Porphyromonas gingivalis GroEL exacerbates orthotopic allograft transplantation vasculopathy via impairment of endothelial cell function.

Orthotopic allograft transplantation (OAT) is a significant approach to addressing organ failure. However, persistent immune responses to the allograft affect chronic rejection, which induces OAT vasculopathy (OATV) and organ failure. Porphyromonas gingivalis can infiltrate remote organs via the bloodstream, thereby intensifying the severity of cardiovascular, respiratory, and neurodegenerative diseases and cancer. GroEL, a virulence factor of P. gingivalis promotes pro-inflammatory cytokine production in host cells, which assumes to play a pivotal role in the pathogenesis of cardiovascular diseases. Although the aggravation of OATV is attributable to numerous factors, the role of GroEL remains ambiguous. Therefore, this study aimed to investigate the impact of GroEL on OATV. Aortic grafts extracted from PVG/Seac rats were transplanted into ACI/NKyo rats and in vitro human endothelial progenitor cell (EPC) and coronary artery endothelial cell (HCAEC) models. The experimental findings revealed that GroEL exacerbates OATV in ACI/NKyo rats by affecting EPC and smooth muscle progenitor cell (SMPC) function and enabling the anomalous accumulation of collagen. In vitro, GroEL spurs endothelial-mesenchymal transition in EPCs, reduces HCAEC tube formation and barrier function by downregulating junction proteins, accelerates HCAEC aging by lowering mitochondrial membrane potential and respiratory function, and impedes HCAEC migration by modulating cytoskeleton-associated molecules. This study suggests that P. gingivalis GroEL could potentially augment OATV by impacting vascular progenitor and endothelial cell functions.

Multidimensional item response theory models for testlet-based doubly bounded data.

A testlet-based visual analogue scale (VAS) is a doubly bounded scaling approach (e.g., from 0% to 100% or from 0 to 1) composed of multiple adjectives, nouns, or sentences (statements/items) within testlets for measuring individuals' attitudes, opinions, or career interests. While testlet-based VASs have many advantages over Likert scales, such as reducing response style effects, the development of proper statistical models for analyzing testlet-based VAS data lags behind. This paper proposes a novel beta copula model and a competing logit-normal model based on the item response theory framework, assessed by Bayesian parameter estimation, model comparison, and goodness-of-fit statistics. An empirical career interest dataset based on a testlet-based VAS design was analyzed using the proposed models. Simulation studies were conducted to assess the two models' parameter recovery. The results show that the beta copula model had superior fit in the empirical data analysis, and also exhibited good parameter recovery in the simulation studies, suggesting that it is a promising statistical approach to testlet-based doubly bounded responses.

Online Parameter Estimation for Student Evaluation of Teaching.

Student evaluation of teaching (SET) assesses students' experiences in a class to evaluate teachers' performance in class. SET essentially comprises three facets: teaching proficiency, student rating harshness, and item properties. The computerized adaptive testing form of SET with an established item pool has been used in educational environments. However, conventional scoring methods ignore the harshness of students toward teachers and, therefore, are unable to provide a valid assessment. In addition, simultaneously estimating teachers' teaching proficiency and students' harshness remains an unaddressed issue in the context of online SET. In the current study, we develop and compare three novel methods-marginal, iterative once, and hybrid approaches-to improve the precision of parameter estimations. A simulation study is conducted to demonstrate that the hybrid method is a promising technique that can substantially outperform traditional methods.

Animal Models for Heart Transplantation Focusing on the Pathological Conditions.

