STAT3 activation in microglia increases pericyte apoptosis in diabetic retinas through TNF-ɑ/AKT/p70S6 kinase signaling.

Diabetic retinopathy (DR) is one of the vascular complications associated with diabetes mellitus. Pericyte loss is an early characteristic phenomenon in DR. However, the mechanism by which pericyte apoptosis occurs in DR is not fully understood. We have focused on the increased STAT3 activation in diabetic retinas because STAT3 activation is associated with inflammation, and persistent chronic inflammation is closely related to retinal lesions. In this study, we demonstrated that STAT3 was activated by IFN-γ and IL-6 that highly expressed in diabetic retinas. We identified TNF-α as a potent inducer of pericyte apoptosis in diabetic retinas from the gene expression analysis and found that STAT3 activation in microglia increased TNF-α expression in the diabetic retinas. We also demonstrated that increased TNF-α expression in microglia caused pericyte apoptosis through downregulating AKT/p70S6 kinase signaling. Moreover, we took advantage of mice lacking STAT3 in microglia and demonstrated that STAT3 ablation in microglia reduced the pericyte apoptosis and TNF-α expression in the diabetic retinas. These results suggest that STAT3 activation in microglia plays an important role in pericyte apoptosis in the diabetic retinas through increased TNF-α expression and provide STAT3 activation in microglia as a potential therapeutic target for preventing pericyte loss in DR.

STAT3 activation in microglia exacerbates hippocampal neuronal apoptosis in diabetic brains.

Diabetes mellitus (DM) characterized by hyperglycemia leads to a variety of complications, including cognitive impairment or memory loss. The hippocampus is a key brain area for learning and memory and is one of the regions that is most sensitive to diabetes. However, the pathogenesis of diabetic neuronal lesion is not yet completely understood. We focused on the association of microglia activation and brain lesions in diabetes. In this study, we investigated whether and how signal transducer and activator of transcription 3 (STAT3) activation in microglia affects neuronal lesions in diabetic brains. Using a streptozotocin-induced type 1 DM model, we showed enhanced hippocampal neuronal apoptosis that was associated with increased STAT3 activation. We found that hyperglycemia increased the expression of inflammatory cytokines such as interferon-γ (IFN-γ) and interleukin-6, in the diabetic hippocampus. In particular, IFN-γ induced autocrine activation of microglia, and STAT3 activation is important for this process. We also demonstrated that STAT3 activation in microglia increased tumor necrosis factor-α (TNF-α) expression; subsequently, TNF-α increased neuronal apoptosis by increasing reactive oxygen species (ROS) levels in the neuronal cells. We also took advantage of mice lacking STAT3 in microglia and demonstrated that depletion of microglial STAT3 reduced neuronal apoptosis in the diabetic hippocampus. Taken together, these results suggest that STAT3 activation in microglia plays an important role in hyperglycemia-induced neuronal apoptosis in the diabetic hippocampus and provide a potential therapeutic benefit of STAT3 inhibition in microglia for preventing diabetic neuronal lesions.

The frequency of impairments in everyday activities due to the overuse of the internet, gaming, or smartphone, and its relationship to health-related quality of life in Korea.

BACKGROUND: This study aimed to investigate the relationships between the frequency of impairments in daily activities due to the overuse of the Internet, gaming, or smartphones (IGS) and sociodemographic characteristics, social relationships (including family) & activities, psychosocial characteristics, health status, and health-related quality of life (HRQoL) of Korean adults. METHODS: Secondary data from the 2017 Community Health Survey, a large-scale sample survey conducted yearly in South Korea, were analyzed for 190,066 adults over 19 years of age. Three categories were created for impairment groups due to IGS overuse: No Impairment, Mild Impairment, and Moderate-to-Severe groups. And between-group differences were examined using a one-way ANOVA for health status measured with the EQ-5D-3 L and chi-square tests for all categorical dependent variables, which included sociodemographic characteristics, social relationships & activities, and psychosocial factors. The association between frequencies of daily activity impairments due to IGS overuse and the dependent variables were examined using a multivariate logistic regression analysis and a linear regression model. RESULTS: Approximately 21,345 (11.23%) of the 190,066 participants reported experiencing impairments in daily activities due to IGS overuse at least once in the previous year and the impairments were more severe in males than females. Participants experiencing impairments in daily activities contacted their friends a significantly higher number of times (4 times or more per month) and engaged in leisure activities more frequently (more than once per month) than those without impairments. There was also a significant positive relationship between IGS overuse and stress, depression, suicidal ideation, and suicide attempts. Among participants aged 19-64, impairments in daily activities due to IGS overuse were associated with a lower HRQoL. Conversely, for those aged 65 and over, mild and moderate-to-severe impairments due to IGS overuse were associated with a significantly higher HRQoL. CONCLUSIONS: Increased impairments in daily activities due to IGS overuse may negatively affect mental health. However, among older adults, the frequency of such impairments was positively associated with HRQoL. This finding could be considered to apply interventions with Internet usage or ICT devices for older adults to enhance their quality of life.

