[Clinical-demographic characteristics of tracheostomized patients with chronic obstructive pulmonary disease].

Tracheostomy (TQT) has emerged as a valuable alternative for patients with orotracheal intubation, especially those under prolonged mechanical ventilation (VMP), as in the case of chronic obstructive pulmonary disease (COPD). This population presents additional challenges, and the available information regarding their progression in specialized centers is limited in Argentina.A descriptive, retrospective, and cross-sectional study was conducted at Santa Catalina Neurorehabilitation Clinic between August 2015 and December 2018.

Additive manufacturing of an ultrastrong, deformable Al alloy with nanoscale intermetallics.

Light-weight, high-strength, aluminum (Al) alloys have widespread industrial applications. However, most commercially available high-strength Al alloys, like AA 7075, are not suitable for additive manufacturing due to their high susceptibility to solidification cracking. In this work, a custom Al alloy AlTiFeCoNi is fabricated by selective laser melting.

Convection-Induced Compositional Patterning at Grain Boundaries in Irradiated Alloys.

We consider the stability of precipitates formed at grain boundaries (GBs) by radiation-induced segregation in dilute alloys subjected to irradiation. The effects of grain size and misorientation of symmetric-tilt GBs are quantified using phase field modeling. A novel regime is identified where, at long times, GBs are decorated by precipitate patterns that resist coarsening.

Tracheostomy Decannulation and Disorders of Consciousness Evolution.

Background: Tracheostomy is a frequent surgical procedure in subjects with chronic disorders of consciousness (DOC). There is no consensus about safety of tracheostomy decannulation in this population.The aim of our study was to estimate if DOC improvement is a predictor for tracheostomy decannulation.

A primer on deep learning and convolutional neural networks for clinicians.

Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks.