Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705–0.745), 0.936 (95% CI, 0.924–0.947), and 0.830 (95%CI, 0.792–0.868), respectively, at the image level and 0.947 (95% CI, 0.912–0.982), 0.691 (95% CI, 0.603–0.779), and 0.816 (95% CI, 0.776–0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493–0.780), 0.944 (95% CI, 0.933–0.955), and 0.731 (95% CI, 0.681–0.780), respectively, at the image level and 0.806 (95% CI, 0.729–0.883), 0.617 (95% CI, 0.523–0.711), and 0.711 (95% CI, 0.660–0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting

Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data

This study investigates the influence of meteorological parameters such as temperature and precipitation on gross primary production (GPP) in the continental United States (CONUS) during boreal summer using satellite-based temperature and precipitation indices and GPP data at various scales (i.e., pixel, county, and state levels). The strong linear relationship between temperature and precipitation indices is presented around the central United States, particularly in the Great Plains, where the year-to-year variation of GPP is very sensitive to meteorological conditions. This sensitive GPP variation is mostly attributable to the semi-arid climate in the Great Plains, where crop productivity and temperature are closely related. The more specific information for the regionality of the relationships across the variables manifests itself at higher resolutions. The impact of the summer meteorological condition on the annual crop yield is particularly significant. Maize and soybean yields show a strong correlation with both Temperature Condition Index (TCI) and Precipitation Condition Index (PCI) in the Great Plains, with a relatively higher relationship with TCI than PCI, which is consistent with the relationship compared with GPP. This study suggests that in-depth investigations into the relationship between maize and soybean yields and the climate are required. The region-dependent relationship between GPP and meteorological conditions in our study would guide agricultural decision making in the future climate

Screening and Probiotic Properties of Lactic Acid Bacteria with Potential Immunostimulatory Activity Isolated from Kimchi

The modulation of the immune system is a major mechanism through which probiotics exert beneficial effects on health. Probiotics, including lactic acid bacteria (LAB), have been reported to enhance innate immunity. The purpose of this study was to screen for LAB strains with excellent immunostimulatory activity isolated from kimchi. We selected five promising strains (Limosilactobacillus fermentum MG5489, Lactococcus lactis MG5542, Lacticaseibacillus paracasei MG5559, Latilactobacillus sakei MG5468, and Latilactobacillus curvatus MG5609) that exhibited immune-stimulating effects by inducing the production of nitric oxide (NO) and pro-inflammatory cytokines such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β in RAW264.7 cells. The selected strains significantly increased phagocytic activity of RAW264.7 cells and nuclear factor-κB (NF-κB) activation. Furthermore, the safety of the selected strains was determined using hemolysis and antibiotic susceptibility tests. The stabilities and adhesion abilities of these strains in the gastrointestinal tract (GIT) were also determined. Taken together, these findings suggest that the strains selected in this study have the potential to be novel probiotics to enhance immunity

Immunostimulatory Activity of Lactic Acid Bacteria Cell-Free Supernatants through the Activation of NF-κB and MAPK Signaling Pathways in RAW 264.7 Cells

Lactic acid bacteria (LAB) can improve host health and has strong potential for use as a health functional food. Specific strains of LAB have been reported to exert immunostimulatory effects. The primary goal of this study was to evaluate the immunostimulatory activities of novel LAB strains isolated from humans and foods and to investigate the probiotic properties of these strains. Cell-free supernatants (CFS) obtained from selected LAB strains significantly increased phagocytosis and level of nitric oxide (NO) and pro-inflammatory cytokines such as tumor necrosis factor (TNF)-α and interleukin (IL)-6 in RAW264.7 macrophage cells. The protein expression of inducible NO synthase (iNOS) and cyclooxygenase (COX)-2, which are immunomodulators, was also upregulated by CFS treatment. CFS markedly induced the phosphorylation of nuclear factor-κB (NF-κB) and MAPKs (ERK, JNK, and p38). In addition, the safety of the LAB strains used in this study was demonstrated by hemolysis and antibiotic resistance tests. Their stability was confirmed under simulated gastrointestinal conditions. Taken together, these results indicate that the LAB strains selected in this study could be useful as probiotic candidates with immune-stimulating activity

