| 2. |
- Attia, Zachi I., et al.
(författare)
-
Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
- 2021
-
Ingår i: Mayo Clinic proceedings. - : ELSEVIER SCIENCE INC. - 0025-6196 .- 1942-5546. ; 96:8, s. 2081-2094
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). Methods: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. Results: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. Conclusion: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control. (C) 2021 Mayo Foundation Medical Education and Research
|
|
| 3. |
- Lara, Patricia, et al.
(författare)
-
Murine astrotactins 1 and 2 have a similar membrane topology and mature via endoproteolytic cleavage catalyzed by a signal peptidase
- 2019
-
Ingår i: Journal of Biological Chemistry. - 0021-9258 .- 1083-351X. ; 294:12, s. 4538-4545
-
Tidskriftsartikel (refereegranskat)abstract
- Astrotactin 1 (Astn1) and Astn2 are membrane proteins that function in glial-guided migration, receptor trafficking, and synaptic plasticity in the brain as well as in planar polarity pathways in the skin. Here we used glycosylation mapping and protease protection approaches to map the topologies of mouse Astn1 and Astn2 in rough microsomal membranes and found that Astn2 has a cleaved N-terminal signal peptide, an N-terminal domain located in the lumen of the rough microsomal membranes (topologically equivalent to the extracellular surface in cells), two transmembrane helices, and a large C-terminal lumenal domain. We also found that Astn1 has the same topology as Astn2, but we did not observe any evidence of signal peptide cleavage in Astn1. Both Astn1 and Astn2 mature through endoproteolytic cleavage in the second transmembrane helix; importantly, we identified the endoprotease responsible for the maturation of Astn1 and Astn2 as the endoplasmic reticulum signal peptidase. Differences in the degree of Astn1 and Astn2 maturation possibly contribute to the higher levels of the C-terminal domain of Astn1 detected on neuronal membranes of the central nervous system. These differences may also explain the distinct cellular functions of Astn1 and Astn2, such as in membrane adhesion, receptor trafficking, and planar polarity signaling.
|
|
