Uncovering spatiotemporal dynamics of the corticothalamic network at ictal onset.

Objective: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human electroencephalographic (EEG) recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci.

Methods: We analyzed 10 patients (aged 2.

Uncovering Spatiotemporal Dynamics of the Corticothalamic Network during Seizures.

Objective: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human EEG recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci.

Methods: We analyzed ten patients (aged 2.

A comparative analysis of machine learning approaches to predict C. difficile infection in hospitalized patients.

Background: Interventions to better prevent or manage Clostridioides difficile infection (CDI) may significantly reduce morbidity, mortality, and healthcare spending.

Methods: We present a retrospective study using electronic health record data from over 700 United States hospitals. A subset of hospitals was used to develop machine learning algorithms (MLAs); the remaining hospitals served as an external test set.

Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification.

Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data.

An Automated System for Reactive Accelerated Aging of Implant Materials with In-Situ Testing.

The efficacy of implantable medical devices is limited by the longevity of devices in the body environment. Due to the aqueous and mobile-ion rich environment of tissue, robust and long-lasting encapsulation materials are critical for chronic implants. Assessing the reliability of medical devices is commonly performed through saline soak tests with reactive oxidative species at elevated temperatures and lifetime data are fit to an Arrhenius model to predict lifetime under physiological conditions.

Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates.

Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D printing techniques. Here, we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging in response to fluid convection through these networks and to diffusion of oxygen and metabolites out of them.

Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions.