Negation mitigates rather than inverts the neural representations of adjectives.

Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.

Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: (1) the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation; and (2) this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous MEG and intracranial EEG. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than jabberwocky. Furthermore, multivariate decoding of normal versus jabberwocky confirmed three dynamic patterns: (1) a phasic pattern following each word, peaking in temporal and parietal areas; (2) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri; and (3) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition.SIGNIFICANCE STATEMENT Starting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multiword sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep neural language models, artificial neural networks trained on text and performing very well on many natural language processing tasks. Then, using a unique combination of MEG and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

The Human Brain Encodes a Chronicle of Visual Events at Each Instant of Time Through the Multiplexing of Traveling Waves.

The human brain continuously processes streams of visual input. Yet, a single image typically triggers neural responses that extend beyond 1s. To understand how the brain encodes and maintains successive images, we analyzed with electroencephalography the brain activity of human subjects while they watched ∼5000 visual stimuli presented in fast sequences. First, we confirm that each stimulus can be decoded from brain activity for ∼1s, and we demonstrate that the brain simultaneously represents multiple images at each time instant. Second, we source localize the corresponding brain responses in the expected visual hierarchy and show that distinct brain regions represent, at each time instant, different snapshots of past stimulations. Third, we propose a simple framework to further characterize the dynamical system of these traveling waves. Our results show that a chain of neural circuits, which each consist of (1) a hidden maintenance mechanism and (2) an observable update mechanism, accounts for the dynamics of macroscopic brain representations elicited by visual sequences. Together, these results detail a simple architecture explaining how successive visual events and their respective timings can be simultaneously represented in the brain.SIGNIFICANCE STATEMENT Our retinas are continuously bombarded with a rich flux of visual input. Yet, how our brain continuously processes such visual streams is a major challenge to neuroscience. Here, we developed techniques to decode and track, from human brain activity, multiple images flashed in rapid succession. Our results show that the brain simultaneously represents multiple successive images at each time instant by multiplexing them along a neural cascade. Dynamical modeling shows that these results can be explained by a hierarchy of neural assemblies that continuously propagate multiple visual contents. Overall, this study sheds new light on the biological basis of our visual experience.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability.

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury.

BACKGROUND: Brain activation in response to spoken motor commands can be detected by electroencephalography (EEG) in clinically unresponsive patients. The prevalence and prognostic importance of a dissociation between commanded motor behavior and brain activation in the first few days after brain injury are not well understood. METHODS: We studied a prospective, consecutive series of patients in a single intensive care unit who had acute brain injury from a variety of causes and who were unresponsive to spoken commands, including some patients with the ability to localize painful stimuli or to fixate on or track visual stimuli. Machine learning was applied to EEG recordings to detect brain activation in response to commands that patients move their hands. The functional outcome at 12 months was determined with the Glasgow Outcome Scale-Extended (GOS-E; levels range from 1 to 8, with higher levels indicating better outcomes). RESULTS: A total of 16 of 104 unresponsive patients (15%) had brain activation detected by EEG at a median of 4 days after injury. The condition in 8 of these 16 patients (50%) and in 23 of 88 patients (26%) without brain activation improved such that they were able to follow commands before discharge. At 12 months, 7 of 16 patients (44%) with brain activation and 12 of 84 patients (14%) without brain activation had a GOS-E level of 4 or higher, denoting the ability to function independently for 8 hours (odds ratio, 4.6; 95% confidence interval, 1.2 to 17.1). CONCLUSIONS: A dissociation between the absence of behavioral responses to motor commands and the evidence of brain activation in response to these commands in EEG recordings was found in 15% of patients in a consecutive series of patients with acute brain injury. (Supported by the Dana Foundation and the James S. McDonnell Foundation.).

Phenytoin induces connective tissue growth factor (CTGF/CCN2) production through NADPH oxidase 4-mediated latent TGFß1 activation in human gingiva fibroblasts: Suppression by curcumin.

