| 1. |
- Cheng, Anying, et al.
(författare)
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Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients
- 2020
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Ingår i: International Journal of Antimicrobial Agents. - : ELSEVIER. - 0924-8579 .- 1872-7913. ; 56:3
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Tidskriftsartikel (refereegranskat)abstract
- The crude mortality rate in critical pneumonia cases with coronavirus disease 2019 (COVID-19) reaches 49%. This study aimed to test whether levels of blood urea nitrogen (BUN) in combination with D-dimer were predictors of in-hospital mortality in COVID-19 patients. The clinical characteristics of 305 COVID19 patients were analysed and were compared between the survivor and non-survivor groups. Of the 305 patients, 85 (27.9%) died and 220 (72.1%) were discharged from hospital. Compared with discharged cases, non-survivor cases were older and their BUN and D-dimer levels were significantly higher ( P < 0.0 0 01). Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses identified BUN and D-dimer levels as independent risk factors for poor prognosis. Kaplan-Meier analysis showed that elevated levels of BUN and D-dimer were associated with increased mortality (logrank, P 0.0 0 01). The area under the curve for BUN combined with D-dimer was 0.94 (95% CI 0.90-0.97), with a sensitivity of 85% and specificity of 91%. Based on BUN and D-dimer levels on admission, a nomogram model was developed that showed good discrimination, with a concordance index of 0.94. Together, initial BUN and D-dimer levels were associated with mortality in COVID-19 patients. The combination of BUN 4.6 mmol/L and D-dimer > 0.845 mu g/mL appears to identify patients at high risk of in-hospital mortality, therefore it may prove to be a powerful risk assessment tool for severe COVID-19 patients. (c) 2020 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.
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| 2. |
- Liu, Huizeng, et al.
(författare)
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Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing
- 2021
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 258
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.
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| 3. |
- Liu, Huizeng, et al.
(författare)
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Evaluation of Ocean Color Atmospheric Correction Methods for Sentinel-3 OLCI Using Global Automatic In Situ Observations
- 2022
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892 .- 1558-0644. ; 60
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Tidskriftsartikel (refereegranskat)abstract
- The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automatic in situ observations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.
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