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting.

BACKGROUND: Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furthermore, there has been a lack of introductory information explaining attention mechanisms to clinical researchers. OBJECTIVE: The aim of this study was to introduce the basic concepts and design approaches of attention mechanisms. In addition, we aimed to empirically assess the potential limitations of current attention mechanisms in terms of prediction and interpretability performance. METHODS: First, the basic concepts and several key considerations regarding attention mechanisms were identified. Second, four approaches to attention mechanisms were suggested according to a two-dimensional framework based on the degrees of freedom and uncertainty awareness. Third, the prediction performance, probability reliability, concentration of variable importance, consistency of attention results, and generalizability of attention results to conventional statistics were assessed in the diabetic classification modeling setting. Fourth, the potential limitations of attention mechanisms were considered. RESULTS: Prediction performance was very high for all models. Probability reliability was high in models with uncertainty awareness. Variable importance was concentrated in several variables when uncertainty awareness was not considered. The consistency of attention results was high when uncertainty awareness was considered. The generalizability of attention results to conventional statistics was poor regardless of the modeling approach. CONCLUSIONS: The attention mechanism is an attractive technique with potential to be very promising in the future. However, it may not yet be desirable to rely on this method to assess variable importance in clinical settings. Therefore, along with theoretical studies enhancing attention mechanisms, more empirical studies investigating potential limitations should be encouraged.

Retinal pigment epithelium-derived transforming growth factor-ß2 inhibits the angiogenic response of endothelial cells by decreasing vascular endothelial growth factor receptor-2 expression.

Transforming growth factor-ß (TGF-ß) is a multifunctional cytokine that is known to modulate various aspects of endothelial cell (EC) biology. Retinal pigment epithelium (RPE) is important for regulating angiogenesis of choriocapillaris and one of the main cell sources of TGF-ß secretion, particularly TGF-ß2. However, it is largely unclear whether and how TGF-ß2 affects angiogenic responses of ECs. In the current study, we demonstrated that TGF-ß2 reduces vascular endothelial growth factor receptor-2 (VEGFR-2) expression in ECs and thereby inhibits vascular endothelial growth factor (VEGF) signaling and VEGF-induced angiogenic responses such as EC migration and tube formation. We also demonstrated that the reduction of VEGFR-2 expression by TGF-ß2 is due to the suppression of JNK signaling. In coculture of RPE cells and ECs, RPE cells decreased VEGFR-2 levels in ECs and EC migration. In addition, we showed that TGF-ß2 derived from RPE cells is involved in the reduction of VEGFR-2 expression and inhibition of EC migration. These results suggest that TGF-ß2 plays an important role in inhibiting the angiogenic responses of ECs during the interaction between RPE cells and ECs and that angiogenic responses of ECs may be amplified by a decrease in TGF-ß2 expression in RPE cells under pathologic conditions.

Angiopoietin 1 attenuates interleukin-6-induced endothelial cell permeability through SHP-1.

