulating therapy trials for patients with CCA.
Dilepton spectra for p+p and p+d reactions at 4.9GeV are calculated. We consider electromagnetic bremsstrahlung also in inelastic reactions. N* and Delta* decay present the major contributions to the pho and omega meson yields.Pion annihilation yields only 1.5% of all pho's in p+d. The pho mass spectrum is strongly distorted due to phase space effects, populating dominantly dilepton masses below 770MeV.
The behavior of hadronic matter at high baryon densities is studied within Ultrarelativistic Quantum Molecular Dynamics (URQMD). Baryonic stopping is observed for Au+Au collisions from SIS up to SPS energies. The excitation function of flow shows strong sensitivities to the underlying equation of state (EOS), allowing for systematic studies of the EOS. Dilepton spectra are calculated with and without shifting the rho pole. Except for S+Au collisions our calculations reproduce the CERES data.
The behavior of hadronic matter at high baryon densities is studied within Ultrarelativistic Quantum Molecular Dynamics (URQMD). Baryonic stopping is observed for Au+Au collisions from SIS up to SPS energies. The excitation function of flow shows strong sensitivities to the underlying equation of state (EOS), allowing for systematic studies of the EOS. Effects of a density dependent pole of the rho-meson propagator on dilepton spectra are studied for different systems and centralities at CERN energies.
A 48 year old patient with dilated cardiomyopathy and chronic acne inversa underwent implantation of a LVAD system (Heartmate II, Thoratec, USA) March 2011. During 2011 and 2012 the patient was repeatedly readmitted for treatment of driveline infection with MRSA. Colonization was controlled with Linezolid and Rifampicin however reoccurred after discontinuation. In August 2012 the LVAD-system was exchanged due to pump dysfunction (HVAD, HeartWare Inc., USA). Postoperatively, the patient presented with ascites which secreted through the driveline exit. Consequently, the abdominal wall was surgically corrected to prevent exit of peritoneal fluid through the driveline, and the patient was discharged with sterile wound swabs. However 6 weeks after discharge the driveline exit wound started secreting pus showing abundant growth of multi resistant staphylococcus aureus (MRSA). With clinical signs of increasing liver failure with regular need for paracentesis, and clinical signs of local infection, a CT scan of the abdomen was performed revealing an enrichment of contrast medium along the driveline and an abscess-like formation on the abdominal wall. Patient was admitted receiving regular dose Daptomycin and Rifampicin. The latter was discontinued after ten days. The abscess, surrounding driveline exit and abdominal wall cavity was excised and vacuum treatment initiated. Total duration of Daptomycin therapy was 3 weeks. While first week skin and wound swabs were still positive for MRSA, all samples were sterile after the second week. Inflammation was monitored by leucocyte count and IL6. The secretion of pus along the driveline ceased, the wound cavity was closed subsequently. After discharge and stop of antibiotics skin and driveline swabs remained negative for MRSA (10 weeks).
Objectives: We sought to investigate whether statin therapy affects the association between preprocedural C-reactive protein (CRP) levels and the risk for recurrent coronary events in patients undergoing coronary stent implantation.
Background: Low-grade inflammation as detected by elevated CRP levels predicts the risk of recurrent coronary events. The effect of inflammation on coronary risk may be attenuated by statin therapy.
Methods: We investigated a potential interrelation among statin therapy, serum evidence of inflammation, and the risk for recurrent coronary events in 388 consecutive patients undergoing coronary stent implantation. Patients were grouped according to the median CRP level (0.6 mg/dl) and to the presence of statin therapy.
Results: A primary combined end point event occurred significantly more frequently in patients with elevated CRP levels without statin therapy (RR [relative risk] 2.37, 95% CI [confidence interval] [1.3 to 4.2]). Importantly, in the presence of statin therapy, the RR for recurrent events was significantly reduced in the patients with elevated CRP levels (RR 1.27 [0.7 to 2.1]) to about the same degree as in patients with CRP levels below 0.6 mg/dl and who did not receive statin therapy (RR 1.1 [0.8 to 1.3]).
Conclusions: Statin therapy significantly attenuates the increased risk for major adverse cardiac events in patients with elevated CRP levels undergoing coronary stent implantation, suggesting that statin the rapy interferes with the detrimental effects of inflammation on accelerated atherosclerotic disease progression following coronary stenting.
The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Attribution and detection of anthropogenic climate change using a backpropagation neural network
(2002)
The climate system can be regarded as a dynamic nonlinear system. Thus traditional linear statistical methods are not suited to describe the nonlinearities of this system which renders it necessary to find alternative statistical techniques to model those nonlinear properties. In addition to an earlier paper on this subject (WALTER et al., 1998), the problem of attribution and detection of the observed climate change is addressed here using a nonlinear Backpropagation Neural Network (BPN). In addition to potential anthropogenic influences on climate (CO2-equivalent concentrations, called greenhouse gases, GHG and SO2 emissions) natural influences on surface air temperature (variations of solar activity, volcanism and the El Niño/Southern Oscillation phenomenon) are integrated into the simulations as well. It is shown that the adaptive BPN algorithm captures the dynamics of the climate system, i.e. global and area weighted mean temperature anomalies, to a great extent. However, free parameters of this network architecture have to be optimized in a time consuming trial-and-error process. The simulation quality obtained by the BPN exceeds the results of those from a linear model by far; the simulation quality on the global scale amounts to 84% explained variance. Additionally the results of the nonlinear algorithm are plausible in a physical sense, i.e. amplitude and time structure. Nevertheless they cover a broad range, e.g. the GHG-signal on the global scale ranges from 0.37 K to 1.65 K warming for the time period 1856-1998. However the simulated amplitudes are situated within the discussed range (HOUGHTON et al., 2001). Additionally the combined anthropogenic effect corresponds to the observed increase in temperature for the examined time period. In addition to that, the BPN succeeds with the detection of anthropogenic induced climate change on a high significance level. Therefore the concept of neural networks can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.