Selective inhibition of HDAC6 promotes bladder cancer radiosensitization and mitigates the radiation-induced CXCL1 signalling.

BACKGROUND: Although trimodality therapy resecting tumours followed by chemoradiotherapy is emerged for muscle-invasive bladder cancer (MIBC), chemotherapy produces toxicities. Histone deacetylase inhibitors have been identified as an effective strategy to enhance cancer radiotherapy (RT). METHODS: We examined the role of HDAC6 and specific inhibition of HDAC6 on BC radiosensitivity by performing transcriptomic analysis and mechanism study. RESULTS: HDAC6 knockdown or HDAC6 inhibitor (HDAC6i) tubacin exerted a radiosensitizing effect, including decreased clonogenic survival, increased H3K9ac and α-tubulin acetylation, and accumulated γH2AX, which are similar to the effect of panobinostat, a pan-HDACi, on irradiated BC cells. Transcriptomics of shHDAC6-transduced T24 under irradiation showed that shHDAC6 counteracted RT-induced mRNA expression of CXCL1, SERPINE1, SDC1 and SDC2, which are linked to cell migration, angiogenesis and metastasis. Moreover, tubacin significantly suppressed RT-induced CXCL1 and radiation-enhanced invasion/migration, whereas panobinostat elevated RT-induced CXCL1 expression and invasion/migration abilities. This phenotype was significantly abrogated by anti-CXCL1 antibody, indicating the key regulator of CXCL1 contributing to BC malignancy. Immunohistochemical evaluation of tumours from urothelial carcinoma patients supported the correlation between high CXCL1 expression and reduced survival. CONCLUSION: Unlike pan-HDACi, the selective HDAC6i can enhance BC radiosensitization and effectively inhibit RT-induced oncogenic CXCL1-Snail-signalling, thus further advancing its therapeutic potential with RT.

An Intelligent Virtual-Reality System With Multi-Model Sensing for Cue-Elicited Craving in Patients With Methamphetamine Use Disorder.

Methamphetamine abuse is getting worse amongst the younger population. While there is methadone or buprenorphine harm-reduction treatment for heroin addicts, there is no drug treatment for addicts with methamphetamine use disorder (MUD). Recently, non-medication treatment, such as the cue-elicited craving method integrated with biofeedback, has been widely used. Further, virtual reality (VR) is proposed to simulate an immersive virtual environment for cue-elicited craving in therapy. In this study, we developed a VR system equipped with flavor simulation for the purpose of inducing cravings for MUD patients in therapy. The VR system was integrated with multi-model sensors, such as an electrocardiogram (ECG), galvanic skin response (GSR) and eye tracking to measure various physiological responses from MUD patients in the virtual environment. The goal of the study was to validate the effectiveness of the proposed VR system in inducing the craving of MUD patients via the physiological data. Clinical trials were performed with 20 MUD patients and 11 healthy subjects. VR stimulation was applied to each subject and the physiological data was measured at the time of pre-VR stimulation and post-VR stimulation. A variety of features were extracted from the raw data of heart rate variability (HRV), GSR and eye tracking. The results of statistical analysis found that quite a few features of HRV, GSR and eye tracking had significant differences between pre-VR stimulation and post-VR stimulation in MUD patients but not in healthy subjects. Also, the data of post-VR stimulation showed a significant difference between MUD patients and healthy subjects. Correlation analysis was made and several features between HRV and GSR were found to be correlated. Further, several machine learning methods were applied and showed that the classification accuracy between MUD and healthy subjects at post-VR stimulation attained to 89.8%. In conclusion, the proposed VR system was validated to effectively induce the drug craving in MUD patients.

Combination of Expert Knowledge and a Genetic Fuzzy Inference System for Automatic Sleep Staging.

