Exploring the possible relationship between skin microbiome and brain cognitive functions: a pilot EEG study.

Human microbiota mainly resides on the skin and in the gut. Human gut microbiota can produce a variety of short chain fatty acids (SCFAs) that affect many physiological functions and most importantly modulate brain functions through the bidirectional gut-brain axis. Similarly, skin microorganisms also have identical metabolites of SCFAs reported to be involved in maintaining skin homeostasis. However, it remains unclear whether these SCFAs produced by skin bacteria can affect brain cognitive functions. In this study, we hypothesize that the brain's functional activities are associated with the skin bacterial population and examine the influence of local skin-bacterial growth on event-related potentials (ERPs) during an oddball task using EEG. Additionally, five machine learning (ML) methods were employed to discern the relationship between skin microbiota and cognitive functions. Twenty healthy subjects underwent three rounds of tests under different conditions-alcohol, glycerol, and water. Statistical tests confirmed a significant increase in bacterial population under water and glycerol conditions when compared to the alcohol condition. The metabolites of bacteria can turn phenol red from red-orange to yellow, confirming an increase in acidity. P3 amplitudes were significantly enhanced in response to only oddball stimulus at four channels (Fz, FCz, and Cz) and were observed after the removal of bacteria when compared with that under the water and glycerol manipulations. By using machine learning methods, we demonstrated that EEG features could be separated with a good accuracy (> 88%) after experimental manipulations. Our results suggest a relationship between skin microbiota and brain functions. We hope our findings motivate further study into the underlying mechanism. Ultimately, an understanding of the relationship between skin microbiota and brain functions can contribute to the treatment and intervention of diseases that link with this pathway.

Impaired Brain-Heart Relation in Patients With Methamphetamine Use Disorder During VR Induction of Drug Cue Reactivity.

Methamphetamine use disorder (MUD) is an illness associated with severe health consequences. Virtual reality (VR) is used to induce the drug-cue reactivity and significant EEG and ECG abnormalities were found in MUD patients. However, whether a link exists between EEG and ECG abnormalities in patients with MUD during exposure to drug cues remains unknown. This is important from the therapeutic viewpoint because different treatment strategies may be applied when EEG abnormalities and ECG irregularities are complications of MUD. We designed a VR system with drug cues and EEG and ECG were recorded during VR exposure. Sixteen patients with MUD and sixteen healthy subjects were recruited. Statistical tests and Pearson correlation were employed to analyze the EEG and ECG. The results showed that, during VR induction, the patients with MUD but not healthy controls showed significant [Formula: see text] and [Formula: see text] power increases when the stimulus materials were most intense. This finding indicated that the stimuli are indiscriminate to healthy controls but meaningful to patients with MUD. Five heart rate variability (HRV) indexes significantly differed between patients and controls, suggesting abnormalities in the reaction of patient's autonomic nervous system. Importantly, significant relations between EEG and HRV indexes changes were only identified in the controls, but not in MUD patients, signifying a disruption of brain-heart relations in patients. Our findings of stimulus-specific EEG changes and the impaired brain-heart relations in patients with MUD shed light on the understanding of drug-cue reactivity and may be used to design diagnostic and/or therapeutic strategies for MUD.

Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder.

Early diagnosis and treatment can reduce the symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) in children, but medical diagnosis is usually delayed. Hence, it is important to increase the efficiency of early diagnosis. Previous studies used behavioral and neuronal data during GO/NOGO task to help detect ADHD and the accuracy differed considerably from 53% to 92%, depending on the employed methods and the number of electroencephalogram (EEG) channels. It remains unclear whether data from a few EEG channels can still lead to a good accuracy of detecting ADHD. Here, we hypothesize that introducing distractions into a VR-based GO/NOGO task can augment the detection of ADHD using 6-channel EEG because children with ADHD are easily distracted. Forty-nine ADHD children and 32 typically developing children were recruited. We use a clinically applicable system with EEG to record data. Statistical analysis and machine learning methods were employed to analyze the data. The behavioral results revealed significant differences in task performance when there are distractions. The presence of distractions leads to EEG changes in both groups, indicating immaturity in inhibitory control. Importantly, the distractions additionally enhanced the between-group differences in NOGO α and γ power, reflecting insufficient inhibition in different neural networks for distraction suppression in the ADHD group. Machine learning methods further confirmed that distractions enhance the detection of ADHD with an accuracy of 85.45%. In conclusion, this system can assist in fast screenings for ADHD and the findings of neuronal correlates of distractions can help design therapeutic strategies.

