Inhibition of Urban Particulate Matter-Induced Airway Inflammation by RIPK3 through the Regulation of Tight Junction Protein Production.

Urban particulate matter (UPM) is a high-hazard cause of various diseases in humans, including in the respiratory tract, skin, heart, and even brain. Unfortunately, there is no established treatment for the damage caused by UPM in the respiratory epithelium. In addition, although RIPK3 is known to induce necroptosis, its intracellular role as a negative regulator in human lungs and bronchial epithelia remains unclear. Here, the endogenous expression of RIPK3 was significantly decreased 6 h after exposure to UPM. In RIPK3-ovexpressed cells, RIPK3 was not moved to the cytoplasm from the nucleus. Interestingly, the overexpression of RIPK3 dramatically decreased TEER and F-actin formation. Its overexpression also decreased the expression of genes for pro-inflammatory cytokines (IL-6 and IL-8) and tight junctions (ZO-1, -2, -3, E-cadherin, and claudin) during UPM-induced airway inflammation. Importantly, overexpression of RIPK3 inhibited the UPM-induced ROS production by inhibiting the activation of iNOS and eNOS and by regulating mitochondrial fission processing. In addition, UPM-induced activation of the iκB and NF-κB signaling pathways was dramatically decreased by RIPK3, and the expression of pro-inflammatory cytokines was decreased by inhibiting the iκB signaling pathway. Our data indicated that RIPK3 is essential for the UPM-induced inflammatory microenvironment to maintain homeostasis. Therefore, we suggest that RIPK3 is a potential therapeutic candidate for UPM-induced pulmonary inflammation.

Clinical characteristics of pediatric patients infected with SARS-CoV-2 versus common human coronaviruses: a national multicenter study.

BACKGROUND: Human coronaviruses (HCoV) cause mild upper respiratory infections; however, in 2019, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged, causing an acute respiratory disease pandemic. Coronaviruses exhibit marked epidemiological and clinical differences. PURPOSE: This study compared the clinical, laboratory, and radiographic findings of children infected with SARS-CoV-2 versus HCoV. METHODS: SARS-CoV-2 data were obtained from the Korea Disease Control and Prevention Agency (KDCA) registry and 4 dedicated coronavirus disease 2019 (COVID-19) hospitals. Medical records of children admitted with a single HCoV infection from January 2015 to March 2020 were collected from 10 secondary/tertiary hospitals. Clinical data included age, sex, underlying disease, symptoms, test results, imaging findings, treatment, and length of hospital stay. RESULTS: We compared the clinical characteristics of children infected with HCoV (n=475) to those of children infected with SARS-CoV-2 (272 from KDCA, 218 from COVID-19 hospitals). HCoV patients were younger than KDCA patients (older than 9 years:3.6% vs. 75.7%; P<0.001) and patients at COVID-19 hospitals (2.0±2.9 vs 11.3±5.3; P<0.001). Patients with SARS-CoV-2 infection had a lower rate of fever (26.6% vs. 66.7%; P<0.001) and fewer respiratory symptoms than those with HCoV infection. Clinical severity, as determined by oxygen therapy and medication usage, was worse in children with HCoV infection. Children and adolescents with SARS-CoV-2 had less severe symptoms. CONCLUSION: Children and adolescents with COVID-19 had a milder clinical course and less severe disease than those with HCoV in terms of symptoms at admission, examination findings, and laboratory and radiology results.

Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis.

Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature's relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio-based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy.

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning.

This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time-frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection.

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory.

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

Epidemiology and clinical features of common community human coronavirus disease.