The regulation of endothelial cell (EC) permeability is critical for the physiological homeostasis of blood vessels and tissues. The elevation of pro-inflammatory cytokines is highly associated with lesions, such as the increased vascular permeability of diabetic retinas. We have previously reported that interleukin-6 (IL-6) increases EC permeability through the downregulation of tight junction protein expression. Angiopoietin 1 (Ang1) has an anti-permeability function, but the effect of Ang1 on vascular permeability induced by inflammatory cytokines is unclear. In the present study, we investigated the effect of Ang1 on IL-6-induced EC permeability and its underlying molecular mechanisms. We demonstrated that Ang1 inhibited the IL-6-induced increase in EC permeability by inhibiting the reductions in the levels of tight junction protein ZO-1 and occludin, which was related to the decrease in vascular endothelial growth factor (VEGF) secretion through the inhibition of STAT3 activation by Ang1. Mechanistically, Ang1 induced the dissociation of the tyrosine phosphatase SHP-1 from the Tie2 receptor and increased the binding of SHP-1 to JAK1, JAK2, and STAT3, which are IL-6 downstream signaling proteins. We conclude that SHP-1 plays an important role in the Ang1-induced inhibition of JAK/STAT3 signaling. These results provide evidence for a potential beneficial role of Ang1 in suppressing the vascular permeability induced by the pro-inflammatory cytokine IL-6 in diabetic retinopathy.

ß-Adrenergic receptor agonists attenuate pericyte loss in diabetic retinas through Akt activation.

Pericytes (PCs) are crucial in maintaining the quiescence of endothelial cells (ECs) and the integrity of EC tight junctions. Especially in diabetic retinopathy (DR), PC loss is one of the early pathologic changes in capillaries of diabetic retinas. Thus, preventing PC loss is beneficial for attenuating vision impairment in patients with DR. Although many studies have revealed the mechanism of PC loss in retinas, little is known about the mechanisms that increase PC survival. We focused on the effect of ß-adrenergic receptor agonists (ß-agonists) on PC loss in diabetic retinas. In this study, ß-agonists increased the cell viability of PCs by increasing PC survival and proliferation. Mechanistically, ß-agonist-induced protein kinase B activation in PCs reduced PC apoptosis in response to various stimuli. ß2-agonists more potently increased PC survival than ß1-agonists. ß2-Agonist reduced vascular leakage and PC loss in retinas of mice with streptozotocin-induced diabetes. In cocultures of PCs and ECs, ß2-agonists restored the altered permeability and ZO-1 expression in ECs induced by PC loss. We concluded that ß-agonists, especially ß2-agonists, increase PC survival, thereby preventing diabetes-induced PC loss in retinas. These results provide a potential therapeutic benefit of ß-agonists for preventing PC loss in DR.-Yun, J.-H., Jeong, H.-S., Kim, K.-J., Han, M. H., Lee, E. H., Lee, K., Cho, C.-H. ß-Adrenergic receptor agonists attenuate pericyte loss in diabetic retinas through Akt activation.

How climate scenarios alter future predictions of field-scale water and nitrogen dynamics and crop yields.

Climate change scenarios are widely used for exploring future changes in environmental systems. However, many aspects of the uncertainties associated with the use of climate change scenarios in environmental systems modeling have not yet been studied sufficiently. We explore how the way that baseline scenarios are defined and general circulation model (GCM) outputs are used affects climate change impact assessments of agricultural systems. Our study builds on a previously validated agricultural systems model, the Root Zone Water Quality Model (RZWQM), coupled with the Decision Support System for Agrotechnology Transfer (DSSAT), which models a tiled-drained field in central Illinois of the United States and uses nine GCM outputs to investigate the effects. Our model simulations demonstrated the following three results. Firstly, the evaluation of climate change impacts presented a significant difference between the types of baseline used. The baseline scenario should be defined using the bias-corrected retrospective GCM outputs. Secondly, once GCM outputs are bias-corrected, the selective use of GCM outputs did not add significant value over using all available GCM outputs to provide more plausible future descriptions of agricultural systems' responses. Notably, however, selective use may have impacts comparable to carbon dioxide (CO2) emission scenarios in the field-scale agricultural climate change impact assessments. Thirdly, raw GCM outputs should be avoided for the predictions of field-scale agricultural systems' responses to climate change. Our findings can help provide a clearer picture of how GCM outputs should be used in agricultural systems modeling and might enable us to have more plausible descriptions of how future agricultural systems might unfold.

Propranolol increases vascular permeability through pericyte apoptosis and exacerbates oxygen-induced retinopathy.