OBJECTIVE AND BACKGROUND: Gingival overgrowth (GO) is a common side effect of some drugs such as anticonvulsants, immunosuppressant, and calcium channel blockers. Among them, the antiepileptic agent phenytoin is the most common agent related to this condition due to its high incidence. Transforming growth factor ß (TGFß) importantly contributes to the pathogenesis of GO. Connective tissue growth factor (CTGF or CCN2) is a key mediator of tissue fibrosis and is positively associated with the degree of fibrosis in GO. We previously showed that Src, c-jun N-terminal kinase, and Smad3 mediate TGFß1-induced CCN2 protein expression in human gingival fibroblasts (HGFs). This study investigates whether phenytoin can induce CCN2 synthesis through activated latent TGFß in HGFs and its mechanisms. METHODS: CCN2 synthesis, latent TGFß1 activation, and cellular reactive oxygen species (ROS) generation in HGFs were studied using western blot analysis, a TGFß1 Emax® ImmunoAssay System, and 2',7'-dichlorodihydrofluorescein diacetate (an oxidation-sensitive fluorescent probe), respectively. RESULTS: Phenytoin significantly stimulated CCN2 synthesis, latent TGFß1 activation, and ROS generation in HGFs. Addition of an TGFß-neutralizing antibody, TGFß receptor kinase inhibitor SB431542, and Smad3 inhibitor SIS3 completely inhibited phenytoin-induced CCN2 synthesis. General antioxidant N-acetylcysteine, NADPH oxidase (NOX) inhibitor diphenylene iodonium, and specific NOX4 inhibitor plumbagin almost completely suppressed phenytoin-induced total cellular ROS and latent TGFß1 activation. Curcumin dose-dependently decreased phenytoin-induced TGFß1 activation and CCN2 synthesis in HGFs. CONCLUSIONS: Our findings indicated that NOX4-derived ROS play pivotal roles in phenytoin-induced latent TGFß1 activation. Molecular targeting the phenytoin/NOX4/ROS/TGFß1 pathway may provide promising strategies for the prevention and treatment of GO. Curcumin-inhibited phenytoin-induced CCN2 synthesis is caused by the suppression of latent TGFß1 activation.

Depression Predicts Chronic Pain Interference in Racially Diverse, Income-Disadvantaged Patients.

BACKGROUND: Chronic pain is one of the most common reason adults seek medical care in the United States, with prevalence estimates ranging from 11% to 40%. Mindfulness meditation has been associated with significant improvements in pain, depression, physical and mental health, sleep, and overall quality of life. Group medical visits are increasingly common and are effective at treating myriad illnesses, including chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence based integrative medicine, and medical group visits and can be used as adjuncts to medications, particularly in diverse underserved populations with limited access to non-pharmacological therapies. OBJECTIVE AND DESIGN: The objective of the present study was to use a blended analytical approach of machine learning and regression analyses to evaluate the potential relationship between depression and chronic pain in data from a randomized clinical trial of IMGV in diverse, income-disadvantaged patients suffering from chronic pain and depression. METHODS: The analytical approach used machine learning to assess the predictive relationship between depression and pain and identify and select key mediators, which were then assessed with regression analyses. It was hypothesized that depression would predict the pain outcomes of average pain, pain severity, and pain interference. RESULTS: Our analyses identified and characterized a predictive relationship between depression and chronic pain interference. This prediction was mediated by high perceived stress, low pain self-efficacy, and poor sleep quality, potential targets for attenuating the adverse effects of depression on functional outcomes. CONCLUSIONS: In the context of the associated clinical trial and similar interventions, these insights may inform future treatment optimization, targeting, and application efforts in racialized, income-disadvantaged populations, demographics often neglected in studies of chronic pain.

Court decisions in criminal proceedings for dental malpractice in Taiwan.

BACKGROUND/PURPOSE: Malpractice claims place heavy economic and emotional burdens on both dentists and patients. Recently, medical malpractice lawsuits are decreasing in prevalence but increasing in severity. The percentage of dental malpractice payments is also growing among the health profession. The present study aimed to explore criminal convictions in dental malpractice litigation and to analyze the factors affecting the judgment in dental disputes in Taiwan. METHODS: The keywords "dentist," "professional negligence," "medical malpractice," and "professional liability" were used to search Taiwan's Law and Regulations Retrieving System for criminal dental malpractice cases in all district courts from January 1, 2000 to June 30, 2021. The eligible judgments were summarized and analyzed. RESULTS: Overall, 425 cases were identified, with 28 dental disputes included in the final analysis. The dentists lost in 10 cases (35.7%). The average claim time was 36.75 ± 16.34 months. Taipei and Taichung dealt with more lawsuit cases (n = 8). Local clinics were the most common institution of the defendants (75%) and had the highest number of convictions (n = 9). Implant dentistry was the most common specialty involved. Expert testimony of the Medical Review Committee (MRC) had a high K coefficient of agreement with court judgments regarding professional negligence (p < 0.001). CONCLUSION: The overall criminal conviction rate was 35.7%. Implant therapy and local clinics had the highest rate of lawsuits and a considerably higher conviction rate. All guilty dentists were fined or given probation. The court judgments were highly consistent with the expert testimony of the MRC.

Severity of periodontitis and salivary interleukin-1ß are associated with psoriasis involvement.

BACKGROUND/PURPOSE: Both psoriasis and periodontal diseases are characterized by an exaggerated immune response to the microbiota residing on epithelial surfaces. This study aimed to explore the associations between the severity of psoriasis and periodontal destruction in patients with psoriasis. METHODS: Thirty-three patients diagnosed with psoriasis were referred from the dermatology clinic of National Taiwan University Hospital. Full-mouth periodontal examination was performed and saliva was collected after patients signed informed consent forms. The Psoriasis Area Severity Index (PASI) as well as clinical periodontal parameters including probing depth (PD), plaque index (PI), gingival index (GI), and clinical attachment level (CAL) were evaluated. Salivary cytokines including interleukin (IL)-1ß, IL-12, IL-17, interferon-γ, and tumor necrosis factor (TNF)-α were tested with the Luminex Bio-Plex system. Anti-inflammatory medication, tobacco use, and underlying comorbidities were included in the analysis. RESULTS: Baseline PASI was significantly associated with PI. PASI at follow-up was positively correlated with CAL ≥ 4 mm (%) and saliva IL-1ß levels. Psoriasis patients who used non-steroidal anti-inflammatory drugs or topical steroids had significantly lower GI, PD ≥ 4 mm (%), and saliva IL-1ß and TNF-α levels. Moreover, a history of tobacco use was associated with higher PD ≥ 4 mm (%). CONCLUSION: PI, CAL, and salivary IL-1ß were associated with PASI. Periodontal severity was associated with psoriasis involvement. Periodontal inflammation in psoriasis may be modified by anti-inflammatory medication and tobacco use. Additional large-scale longitudinal and mechanistic studies are needed.

Differential Brain Mechanisms of Selection and Maintenance of Information during Working Memory.

Working memory is our ability to select and temporarily hold information as needed for complex cognitive operations. The temporal dynamics of sustained and transient neural activity supporting the selection and holding of memory content is not known. To address this problem, we recorded magnetoencephalography in healthy participants performing a retro-cue working memory task in which the selection rule and the memory content varied independently. Multivariate decoding and source analyses showed that selecting the memory content relies on prefrontal and parieto-occipital persistent oscillatory neural activity. By contrast, the memory content was reactivated in a distributed occipitotemporal posterior network, preceding the working memory decision and in a different format than during the visual encoding. These results identify a neural signature of content selection and characterize differentiated spatiotemporal constraints for subprocesses of working memory.SIGNIFICANCE STATEMENT Our brain selects and maintains information during short time windows in a way that is essential to reasoning and learning. Recent advances in multivariate analysis of brain activity allowed the characterization of brain regions that stores the memory. We applied multivariate analysis to time-resolved brain signals to characterize the spatiotemporal signature underlying these subprocesses. The selection of information relies on sustained oscillatory activity in a network that includes the ventrolateral prefrontal cortex while memory content is transiently replayed in an occipitotemporal network that differs from encoding. Our results characterized differentiated spatiotemporal activity underlying encoding, selection, and maintenance of information during working memory.

Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations.

Identifying causes solely from observations can be particularly challenging when i) the factors under investigation are difficult to manipulate independently from one-another and ii) observations are high-dimensional. To address this issue, we introduce ''Back-to-Back'' regression (B2B), a linear method designed to efficiently estimate, from a set of correlated factors, those that most plausibly account for multidimensional observations. First, we prove the consistency of B2B, its links to other linear approaches, and show how it can provide a robust, unbiased and interpretable scalar estimate for each factor. Second, we use a variety of simulated data to show that B2B can outperform forward modeling ("encoding"), backward modeling ("decoding") as well as cross-decomposition modeling (i.e. canonical correlation analysis and partial least squares) on causal identification when the factors and the observations are not orthogonal. Finally, we apply B2B to a hundred magneto-encephalography recordings and to a hundred functional Magnetic Resonance Imaging recordings acquired while subjects performed a 1 â€‹h reading task. B2B successfully disentangles the respective contribution of collinear factors such as word length, word frequency in the early visual and late associative cortical responses respectively. B2B compared favorably to other standard techniques on this disentanglement. We discuss how the speed and the generality of B2B sets promising foundations to help identify the causal contributions of covarying factors from high-dimensional observations.

Traffic-related particulate matter affects behavior, inflammation, and neural integrity in a developmental rodent model.

Recent studies indicate that exposure to airborne particulate matter (PM) is associated with cognitive delay, depression, anxiety, autism, and neurodegenerative diseases; however, the role of PM in the etiology of these outcomes is not well-understood. Therefore, there is a need for controlled animal studies to better elucidate the causes and mechanisms by which PM impacts these health outcomes. We assessed the effects of gestational and early life exposure to traffic-related PM on social- and anxiety-related behaviors, cognition, inflammatory markers, and neural integrity in juvenile male rats. Gestating and lactating rats were exposed to PM from a Boston (MA, USA) traffic tunnel for 5 h/day, 5 days/week for 6 weeks (3 weeks gestation, 3 weeks lactation). The target exposure concentration for the fine fraction of nebulized PM, measured as PM2.5, was 200 µg/m3. To assess anxiety and cognitive function, F1 male juveniles underwent elevated platform, cricket predation, nest building, social behavior and marble burying tests at 32-60 days of age. Upon completion of behavioral testing, multiple cytokines and growth factors were measured in these animals and their brains were analyzed with diffusion tensor MRI to assess neural integrity. PM exposure had no effect on litter size or weight, or offspring growth; however, F1 litters developmentally exposed to PM exhibited significantly increased anxiety (p = 0.04), decreased cognition reflected in poorer nest-organization (p = 0.04), and decreased social play and allogrooming (p = 0.003). MRI analysis of ex vivo brains revealed decreased structural integrity of neural tissues in the anterior cingulate and hippocampus in F1 juveniles exposed to PM (p < 0.01, p = 0.03, respectively). F1 juvenile males exposed to PM also exhibited significantly decreased plasma levels of both IL-18 (p = 0.03) and VEGF (p = 0.04), and these changes were inversely correlated with anxiety-related behavior. Chronic exposure of rat dams and their offspring to traffic-related PM during gestation and lactation decreases social behavior, increases anxiety, impairs cognition, decreases levels of inflammatory and growth factors (which are correlated with behavioral changes), and disrupts neural integrity in the juvenile male offspring. Our findings add evidence that exposure to traffic-related air pollution during gestation and lactation is involved in the etiology of autism spectrum disorder and other disorders which include social and cognitive deficits and/or increased anxiety.

What Limits Our Capacity to Process Nested Long-Range Dependencies in Sentence Comprehension?

Sentence comprehension requires inferring, from a sequence of words, the structure of syntactic relationships that bind these words into a semantic representation. Our limited ability to build some specific syntactic structures, such as nested center-embedded clauses (e.g., "The dog that the cat that the mouse bit chased ran away"), suggests a striking capacity limitation of sentence processing, and thus offers a window to understand how the human brain processes sentences. Here, we review the main hypotheses proposed in psycholinguistics to explain such capacity limitation. We then introduce an alternative approach, derived from our recent work on artificial neural networks optimized for language modeling, and predict that capacity limitation derives from the emergence of sparse and feature-specific syntactic units. Unlike psycholinguistic theories, our neural network-based framework provides precise capacity-limit predictions without making any a priori assumptions about the form of the grammar or parser. Finally, we discuss how our framework may clarify the mechanistic underpinning of language processing and its limitations in the human brain.

Robust EEG-based cross-site and cross-protocol classification of states of consciousness.

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

Electrophysiological Responses in the Ventral Temporal Cortex During Reading of Numerals and Calculation.

Recent evidence suggests that specific neuronal populations in the ventral temporal cortex show larger electrophysiological responses to visual numerals compared with morphologically similar stimuli. This study investigates how these responses change from simple reading of numerals to the active use of numerals in an arithmetic context. We recorded high-frequency broadband (HFB) signals, a reliable measure for local neuronal population activity, while 10 epilepsy patients implanted with subdural electrodes performed separate numeral reading and calculation tasks. We found that calculation increased activity in the posterior inferior temporal gyrus (ITG) with a factor of approximately 1.5 over the first 500 ms of calculation, whereas no such increase was noted for reading numerals without calculation or reading and judging memory statements. In a second experiment conducted in 2 of the same subjects, we show that HFB responses increase in a systematic manner when the single numerals were presented successively in a calculation context: The HFB response in the ITG, to the second and third numerals (i.e., b and c in a + b = c), was approximately 1.5 times larger than the responses to the first numeral (a). These results provide electrophysiological evidence for modulation of local neuronal population responses to visual stimuli based on increasing task demands.

Disruption of hierarchical predictive coding during sleep.

When presented with an auditory sequence, the brain acts as a predictive-coding device that extracts regularities in the transition probabilities between sounds and detects unexpected deviations from these regularities. Does such prediction require conscious vigilance, or does it continue to unfold automatically in the sleeping brain? The mismatch negativity and P300 components of the auditory event-related potential, reflecting two steps of auditory novelty detection, have been inconsistently observed in the various sleep stages. To clarify whether these steps remain during sleep, we recorded simultaneous electroencephalographic and magnetoencephalographic signals during wakefulness and during sleep in normal subjects listening to a hierarchical auditory paradigm including short-term (local) and long-term (global) regularities. The global response, reflected in the P300, vanished during sleep, in line with the hypothesis that it is a correlate of high-level conscious error detection. The local mismatch response remained across all sleep stages (N1, N2, and REM sleep), but with an incomplete structure; compared with wakefulness, a specific peak reflecting prediction error vanished during sleep. Those results indicate that sleep leaves initial auditory processing and passive sensory response adaptation intact, but specifically disrupts both short-term and long-term auditory predictive coding.

Arecoline activates latent transforming growth factor ß1 via mitochondrial reactive oxygen species in buccal fibroblasts: Suppression by epigallocatechin-3-gallate.

BACKGROUND/PURPOSE: Oral submucous fibrosis (OSF) is a premalignant condition caused by the chewing of areca nut (AN). Transforming growth factor ß (TGFß) plays a central role in the pathogenesis of OSF. Connective tissue growth factor (CTGF or CCN2) and early growth response-1 (Egr-1) are important mediators in the fibrotic response to TGFß in several fibrotic disorders including OSF. Arecoline, a major AN alkaloid, induced the synthesis of CCN2 and Egr-1 in human buccal mucosal fibroblast (BMFs). The aims of this study were to investigate whether arecoline-induced CCN2 and Egr-1 syntheses are mediated through TGFß1 signaling and to inspect the detailed mechanisms involved. METHODS: Western blot and TGFß1 Emax® ImmunoAssay were used to measure the effect of arecoline on the TGFß signaling pathways. 2',7'-dichlorodihydrofluorescein diacetate and MitoSOX™ Red were used to measure the effect of arecoline on the cellular and mitochondrial reactive oxygen species (ROS). RESULTS: Arecoline induced latent TGFß1 activation, Smad2 phosphorylation, and mitochondrial and total cellular ROS in BMFs. TGFß-neutralizing antibody completely inhibited the arecoline-induced synthesis of CCN2 and Egr-1. Mito-TEMPO, a mitochondria-targeted antioxidant, completely suppressed arecoline-induced latent TGFß1 activation and mitochondrial and total cellular ROS. Epigallocatechin-3-gallate (EGCG) dose-dependently inhibited arecoline-induced TGFß1 activation and mitochondrial ROS in BMFs. CONCLUSION: Our results indicated that arecoline-induced mitochondrial ROS plays pivotal roles in the activation of latent TGFß1 leading to the initiation of TGFß1 signaling and subsequent increase in the synthesis of CCN2 and Egr-1. EGCG can be a useful agent in the chemoprevention and treatment of OSF.

