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Aim: Coronavirus disease 2019 (COVID‐19) has been proposed as triggering autoimmunity. The aim of this study was to evaluate the presence and clinical significance of autoantibodies in patients with COVID‐19. Methods: We retrospectively collected data from 245 patients who were hospitalized for COVID‐19. All patients were tested for the presence of antinuclear antibody (ANA), rheumatoid factor (RF), anti‐citrullinated peptide antibody (ACPA), and anti‐cytoplasmic neutrophil antibody (ANCA). Risk factors for death and critical COVID‐19, defined as the need for invasive mechanical ventilation or extracorporeal membrane oxygenation, were analyzed. Results: Ninety (36.7%) patients tested positive for ANA, and 51 (20.8%) patients tested positive for RF. Three patients each (1.2%) tested positive for ACPA and ANCA. RF‐positive patients had higher rates of invasive mechanical ventilation and death than RF‐negative patients (70.6% vs 28.4%, P < 0.001 and 45.1% vs 18.6%, P < 0.001, respectively). Underlying lung disease, kidney disease, heart disease, quick COVID severity index (qCSI), and lactate dehydrogenase (LDH) were associated with in‐hospital death. RF (odds ratio [OR] 7.31, 95% CI 2.50–21.37, P < 0.001), qCSI (OR 1.42, 95% CI 1.19–1.69, P < 0.001), and LDH (OR 1.004, 95% CI 1.002–1.005, P < 0.001) were associated with critical COVID‐19. Combination of RF, qCSI, and LDH showed good prognostic value (area under the curve = 0.903, P < 0.001) for critical COVID‐19. Conclusions: ANA and RF were frequently detected in COVID‐19 patients. RF could be a risk factor for critical COVID‐19. The results of this study suggest immune dysfunction contributes to the complications of COVID‐19. [ABSTRACT FROM AUTHOR]

• The influence of equifinality on the hybrid model accuracy was assessed. • The hybrid model was developed by coupling SWAT with LSTM. • The two hybrid models constrained by RS-ET or not were compared. • The hybrid models showed better predictions than standalone SWAT and LSTM. • Among the hybrid models, those with RS-ET indicated enhanced performances. Hybrid models coupling process-based models with deep learning models have been widely used for estimating water quantity and quality. However, the impacts of uncertainty within process-based models on the performance of hybrid models remained largely unknown. This study focused on assessing the impact of uncertainties on a hybrid model that combines Soil and Water Assessment Tool (SWAT) with Long Short-Term Memory (LSTM) for estimating streamflow and suspended solids (SS). This study applied the output of SWAT as input for LSTM to make a hybrid model. By incorporating an additional constraint, remotely sensed evapotranspiration (RS-ET), the performance of hybrid models with and without considering RS-ET was evaluated at various temporal and spatial scales. Furthermore, various input settings (default and calibrated SWAT and inclusion/exclusion of precipitation) were considered. The results showed that the hybrid models tended to provide accurate estimations of monthly streamflow and SS compared to standalone SWAT and LSTM. The addition of precipitation to hybrid models did not make noticeable improvements for streamflow and SS. The hybrid models constrained by RS-ET tended to have improved estimations on streamflow and SS at daily and monthly scales compared to those unconstrained by RS-ET. Similar results were also observed at the sub-watershed level. These insights highlighted the potential of hybrid models to enhance hydrological estimations and underscore the importance of incorporating additional constraints to reduce uncertainties. Such advancements have practical implications for water resource management and decision-making processes, enabling more reliable and accurate estimations in applications of hybrid models. [ABSTRACT FROM AUTHOR]

Aim: To evaluate clinical characteristics and natural history of non‐radiographic axial spondyloarthritis (nr‐axSpA) using KOrean Nonradiographic Axial SPondyloArthritis (KONASPA) data. Methods: Data were collected from 11 centers in South Korea. A total of 278 patients with nr‐axSpA from January 2018 to July 2020 were included. Demographic data, clinical features, comorbidities, disease activity, medications, and laboratory results were collected. Results: Mean age at symptom onset was 28.2 ± 14.2 years. Of 278 patients, 152 (54.7%) were male. Mean Bath Ankylosing Spondylitis Disease Activity Index at diagnosis was 3.5 ± 2.1. Dyslipidemia was the most common comorbidity (8.4%), followed by hypertension (6.1%). Mean age at diagnosis of nr‐axSpA was older in female patients than in male patients (31.8 ± 15.8 years vs 24.9 ± 12.0 years, P < 0.001). Enthesitis and uveitis were more frequently found in female patients than in male patients. Thirty‐one (11.1%) participants with nr‐axSpA progressed to ankylosing spondylitis. The median follow‐up duration was 48 months. In multivariable Cox regression analysis, age at symptom onset (hazard ratio [HR] 0.93, 95% confidence interval (CI) 0.88‐0.97, P = 0.006), body mass index (BMI) (HR 1.24, 95% CI 1.06‐1.44, P = 0.005) and sacroiliitis grade (HR 1.86, 95% CI 1.19‐2.92, P = 0.006) were associated with progression to ankylosing spondylitis. Conclusions: Results of nationwide data revealed that women with nr‐axSpA showed a late disease onset and more extra‐articular manifestations than men. Young age at symptom onset, high BMI, and presence of radiographic sacroiliitis at diagnosis were risk factors for progression to AS. [ABSTRACT FROM AUTHOR]

The soil vulnerability index (SVI) classifies vulnerability to cropland pollutant transport by surface runoff and leaching, helping to categorize areas with a high contribution of pollutant loads. This study aims to 1) adopt the SVI classification scheme in South Korea and 2) suggest a modified SVI classification scheme for surface runoff to anticipate climate change impacts. The precipitation amount was considered in an original scheme to make a modified scheme. The results with an original SVI classification scheme predicted that the SVI surface runoff vulnerability was relatively greater in areas with a steep slope than in those with a plain topography, and a high level of the SVI leaching vulnerability was frequently observed in plain areas. When a modified SVI classification scheme was applied, "High" vulnerability class increased from 44.0% to 57.4% and the greater increase rate was observed in the regions susceptible to heavy precipitation defined as a day with more than 80 mm precipitation. When compared with observational suspended solids, the results with a modified SVI classification scheme were more consistent with observations than those with an original SVI classification scheme. Relative to the baseline period, the SVI surface runoff vulnerability of "High" class increased by 47.1–88.7% during the climate change period. When the number of vulnerability classes was increased, the differences on "Very extremely high" class between the baseline and projection periods were clear with the increase rate of 16.4–343%. The results with an original SVI classification scheme matched well with the topographic characteristics, and a modified SVI classification scheme captured the impacts of regional precipitation patterns and climate change impacts. As a first approach to adopting the SVI classification system to South Korea, increasing the number of classifications in regions with excessive rainfall amounts can improve identifying those most vulnerable areas. • The SVI was developed to classify soil vulnerability classes to cropland pollutant. • The SVI classification scheme was tested on croplands across South Korea. • The SVI classification results agreed well with topographic characteristics. • An original classification scheme was modified to consider precipitation amount. • A modified classification scheme showed enhanced SVI classification results. [ABSTRACT FROM AUTHOR]