Retinopathy of prematurity (ROP) is an eye disease that causes blindness due to delayed vascular growth, retinal ischemia, and resulting abnormal angiogenesis. Nonselective ß-antagonist propranolol is in clinical trials for the treatment of ROP due to its effect of reducing VEGF expression and inhibiting retinal angiogenesis in oxygen-induced ROP models (OIR), but the mechanism by which propranolol acts on ROP vessels is still unclear. In the present study, we have focused on the effect of propranolol on pericyte survival and vascular permeability. We demonstrated that propranolol increases pericyte apoptosis more sensitively than endothelial cells (ECs), thereby weakening EC tight junctions to increase endothelial permeability in co-cultures of pericytes and ECs. Mechanistically, pericyte apoptosis by propranolol was due to the inhibition of Akt signaling pathway. We also demonstrated that propranolol increases pericyte loss and vascular permeability of retinal vessels in a mouse model of OIR. These results suggest that propranolol may be negative for blood vessels in retinas of OIR, and that the efficacy of propranolol for the treatment of ROP needs to be more thoroughly verified.

Exploring the effects of nitrogen fertilization management alternatives on nitrate loss and crop yields in tile-drained fields in Illinois.

It is vital to manage the excessive use of nitrogen (N) fertilizer in corn production, the single largest consumer of N fertilizer in the United States, in order to achieve more sustainable agroecosystems. This study comprehensively explored the effects of N fertilization alternatives on nitrate loss and crop yields using the Root Zone Water Quality Model (RZWQM) in tile-drained fields in central Illinois. The RZWQM was tested for the prediction of tile flow, nitrate loss, and crop yields using eight years (1993-2000) of observed data and showed satisfactory model performances from statistical and graphical evaluations. Our model simulations demonstrated the maximum return to nitrogen (MRTN) rate (193 kgha-1), a newly advised N recommendation by the Illinois Nutrient Loss Reduction Strategy (INLRS), can be further reduced. Nitrate loss was reduced by 10.3% and 29.8%, but corn yields decreased by 0.3% and 1.9% at 156 and 150 kgha-1 of N fertilizer rate in the study sites A and E, respectively. Although adjustment of N fertilization timing presented a further reduction in nitrate loss, there was no optimal timing to ensure nitrate loss reduction and corn productivity. For site A, 100% spring application was the most productive and 40% fall, 10% pre-plant, and 50% side dress application generated the lowest nitrate loss. For site E, the conventional N application timing was verified as the best practice in both corn production and nitrate loss reduction. Compared to surface broadcast placement, injected N fertilizer in spring increased corn yield, but may also escalate nitrate loss. This study presented the need of an adaptive N fertilizer management due to the heterogeneity in agricultural systems, and raised the importance of timing and placement of N fertilizer, as well as further reduction in fertilizer rate to devise a better in-field N management practice.

Improving nitrate load simulation of the SWAT model in an extensively tile-drained watershed.

Subsurface drainage systems are effective management practices employed to remove excess soil water, thereby improving soil aeration and crop productivity. However, these systems can also contribute to water quality issues by enhancing nitrate leaching and loads from agricultural fields. The Soil and Water Assessment Tool (SWAT) is commonly used to assess nitrate loads and long-term water quality impacts from agricultural watersheds. However, the current SWAT model oversimplifies nitrate transport processes by assuming a linear relationship between nitrate concentrations in tile flow and soil nitrate content. It also neglects the time lag between nitrate loading and transport with the flow. This study aimed to enhance the accuracy of nitrate load prediction by revising the subsurface drainage routine in the SWAT model. The revised routine was tested using flow and nitrate load measurements from a typical tile-drained watershed in east-central Illinois, U.S. The results demonstrated that the revised SWAT nitrate routine outperformed the current one in simulating nitrate transport at field and watershed scales. The revised routine improved the nitrate load prediction from an "unacceptable" to a "satisfactory" or "good" rating on the field scale. A sensitivity analysis conducted using the revised nitrate module showed the parameters directly associated with transpiration, groundwater discharge to the reach, the lag time of tile flow, and channel flow hydraulics were the most sensitive in nitrate load simulation. In addition, different tile depth scenarios were modeled to evaluate variation in the amount of surface runoff, tile flow, and nitrate loads by the surface flow and tile flow. The results of tile configuration scenarios agreed with understanding the tile flow process. The test results demonstrated the potential of the revised SWAT nitrate module as a tool to accurately evaluate the effects of tile drainage systems on water quality.

Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm.

