Unsupervised Feature-Construction-Based Motor Fault Diagnosis.

Any bearing faults are a leading cause of motor damage and bring economic losses. Fast and accurate identification of bearing faults is valuable for preventing damaging the whole equipment and continuously running industrial processes without interruption. Vibration signals from a running motor can be utilized to diagnose a bearing health condition. This study proposes a detection method for bearing faults based on two types of neural networks from motor vibration data. The proposed method uses an autoencoder neural network for constructing a new motor vibration feature and a feed-forward neural network for the final detection. The constructed signal feature enhances the prediction performance by focusing more on a fault type that is difficult to detect. We conducted experiments on the CWRU bearing datasets. The experimental study shows that the proposed method improves the performance of the feed-forward neural network and outperforms the other machine learning algorithms.

A Mood Semantic Awareness Model for Emotional Interactive Robots.

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human-machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users' degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user's interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model's performance in terms of dialogue quality and user experience.

Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction.

Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al., which could obviously improve recognition speed based on lightweight skeleton node calculation, and improve recognition accuracy based on occluded skeleton node prediction analysis in order to effectively solve the behavior occlusion problem. The experiments show that our proposed All-MSFCF model has a video action recognition average accuracy rate of 92.7% for eight kinds of abnormal behavior recognition. Although our proposed lightweight L-MSFCF model has an 87.3% average accuracy rate, its average recognition speed is 62.7% higher than the full-skeleton recognition model, which is more suitable for solving real-time tracing problems. Moreover, our proposed Trajectory Prediction Tracking (TPT) model could real-time predict the moving positions based on the dynamically selected core skeleton node calculation, especially for the short-term prediction within 15 frames and 30 frames that have lower average loss errors.

EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.

A variety of technologies that could enhance driving safety are being actively explored, with the aim of reducing traffic accidents by accurately recognizing the driver's state. In this field, three mainstream detection methods have been widely applied, namely visual monitoring, physiological indicator monitoring and vehicle behavior analysis. In order to achieve more accurate driver state recognition, we adopted a multi-sensor fusion approach. We monitored driver physiological signals, electroencephalogram (EEG) signals and electrocardiogram (ECG) signals to determine fatigue state, while an in-vehicle camera observed driver behavior and provided more information for driver state assessment. In addition, an outside camera was used to monitor vehicle position to determine whether there were any driving deviations due to distraction or fatigue. After a series of experimental validations, our research results showed that our multi-sensor approach exhibited good performance for driver state recognition. This study could provide a solid foundation and development direction for future in-depth driver state recognition research, which is expected to further improve road safety.

Methylation Signature for Prediction of Progression Free Survival in Surgically Treated Clear Cell Renal Cell Carcinoma.

BACKGROUND: Little is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC). METHODS: Methylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccRCC and normal tissue from 12 patients. Using quantitative PSQ analysis, tumor-specific hypermethylated genes were validated in 25 independent cohorts and their clinical relevance was also verified in 152 independent cohorts. RESULTS: Using genome-wide methylation array, Zinc finger protein 278 (ZNF278), Family with sequence similarity 155 member A (FAM155A) and Dipeptidyl peptidase 6 (DPP6) were selected for tumor-specific hypermethylated genes in primary ccRCC. The promoter methylation of these genes occurred more frequently in ccRCC than normal kidney in independent validation cohort. The hypermethylation of three genes were associated with advanced tumor stage and high grade tumor in ccRCC. During median follow-up of 39.2 (interquartile range, 15.4-79.1) months, 22 (14.5%) patients experienced distant metastasis. Multivariate analysis identified the methylation status of these three genes, either alone, or in a combined risk score as an independent predictor of distant metastasis. CONCLUSION: The promoter methylation of ZNF278, FAM155A and DPP6 genes are associated with aggressive tumor phenotype and early development of distant metastasis in patients with surgically treated ccRCC. These potential methylation markers, either alone, or in combination, could provide novel targets for development of individualized therapeutic and prevention regimens.

Risk Scoring System for Prognosis Estimation of Multivessel Disease Among Patients with ST-Segment Elevation Myocardial Infarction.

Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS to estimate the prognosis of patients with MVD in STEMI.We used the Korean Acute Myocardial Infarction Registry (KAMIR) to identify 2,030 STEMI patients with MVD who underwent appropriate percutaneous coronary intervention (PCI). Their data were analyzed to develop a new RSS. The prognostic power of this RSS was validated with 2,556 STEMI patients with MVD in the Korean Working Group on Myocardial Infarction Registry (KORMI).Six prognostic factors related to all-cause death in STEMI patients with MVD were age, serum creatinine, Killip Class, lower body weight, decrease in left ventricular ejection fraction, and history of cerebrovascular disease. The RSS for all-cause death was constructed using these risk factors and their statistical weight. The RSS had appropriate performance (c-index: 0.72) in the KORMI validation cohort.We developed a novel RSS that estimates all-cause death in the year following discharge for patients with MVD in STEMI appropriately treated by PCI. This novel RSS was transformed into a simple linear risk score to yield a simplified estimate prognosis of MVD among STEMI patients.

CAME: identification of chromatin accessibility from nucleosome occupancy and methylome sequencing.

Motivation: Chromatin accessibility plays a key role in epigenetic regulation of gene activation and silencing. Open chromatin regions allow regulatory elements such as transcription factors and polymerases to bind for gene expression while closed chromatin regions prevent the activity of transcriptional machinery. Recently, Methyltransferase Accessibility Protocol for individual templates-Bisulfite Genome Sequencing (MAPit-BGS) and nucleosome occupancy and methylome sequencing (NOMe-seq) have been developed for simultaneously profiling chromatin accessibility and DNA methylation on single molecules. Therefore, there is a great demand in developing computational methods to identify chromatin accessibility from MAPit-BGS and NOMe-seq. Results: In this article, we present CAME (Chromatin Accessibility and Methylation), a seed-extension based approach that identifies chromatin accessibility from NOMe-seq. The efficiency and effectiveness of CAME were demonstrated through comparisons with other existing techniques on both simulated and real data, and the results show that our method not only can precisely identify chromatin accessibility but also outperforms other methods. Availability and Implementation: CAME is implemented in java and the program is freely available online at http://sourceforge.net/projects/came/. Contacts: jechoi@gru.edu or khryu@dblab.chungbuk.ac.kr. Supplementary information: Supplementary data are available at Bioinformatics online.

Feasibility and accessibility of electronic patient-reported outcome measures using a smartphone during routine chemotherapy: a pilot study.

PURPOSE: There is growing interest in integrating electronic patient-reported outcome (PRO) measures into routine oncology practice for symptom monitoring. Here, we evaluated the feasibility and accessibility of electronic PRO measures using a smartphone (PRO-SMART) for cancer patients receiving routine chemotherapy. METHODS: The proposed PRO-SMART application obtains daily personal health record (PHR) data from cancer patients via a smartphone. An analysis report of cumulative PHR data is provided to the clinician in a format suitable for upload to electronic medical records (EMRs). Cancer outpatients who had received at least two cycles of chemotherapy and who were scheduled for two more cycles were enrolled. RESULTS: Between February 2015 and December 2016, 111 patients were screened and 101 of these were included. One-hundred patients used PRO-SMART at least once and were included in the final analysis (90.1% overall accessibility among all screened patients). The number of symptomatic adverse events (AEs) related to chemotherapy recorded in EMRs (mean ± standard deviation [SD]) increased from 0.92 ± 0.80 to 2.26 ± 1.80 (P < 0.001), and grading of AEs increased from 0.81 ± 0.69 to 1.00 ± 0.62 (P = 0.029). After using PRO-SMART, the numeric rating scale for pain (mean ± SD) increased from 0.20 ± 0.72 to 0.99 ± 1.55 (P < 0.001). A patient-reported questionnaire revealed that 64.2% of patients found it useful and 83% found it easy to use. CONCLUSIONS: This study suggests that the proposed PRO-SMART is feasible and accessible for assessment of symptomatic AEs in cancer patients receiving chemotherapy for a prospective randomized trial.

Positive Effect of Baby-Friendly Hospital Initiatives on Improving Mothers' Intention for Successful Breastfeeding in Korea.

BACKGROUND: In Korea, the breastfeeding (BF) rate of infants aged 6 months or more is drastically decreasing, and this phenomenon is particularly worrisome for the future health of the population. The present study aimed to identify an antenatal strategy for initiation and continuation of human BF, and to identify how Baby-Friendly Hospitals (BFHs) may positively influence the intention to breastfeed. METHODS: A total of 414 pregnant Korean antenatal women were surveyed using questionnaires to determine current knowledge of the benefits of human breast milk, whether they planned to breastfeed after delivery, to continue BF after reinstatement in the workforce, are willing to abide by rooming-in care for infants, and plan to give birth at BFHs. RESULTS: We found that planning room-in care, greater awareness of BF benefits for infant and mother, participation in antenatal education programs, and provision of BF facilities in the workplace were positively associated with plans for exclusive breastfeeding (EBF) and longer BF duration. The mothers who planned to give birth at BFHs also desired to breastfeed immediately after birth, implement in-room care, continue BF at their workplace, participate in antenatal BF educational programs, and were more aware of the benefits of BF. CONCLUSION: If the beneficial effects of BFHs were well known to individuals, these would enhance the success rate of BF in Korea. Antenatal education and consequent acquisition of better knowledge of the benefits of BF are important for increasing the rate of BF practices.

A Single Center Analysis of the Positivity of Hepatitis B Antibody after Neonatal Vaccination Program in Korea.

The antibody to hepatitis B surface antigen (anti-HBs) seropositivity rate after 3 doses of hepatitis B virus (HBV) vaccination during infancy period is known to be higher than 90%. However, a considerable number of vaccines do not form protective anti-HBs or chronologic decrease of anti-HBs. We retrospectively collected data of HBV serologic test results in 20,738 individuals from 2000 to 2015. After exclusion criteria were applied, 19,072 individuals were included. We analyzed the anti-HBs seropositivity rate, anti-HBs disappearance rate, anti-HBs positive seroconversion rate after receiving a booster vaccine, and the difference in anti-HBs positivity between the 2 groups; group A (born before 2005, while both recombinant vaccines and plasma-derived vaccines were used) and group B (born after 2005, when only recombinant vaccines were used by national regulation). The anti-HBs seropositivity rate was 55.8%, but there was a significant difference in the rate of seropositivity for anti-HBs between the group A and B (53.0% vs. 78.1%, P < 0.001). There was no significant age-adjusted difference in the mean seropositivity rate between the 2 groups (P = 0.058). In addition, the anti-HBs positivity rate was significantly lower in the group A as compared with the group B during infancy (83.1% vs. 92.1%, P < 0.001). A total of 1,106 anti-HBs-positive subjects underwent serologic tests more than twice. Of these, 217 subjects (19.6%) showed anti-HBs disappearance. After booster vaccinations, 87.4% (83/95) achieved seroconversion from seronegative to seropositive. Our results highlight the importance of lifelong protection against HBV and the possible necessity of booster vaccination after adolescent period.

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.

BACKGROUND: Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments. RESULTS: In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results. CONCLUSION: Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

Pharmacokinetic properties of trifolirhizin, (-)-maackiain, (-)-sophoranone and 2-(2,4-dihydroxyphenyl)-5,6-methylenedioxybenzofuran after intravenous and oral administration of Sophora tonkinensis extract in rats.

1. SKI3301, a standardized dried 50% ethanolic extracts of Sophora tonkinensis, contains four marker compounds (trifolirhizin, TF; (-)-maackiain, Maack; (-)-sophoranone, SPN, and (2-(2,4-dihydroxyphenyl)-5,6-methylenedioxybenzofuran, ABF), is being developed as an herbal medicine for the treatment of asthma in Korea. This study investigates the pharmacokinetic properties of SKI3301 extract in rats. 2. The dose-proportional AUCs suggest linear pharmacokinetics of TF, Maack, SPN and ABF in the SKI3301 extract intravenous dose range of 5-20 mg/kg. After the oral administration of 200-1000 mg/kg of the extract, TF and Maack exhibited non-linearity due to the saturation of gastrointestinal absorption. However, linear pharmacokinetics of SPN and ABF were observed. 3. The absorptions of TF, Maack, SPN and ABF in the extract were increased relative to those of the respective pure forms due to the increased solubility and/or the decreased metabolism by other components in the SKI3301 extract. 4. No accumulation was observed after multiple dosing, and the steady-state pharmacokinetics of TF, Maack, SPN and ABF were not significantly different from those after a single oral administration of the extract. 5. The pharmacokinetics of TF, SPN and ABF were not significantly different between male and female rats after oral administration of the extract, but a significant gender difference in the pharmacokinetics of Maack in rats was observed. 6. Our findings may help to comprehensively elucidate the pharmacokinetic characteristics of TF, Maack, SPN and ABF and provide useful information for the clinical application of SKI3301 extract.

Flavonoids and arylbenzofurans from the rhizomes and roots of Sophora tonkinensis with IL-6 production inhibitory activity.

Three new compounds (1-3) and 20 known compounds were isolated from the rhizomes and roots of Sophora tonkinensis, and all the isolates were tested for their inhibitory activity against IL-6 production in HMC-1 cells stimulated by PMA plus ionophore, A23187. Of the tested compounds, compounds 1, 5, 9, and 21 were found to potently inhibit IL-6 production with IC50 values of 1.62, 0.73, 3.01, and 4.02 µM, respectively.

Simultaneous determination of trifolirhizin, (-)-maackiain, (-)-sophoranone, and 2-(2,4-dihydroxyphenyl)-5,6-methylenedioxybenzofuran from Sophora tonkinensis in rat plasma by liquid chromatography with tandem mass spectrometry and its application to a pharmacokinetic study.

A new liquid chromatography with tandem mass spectrometry method was developed and validated for the simultaneous determination of trifolirhizin, (-)-maackiain, (-)-sophoranone, and 2-(2,4-dihydroxyphenyl)-5,6-methylenedioxybenzofuran from Sophora tonkinensis in rat plasma using chlorpropamide as an internal standard. Plasma samples (50 µL) were prepared using a simple deproteinization procedure with 150 µL of acetonitrile containing 100 ng/mL of chlorpropamide. Chromatographic separation was carried out on an Acclaim RSLC120 C18 column (2.1 × 100 mm, 2.2 µm) using a gradient elution consisting of 7.5 mM ammonium acetate and acetonitrile containing 0.1% formic acid (0.4 mL/min flow rate, 7.0 min total run time). The detection and quantitation of all analytes were performed in selected reaction monitoring mode under both positive and negative electrospray ionization. This assay was linear over concentration ranges of 50-5000 ng/mL (trifolirhizin), 25-2500 ng/mL ((-)-maackiain), 5-250 ng/mL ((-)-sophoranone), and 1-250 ng/mL 2-(2,4-dihydroxyphenyl)-5,6-methylenedioxybenzofuran) with a lower limit of quantification of 50, 25, 5, and 1 ng/mL for trifolirhizin, (-)-maackiain, (-)-sophoranone, and 2-(2,4-dihydroxyphenyl)-5,6-methylenedioxybenzofuran, respectively. All the validation data, including the specificity, precision, accuracy, recovery, and stability conformed to the acceptance requirements. The results indicated that the developed method is sufficiently reliable for the pharmacokinetic study of the analytes following oral administration of Sophora tonkinensis extract in rats.

HOXA9, ISL1 and ALDH1A3 methylation patterns as prognostic markers for nonmuscle invasive bladder cancer: array-based DNA methylation and expression profiling.

DNA methylation patterns are associated with the development and prognosis of cancer. The aim of this study was to identify novel methylation markers for the prediction of patient outcomes using microarray analysis of DNA methylation and RNA expression patterns in samples from long-term follow-up patients with nonmuscle invasive bladder cancer (NMIBC). A total of 187 human bladder specimens were used for microarray array or pyrosequencing (PSQ) analyses: 6 normal controls (NC) and 181 NMIBC. Tumor-specific hypermethylated genes were selected from a data set comprising 24 matched microarray-based DNA methylation and gene expression profiles (6 controls and 18 NMIBC), and their clinical relevance was verified by quantitative PSQ analysis. The methylation status of Homeobox A9 (HOXA9), ISL LIM homeobox 1 (ISL1) and Aldehyde dehydrogenase 1 family, member A3 (ALDH1A3) was significantly associated with decreased gene expression levels and aggressive clinicopathological characteristics. Multivariate regression analyses showed that hypermethylation of these genes was an independent predictor of disease recurrence (HOXA9, ISL1 and ALDH1A3, either alone or in combination) and progression (ISL1 and ALDH1A3, either alone or in combination) (each p < 0.05). The results of this study suggest that these novel methylation markers are independent prognostic indicators in NMIBC patients, which may facilitate the assessment of disease recurrence and progression in NMIBC patients and inform clinical decision making regarding treatment.

An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.

MOTIVATION: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.

Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction.

The knowledge discovery has been widely applied to mine significant knowledge from medical data. Nevertheless, previous studies have produced large numbers of imprecise patterns. To reduce the number of imprecise patterns, we need an approach that can discover interesting patterns that connote causality between antecedent and consequence in a pattern. In this paper, we propose association rule mining method that can discover interesting patterns that include medical knowledge in Korean acute myocardial infarction registry that consists of 1,247 young adults collected by 51 participating hospitals since 2005. Proposed method can remove imprecise patterns and discover target patterns that include associations between blood factors and disease history. The association that blood factors affect to disease history is defined as target pattern. In our experiments, the interestingness of a target pattern is evaluated in terms of statistical measures such as lift, leverage, and conviction. We discover medical knowledge that glucose, smoking, triglyceride total cholesterol, and creatinine are associated with diabetes and hypertension in Korean young adults with acute myocardial infarction.

CKD-516 potentiates the anti-cancer activity of docetaxel against epidermal growth factor receptor tyrosine kinase inhibitor-resistant lung cancer.

Lung cancer is the leading cause of cancer death. Although docetaxel has been used as a second- or third-line treatment for non-small cell lung cancer (NSCLC), the objective response rate is less than 10%. Hence, there is a need to improve the clinical efficacy of docetaxel monotherapy; combination therapy should be considered. Here, we show that CKD-516, a vascular disruption agent, can be combined with docetaxel to treat epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI)-resistant NSCLC. CKD-516 was orally bioavailable; neither CKD-516 nor docetaxel affected the mean plasma concentration-time profile or pharmacokinetic parameters of the other drug. CKD-516 and docetaxel synergistically inhibited the growth of H1975 (with an L858R/T790M double mutation of EGFR) and A549 (with a KRAS mutation) lung cancer cell lines. In addition, docetaxel plus CKD-516 delayed tumor growth in-and extended the lifespan of-tumor-bearing mice. Thus, combination CKD-516 and docetaxel therapy could be used to treat EGFR-TKI-resistant NSCLC.

A novel HDAC6 inhibitor, CKD-504, is effective in treating preclinical models of huntington's disease.

Huntington's disease (HD) is a neurodegenerative disorder, of which pathogenesis is caused by a polyglutamine expansion in the amino-terminus of huntingtin gene that resulted in the aggregation of mutant HTT proteins. HD is characterized by progressive motor dysfunction, cognitive impairment and neuropsychiatric disturbances. Histone deacetylase 6 (HDAC6), a microtubule-associated deacetylase, has been shown to induce transport- and release-defect phenotypes in HD models, whilst treatment with HDAC6 inhibitors ameliorates the phenotypic effects of HD by increasing the levels of α-tubulin acetylation, as well as decreasing the accumulation of mutant huntingtin (mHTT) aggregates, suggesting HDAC6 inhibitor as a HD therapeutics. In this study, we employed in vitro neural stem cell (NSC) model and in vivo YAC128 transgenic (TG) mouse model of HD to test the effect of a novel HDAC6 selective inhibitor, CKD-504, developed by Chong Kun Dang (CKD Pharmaceutical Corp., Korea). We found that treatment of CKD-504 increased tubulin acetylation, microtubule stabilization, axonal transport, and the decrease of mutant huntingtin protein in vitro. From in vivo study, we observed CKD-504 improved the pathology of Huntington's disease: alleviated behavioral deficits, increased axonal transport and number of neurons, restored synaptic function in corticostriatal (CS) circuit, reduced mHTT accumulation, inflammation and tau hyperphosphorylation in YAC128 TG mouse model. These novel results highlight CKD-504 as a potential therapeutic strategy in HD. [BMB Reports 2023; 56(3): 178-183].

Novel Histone Deacetylase 6 Inhibitor Confers Anti-inflammatory Effects and Enhances Gut Barrier Function.

Background/Aims: The purpose of the current study was to examine the anti-inflammatory effects of CKD-506, a novel histone deacetylase 6 inhibitor, on human peripheral blood mononuclear cells (PBMCs) and CD4+ T cells and to explore the relationship between CKD-506 and gut epithelial barrier function. Methods: Lipopolysaccharide-stimulated human PBMCs from inflammatory bowel disease (IBD) patients were treated with CKD-506, and tumor necrosis factor (TNF)-α expression was measured using an enzyme-linked immunosorbent assay. The proliferation of CD4+ T cells from IBD patients was evaluated using flow cytometric analysis. The effects of CKD-506 on gut barrier function in a cell line and colon organoids, based on examinations of mRNA production, goblet cell differentiation, and E-cadherin recovery, were investigated using quantitative reverse transcription polymerase chain reaction, immunofluorescence, and a fluorescein isothiocyanate-dextran permeability assay. Results: Secretion of TNF-α, a pivotal pro-inflammatory mediator in IBD, by lipopolysaccharide-triggered PBMCs was markedly decreased by CKD-506 treatment in a dose-dependent manner and to a greater extent than by tofacitinib or tubastatin A treatment. E-cadherin mRNA expression and goblet cell differentiation increased significantly and dose-dependently in HT-29 cells in response to CKD-506, and inhibition of E-cadherin loss after TNF-α stimulation was significantly reduced both in HT-29 cells and gut organoids. Caco-2 cells treated with CKD-506 showed a significant reduction in barrier permeability in a dose-dependent manner. Conclusions: The present study demonstrated that CKD-506 has anti-inflammatory effects on PBMCs and CD4 T cells and improves gut barrier function, suggesting its potential as a small-molecule therapeutic option for IBD.