Bacillus amyloliquefaciens Increases the GABA in Rice Seed for Upregulation of Type I Collagen in the Skin.

Biosynthesis of gamma-aminobutyric acid (GABA) can be achieved by naturally occurring microorganisms with the advantages of cost-effectiveness and safety. In this study, Bacillus amyloliquefaciens EH-9 strain (B. amyloliquefaciens EH-9), a soil bacterium, was used to promote the accumulation of GABA in germinated rice seed. Further, the topical application of supernatant from rice seed co-cultivated with soil B. amyloliquefaciens EH-9 can significantly increase the production of type I collagen (COL1) in the dorsal skin of mice. The knocking down of the GABA-A receptor (GABAA) significantly reduced the production of COL1 in the NIH/3T3 cells and in the dorsal skin of mice. This result suggests that topical application of GABA can promote the biosynthesis of COL1 via its interaction with the GABAA receptor in the dorsal skin of mice. In summary, our findings illustrate for the first time that soil B. amyloliquefaciens EH-9 elicits GABA production in germinated rice seed to upregulate the formation of COL1 in the dorsal skin of mice. This study is translational because the result shows a potential remedy for skin aging by stimulating COL1 synthesis using biosynthetic GABA associated with B. amyloliquefaciens EH-9.

Evaluation of a decided sample size in machine learning applications.

BACKGROUND: An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets and three real datasets to derive the criteria for sample size. We systematically increase the sample size, starting from 16, by randomly sampling and examine the impact of sample size on classifiers' performance and both effect sizes. Tenfold cross-validation was used to quantify the accuracy. RESULTS: The results demonstrate that the effect sizes and the classification accuracies increase while the variances in effect sizes shrink with the increment of samples when the datasets have a good discriminative power between two classes. By contrast, indeterminate datasets had poor effect sizes and classification accuracies, which did not improve by increasing sample size in both simulated and real datasets. A good dataset exhibited a significant difference in average and grand effect sizes. We derived two criteria based on the above findings to assess a decided sample size by combining the effect size and the ML accuracy. The sample size is considered suitable when it has appropriate effect sizes (≥ 0.5) and ML accuracy (≥ 80%). After an appropriate sample size, the increment in samples will not benefit as it will not significantly change the effect size and accuracy, thereby resulting in a good cost-benefit ratio. CONCLUSION: We believe that these practical criteria can be used as a reference for both the authors and editors to evaluate whether the selected sample size is adequate for a study.

Extracellular electrons transferred from honey probiotic Bacillus circulans inhibits inflammatory acne vulgaris.

Bacillus circulans (B. circulans) is widely used as an electrogenic bacterium in microbial fuel cell (MFC) technology. This study evaluated whether B. circulans can ferment glucose to generate electricity and mitigate the effects of human skin pathogens. The electricity production of B. circulans was examined by measuring the voltage difference and verified using a ferrozine assay in vitro. To investigate the fermentation effects of B. circulans on inhibition of human skin pathogens, Cutibacterium acnes (C. acnes) was injected intradermally into mice ears to induce an inflammatory response. The results revealed that the glucose-B. circulans co-culture enhanced electricity production and significantly supressed C. acnes growth. The addition of roseoflavin to inhibit flavin production considerably reduced the electrical energy generated by B. circulans through metabolism and, in vivo test, recovered C. acnes count and macrophage inflammatory protein 2 (MIP-2) levels. This suggests that B. circulans can generate electrons that affect the growth of C. acnes through flavin-mediated electron transfer and alleviate the resultant inflammatory response. Our findings demonstrate that probiotics separated from natural substances and antimicrobial methods of generating electrical energy through carbon source fermentation can help in the treatment of bacterial infections.

Neuronal Abnormalities Induced by an Intelligent Virtual Reality System for Methamphetamine Use Disorder.