Influencing Factors and Consequences of Workplace Bullying among Nurses: A Structural Equation Modeling

Purpose: The purpose of this study was to build and test a model outlining the factors related to workplace bullying among nurses. The hypothesized model included authentic leadership and a relationship-oriented organizational culture as influencing factors, symptom experience and turnover intention as consequences, and positive psychological capital as a mediator of workplace bullying among nurses. Methods: We obtained structured questionnaire data from 301 nurses working at hospitals in South Korea. Based on these data, the developed model was verified via a structural equation modeling analysis using SPSS and AMOS program. Results: The fit indices of the hypothesized model satisfied recommended levels; χ2 = 397.58 (p < .001), normed χ2 (χ2/df) = 1.82, RMR = .05, TLI = .93, CFI = .94, RMSEA = .05. A relationship-oriented organizational culture had a direct effect on workplace bullying (β = −.48, p < .001). Furthermore, workplace bullying had a direct effect on symptom experience (β = .36, p < .001), and this relationship was mediated by positive psychological capital (β = .15, p = .003). Workplace bullying also had an indirect effect on turnover intention (β = .20, p = .007). Finally, symptom experience had a direct effect on turnover intention (β = .31, p = .002). Conclusion: These results suggest that workplace bullying among nurses may be prevented by constructing a relationship-oriented organizational culture, as long as employees have sufficient positive psychological capital. In this regard, workplace bullying among nurses should be addressed using a comprehensive strategy that considers both individual and organizational factors. Keywords: bullying, leadership, nurses, organizational culture, personnel turnove

Antioxidant Activity and Probiotic Properties of Lactic Acid Bacteria

Oxidative stress, which can cause imbalance in the body by damaging cells and tissues, arises from the immoderate production of reactive oxygen species (ROS)/reactive nitrogen species (RNS). Therefore, external supplements having antioxidant activity are required for reducing oxidative stress. In our study, we investigated DPPH and ABTS radical scavenging ability, and the inhibition effect on the nitric oxide (NO) production of 15 food-derived bacterial strains in LPS-activated RAW264.7 cells. Among these LAB strains, eight strains with an excellent inhibition effect on NO production were selected through comparisons within the same genera. Moreover, the selected strains, including Leuconostoc mesenteroides MG860, Leu. citreum MG210, Pediococcus acidilactici MG5001, P. pentosaceus MG5078, Weissella cibaria MG5090, Levilactobacillus brevis MG5306, Latilactobacillus curvatus MG5020, and Latilactobacillus sakei MG5048 diminished the inducible nitric oxide synthase (iNOS)/cyclooxygenase-2 (COX-2) expression. In addition, the stability and adhesion ability of the eight LAB strains in the gastrointestinal tract were determined. In conclusion, the selected strains have potential as new probiotics with antioxidant effects

Antioxidant Activity and Probiotic Properties of Lactic Acid Bacteria

Oxidative stress, which can cause imbalance in the body by damaging cells and tissues, arises from the immoderate production of reactive oxygen species (ROS)/reactive nitrogen species (RNS). Therefore, external supplements having antioxidant activity are required for reducing oxidative stress. In our study, we investigated DPPH and ABTS radical scavenging ability, and the inhibition effect on the nitric oxide (NO) production of 15 food-derived bacterial strains in LPS-activated RAW264.7 cells. Among these LAB strains, eight strains with an excellent inhibition effect on NO production were selected through comparisons within the same genera. Moreover, the selected strains, including Leuconostoc mesenteroides MG860, Leu. citreum MG210, Pediococcus acidilactici MG5001, P. pentosaceus MG5078, Weissella cibaria MG5090, Levilactobacillus brevis MG5306, Latilactobacillus curvatus MG5020, and Latilactobacillus sakei MG5048 diminished the inducible nitric oxide synthase (iNOS)/cyclooxygenase-2 (COX-2) expression. In addition, the stability and adhesion ability of the eight LAB strains in the gastrointestinal tract were determined. In conclusion, the selected strains have potential as new probiotics with antioxidant effects

Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II

To produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 mu m (PM2.5), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise. Here, we used ML techniques to directly estimate ground-level PM2.5 concentrations over South Korea using top-of-atmosphere (TOA) reflectance from the Geostationary Ocean Color Imager I (GOCI-I) and its next generation GOCI-II with improved spatial, spectral, and temporal resolu-tions. Three ML techniques were used to estimate ground-level PM2.5 concentrations: random forest, light gradient boosting machine (LGBM), and artificial neural network. Three schemes were examined based on the input feature composition of the GOCI spectral bands: scheme 1 using all GOCI-I bands, scheme 2 using only GOCI-II bands that overlap with GOCI-I bands, and scheme 3 using all GOCI-II bands. The results showed that LGBM performed better than the other ML models. GOCI-II-based schemes 2 and 3 (determination of coefficient (R2) = 0.85 and 0.85 and root-mean-square-error (RMSE) = 7.69 and 7.82 mu g/m3, respectively) performed slightly better than GOCI-I-based scheme 1 (R2 = 0.83 and RMSE = 8.49 mu g/m3). In particular, TOA reflectance at a new channel (380 nm) of GOCI-II was identified as the most contributing variable, given its high sensitivity to aerosols. The long-term estimation of PM2.5 concentrations using the proposed models was examined for ground stations located in two major cities. GOCI-II-based models produced a more detailed spatial distribution of PM2.5 concentrations owing to their higher spatial resolution (i.e., 250 m). The use of TOA reflectance data, instead of AOD and other aerosol products commonly used in previous studies, reduced the missing rate of the estimated ground-level PM2.5 concentrations by up to 50%. Our results indicate that the proposed approach using TOA reflectance data from geostationary satellite sensors has great potential for estimating ground-level PM2.5 concentrations for operational purposes

Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data

This study identified a relationship between the El Nino-Southern Oscillation (ENSO) and East African drought during the two rainy seasons (i.e., short rain from October to December and long rain from March to May). ENSO shows a positive relationship with the East African short rain during the entire period analyzed (1949-2016). Meanwhile, a statistically significant relationship between ENSO and East African long rain appears only in a recent period (2000-2016), which is unprecedented in the past 50 years before the 2000s. The strengthened interannual relationship between ENSO and East African long rain is associated with distinguished Indian Ocean Walker cell in boreal spring, implying that their relationship could be affected by either multidecadal natural variability or anthropogenic forcing. Various satellite-based drought indices which consider vegetation health, land surface temperature, evapotranspiration, and precipitation with 1 km spatial resolution showed a robust relationship between ENSO and East African drought in the recent period (2000-2016) during the both rainy seasons. In the case studies of June 2005, August 2007, and November 2010, the anomalous wet condition in East Africa during the mature phase of El Nino became dry as La Nina developed in the following year, thereby a lagged response was observed in vegetation-related drought indices and long-term meteorological drought indices. Satellite-based high resolution (1 km) drought indices often showed heterogeneous drought patterns under the same drought condition from reanalysis data at coarse resolution (2.5 degrees), indicating the importance of spatiotemporally continuous high-resolution measurements for drought monitoring in East Africa. Consequently, the synergetic use of high resolution satellite observations and reanalysis data is crucial to provide the effective monitoring, assessment, and seasonal outlook of East African drought

Prediction of drought on pentad scale using remote sensing data and MJO index through random forest over East Asia

Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20??-50??N, 90??-150??E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden-Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions