The experiences and perspectives of abortion seekers who travel for care: a qualitative evidence synthesis.

This is a protocol for a Cochrane Review (qualitative). The objectives are as follows: This QES aims to address the following question: What are the lived experiences and perceptions of abortion seekers (i.e.

Morphometric characterization and temporal temperature measurements during hepatic microwave ablation in swine.

Purpose: Heat-induced destruction of cancer cells via microwave ablation (MWA) is emerging as a viable treatment of primary and metastatic liver cancer. Prediction of the impacted zone where cell death occurs, especially in the presence of vasculature, is challenging but may be achieved via biophysical modeling. To advance and characterize thermal MWA for focal cancer treatment, an in vivo method and experimental dataset were created for assessment of biophysical models designed to dynamically predict ablation zone parameters, given the delivery device, power, location, and proximity to vessels.

In-line miniature 3D-printed pressure-cycled ventilator maintains respiratory homeostasis in swine with induced acute pulmonary injury.

The COVID-19 pandemic demonstrated the need for inexpensive, easy-to-use, rapidly mass-produced resuscitation devices that could be quickly distributed in areas of critical need. In-line miniature ventilators based on principles of fluidics ventilate patients by automatically oscillating between forced inspiration and assisted expiration as airway pressure changes, requiring only a continuous supply of pressurized oxygen. Here, we designed three miniature ventilator models to operate in specific pressure ranges along a continuum of clinical lung injury (mild, moderate, and severe injury).

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.

Federated Learning used for predicting outcomes in SARS-COV-2 patients.

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.

Safe and Effective Deployment of Personnel to Support the Ebola Response - West Africa.

From the initial task of getting "50 deployers within 30 days" into the field to support the 2014-2016 Ebola virus disease (Ebola) epidemic response in West Africa to maintaining well over 200 staff per day in the most affected countries (Guinea, Liberia, and Sierra Leone) during the peak of the response, ensuring the safe and effective deployment of international responders was an unprecedented accomplishment by CDC. Response experiences shared by CDC deployed staff returning from West Africa were quickly incorporated into lessons learned and resulted in new activities to better protect the health, safety, security, and resiliency of responding personnel. Enhanced screening of personnel to better match skill sets and experience with deployment needs was developed as a staffing strategy.

Characterizing retinal structure injury in African-Americans with multiple sclerosis.

To examine retinal structure injury in African-Americans (AA) with Multiple Sclerosis (MS) compared to Caucasians (CA) with MS, we used spectral domain optical-coherence tomography (OCT) in this cross sectional study. The peripapillary retinal nerve fiber layer (pRNFL) and macular volume of 234 MS patients (149 CA; 85 AA) and 74 healthy controls (60 CA; 17 AA) were measured. Intra-retinal segmentation was performed to obtain retinal nerve fiber (RNFL), ganglion cell (GCL), inner plexiform (IPL), inner nuclear (INL), outer plexiform (OPL), outer nuclear (ONL), retinal pigment epithelium (RPE), and photoreceptor (PR) layer volumes.