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Objective This study aimed to assess the efficacy and safety of intravenous ramosetron for pain relief in patients with fibromyalgia (FM) unresponsive to conventional treatments. Methods In this prospective, double-blind, placebo-controlled trial, 80 FM patients were randomly allocated to receive either placebo (n  = 40) or ramosetron (n  = 40) at a dosage of 0.3 mg/day intravenously for five consecutive days. The primary outcome was the reduction in pain intensity at the end of the treatment period, evaluated using a visual analogue scale (VAS). Secondary outcome measures included the FM Impact Questionnaire, Beck Depression Inventory (BDI), Multi-Dimensional Health Assessment Questionnaire (MDHAQ), EQ-5D and State-Trait Anxiety Inventory on days 5 (end of treatment), 7, 10 and 28. Safety was continuously monitored throughout the study. Results At the end of the treatment phase, the ramosetron group demonstrated a significantly greater reduction in VAS pain scores compared with the placebo group (1.18 ± 1.60 vs 0.54 ± 1.59, P  < 0.05). Additionally, the ramosetron group exhibited significant improvements in BDI (4.42 ± 5.18 vs 1.33 ± 4.87, P  < 0.05) and MDHAQ pain scale (0.37 ± 0.74 vs 0.04 ± 0.52, P  < 0.05) scores. However, these improvements in pain VAS and BDI scores were not sustained through day 28. The safety profile of ramosetron was favorable, with gastrointestinal symptoms, particularly constipation, being the most commonly reported adverse events. Conclusions Intravenous administration of ramosetron provided safe and effective short-term relief of pain intensity in FM patients with inadequate response to standard treatments. [ABSTRACT FROM AUTHOR]

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]

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]