Methamphetamine use disorder (MUD) is a brain disease that leads to altered regional neuronal activity. Virtual reality (VR) is used to induce the drug cue reactivity. Previous studies reported significant frequency-specific neuronal abnormalities in patients with MUD during VR induction of drug craving. However, whether those patients exhibit neuronal abnormalities after VR induction that could serve as the treatment target remains unclear. Here, we used an integrated VR system for inducing drug related changes and investigated the neuronal abnormalities after VR exposure in patients. Fifteen patients with MUD and ten healthy subjects were recruited and exposed to drug-related VR environments. Resting-state EEG were recorded for 5 minutes twice-before and after VR and transformed to obtain the frequency-specific data. Three self-reported scales for measurement of the anxiety levels and impulsivity of participants were obtained after VR task. Statistical tests and machine learning methods were employed to reveal the differences between patients and healthy subjects. The result showed that patients with MUD and healthy subjects significantly differed in Θ, α, and γ power changes after VR. These neuronal abnormalities in patients were associated with the self-reported behavioral scales, indicating impaired impulse control. Our findings of resting-state EEG abnormalities in patients with MUD after VR exposure have the translational value and can be used to develop the treatment strategies for methamphetamine use disorder.

A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket.

Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.

Low-frequency oscillations in cortical level to help diagnose task-specific dystonia.

Task-specific dystonia is a neurological movement disorder that abnormal contractions of muscles result in the twisting of fixed postures or muscle spasm during specific tasks. Due to the rareness and the pathophysiology of the disease, there is no test to confirm the diagnosis of task-specific dystonia, except comprehensive observations by the experts. Evidence from neural electrophysiological data suggests that enhanced low frequency (4-12 Hz) oscillations in the subcortical structure of the globus pallidus were associated with the pathological abnormalities concerning ß and γ rhythms in motor areas and motor cortical network in patients with task-specific dystonia. However, whether patients with task-specific dystonia have any low-frequency abnormalities in motor cortical areas remains unclear. In this study, we hypothesized that low-frequency abnormalities are present in core motor areas and motor cortical networks in patients with task-specific dystonia during performing the non-symptomatic movements and those low-frequency abnormalities can help the diagnosis of this disease. We tested this hypothesis by using EEG, effective connectivity analysis, and a machine learning method. Fifteen patients with task-specific dystonia and 15 healthy controls were recruited. The machine learning method identified 8 aberrant movement-related network connections concerning low frequency, ß and γ frequencies, which enabled the separation of the data of patients from those of controls with an accuracy of 90%. Importantly, 7 of the 8 aberrant connections engaged the premotor area contralateral to the affected hand, suggesting an important role of the premotor area in the pathological abnormities. The patients exhibited significantly lower low frequency activities during the movement preparation and significantly lower ß rhythms during movements compared with healthy controls in the core motor areas. Our findings of low frequency- and ß-related abnormalities at the cortical level and aberrant motor network could help diagnose task-specific dystonia in the clinical setting, and the importance of the contralesional premotor area suggests its diagnostic potential for task-specific dystonia.

Propionic acid produced by Cutibacterium acnes fermentation ameliorates ultraviolet B-induced melanin synthesis.

Ultraviolet irradiation induces melanin accumulation, which can be reduced by the use of chemical whitening products. However, the associated safety concerns of such products have prompted the search for natural and harmless alternatives. This study aimed to identify a natural acidic formulation to reduce skin pigmentation. The metabolite propionic acid (CH3CH2COOH, PA) was the most abundant fatty acid in the filtrate from Pluronic F68 (PF68) fermentation of Cutibacterium acnes (C. acnes) and reduced the DOPA-positive melanocytes by significantly inhibiting cellular tyrosinase activity via binding to the free fatty acid receptor 2 (FFAR2). Moreover, 4 mM PA treatment did not alter melanocyte proliferation, indicating that it is an effective solution for hyperpigmentation, causing no cellular damage. The reduced DOPA-positive melanocytes and tyrosinase activity were also observed in mice ear skin tissue injected with a mixture of C. acnes and PF68, supporting that the inhibition of melanogenesis is likely to be mediated through fermentation metabolites from C. acnes fermentation using PF68 as a carbon source. Additionally, PA did not affect the growth of its parent bacteria C. acnes, hence is a potent fermentation metabolite that does not disrupt the balance of the skin microbiome.

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.

Synchrony Between Default-Mode and Sensorimotor Networks Facilitates Motor Function in Stroke Rehabilitation: A Pilot fMRI Study.

Stroke is the most common cause of complex disability in Taiwan. After stroke onset, persistent physical practice or exercise in the rehabilitation procedure reorganizes neural assembly for reducing motor deficits, known as neuroplasticity. Neuroimaging literature showed rehabilitative effects specific to the brain networks of the sensorimotor network (SMN) and default-mode network (DMN). However, whether between-network interactions facilitate the neuroplasticity after stroke rehabilitation remains a mystery. Therefore, we conducted the longitudinal assessment protocol of stroke rehabilitation, including three types of clinical evaluations and two types of functional magnetic resonance imaging (fMRI) techniques (resting state and grasp task). Twelve chronic stroke patients completed the rehabilitation protocol for at least 24 h and finished the three-time assessments: before, after rehabilitation, and 1 month after the cessation of rehabilitation. For comparison, age-matched normal controls (NC) underwent the same fMRI evaluation once without repeated measure. Increasing scores of the Fugl-Meyer assessment (FMA) and upper extremity performance test reflected the enhanced motor performances after the stroke rehabilitation process. Analysis of covariance (ANCOVA) results showed that the connections between posterior cingulate cortex (PCC) and iM1 were persistently enhanced in contrast to the pre-rehabilitation condition. The interactions between PCC and SMN were positively associated with motor performances. The enhanced cross-network connectivity facilitates the motor recovery after stroke rehabilitation, but the cross-network interaction was low before the rehabilitation process, similar to the level of NCs. Our findings suggested that cross-network connectivity plays a facilitatory role following the stroke rehabilitation, which can serve as a neurorehabilitative biomarker for future intervention evaluations.

Distinguishing the Visual Working Memory Training and Practice Effects by the Effective Connectivity During n-back Tasks: A DCM of ERP Study.

Visual working memory (WM) training and practice can result in improved task performance and increased P300 amplitude; however, only training can yield N160 enhancements. N160 amplitudes are related to the spatial attention, the detection of novelty and the inhibitory control, while P300 amplitudes are related to the selective attention. Therefore, it could be speculated that the mechanisms underlying N160 and P300 production may differ to accommodate to their functions. Based on the different N160 engagements and different functional roles of N160 and P300, we hypothesized that the effects of visual WM training and practice can be dissociated by their brain effective connectivity patterns. We compared different neural connectivity configurations for the main task-related brain activities including N160 and P300 during the visual three-back task in subjects after visual WM training (the WM group) and after repetitive task practice (the control group). The behavioral result shows significantly greater improvement in accuracy after training and suggests that visual WM training can boost the learning process of this simple task. The N160 peak amplitude increased significantly after training over the anterior and posterior brain areas but decreased after practice over the posterior areas, indicating different mechanisms for mediating the training and practice effects. In support of our hypothesis, we observed that visual WM training alters the frontal-parietal connections, which comprise the executive control network (ECN) and the dorsal attention network (DAN), whereas practice modulates the parietal-frontal connections underpinning P300 production for selective attention. It should be noted that the analytic results in this study are conditional on the plausible models being tested and the experimental settings. Studies that employ different tasks, devices and plausible models may lead to different results. Nevertheless, our findings provide a reference for distinguishing the visual WM training and practice effects by the underlying neuroplasticity.

Curcumin Promotes Connexin 43 Degradation and Temozolomide-Induced Apoptosis in Glioblastoma Cells.

Glioblastoma (GBM) is the most commonly occurring tumor in the cerebral hemispheres. Currently, temozolomide (TMZ), an alkylating agent that induces DNA strand breaks, is considered the frontline chemotherapeutic agent for GBM. Despite its frontline status, GBM patients commonly exhibit resistance to TMZ treatment. We have recently established and characterized TMZ-resistant human glioma cells. The aim of this study is to investigate whether curcumin modulates cell apoptosis through the alternation of the connexin 43 (Cx43) protein level in TMZ-resistant GBM. Overexpression of Cx43, but not ATP-binding cassette transporters (ABC transporters), was observed (approximately 2.2-fold) in TMZ-resistant GBM cells compared to the Cx43 levels in parental GBM cells. Furthermore, at a concentration of 10 µ M, curcumin significantly reduced Cx43 protein expression by about 40%. In addition, curcumin did not affect the expression of other connexins like Cx26 or epithelial-to-mesenchymal transition (EMT) proteins such as ß -catenin or α E-catenin. Curcumin treatment led to an increase in TMZ-induced cell apoptosis from 4% to 8%. Importantly, it did not affect the mRNA expression level of Cx43. Concomitant treatment with the translation inhibitor cycloheximide (CHX) exerted additional effects on Cx43 degradation. Treatment with the autophagy inhibitor 3-MA (methyladenine) did not affect the curcumin-induced Cx43 degradation. Interestingly, treatment with the proteasome inhibitor MG132 (carbobenzoxy-Leu-Leu-leucinal) significantly negated the curcumin-induced Cx43 degradation, which suggests that curcumin-induced Cx43 degradation occurs through the ubiquitin-proteasome pathway.

Effects of Oxytocin on Fear Memory and Neuroinflammation in a Rodent Model of Posttraumatic Stress Disorder.

Posttraumatic stress disorder (PTSD) is a trauma-induced mental disorder characterized by fear extinction abnormalities, which involve biological dysfunctions among fear circuit areas in the brain. Oxytocin (OXT) is a neuropeptide that regulates sexual reproduction and social interaction and has recently earned specific attention due to its role in adjusting neurobiological and behavioral correlates of PTSD; however, the mechanism by which this is achieved remains unclear. The present study aimed to examine whether the effects of OXT on traumatic stress-induced abnormalities of fear extinction (specifically induced by single prolonged stress (SPS), an animal model of PTSD) are associated with pro-inflammatory cytokines. Seven days after SPS, rats received intranasal OXT 40 min before a cue-dependent Pavlovian fear conditioning-extinction test in which rats' freezing degree was used to reflect the outcome of fear extinction. We also measured mRNA expression of IL-1ß, IFN-γ, and TNF-α in the medial prefrontal cortex (mPFC), hippocampus, and amygdala at the end of the study, together with plasma oxytocin, corticosterone, IL-1ß, IFN-γ, and TNF-α, to reflect the central and peripheral changes of stress-related hormones and cytokines after SPS. Our results suggested that intranasal OXT effectively amends the SPS-impaired behavior of fear extinction retrieval. Moreover, it neurochemically reverses the SPS increase in pro-inflammatory cytokines; thus, IL-1ß and IFN-γ can be further blocked by the OXT antagonist atosiban (ASB) in the hippocampus. Peripheral profiles revealed a similar response pattern to SPS of OXT and corticosterone (CORT), and the SPS-induced increase in plasma levels of IL-1ß and TNF-α could be reduced by OXT. The present study suggests potential therapeutic effects of OXT in both behavioral and neuroinflammatory profiles of PTSD.

Differential Characterization of Temozolomide-Resistant Human Glioma Cells.

Glioblastoma multiforme (GBM) is the most common type of primary and malignant tumor occurring in the adult central nervous system. Temozolomide (TMZ) has been considered to be one of the most effective chemotherapeutic agents to prolong the survival of patients with glioblastoma. Many glioma cells develop drug-resistance against TMZ that is mediated by increasing O-6-methylguanine-DNA methyltransferase (MGMT) levels. The expression of connexin 43 was increased in the resistant U251 subline compared with the parental U251 cells. The expression of epithelial-mesenchymal transition (EMT)-associated regulators, including vimentin, N-cadherin, and ß-catenin, was reduced in the resistant U251 subline. In addition, the resistant U251 subline exhibited decreased cell migratory activity and monocyte adhesion ability compared to the parental U251 cells. Furthermore, the resistant U251 subline also expressed lower levels of vascular cell adhesion molecule (VCAM)-1 after treatment with recombinant tumor necrosis factor (TNF)-α. These findings suggest differential characteristics in the drug-resistant GBM from the parental glioma cells.

EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency) network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG) datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation). In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT) score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

Pulmonary hypertension in patients on chronic hemodialysis and with heart failure.

Pulmonary hypertension (PH) is linked to chronic kidney disease. However, few studies have examined the prevalence, risk factors, or outcomes of PH in patients with chronic hemodialysis and concomitant heart failure. This retrospective cohort study enrolled 160 patients with a history of acute decompensated heart failure after maintenance hemodialysis therapy. All patients were prospectively observed until December 2013 or death. PH was defined as pulmonary artery systolic pressure >35 mmHg, as determined through echocardiography. Fifty-one (32%) patients had PH, more of whom were female (70% vs. 52%, P = 0.04). The patients with PH had a lower body mass index (21.8 vs. 23.0, P = 0.03), higher cardiothoracic ratio (55% vs. 52%, P = 0.006), larger left atrium (38.5 vs. 35.7 mm, P = 0.01), and an increased proportion of mitral regurgitation (MR) (73% vs. 38%, P < 0.001) compared with the patients who did not have PH. In the multivariate regression analysis, MR was associated most strongly with PH (odds ratio 3.75, 95% confidence interval [CI]: 1.67-8.43, P = 0.001). In the multivariate Cox proportional hazard models, PH was related independently to all-cause mortality (hazard ratio [HR], 3.11; 95% CI, 1.53-6.31; P = 0.002) and combined cardiovascular events (HR, 2.71; 95% CI, 1.66-4.44; P < 0.001) after the model was adjusted for conventional cardiovascular risk factors. PH is related to MR and independently associated with increased all-cause mortality and cardiovascular events in patients with chronic hemodialysis and heart failure.

Genetic Polymorphisms of IL-17F and TRAF3IP2 Could Be Predictive Factors of the Long-Term Effect of Infliximab against Crohn's Disease.

BACKGROUND: We aimed to identify certain genes related to response to infliximab (IFX) and biomarkers to predict the IFX effect for Japanese Crohn's disease (CD) patients by performing an association study of single nucleotide polymorphisms (SNPs) in candidate genes in the interleukin- (IL-) 17 signaling pathway with response to IFX after 1 year of treatment. METHODS: A total of 103 patients were divided into two groups, responders and nonresponders. Twenty-eight tag SNPs in 5 genes were genotyped. The frequencies of alleles and genotypes of each SNP were compared between responders and nonresponders in three different inheritance models. A genetic test was performed using a combination of the associated SNPs as biomarkers. RESULTS: Multivariate logistic regression analysis indicated that the four variable factors, concomitant use of immunomodulators, penetrating disease, a G/G genotype of rs766748 in IL-17F, and a C/C or C/A genotype of rs1883136 in TRAF3IP2, independently contributed to response to IFX after 1 year of treatment. Genetic test using the polymorphisms of these genes perfectly predicted the responder and nonresponder CD patients with both concomitant use of immunomodulators and penetrating disease. CONCLUSION: IL17F and TRAF3IP2 are one of IFX-related genes, useful as biomarkers of IFX response, and may be target molecules for new therapeutic drugs.

Neuronal correlates of a virtual-reality-based passive sensory P300 network.

P300, a positive event-related potential (ERP) evoked at around 300 ms after stimulus, can be elicited using an active or passive oddball paradigm. Active P300 requires a person's intentional response, whereas passive P300 does not require an intentional response. Passive P300 has been used in incommunicative patients for consciousness detection and brain computer interface. Active and passive P300 differ in amplitude, but not in latency or scalp distribution. However, no study has addressed the mechanism underlying the production of passive P300. In particular, it remains unclear whether the passive P300 shares an identical active P300 generating network architecture when no response is required. This study aims to explore the hierarchical network of passive sensory P300 production using dynamic causal modelling (DCM) for ERP and a novel virtual reality (VR)-based passive oddball paradigm. Moreover, we investigated the causal relationship of this passive P300 network and the changes in connection strength to address the possible functional roles. A classical ERP analysis was performed to verify that the proposed VR-based game can reliably elicit passive P300. The DCM results suggested that the passive and active P300 share the same parietal-frontal neural network for attentional control and, underlying the passive network, the feed-forward modulation is stronger than the feed-back one. The functional role of this forward modulation may indicate the delivery of sensory information, automatic detection of differences, and stimulus-driven attentional processes involved in performing this passive task. To our best knowledge, this is the first study to address the passive P300 network. The results of this study may provide a reference for future clinical studies on addressing the network alternations under pathological states of incommunicative patients. However, caution is required when comparing patients' analytic results with this study. For example, the task presented here is not applicable to incommunicative patients.