Cardiac transplant recipients face many complications due to transplant rejection. Scientists must conduct animal experiments to study disease onset mechanisms and develop countermeasures. Therefore, many animal models have been developed for research topics including immunopathology of graft rejection, immunosuppressive therapies, anastomotic techniques, and graft preservation techniques. Small experimental animals include rodents, rabbits, and guinea pigs. They have a high metabolic rate, high reproductive rate, small size for easy handling, and low cost. Additionally, they have genetically modified strains for pathological mechanisms research; however, there is a lacuna, as these research results rarely translate directly to clinical applications. Large animals, including canines, pigs, and non-human primates, have anatomical structures and physiological states that are similar to those of humans; therefore, they are often used to validate the results obtained from small animal studies and directly speculate on the feasibility of applying these results in clinical practice. Before 2023, PubMed Central® at the United States National Institute of Health's National Library of Medicine was used for literature searches on the animal models for heart transplantation focusing on the pathological conditions. Unpublished reports and abstracts from conferences were excluded from this review article. We discussed the applications of small- and large-animal models in heart transplantation-related studies. This review article aimed to provide researchers with a complete understanding of animal models for heart transplantation by focusing on the pathological conditions created by each model.

Tumor Necrosis Factor Superfamily 14 (LIGHT) Restricts Neovascularization by Decreasing Circulating Endothelial Progenitor Cells and Function.

Tumor necrosis factor superfamily 14 (TNFSF14) is also known as the LT-related inducible ligand (LIGHT). It can bind to the herpesvirus invasion mediator and lymphotoxin-ß receptor to perform its biological activity. LIGHT has multiple physiological functions, including strengthening the synthesis of nitric oxide, reactive oxygen species, and cytokines. LIGHT also stimulates angiogenesis in tumors and induces the synthesis of high endothelial venules; degrades the extracellular matrix in thoracic aortic dissection, and induces the expression of interleukin-8, cyclooxygenase-2, and cell adhesion molecules in endothelial cells. While LIGHT induces tissue inflammation, its effects on angiogenesis after tissue ischemia are unclear. Thus, we analyzed these effects in the current study. In this study, the animal model of hind limb ischemia surgery in C57BL/6 mice was performed. Doppler ultrasound, immunohistochemical staining, and Western blotting were employed to analyze the situation of angiogenesis. In addition, human endothelial progenitor cells (EPCs) were used for in vitro studies to analyze the possible mechanisms. The results in the animal study showed that LIGHT injection inhibited angiogenesis in ischemic limbs. For the in vitro studies, LIGHT inhibited the expression of integrins and E-selectin; decreased migration and tube formation capabilities, mitochondrial respiration, and succinate dehydrogenase activity; and promoted senescence in EPCs. Western blotting revealed that the impairment of EPC function by LIGHT may be due to its effects on the proper functioning of the intracellular Akt signaling pathway, endothelial nitrite oxide synthase (eNOS), and mitochondrial respiration. In conclusion, LIGHT inhibits angiogenesis after tissue ischemia. This may be related to the clamped EPC function.

E-cigarettes Induce Dysregulation of Autophagy Leading to Endothelial Dysfunction in Pulmonary Arterial Hypertension.

Given the increasing popularity of electronic cigarettes (e-cigs), it is imperative to evaluate the potential health risks of e-cigs, especially in users with preexisting health concerns such as pulmonary arterial hypertension (PAH). The aim of the present study was to investigate whether differential susceptibility exists between healthy and patients with PAH to e-cig exposure and the molecular mechanisms contributing to it. Patient-specific induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) from healthy individuals and patients with PAH were used to investigate whether e-cig contributes to the pathophysiology of PAH and affects EC homeostasis in PAH. Our results showed that PAH iPSC-ECs showed a greater amount of damage than healthy iPSC-ECs upon e-cig exposure. Transcriptomic analyses revealed that differential expression of Akt3 may be responsible for increased autophagic flux impairment in PAH iPSC-ECs, which underlies increased susceptibility upon e-cig exposure. Moreover, knockdown of Akt3 in healthy iPSC-ECs significantly induced autophagic flux impairment and endothelial dysfunction, which further increased with e-cig treatment, thus mimicking the PAH cell phenotype after e-cig exposure. In addition, functional disruption of mTORC2 by knocking down Rictor in PAH iPSC-ECs caused autophagic flux impairment, which was mediated by downregulation of Akt3. Finally, pharmacological induction of autophagy via direct inhibition of mTORC1 and indirect activation of mTORC2 with rapamycin reverses e-cig-induced decreased Akt3 expression, endothelial dysfunction, autophagic flux impairment, and decreased cell viability, and migration in PAH iPSC-ECs. Taken together, these data suggest a potential link between autophagy and Akt3-mediated increased susceptibility to e-cig in PAH.