BACKGROUND: We would evaluate the epidemiology, clinical aspects, and prognostic factors of patients of all ages admitted with human corona virus (HCoV). METHODS: This study was retrospectively performed at five university teaching hospitals between 1st January 2018 and 31th March 2020. Routine molecular testing using for multiplex real-time reverse transcription-polymerase chain reaction (RT-PCR) methods was conducted on the respiratory viruses. We assessed the demographics, laboratory findings, and treatment of patients infected with coronavirus. RESULTS: There were 807 coronavirus-infected patients from 24,311 patients with respiratory virus PCR test admitted to five hospitals over 27 months. All-cause mortality rates of patients admitted for seasonal HCoV disease were 3.1% in all patients and 10.8% in patients aged ≥18 years. The Cox proportional hazard regression analysis was performed in patients aged ≥18 years. After adjusting for other clinical variables, general weakness symptoms [hazard ratio (HR), 2.651; 95% confidence interval (CI), 1.147-6.125, P=0.023], National Early Warning Score (NEWS) ≥2 (HR, 5.485; 95% CI, 1.261-23.858, P=0.023), and coronavirus subtype OC43 (HR, 2.500; 95% CI, 1.060-5.897, P=0.036) were significantly associated with death from coronavirus. CONCLUSIONS: Coronavirus infection can reveal a higher mortality rate in patients of ≥18 than those of <18 years, thus, adult patients require more careful treatment. Furthermore, in adult patients, the factors associated with death from coronavirus include general weakness symptoms, NEWS higher than 2, and OC43 subtype.

Shoulder Pain Treated by Manual Acupuncture and Pharmacopuncture Following Origin/Insertion Technique of Applied Kinesiology: A Case Series of Two Patients.

The aim of this study was to report on the improvement of shoulder pain resulting from disorders of the rotator cuff such as impingement syndrome and adhesive capsulitis, by manual acupuncture (MA) and pharmacopuncture (PA) following origin/insertion technique (OIT) of applied kinesiology (AK). Two patients were treated with MA and PA after OIT on shoulder muscles. The Numerical Rating Scale and the assessment of the Japanese Orthopedic Association scores were used to assess the pain, and ultrasound images were taken to compare treatment outcome. This study showed that MA and PA following OIT may be an effective treatment for impingement syndrome and adhesive capsulitis.

The effect of hidden female smoking on the relationship between smoking and cardiovascular disease.

BACKGROUND: Smoking is a known risk factor for cardiovascular morbidity and mortality, but several Korean studies have shown differing results on the association of current smoking status and the risk of cardiovascular disease (CVD). The aim of the present study was to investigate the association between smoking status and CVD (myocardial infarction and stroke) using national representative populationbased samples. The aim was also to investigate the effects of hidden smokers on the association between CVD and smoking. METHODS: Data were acquired from 28,620 participants (12,875 men and 15,745 women), age 19 years or older, who participated in the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2008 to 2016. RESULTS: The multivariable logistic regression analysis showed that ex-smoking status was correlated with CVD when self-reported (odds ratio [OR]: 1.62; 95% confidence interval [CI]: 1.20-2.19) and for survey-cotinine verified-smoking status (OR: 1.57; 95% CI: 1.20-2.19). Interestingly, the present study showed current smoking was not significantly associated with CVD. For the effect of sex on smoking and CVD, self-reported and survey-cotinine-verified ex-smoking status were correlated with CVD in males (OR: 1.45; 95% CI: 1.04-2.04 and OR: 1.43; 95% CI: 1.02-2.02) and in females (OR: 2.74; 95% CI: 1.59-4.71 and OR: 2.92; 95% CI: 1.64-5.18). The ratios of cotinine-verified to self-reported smoking rates were 1.95 for women and 1.08 for men. CONCLUSIONS: In the current study, while ex-smoking status was significantly associated with CVD, current smoking status was not. Female ex-smoking status had a higher adjusted odds ratio for CVD than males compared to non-smoking status. An effect of hidden female smoking was also found on the association between smoking status and CVD in Korean adults.

Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time-frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network.

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder-based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

Down-regulation of diesel particulate matter-induced airway inflammation by the PDZ motif peptide of ZO-1.

Although diesel airborne particulate matter (PM2.5) has been known to play a role in many human diseases, there is no direct evidence that therapeutic drugs or proteins can diminish PM2.5-induced diseases. Nevertheless, studies examining the negative control mechanisms of PM2.5-induced diseases are critical to develop novel therapeutic medications. In this study, the consensus PDZ peptide of ZO-1 inhibited PM2.5-induced inflammatory cell infiltration, pro-inflammatory cytokine gene expression, and TEER in bronchoalveolar lavage (BAL) fluid and AM cells. Our data indicated that the PDZ domain in ZO-1 is critical for regulation of the PM2.5-induced inflammatory microenvironment. Therefore, the PDZ peptide may be a potential therapeutic candidate during PM-induced respiratory diseases.