Significant association of rheumatoid arthritis-related inflammatory markers with non-surgical periodontal therapy.

BACKGROUND/PURPOSE: Chronic periodontitis (CP) and rheumatoid arthritis (RA) are the most common chronic inflammatory diseases and their immunopathogenesis is similar. The aim of this study was to evaluate the effect of non-surgical periodontal treatment on the serum levels of RA-related inflammatory markers in patients with chronic periodontitis. METHODS: Thirty-one Taiwanese adults with CP were included. Demographics and periodontal parameters, including probing depth, clinical attachment level, and number of remaining teeth in the oral cavity, were recorded. All subjects received non-surgical periodontal treatment such as scaling and subgingival root planing. Serum samples were collected before and after the treatment. Serum levels of anti-citrullinated protein antibodies (ACPA), rheumatoid factor, tumor necrosis factor-α (TNF-α), C-reactive protein, interleukin-1ß (IL-1ß), and Interleukin-6 (IL-6) were measured using an enzyme-linked immunosorbent assay. RESULTS: Non-surgical periodontal treatment significantly reduced the serum ACPA (p = 0.015) and TNF-α levels (p = 0.026) in CP patients, particularly in patients with generalized CP. Furthermore, there was a significant and positive correlation between the number of extracted teeth and the reduction in the serum ACPA (p = 0.05) and IL-1ß levels (p = 0.029) after non-surgical periodontal treatment. CONCLUSION: Non-surgical periodontal therapy may aid in the control of RA-related inflammatory markers in patients with CP. A large-scale study with well-defined populations is needed to clarify the benefit of non-surgical periodontal therapy.

Evidence of Altered Brain Responses to Nicotine in an Animal Model of Attention Deficit/Hyperactivity Disorder.

INTRODUCTION: Individuals with attention deficit/hyperactivity disorder (ADHD) are susceptible to earlier and more severe nicotine addiction. To shed light on the relationship between nicotine and ADHD, we examined nicotine's effects on functional brain networks in an animal model of ADHD. METHODS: Awake magnetic resonance imaging was used to compare functional connectivity in adolescent (post-natal day 44 ± 2) males of the spontaneously hypertensive rat (SHR) strain and two control strains, Wistar-Kyoto and Sprague-Dawley (n = 16 each). We analyzed functional connectivity immediately before and after nicotine exposure (0.4 mg/kg base) in naïve animals, using a region-of-interest approach focussing on 16 regions previously implicated in reward and addiction. RESULTS: Relative to the control groups, the SHR strain demonstrated increased functional connectivity between the ventral tegmental area (VTA) and retrosplenial cortex in response to nicotine, suggesting an aberrant response to nicotine. In contrast, increased VTA-substantia nigra connectivity in response to a saline injection in the SHR was absent following a nicotine injection, suggesting that nicotine normalized function in this circuit. CONCLUSIONS: In the SHR, nicotine triggered an atypical response in one VTA circuit while normalizing activity in another. The VTA has been widely implicated in drug reward. Our data suggest that increased susceptibility to nicotine addiction in individuals with ADHD may involve altered responses to nicotine involving VTA circuits. IMPLICATIONS: Nicotine addiction is more common among individuals with ADHD. We found that two circuits involving the VTA responded differently to nicotine in animals that model ADHD in comparison to two control strains. In one circuit, nicotine normalized activity that was abnormal in the ADHD animals, while in the other circuit nicotine caused an atypical brain response in the ADHD animals. The VTA has been implicated in drug reward. Our results would be consistent with an interpretation that nicotine may normalize abnormal brain activity in ADHD, and that nicotine may be more rewarding for individuals with ADHD.