Efficient Metropolis-Hastings Robbins-Monro Algorithm for High-Dimensional Diagnostic Classification Models.

The expectation-maximization (EM) algorithm is a commonly used technique for the parameter estimation of the diagnostic classification models (DCMs) with a prespecified Q-matrix; however, it requires O(2 K ) calculations in its expectation-step, which significantly slows down the computation when the number of attributes, K, is large. This study proposes an efficient Metropolis-Hastings Robbins-Monro (eMHRM) algorithm, needing only O(K + 1) calculations in the Monte Carlo expectation step. Furthermore, the item parameters and structural parameters are approximated via the Robbins-Monro algorithm, which does not require time-consuming nonlinear optimization procedures. A series of simulation studies were conducted to compare the eMHRM with the EM and a Metropolis-Hastings (MH) algorithm regarding the parameter recovery and execution time. The outcomes presented in this article reveal that the eMHRM is much more computationally efficient than the EM and MH, and it tends to produce better estimates than the EM when K is large, suggesting that the eMHRM is a promising parameter estimation method for high-dimensional DCMs.

Genome-wide differential expression profiling of lncRNAs and mRNAs in human induced pluripotent stem cell-derived endothelial cells exposed to e-cigarette extract.

BACKGROUND: Electronic-cigarette (e-cig) usage, particularly in the youth population, is a growing concern. It is known that e-cig causes endothelial dysfunction, which is a risk factor for the development of cardiovascular diseases; however, the mechanisms involved remain unclear. We hypothesized that long noncoding RNAs (lncRNAs) may play a role in e-cig-induced endothelial dysfunction. METHODS: Here, we identified lncRNAs that are dysregulated in human induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) following 24 h of e-cig aerosol extract treatment via microarray analysis. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analyses of the dysregulated mRNAs following e-cig exposure and constructed co-expression networks of the top 5 upregulated lncRNAs and the top 5 downregulated lncRNAs and the mRNAs that are correlated with them. Furthermore, the functional effects of knocking down lncRNA lung cancer-associated transcript 1 (LUCAT1) on EC phenotypes were determined as it was one of the significantly upregulated lncRNAs following e-cig exposure based on our profiling. RESULTS: 183 lncRNAs and 132 mRNAs were found to be upregulated, whereas 297 lncRNAs and 413 mRNAs were found to be downregulated after e-cig exposure. We also observed that e-cig caused dysregulation of endothelial metabolism resulting in increased FAO activity, higher mitochondrial membrane potential, and decreased glucose uptake and glycolysis. These results suggest that e-cig alters EC metabolism by increasing FAO to compensate for energy deficiency in ECs. Finally, the knockdown of LUCAT1 prevented e-cig-induced EC dysfunction by maintaining  vascular barrier, reducing reactive oxygen species level, and increasing migration capacity. CONCLUSION: This study identifies an expression profile of differentially expressed lncRNAs and several potential regulators and pathways in ECs exposed to e-cig, which provide insights into the regulation of lncRNAs and mRNAs and the role of lncRNA and mRNA networks in ECs associated e-cig exposure.

Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data.

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for "complete" item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of "don't know" item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates.

A note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models.

Using Louis' formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation-maximization (EM) algorithm has converged. However, Louis' formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis' formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis' formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis' formula with previously existing methods. Results from these presentations suggest that Louis' formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.

Extract of Pyrus nivalis enhances phagocytosis in lungs after particles matter exposure in BALB/c mice.

During past few decades, studies have demonstrated that particulate matter (PM) is the most serious environmental pollutant in industrial countries which mainly contributes for increasing prevalence of chronic respiratory inflammatory diseases. Healthy food supplements to prevent the inflammatory diseases are common and show notable effects. The effects of the extract of Pyrus nivalis, a common fruit and herbal medicine in Taiwan, on PM-induced airway inflammation in mice were investigated by feeding the extract orally for 21 days. Results obtained from lung histology and bronchial alveolar lavage fluid (BALF) cell profile showed that oral feeding of P. nivalis extract did not affect the airway inflammation. However, it increased the phagocytic activity in BALF cells and induced M1-dominant macrophage differentiation in lungs. Our study showed that extract of P. nivalis might present the beneficial and therapeutic potential for clearance of PM and pathogens in airway. PRACTICAL APPLICATIONS: Pyrus nivalis is a common fruit and also used extensively in Chinese herbology. The pharmacological effects of P. nivalis have been reported in ancient Chinese Medical literature and known to possess anti-asthma and anti-cough properties as well as to enhance function of lungs. In this study, we found that orally feeding the extract of P. nivalis did not induce airway inflammation and affect particulate matter (PM)-induced inflammatory cells infiltration in lungs but increased phagocytosis with or without PM treatment which might indicate its therapeutic potential for clearance of PM and pathogens in airway.

Particulate matters increase epithelial-mesenchymal transition and lung fibrosis through the ETS-1/NF-κB-dependent pathway in lung epithelial cells.

BACKGROUND: Particulate matters (PMs) in ambient air pollution are closely related to the incidence of respiratory diseases and decreased lung function. Our previous report demonstrated that PMs-induced oxidative stress increased the expression of proinflammatory intracellular adhesion molecule-1 (ICAM-1) through the IL-6/AKT/STAT3/NF-κB pathway in A549 cells. However, the role of O-PMs in epithelial-mesenchymal transition (EMT) development and pulmonary fibrosis and the related mechanisms have not been determined. The aim of this study was to investigate the effects of O-PMs on the pathogenesis of EMT and pulmonary fibrosis as well as the expression of ETS-1 and NF-κB p65, in vitro and in vivo. RESULTS: O-PMs treatment induced EMT development, fibronectin expression, and cell migration. O-PMs affected the expression of the EMT-related transcription factors NF-κB p65 and ETS-1. Interference with NF-κB p65 significantly decreased O-PMs-induced fibronectin expression. In addition, O-PMs affected the expression of fibronectin, E-cadherin, and vimentin through modulating ETS-1 expression. ATN-161, an antagonist of integrin α5ß1, decreased the expression of fibronectin and ETS-1 and EMT development. EMT development and the expression of fibronectin and ETS-1 were increased in the lung tissue of mice after exposure to PMs for 7 and 14 days. There was a significant correlation between fibronectin and ETS-1 expression in human pulmonary fibrosis tissue. CONCLUSION: O-PMs can induce EMT and fibronectin expression through the activation of transcription factors ETS-1 and NF-κB in A549 cells. PMs can induce EMT development and the expression of fibronectin and ETS-1 in mouse lung tissues. These findings suggest that the ETS-1 pathway could be a novel and alternative mechanism for EMT development and pulmonary fibrosis.

A Constrained Metropolis-Hastings Robbins-Monro Algorithm for Q Matrix Estimation in DINA Models.

In diagnostic classification models (DCMs), the Q matrix encodes in which attributes are required for each item. The Q matrix is usually predetermined by the researcher but may in practice be misspecified which yields incorrect statistical inference. Instead of using a predetermined Q matrix, it is possible to estimate it simultaneously with the item and structural parameters of the DCM. Unfortunately, current methods are computationally intensive when there are many attributes and items. In addition, the identification constraints necessary for DCMs are not always enforced in the estimation algorithms which can lead to non-identified models being considered. We address these problems by simultaneously estimating the item, structural and Q matrix parameters of the Deterministic Input Noisy "And" gate model using a constrained Metropolis-Hastings Robbins-Monro algorithm. Simulations show that the new method is computationally efficient and can outperform previously proposed Bayesian Markov chain Monte-Carlo algorithms in terms of Q matrix recovery, and item and structural parameter estimation. We also illustrate our approach using Tatsuoka's fraction-subtraction data and Certificate of Proficiency in English data.