Association between cataract and cotinine-verified smoking status in 11 435 Korean adults using Korea National Health and Nutrition Examination Survey data from 2008 to 2016.

PURPOSE: To investigate the association between cataract and cotinine-verified smoking status. SETTING: Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, South Korea. DESIGN: Retrospective study. METHODS: Participants were randomly selected using data collected by the Korea National Health and Nutrition Examination Survey from 2008 to 2016. Participants completed a questionnaire to self-report smoking status and a history of cataract, among other variables. To identify the relationship between cataract and smoking, a new variable was used to define smoking status, survey-cotinine-verified smoking status (SCS)-the combination of self-reported smoking status and cotinine-verified smoking status- and thus identify hidden smokers. RESULTS: In total, 11 435 participants were eligible for final analysis. The study comprised 4925 men and 6510 women; the mean age was 52.86 ± 16.83 years (median: 54 years). Of 2292 SCS smokers, 382 (16.7%) were nonsmokers according to their self-report. Notably, the ratio of the cotinine-verified to self-reported smoking rate of women was greater than that of men, 1.60 and 1.06, respectively. This indicated that female hidden smokers may affect the results of studies based on self-reported questionnaires. Multivariate logistic regression analysis showed that smoking was correlated with cataract (odds ratio [OR], 1.37 [95% CI, 1.07-1.76]; OR, 1.35 [CI, 1.12-1.64]; and OR, 1.36 [CI, 1.10-1.69]) for self-reported, cotinine-verified, and SCS, respectively. No statistically significant sex difference was found. CONCLUSION: Smoking was associated with cataract, but it did not vary by sex. Female hidden smoking must be considered when investigating the association between smoking and cataract based on self-reported questionnaires.

Re-use of health masks after autoclaving.

With the rapid global spread of the new coronavirus and risk of pneumonia from COVID-19 infection, wearing a mask has become an essential defense for all frontline doctors, nurses, and healthcare professionals. Plus, the rise in demand for masks from the general public means the worldwide supply of masks is insufficient, which has led to an increase in the reuse of disposable masks. Therefore, this study compared the impact of autoclaving (steaming) and 70% ethyl alcohol treatment for decontaminating masks, as both methods can easily be used at home. The autoclaved masks showed a better filtration efficiency than the 70% ethyl alcohol-treated masks. A further investigation of 8 used KF 94 masks (filtration efficiency >94%) also showed that autoclaving for decontamination was limited to two times. Moreover, a kitchen towel mask, a popular homemade alternative, did not show sufficient filtration efficiency.

Mortality and Prognostic Prediction in Very Elderly Patients With Severe Pneumonia.

INTRODUCTION: Although prognostic prediction scores for pneumonia such as CURB-65 score or pneumonia severity index (PSI) are widely used, there were a few studies in very elderly patients. The aim of the study was to validate prognostic prediction scores for severe pneumonia and investigate risk factors associated with in-hospital mortality of severe pneumonia in very elderly patients. METHODS: During the 6-year study period (from October 2012 to May 2018), 160 patients aged 80 or older admitted to medical intensive unit were analyzed retrospectively. Pneumonia severity was evaluated using CURB-65 score, PSI, Sequential Organ Failure Assessment (SOFA) scores, A-DROP, I-ROAD, UBMo index, SOAR score, and lactate. The outcome was in-hospital mortality. RESULTS: The median age was 85 years (interquartile range: 82-88). Nursing home residents accounted for 71 (44.4%) and in-hospital mortality was 40 (25.0%). Logistic regression showed that chronic lung, mechanical ventilation, hemodialysis, and albumin were associated with in-hospital mortality of pneumonia. Using the receiver operating characteristics curve for predicting mortality, the area under the curve in pneumonia was 0.65 for the SOFA score, 0.61 for the CURB-65 score, 0.52 for the PSI, 0.58 for the A-DROP, 0.52 for the I-ROAD, 0.54 for UBMo index, 0.59 for SOAR score, and 0.65 for lactate. CONCLUSION: The performances of the CURB-65 and PSI are not excellent in very elderly patients with pneumonia. Further studies are needed to improve the performance of prognostic prediction scores in elderly patients.