OBJECTIVE: In this paper, the genetic fuzzy inference system based on expert knowledge for automatic sleep staging was developed. METHODS: Eight features, including temporal and spectrum analyses of the EEG and EMG signals, were utilized as the input variables. The fuzzy rules and the fuzzy sets were constructed based on expert knowledge and the distributions of feature values at different sleep stages. Three experiments were designed to develop and evaluate the proposed system. PSGs of 32 healthy subjects and 16 subjects with insomnia were included in the experiment to develop and evaluate the proposed method. Finally, a complete sleep scoring system integrating two fuzzy inference models with robust performance on various subject groups is developed. RESULTS: The overall agreement and kappa coefficient of this integrated system applied to PSG data from 8 subjects with good sleep efficiency, 8 subjects with poor sleep efficiency and 8 subjects with insomnia were 86.44 % and 0.81, respectively. CONCLUSION: Due to the high performance of the proposed system, it is expected to integrate the proposed method with various PSG systems for sleep monitoring in clinical or homecare applications in the future. SIGNIFICANCE: An automatic sleep staging system integrating knowledge of the experts in scoring of PSG data and the elasticity of fuzzy systems in reasoning and decision making is proposed and the robustness and clinical applicability of the proposed method is demonstrated on data from healthy subjects and subjects with insomnia.

Multiscale Entropy of Electroencephalogram as a Potential Predictor for the Prognosis of Neonatal Seizures.

OBJECTIVE: Increasing animal studies supported the harmful effects of prolonged or frequent neonatal seizures in developing brain, including increased risk of later epilepsy. Various nonlinear analytic measures had been applied to investigate the change of brain complexity with age. This study focuses on clarifying the relationship between later epilepsy and the changes of electroencephalogram (EEG) complexity in neonatal seizures. METHODS: EEG signals from 19 channels of the whole brain from 32 neonates below 2 months old were acquired. The neonates were classified into 3 groups: 9 were normal controls, 9 were neonatal seizures without later epilepsy, and 14 were neonatal seizures with later epilepsy. Sample entropy (SamEn), multiscale entropy (MSE) and complexity index (CI) were analyzed. RESULTS: Although there was no significant change in SamEn, the CI values showed significantly decreased over Channels C3, C4, and Cz in patients with neonatal seizures and later epilepsy compared with control group. More multifocal epileptiform discharges in EEG, more abnormal neuroimaging findings, and higher incidence of future developmental delay were noted in the group with later epilepsy. CONCLUSIONS: Decreased MSE and CI values in patients with neonatal seizures and later epilepsy may reflect the mixed effects of acute insults, underlying brain immaturity, and prolonged seizures-related injuries. The analysis of MSE and CI can therefore provide a quantifiable and accurate way to decrypt the mystery of neonatal seizures, and could be a promising predictor.

Single-virus tracking approach to reveal the interaction of Dengue virus with autophagy during the early stage of infection.

Dengue virus (DENV) is one of the major infectious pathogens worldwide. DENV infection is a highly dynamic process. Currently, no antiviral drug is available for treating DENV-induced diseases since little is known regarding how the virus interacts with host cells during infection. Advanced molecular imaging technologies are powerful tools to understand the dynamics of intracellular interactions and molecular trafficking. This study exploited a single-virus particle tracking technology to address whether DENV interacts with autophagy machinery during the early stage of infection. Using confocal microscopy and three-dimensional image analysis, we showed that DENV triggered the formation of green fluorescence protein-fused microtubule-associated protein 1A/1B-light chain 3 (GFP-LC3) puncta, and DENV-induced autophagosomes engulfed DENV particles within 15-min postinfection. Moreover, single-virus particle tracking revealed that both DENV particles and autophagosomes traveled together during the viral infection. Finally, in the presence of autophagy suppressor 3-methyladenine, the replication of DENV was inhibited and the location of DENV particles spread in cytoplasma. In contrast, the numbers of newly synthesized DENV were elevated and the co-localization of DENV particles and autophagosomes was detected while the cells were treated with autophagy inducer rapamycin. Taken together, we propose that DENV particles interact with autophagosomes at the early stage of viral infection, which promotes the replication of DENV.