Optimization of Pt-Oxygen-Containing Species Anodes for Ethanol Oxidation Reaction: High Performance of Pt-AuSnOx Electrocatalyst.

Pt-oxygen-containing species (Pt-OCS) catalysts, in which OCS (e.g., metal-oxides) are decorated on a Pt surface, possess enhanced ethanol oxidation reaction (EOR) activity and stability compared with pure Pt and are promising in practical applications of direct ethanol fuel cells. We investigate the promotion roles of Pt-OCS electrocatalysts toward the EOR via a combination of density functional theory (DFT) calculations and experiments, providing a rational design strategy for Pt-OCS catalysts. It is revealed that Pt-AuO and Pt-SnO excel in EOR activity and stability, respectively, among the DFT screening of various Pt-OCS systems, and this is confirmed by the following experiments. Moreover, an optimized Pt-AuSnO catalyst is proposed by DFT calculations, taking advantage of both Pt-AuO and Pt-SnO. The as-prepared Pt-AuSnO catalyst delivers an EOR activity that is 9.7 times higher than that of Pt and shows desired stability. These findings are expected to elucidate the mechanistic insights into Pt-OCS materials and lead to advanced EOR electrocatalysts.

Item Response Theory Modeling for Examinee-selected Items with Rater Effect.

Some large-scale testing requires examinees to select and answer a fixed number of items from given items (e.g., select one out of the three items). Usually, they are constructed-response items that are marked by human raters. In this examinee-selected item (ESI) design, some examinees may benefit more than others from choosing easier items to answer, and so the missing data induced by the design become missing not at random (MNAR). Although item response theory (IRT) models have recently been developed to account for MNAR data in the ESI design, they do not consider the rater effect; thus, their utility is seriously restricted. In this study, two methods are developed: the first one is a new IRT model to account for both MNAR data and rater severity simultaneously, and the second one adapts conditional maximum likelihood estimation and pairwise estimation methods to the ESI design with the rater effect. A series of simulations was then conducted to compare their performance with those of conventional IRT models that ignored MNAR data or rater severity. The results indicated a good parameter recovery for the new model. The conditional maximum likelihood estimation and pairwise estimation methods were applicable when the Rasch models fit the data, but the conventional IRT models yielded biased parameter estimates. An empirical example was given to illustrate these new initiatives.

A General Unfolding IRT Model for Multiple Response Styles.

It is commonly known that respondents exhibit different response styles when responding to Likert-type items. For example, some respondents tend to select the extreme categories (e.g., strongly disagree and strongly agree), whereas some tend to select the middle categories (e.g., disagree, neutral, and agree). Furthermore, some respondents tend to disagree with every item (e.g., strongly disagree and disagree), whereas others tend to agree with every item (e.g., agree and strongly agree). In such cases, fitting standard unfolding item response theory (IRT) models that assume no response style will yield a poor fit and biased parameter estimates. Although there have been attempts to develop dominance IRT models to accommodate the various response styles, such models are usually restricted to a specific response style and cannot be used for unfolding data. In this study, a general unfolding IRT model is proposed that can be combined with a softmax function to accommodate various response styles via scoring functions. The parameters of the new model can be estimated using Bayesian Markov chain Monte Carlo algorithms. An empirical data set is used for demonstration purposes, followed by simulation studies to assess the parameter recovery of the new model, as well as the consequences of ignoring the impact of response styles on parameter estimators by fitting standard unfolding IRT models. The results suggest the new model to exhibit good parameter recovery and seriously biased estimates when the response styles are ignored.

Fitting item response unfolding models to Likert-scale data using mirt in R.

While a large family of unfolding models for Likert-scale response data have been developed for decades, very few applications of these models have been witnessed in practice. There may be several reasons why these have not appeared more widely in published research, however one obvious limitation appears to be the absence of suitable software for model estimation. In this article, the authors demonstrate how the mirt package can be adopted to estimate parameters from various unidimensional and multidimensional unfolding models. To concretely demonstrate the concepts and recommendations, a tutorial and examples of R syntax are provided for practical guidelines. Finally, the performance of mirt is evaluated via parameter-recovery simulation studies to demonstrate its potential effectiveness. The authors argue that, armed with the mirt package, applying unfolding models to Likert-scale data is now not only possible but can be estimated to real-datasets with little difficulty.

Curcumin accelerates cutaneous wound healing via multiple biological actions: The involvement of TNF-α, MMP-9, α-SMA, and collagen.

Curcumin, a constituent of the turmeric plant, has antitumor, anti-inflammatory, and antioxidative effects, but its effects on wound healing are unclear. We created back wounds in 72 mice and treated them with or without topical curcumin (0.2 mg/mL) in Pluronic F127 gel (20%) daily for 3, 5, 7, 9, and 12 days. Healing in wounds was evaluated from gross appearance, microscopically by haematoxylin and eosin staining, by immunohistochemistry for tumour necrosis factor alpha and alpha smooth muscle actin, and by polymerase chain reaction amplification of mRNA expression levels. Treatment caused fast wound closure with well-formed granulation tissue dominated by collagen deposition and regenerating epithelium. Curcumin increased the levels of tumour necrosis factor alpha mRNA and protein in the early phase of healing, which then decreased significantly. However, these levels remained high in controls. Levels of collagen were significantly higher in curcumin-treated wounds. Immunohistochemical staining for alpha smooth muscle actin was increased in curcumin-treated mice on days 7 and 12. Curcumin treatment significantly suppressed matrix metallopeptidase-9 and stimulated alpha smooth muscle levels in tumour necrosis factor alpha-treated fibroblasts via nuclear factor kappa B signalling. Thus, topical curcumin accelerated wound healing in mice by regulating the levels of various cytokines.

The effects of wild bitter gourd fruit extracts on ICAM-1 expression in pulmonary epithelial cells of C57BL/6J mice and microRNA-221/222 knockout mice: Involvement of the miR-221/-222/PI3K/AKT/NF-κB pathway.

BACKGROUND: The extracts from wild bitter gourd fruit (WBGE) were reported to possess numerous pharmacological activities. However, the anti-inflammatory effects of WBGE on human lung epithelial cells and the underlying mechanisms have not been determined. PURPOSE: To evaluate the molecular basis of the effects of WBGE on intercellular adhesion molecule-1 (ICAM-1) expression in alveolar epithelial (A549) cells, C57BL/6 wild-type (WT) mice and microRNA (miR)-221/-222 knockout (KO) mice with or without tumor necrosis factor (TNF-α; 3 ng/ml) treatment. STUDY DESIGN/METHODS: WT mice and miR-221/-222 KO mice were fed a control diet and divided into four groups (C: control mice; T: treated with TNF-α alone; WBGE/T: pretreated with WBGE and then stimulated with TNF-α; WBGE: treated with WBGE alone). The effects of WBGE on ICAM-1 expression and the related signals in A549 cells and mice with or without TNF-α treatment were examined by Western blot and immunofluorescent staining. RESULTS: WBGE significantly decreased the TNF-α-induced ICAM-1 expression in A549 cells through the inhibition of phosphoinositide 3-kinase (PI3K)/ protein kinase B (AKT)/ nuclear factor- kappa B (NF-κB)/ inhibitor of NF-κB (IκB) phosphorylation and decreased leukocyte adhesion. In addition, WBGE reduced endogenous ICAM-1 expression and upregulated miR-221/-222 expression. The overexpression of miR-222 decreased PI3K/AKT/NF-κB/IκB and ICAM-1 expression, which resulted in reducing monocyte adhesion. Moreover, WBGE reduced ICAM-1 expression in lung tissues of WT mice with or without TNF-α treatment and upregulated miR-221/222. WBGE did not affect the miR-221/-222 level and had little effect on ICAM-1 expression in miR-221/-222 KO mice. CONCLUSIONS: These results suggest that WBGE reduced ICAM-1 expression both under in vitro and in vivo conditions. The protective effects were mediated partly through the miR-221/-222/PI3K/AKT/NF-κB pathway.