Completion rate of latent tuberculosis infection treatment in patients aged 65 years and older.

INTRODUCTION: There are insufficient data on the treatment of latent tuberculosis infection (LTBI) in elderly patients. We investigated the completion rate of treatment in elderly LTBI patients. METHODS: A retrospective multicentre study was conducted at five university hospitals in South Korea. We reviewed the electronic medical records of patients aged 65 years and older who were diagnosed with LTBI via positive interferon-gamma release assay results between January 2016 and December 2018. Treatment completion was defined as ingestion of more than 80% of all prescribed medications without loss to follow-up. RESULTS: During the study period, 127 LTBI patients aged 65 years and older visited outpatient department. Among them, 77 patients aged 65-78 years (median age, 69 years [interquartile range, 66-71 years]) who received LTBI treatment were analysed. Common reasons for IGRA testing in elderly patients were health-care worker (n = 33, 42.9%) and household contact with infectious TB patients (n = 18, 23.4%). The overall completion rate of LTBI treatment was 83.1% (n = 64), and the completion rate of 3-month isoniazid plus rifampin regimen was 88.4%. Adverse effects were reported in 23 patients (29.9%), and an increase in aminotransferase level was the most common adverse effect (n = 11, 14.3%). Three patients (3.9%) with the adverse effect discontinued treatment and 10 (13.0%) patients were lost to follow-up. CONCLUSIONS: LTBI treatment in patients aged 65-78 years was relatively well tolerated. In LTBI treatment in elderly patients, the majority of discontinuation of treatment was due to loss to follow-up rather than adverse effects of anti-TB medications.

Activation of the Nrf2/HO-1 signaling pathway contributes to the protective effects of baicalein against oxidative stress-induced DNA damage and apoptosis in HEI193 Schwann cells.

Baicalein, a flavonoid extracted from the roots of Scutellaria baicalensis Georgi., has various pharmacological effects due to its high antioxidant activity. However, no study has yet been conducted on the protective efficacy of baicalein against oxidative stress in Schwann cells. In this study, we evaluated the protective effect of baicalein on DNA damage and apoptosis induced by hydrogen peroxide (H2O2) in HEI193 Schwann cells. For this purpose, HEI193 cells exposed to H2O2 in the presence or absence of baicalein were applied to cell viability assay, immunoblotting, Nrf2-specific small interfering RNA (siRNA) transfection, comet assay, and flow cytometry analyses. Our results showed that baicalein effectively inhibited H2O2-induced cytotoxicity and DNA damage associated with the inhibition of reactive oxygen species (ROS) accumulation. Baicalein also weakened H2O2-induced mitochondrial dysfunction, increased the Bax/Bcl-2 ratio, activated caspase-9 and -3, and degraded poly(ADP-ribose) polymerase. In addition, baicalein increased not only the expression but also the phosphorylation of nuclear factor-erythroid 2 related factor 2 (Nrf2) and promoted the expression of heme oxygenase-1 (HO-1), a critical target enzyme of Nrf2, although the expression of kelch-like ECH-associated protein-1 was decreased. However, the inhibition of Nrf2 expression by transfection with Nrf2-siRNA transfection abolished the expression of HO-1 and antioxidant potential of baicalein. These results demonstrate that baicalein attenuated H2O2-induced apoptosis through the conservation of mitochondrial function while eliminating ROS in HEI193 Schwann cells, and the antioxidant efficacy of baicalein implies at least a Nrf2/HO-1 signaling pathway-dependent mechanism. Therefore, it is suggested that baicalein may have a beneficial effect on the prevention and treatment of peripheral neuropathy induced by oxidative stress.

Isorhamnetin alleviates lipopolysaccharide-induced inflammatory responses in BV2 microglia by inactivating NF-κB, blocking the TLR4 pathway and reducing ROS generation.

