A comparison of stage-specific all-cause mortality between testicular sex cord stromal tumors and germ cell tumors: results from the National Cancer Database.

BackgroundTesticular sex cord stromal tumors (SCSTs) are managed similarly to germ cell tumors (GCTs); however, few studies have directly compared outcomes between these tumor types. Using the National Cancer Database (NCDB), we sought to compare overall and stage-specific all-cause mortality (ACM) between SCSTs versus GCTs.MethodsNCDB was queried for patients diagnosed with SCSTs and GCTs between 2004 and 2013. Descriptive statistics were used to compare sociodemographic and clinical characteristics between groups. Univariable and multivariable Cox proportional hazards regression analyses were used to assess associations with ACM.ResultsWe identified 42,192 patients diagnosed with testicular cancer between 2004 and 2013, with 280 having SCSTs and 41,912 patients having GCTs. Median age for SCSTs and GCTs was 45 (interquartile range [IQR] 34-59) and 34 (IQR 27-43), respectively (p < 0.001). Median follow-up was 39 and 52 months, respectively. Overall, patients with SCSTs had greater risk of ACM compared to those with GCTs (HR 1.69, 95% CI 1.14-2.50). Private insurance, greater education, and fewer comorbidities were associated with reduced risk of ACM (p < 0.05 for all). Among those with stage I disease, tumor type was not associated with ACM on multivariable analysis. Among those with stage II/III disease, patients with SCSTs had increased risk of ACM compared to patients with GCTs (HR 3.29, 95% CI 1.89-5.72).ConclusionsPatients with advanced SCSTs had worse survival outcomes compared to those with advanced GCTs. These data suggest a need for further investigation to ascertain effective management recommendations for SCSTs

Enhancement of ferromagnetism by nickel doping in the 112 cobaltite EuBaCo2O5.50

The study of the ordered oxygen deficient perovskite EuBaCo2-xNixO5.50 shows that the doping of cobalt sites by nickel induces a strong ferromagnetic component at low temperature in the antiferromagnetic matrix of EuBaCo2O5.50. This system exhibits indeed phase separation, i.e. consists of ferromagnetic domains embedded in the antiferromagnetic matrix of EuBaCo2O5.50. Besides, a magnetic transition is observed for the first time at 40K in the undoped and nickel doped phases, which can be attributed to the ferromagnetic ordering of the Eu3+ moments below this temperature. Moreover sharp ultra magnetization multisteps are observed below 5K, characteristic of motion of domain walls in a strong pinning system and very different from any metamagnetic transition

Schizophrenia Classification using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level

Disrupted Functional and Structural Connectivity Measures Have Been Used to Distinguish Schizophrenia Patients from Healthy Controls. Classification Methods based on Functional Connectivity Derived from EEG Signals Are Limited by the Volume Conduction Problem. Recorded Time Series at Scalp Electrodes Capture a Mixture of Common Sources Signals, Resulting in Spurious Connections. We Have Transformed Sensor Level Resting State EEG Times Series to Source Level EEG Signals Utilizing a Source Reconstruction Method. Functional Connectivity Networks Were Calculated by Computing Phase Lag Values between Brain Regions at Both the Sensor and Source Level. Brain Complex Network Analysis Was Used to Extract Features and the Best Features Were Selected by a Feature Selection Method. a Logistic Regression Classifier Was Used to Distinguish Schizophrenia Patients from Healthy Controls at Five Different Frequency Bands. the Best Classifier Performance Was based on Connectivity Measures Derived from the Source Space and the Theta Band.The Transformation of Scalp EEG Signals to Source Signals Combined with Functional Connectivity Analysis May Provide Superior Features for Machine Learning Applications

Enhanced Neurologic Concept Recognition using a Named Entity Recognition Model based on Transformers

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks to One based on Bidirectional Encoder Representation from Transformers. Models Were Evaluated for Accuracy of Text Span Identification on Three Text Corpora: Physician Notes from an Electronic Health Record, Case Histories from Neurologic Textbooks, and Clinical Synopses from an Online Database of Genetic Diseases. Both Models Performed Best on the Professionally-Written Clinical Synopses and Worst on the Physician-Written Clinical Notes. Both Models Performed Better When Signs and Symptoms Were Represented as Shorter Text Spans. Consistent with Prior Studies that Examined the Recognition of Diseases and Drugs, the Model based on Bidirectional Encoder Representations from Transformers Outperformed the Model based on Convolutional Neural Networks for Recognizing Signs and Symptoms. Recall for Signs and Symptoms Ranged from 59.5% to 82.0% and Precision Ranged from 61.7% to 80.4%. with Further Advances in NLP, Fully Automated Recognition of Signs and Symptoms in Electronic Health Records and the Medical Literature Should Be Feasible

