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PURPOSE: NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health. PARTICIPANTS: As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months). FINDINGS TO DATE: The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data. FUTURE PLANS: The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.
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Transtorno Depressivo Maior , Serviços de Saúde Mental , Adulto , Registros Eletrônicos de Saúde , Feminino , Humanos , Masculino , Saúde Mental , Processamento de Linguagem NaturalRESUMO
Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make this a formidable task. We have developed a hybrid semimechanistic modeling methodology to investigate CKD progression. The model is represented as a system of ordinary differential equations with embedded neural networks and takes into account complex disease progression pathways, feedback loops, and effects of 53 medications to generate time trajectories of eight clinical biomarkers that capture CKD progression due to various risk factors. The model was applied to real world data of US patients with CKD to map the available longitudinal information onto a set of time-invariant patient-specific parameters with a clear biological interpretation. These parameters describing individual patients were used to segment the cohort using a clustering approach. Model-based simulations were conducted to investigate cluster-specific treatment strategies. The model was able to reliably reproduce the variability in biomarkers across the cohort. The clustering procedure segmented the cohort into five subpopulations - four with enhanced sensitivity to a specific risk factor (hypertension, hyperlipidemia, hyperglycemia, or impaired kidney) and one that is largely insensitive to any of the risk factors. Simulation studies were used to identify patient-specific strategies to restrain or prevent CKD progression through management of specific risk factors. The semimechanistic model enables identification of disease progression phenotypes using longitudinal data that aid in prioritizing treatment strategies at individual patient level.
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Registros Eletrônicos de Saúde , Insuficiência Renal Crônica , Estudos de Coortes , Comorbidade , Progressão da Doença , Humanos , Fatores de RiscoRESUMO
Spintronics relies on magnetization switching through current-induced spin torques. However, because spin transfer torque for ferromagnets is a surface torque, a large switching current is required for a thick, thermally stable ferromagnetic cell, and this remains a fundamental obstacle for high-density non-volatile applications with ferromagnets. Here, we report a long spin coherence length and associated bulk-like torque characteristics in an antiferromagnetically coupled ferrimagnetic multilayer. We find that a transverse spin current can pass through >10-nm-thick ferrimagnetic Co/Tb multilayers, whereas it is entirely absorbed by a 1-nm-thick ferromagnetic Co/Ni multilayer. We also find that the switching efficiency of Co/Tb multilayers partially reflects a bulk-like torque characteristic, as it increases with ferrimagnet thickness up to 8 nm and then decreases, in clear contrast to the 1/thickness dependence of ferromagnetic Co/Ni multilayers. Our results on antiferromagnetically coupled systems will invigorate research towards the development of energy-efficient spintronics.
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We explore the second order bilinear magnetoelectric resistance (BMER) effect in the d-electron-based two-dimensional electron gas (2DEG) at the SrTiO_{3}(111) surface. We find evidence of a spin-split band structure with the archetypal spin-momentum locking of the Rashba effect for the in-plane component. Under an out-of-plane magnetic field, we find a BMER signal that breaks the sixfold symmetry of the electronic dispersion, which is a fingerprint for the presence of a momentum-dependent out-of-plane spin component. Relativistic electronic structure calculations reproduce this spin texture and indicate that the out-of-plane component is a ubiquitous property of oxide 2DEGs arising from strong crystal field effects. We further show that the BMER response of the SrTiO_{3}(111) 2DEG is tunable and unexpectedly large.
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Spin-orbit torques, which utilize spin currents arising from the spin-orbit coupling, offer a novel method for the electrical switching of the magnetization with perpendicular anisotropy. However, the necessity of an external magnetic field to achieve deterministic switching is an obstacle for realizing practical spin-orbit torque devices with all-electric operation. Here, we report field-free spin-orbit torque switching by exploiting the domain-wall motion in an anti-notched microwire with perpendicular anisotropy, which exhibits multidomain states stabilized by the domain-wall surface tension. The combination of spin-orbit torque, Dzyaloshinskii-Moriya interactions, and domain-wall surface-tension-induced geometrical pinning allows the deterministic control of the domain wall and offers a novel method to achieve a field-free spin-orbit torque switching. Our work demonstrates the proof of concept of a perpendicular memory cell that can be readily adopted in three-terminal magnetic memory.
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The two-dimensional electron gas (2DEG) formed at the interface between SrTiO3 (STO) and LaAlO3 (LAO) insulating layer is supposed to possess strong Rashba spin-orbit coupling. To date, the inverse Edelstein effect (i.e., spin-to-charge conversion) in the 2DEG layer is reported. However, the direct effect of charge-to-spin conversion, an essential ingredient for spintronic devices in a current-induced spin-orbit torque scheme, has not been demonstrated yet. Here we show, for the first time, a highly efficient spin generation with the efficiency of â¼6.3 in the STO/LAO/CoFeB structure at room temperature by using spin torque ferromagnetic resonance. In addition, we suggest that the spin transmission through the LAO layer at a high temperature range is attributed to the inelastic tunneling via localized states in the LAO band gap. Our findings may lead to potential applications in the oxide insulator based spintronic devices.
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Topological insulators with spin-momentum-locked topological surface states are expected to exhibit a giant spin-orbit torque in the topological insulator/ferromagnet systems. To date, the topological insulator spin-orbit torque-driven magnetization switching is solely reported in a Cr-doped topological insulator at 1.9 K. Here we directly show giant spin-orbit torque-driven magnetization switching in a Bi2Se3/NiFe heterostructure at room temperature captured using a magneto-optic Kerr effect microscope. We identify a large charge-to-spin conversion efficiency of ~1-1.75 in the thin Bi2Se3 films, where the topological surface states are dominant. In addition, we find the current density required for the magnetization switching is extremely low, ~6 × 105 A cm-2, which is one to two orders of magnitude smaller than that with heavy metals. Our demonstration of room temperature magnetization switching of a conventional 3d ferromagnet using Bi2Se3 may lead to potential innovations in topological insulator-based spintronic applications.
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The magnitude of spin-orbit torque (SOT), exerted to a ferromagnet (FM) from an adjacent heavy metal (HM), strongly depends on the amount of spin current absorbed in the FM. We exploit the large spin absorption at the Ru interface to manipulate the SOTs in HM/FM/Ru multilayers. While the FM thickness is smaller than its spin dephasing length of 1.2 nm, the top Ru layer largely boosts the absorption of spin currents into the FM layer and substantially enhances the strength of SOT acting on the FM. Spin-pumping experiments induced by ferromagnetic resonance support our conclusions that the observed increase in the SOT efficiency can be attributed to an enhancement of the spin-current absorption. A theoretical model that considers both reflected and transmitted mixing conductances at the two interfaces of FM is developed to explain the results.