Isorhamnetin, which is a flavonoid predominantly found in fruits and leaves of various plants, including Hippophae rhamnoides L. and Oenanthe javanica (Blume) DC, is known to possess various pharmacological effects. However, the anti­inflammatory potential of isorhamnetin remains poorly studied. Therefore, the present study aimed to investigate the inhibitory potential of isorhamnetin against inflammatory responses in lipopolysaccharide (LPS)­stimulated BV2 microglia. To measure the effects of isorhamnetin on inflammatory mediators and cytokines, and reactive oxygen species (ROS) generation, the following methods were used: cell viability assay, griess assay, ELISA, reverse transcriptase­polymerase chain reaction, flow cytometry, western blotting and immunofluorescence staining. The results revealed that isorhamnetin significantly suppressed LPS­induced secretion of pro­inflammatory mediators, including nitric oxide (NO) and prostaglandin E2, without exhibiting significant cytotoxicity. Consistent with these results, isorhamnetin inhibited LPS­stimulated expression of regulatory enzymes, including inducible NO synthase and cyclooxygenase­2 in BV2 cells. Isorhamnetin also downregulated LPS­induced production and expression of pro­inflammatory cytokines, such as tumor necrosis factor­α and interleukin­1ß. The mechanism underlying the anti­inflammatory effects of isorhamnetin was subsequently evaluated; this flavonoid inhibited the nuclear factor (NF)­κB signaling pathway by disrupting degradation and phosphorylation of inhibitor κB­α in the cytoplasm and blocking translocation of NF­κB p65 into the nucleus. In addition, isorhamnetin effectively suppressed LPS­induced expression of Toll­like receptor 4 (TLR4) and myeloid differentiation factor 88. It also suppressed the binding of LPS with TLR4 in BV2 cells. Furthermore, isorhamnetin markedly reduced LPS­induced generation of ROS in BV2 cells, thus indicating a strong antioxidative effect. Collectively, these results suggested that isorhamnetin may suppress LPS­mediated inflammatory action in BV2 microglia through inactivating the NF­κB signaling pathway, antagonizing TLR4 and eliminating ROS accumulation. Further studies are required to fully understand the anti­inflammatory effects associated with the antioxidant capacity of isorhamnetin; however, the findings of the present study suggested that isorhamnetin may have potential benefits in inhibiting the onset and treatment of neuroinflammatory diseases.

Cordycepin inhibits lipopolysaccharide-induced cell migration and invasion in human colorectal carcinoma HCT-116 cells through down-regulation of prostaglandin E2 receptor EP4.

Prostaglandin E2 (PGE2), a major product of cyclooxygenase-2 (COX-2), plays an important role in the carcinogenesis of many solid tumors, including colorectal cancer. Because PGE2 functions by signaling through PGE2 receptors (EPs), which regulate tumor cell growth, invasion, and migration, there has been a growing amount of interest in the therapeutic potential of targeting EPs. In the present study, we investigated the role of EP4 on the effectiveness of cordycepin in inhibiting the migration and invasion of HCT116 human colorectal carcinoma cells. Our data indicate that cordycepin suppressed lipopolysaccharide (LPS)-enhanced cell migration and invasion through the inactivation of matrix metalloproteinase (MMP)-9 as well as the down-regulation of COX-2 expression and PGE2 production. These events were shown to be associated with the inactivation of EP4 and activation of AMP-activated protein kinase (AMPK). Moreover, the EP4 antagonist AH23848 prevented LPS-induced MMP-9 expression and cell invasion in HCT116 cells. However, the AMPK inhibitor, compound C, as well as AMPK knockdown via siRNA, attenuated the cordycepin-induced inhibition of EP4 expression. Cordycepin treatment also reduced the activation of CREB. These findings indicate that cordycepin suppresses the migration and invasion of HCT116 cells through modulating EP4 expression and the AMPK-CREB signaling pathway. Therefore, cordycepin has the potential to serve as a potent anti-cancer agent in therapeutic strategies against colorectal cancer metastasis. [BMB Reports 2018; 51(10): 533-538].