Tau Kinetics in Alzheimer\u27s Disease

The Cytoskeletal Protein Tau is Implicated in the Pathogenesis of Alzheimer\u27s Disease Which is Characterized by Intra-Neuronal Neurofibrillary Tangles Containing Abnormally Phosphorylated Insoluble Tau. Levels of Soluble Tau Are Elevated in the Brain, the CSF, and the Plasma of Patients with Alzheimer\u27s Disease. to Better Understand the Causes of These Elevated Levels of Tau, We Propose a Three-Compartment Kinetic Model (Brain, CSF, and Plasma). the Model Assumes that the Synthesis of Tau Follows Zero-Order Kinetics (Uncorrelated with Compartmental Tau Levels) and that the Release, Absorption, and Clearance of Tau is Governed by First-Order Kinetics (Linearly Related to Compartmental Tau Levels). Tau that is Synthesized in the Brain Compartment Can Be Released into the Interstitial Fluid, Catabolized, or Retained in Neurofibrillary Tangles. Tau Released into the Interstitial Fluid Can Mix with the CSF and Eventually Drain to the Plasma Compartment. However, Losses of Tau in the Drainage Pathways May Be Significant. the Kinetic Model Estimates Half-Life of Tau in Each Compartment (552 H in the Brain, 9.9 H in the CSF, and 10 H in the Plasma). the Kinetic Model Predicts that an Increase in the Neuronal Tau Synthesis Rate or a Decrease in Tau Catabolism Rate Best Accounts for Observed Increases in Tau Levels in the Brain, CSF, and Plasma Found in Alzheimer\u27s Disease. Furthermore, the Model Predicts that Increases in Brain Half-Life of Tau in Alzheimer\u27s Disease Should Be Attributed to Decreased Tau Catabolism and Not to Increased Tau Synthesis. Most Clearance of Tau in the Neuron Occurs through Catabolism Rather Than Release to the CSF Compartment. Additional Experimental Data Would Make Ascertainment of the Model Parameters More Precise

Treatment regimens and outcomes in severe and moderate haemophilia A in the UK: The THUNDER study

Introduction: The THUNDER study provides an analysis of treatment patterns and outcomes in UK patients with severe or moderate haemophilia A (SHA/MHA) in 2015. Methods: Patients with SHA or MHA registered with the UK National Haemophilia Database (NHD) were segregated by severity, inhibitor status and age. Haemophilia joint health score (HJHS) was derived from NHD records and treatment regimen and annualized bleed/joint‐bleed rate (ABR/AJBR) from Haemtrack (HT) in HT‐compliant patients. Results: We report 1810 patients with SHA and 864 with MHA. Prophylaxis was used in 94.9% (n = 130/137) of HT‐compliant children <12 years with SHA, falling to 74.1% (n = 123/166) aged ≥40 years. Median ABR increased with age (1.0, IQR 0.0‐5.0, <12 years; 3.0 IQR, 1.0‐8.0, ≥40 years). Inhibitors were present in 159 (8.8%) SHA and 34 (3.9%) MHA. Median ABR increased from 2.0 (<12 years) to 21.(≥40 years) in SHA inhibitor patients using prophylaxis. Prophylaxis was used by 68.8% of HT‐compliant MHA patients (n = 106) (median FVIII baseline 0.01 IU/mL) associated with a median (IQR) ABR of 3.0 (1.0‐7.0). Median HJHS (n = 453) increased with age in SHA and MHA. Median (IQR) HJHS was higher in SHA inhibitor (17.0, 0.0‐64.5) than non‐ or past inhibitor patients (7.0, 0.0‐23.0). Conclusions: Increasing ABR with age persists despite current prophylaxis regimens.SHA and MHA had similar ABR/AJBR and HJHS, leading to a suspicion that a subgroup of MHA may be relatively undertreated. More intensive prophylaxis may improve outcomes, but this requires further study

Intelligent energy management based on SCADA system in a real Microgrid for smart building applications

Energy management is one of the main challenges in Microgrids (MGs) applied to Smart Buildings (SBs). Hence, more studies are indispensable to consider both modeling and operating aspects to utilize the upcoming results of the system for the different applications. This paper presents a novel energy management architecture model based on complete Supervisory Control and Data Acquisition (SCADA) system duties in an educational building with an MG Laboratory (Lab) testbed, which is named LAMBDA at the Electrical and Energy Engineering Department of the Sapienza University of Rome. The LAMBDA MG Lab simulates in a small scale a SB and is connected with the DIAEE electrical network. LAMBDA MG is composed of a Photovoltaic generator (PV), a Battery Energy Storage System (BESS), a smart switchboard (SW), and different classified loads (critical, essential, and normal) some of which are manageable and controllable (lighting, air conditioning, smart plugs operating into the LAB). The aim of the LAMBDA implementation is making the DIAEE smart for energy saving purposes. In the LAMBDA Lab, the communication architecture consists in a complex of master/slave units and actuators carried out by two main international standards, Modbus (industrial serial standard for electrical and technical monitoring systems) and Konnex (an open standard for commercial and domestic building automation). Making the electrical department smart causes to reduce the required power from the main grid. Hence, to achieve the aims, results have been investigated in two modes. Initially, the real-time mode based on the SCADA system, which reveals real daily power consumption and production of different sources and loads. Next, the simulation part is assigned to shows the behavior of the main grid, loads and BESS charging and discharging based on energy management system. Finally, the proposed model has been examined in different scenarios and evaluated from the economic aspect