BACKGROUND: Proper maintenance of hypnosis is crucial for ensuring the safety of patients undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been developed to monitor hypnotic levels. However, the black-box nature of the algorithm coupled with the hardware makes it challenging to understand the underlying mechanisms of the algorithms and integrate them with other monitoring systems, thereby limiting their use. OBJECTIVE: We propose an interpretable deep learning model that forecasts BIS values 25 s in advance using 30 s electroencephalogram (EEG) data. MATERIAL AND METHODS: The proposed model utilized EEG data as a predictor, which is then decomposed into amplitude and phase components using fast Fourier Transform. An attention mechanism was applied to interpret the importance of these components in predicting BIS. The predictability of the model was evaluated on both regression and binary classification tasks, where the former involved predicting a continuous BIS value, and the latter involved classifying a dichotomous status at a BIS value of 60. To evaluate the interpretability of the model, we analyzed the attention values expressed in the amplitude and phase components according to five ranges of BIS values. The proposed model was trained and evaluated using datasets collected from two separate medical institutions. RESULTS AND CONCLUSION: The proposed model achieved excellent performance on both the internal and external validation datasets. The model achieved a root-mean-square error of 6.614 for the regression task, and an area under the receiver operating characteristic curve of 0.937 for the binary classification task. Interpretability analysis provided insight into the relationship between EEG frequency components and BIS values. Specifically, the attention mechanism revealed that higher BIS values were associated with increased amplitude attention values in high-frequency bands and increased phase attention values in various frequency bands. This finding is expected to facilitate a more profound understanding of the BIS prediction mechanism, thereby contributing to the advancement of anesthesia technologies.

Hyperglycemia-induced oxidative stress promotes tumor metastasis by upregulating vWF expression in endothelial cells through the transcription factor GATA1.

Diabetes mellitus (DM) characterized by hyperglycemia is a chronic metabolic disorder that leads to many symptoms and vascular complications. Despite the close association between DM and cancer progression, the response and role of endothelial cells (ECs) under diabetic conditions in tumor metastasis remain to be elucidated. In this study, we sought to determine whether and how ECs under diabetic conditions contribute to tumor metastasis. We have taken advantage of syngeneic mouse tumor models of Lewis lung carcinoma (LLC) and melanoma (B16F10) cells and a streptozotocin (STZ)-induced hyperglycemia model. We demonstrated that hyperglycemia increased the metastasis of LLC and B16F10 cells in an experimental metastasis model with an intravenous injection of the tumor cells. We also found that hyperglycemia promoted lung metastasis of tumor cells by increasing the adhesiveness of ECs to facilitate the adhesion of tumor cells to ECs rather than affecting the metastatic behavior of tumor cells themselves. From the analysis of gene expression in primary lung ECs from STZ-treated mice, we identified that vWF promoted the adhesion of tumor cells to ECs and the transendothelial migration of tumor cells. Mechanistically, hyperglycemia-induced oxidative stress in ECs, and increased oxidative stress enhanced vWF expression in ECs through an increase in the transcription factor GATA1. These results provide evidence for the role of vWF in ECs in promoting hyperglycemia-induced tumor metastasis and potential therapeutic targets for the regulation of vWF expression in ECs and hyperglycemia-induced tumor metastasis.

Oleanolic Acids Inhibit Vascular Endothelial Growth Factor Receptor 2 Signaling in Endothelial Cells: Implication for Anti-Angiogenic Therapy.

Angiogenesis must be precisely controlled because uncontrolled angiogenesis is involved in aggravation of disease symptoms. Vascular endothelial growth factor (VEGF)/VEGF receptor 2 (VEGFR-2) signaling is a key pathway leading to angiogenic responses in vascular endothelial cells (ECs). Therefore, targeting VEGF/VEGFR-2 signaling may be effective at modulating angiogenesis to alleviate various disease symptoms. Oleanolic acid was verified as a VEGFR-2 binding chemical from anticancer herbs with similar binding affinity as a reference drug in the Protein Data Bank (PDB) entry 3CJG of model A coordination. Oleanolic acid effectively inhibited VEGF-induced VEGFR-2 activation and angiogenesis in HU-VECs without cytotoxicity. We also verified that oleanolic acid inhibits in vivo angiogenesis during the development and the course of the retinopathy of prematurity (ROP) model in the mouse retina. Taken together, our results suggest a potential therapeutic benefit of oleanolic acid for inhibiting angiogenesis in proangiogenic diseases, including retinopathy.