Risk factors associated with acute kidney injury in a cohort of hospitalized patients with COVID-19.

BACKGROUND: Patients with COVID-19 have a high incidence of acute kidney injury (AKI), which is associated with mortality. The objective of the study was to determine the factors associated with AKI in patients with COVID-19. METHODOLOGY: A retrospective cohort was established in two university hospitals in Bogotá, Colombia. Adults hospitalized for more than 48 h from March 6, 2020, to March 31, 2021, with confirmed COVID-19 were included. The main outcome was to determine the factors associated with AKI in patients with COVID-19 and the secondary outcome was estimate the incidence of AKI during the 28 days following hospital admission. RESULTS: A total of 1584 patients were included: 60.4% were men, 738 (46.5%) developed AKI, 23.6% were classified as KDIGO 3, and 11.1% had renal replacement therapy. The risk factors for developing AKI during hospitalization were male sex (OR 2.28, 95% CI 1.73-2.99), age (OR 1.02, 95% CI 1.01-1.03), history of chronic kidney disease (CKD) (OR 3.61, 95% CI 2.03-6.42), High Blood Pressure (HBP) (OR 6.51, 95% CI 2.10-20.2), higher qSOFA score to the admission (OR 1.4, 95% CI 1.14-1.71), the use of vancomycin (OR 1.57, 95% CI 1.05-2.37), piperacillin/tazobactam (OR 1.67, 95% CI 1.2-2.31), and vasopressor support (CI 2.39, 95% CI 1.53-3.74). The gross hospital mortality for AKI was 45.5% versus 11.7% without AKI. CONCLUSIONS: This cohort showed that male sex, age, history of HBP and CKD, presentation with elevated qSOFA, in-hospital use of nephrotoxic drugs and the requirement for vasopressor support were the main risk factors for developing AKI in patients hospitalized for COVID-19.

Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses.

Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering and artificial vision comes with new theories and tools that, along with the dawn of modern artificial intelligence, constitute a promising framework for the further development of neurotechnology. In the framework of the development of a Cortical Visual Neuroprosthesis for the blind (CORTIVIS), we are now facing the challenge of developing not only computationally powerful tools and flexible approaches that will allow us to provide some degree of functional vision to individuals who are profoundly blind. In this work, we propose a general neuroprosthesis framework composed of several task-oriented and visual encoding modules. We address the development and implementation of computational models of the firing rates of retinal ganglion cells and design a tool - Neurolight - that allows these models to be interfaced with intracortical microelectrodes in order to create electrical stimulation patterns that can evoke useful perceptions. In addition, the developed framework allows the deployment of a diverse array of state-of-the-art deep-learning techniques for task-oriented and general image pre-processing, such as semantic segmentation and object detection in our system's pipeline. To the best of our knowledge, this constitutes the first deep-learning-based system designed to directly interface with the visual brain through an intracortical microelectrode array. We implement the complete pipeline, from obtaining a video stream to developing and deploying task-oriented deep-learning models and predictive models of retinal ganglion cells' encoding of visual inputs under the control of a neurostimulation device able to send electrical train pulses to a microelectrode array implanted at the visual cortex.