Can Transcutaneous Laryngeal Ultrasonography Systematically Quantify Vocal Fold Movement?-A Feasibility Study.

OBJECTIVES: Several studies have assessed adult vocal fold movement using transcutaneous laryngeal ultrasonography (TLUSG) during the perioperative period of thyroidectomy. However, the movement was not objectively quantified. This study aimed to provide a feasible and objective method for assessing vocal fold movement using TLUSG. STUDY DESIGN: Feasibility study. METHODS: TLUSG was performed during calm breathing and breath-holding in healthy adult volunteers. The morphology and anatomy of the larynx were observed and measured using an ultrasonic self-contained measurement function. At the end of the calm inspiratory and breath-holding phases, vocal fold angle, vocal fold length, distance from vocal process to the midline, distance from anterior vocal commissure to arytenoid cartilage, distance from false vocal fold to the midline, and distance from the anterior horn of thyroid cartilage to false vocal fold were measured. Data were analyzed using a t test (significance <0.05). RESULTS: The ultrasonic images were satisfactory in all 40 healthy adult volunteers (age 19-35 years; body mass index 18.55-23.93 kg/m2; 20 men and 20 women). There were no significant differences in all laryngeal parameters between the left and right sides in both phases (P > 0.05). Moreover, all differences in laryngeal parameters between the end of the calm inspiratory phase and the breath-holding phase were statistically significant (P < 0.05), regardless of sex. CONCLUSION: The relevant positional parameters of the vocal fold, arytenoid cartilage, and false vocal fold and their differences before and after vocal fold movement in healthy adult volunteers can be obtained objectively using TLUSG.

Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization.

Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively. We use a power spectrum density parameterization method to separate the periodic and aperiodic components of the signals, and validate their roles in epilepsy detection and prediction on two public datasets. The average classification accuracy of the periodic and aperiodic components for 10 clinical tasks on the Bonn EEG database were 73.9% and 96.68%, respectively, and increases to 98.88% when combined. For 22 patients on the CHB-MIT Long-term EEG database, the combined features achieve an average detection accuracy of 99.95% and successfully predict all seizures with low false prediction rates. We conclude that both the periodic and aperiodic components of the EEG power spectrum contributed to discriminating different stages of epilepsy, but the aperiodic neural activity played a decisive role in classification. This discovery has significant implications for diagnosing epileptic seizures and providing personalized brain activity information to improve the accuracy and efficiency of epilepsy detection.

Aberrant temporal correlations of ongoing oscillations in disorders of consciousness on multiple time scales.

Changes in neural oscillation amplitude across states of consciousness has been widely reported, but little is known about the link between temporal dynamics of these oscillations on different time scales and consciousness levels. To address this question, we analyzed amplitude fluctuation of the oscillations extracted from spontaneous resting-state EEG recorded from the patients with disorders of consciousness (DOC) and healthy controls. Detrended fluctuation analysis (DFA) and measures of life-time and waiting-time were employed to characterize the temporal structure of EEG oscillations on long time scales (1-20 s) and short time scales (< 1 s), in groups with different consciousness states: patients in minimally conscious state (MCS), patients with unresponsive wakefulness syndrome (UWS) and healthy subjects. Results revealed increased DFA exponents that implies higher long-range temporal correlations (LRTC), especially in the central brain area in alpha and beta bands. On short time scales, declined bursts of oscillations were also observed. All the metrics exhibited lower individual variability in the UWS or MCS group, which may be attributed to the reduced spatial variability of oscillation dynamics. In addition, the temporal dynamics of EEG oscillations showed significant correlations with the behavioral responsiveness of patients. In summary, our findings shows that loss of consciousness is accompanied by alternation of temporal structure in neural oscillations on multiple time scales, and thus may help uncover the mechanism of underlying neuronal correlates of consciousness. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09852-9.

Effects of age, sex, and education on California Verbal Learning Test-II performance in a Chinese-speaking population.

The California Verbal Learning Test-Second Edition (CVLT-II), is a commonly used tool to assess episodic memory. This study analyzed learning and memory characteristics in a cognitively healthy Chinese population, as well as the effects of age, sex and education on CVLT-II factors. In total, 246 healthy people aged 20-80 years and 29 persons with multiple sclerosis (MS) were included in this study and completed the CVLT-II. Factors including total learning, learning strategy, serial position effects, short-delay free and cued recall, long-delay free and cued recall, repetitions and intrusions during recall, hits and false positives of recognition, and total recognition discriminability were calculated. The effects of age, sex and education on these factors were analyzed using ANCOVA or independent two-sample t-tests and further confirmed by multiple regression analysis. The regression-based normative data were then computed by the equivalent scores method. Moreover, differences in learning and memory were compared between persons with MS and age-, sex- and education-matched healthy individuals. Most CVLT-II factors significantly differed between different age and education groups; in particular, better performance in total learning, recall, semantic clustering and recognition was observed in the younger and more educated groups than in the older and less educated groups. Male participants showed higher recency effect scores, more repetitions and fewer hits than female participants. Compared with healthy individuals, persons with MS showed extensive impairments in memory processes, such as learning, recall, learning strategy and recognition (p < 0.05). These findings indicated that verbal learning and memory were highly dependent on age and educational level but not strongly affected by sex. The CVLT-II effectively assesses episodic memory impairment in the Chinese-speaking population.

Disrupted Control Architecture of Brain Network in Disorder of Consciousness.

The human brain controls various cognitive functions via the functional coordination of multiple brain regions in an efficient and robust way. However, the relationship between consciousness state and the control mode of brain networks is poorly explored. Using multi-channel EEG, the present study aimed to characterize the abnormal control architecture of functional brain networks in the patients with disorders of consciousness (DOC). Resting state EEG data were collected from 40 DOC patients with different consciousness levels and 24 healthy subjects. Functional brain networks were constructed in five different EEG frequency bands and the broadband in the source level. Subsequently, a control architecture framework based on the minimum dominating set was applied to investigate the of control mode of functional brain networks for the subjects with different conscious states. Results showed that regardless of the consciousness levels, the functional networks of human brain operate in a distributed and overlapping control architecture different from that of random networks. Compared to the healthy controls, the patients have a higher control cost manifested by more minimum dominating nodes and increased degree of distributed control, especially in the alpha band. The ability to withstand network attack for the control architecture is positive correlated with the consciousness levels. The distributed of control increased correlation levels with Coma Recovery Scale-Revised score and improved separation between unresponsive wakefulness syndrome and minimal consciousness state. These findings may benefit our understanding of consciousness and provide potential biomarkers for the assessment of consciousness levels.

Application of an ultrasound semi-quantitative assessment in the degradation of silk fibroin scaffolds in vivo.

BACKGROUND: Research on the degradation of silk fibroin (SF) scaffolds in vivo lacks uniform and effective standards and experimental evaluation methods. This study aims to evaluate the application of ultrasound in assessing the degradation of SF scaffolds. METHODS: Two groups of three-dimensional regenerated SF scaffolds (3D RSFs) were implanted subcutaneously into the backs of Sprague-Dawley rats. B-mode ultrasound and hematoxylin and eosin (HE) staining were performed on days 3, 7, 14, 28, 56, 84, 112, 140, and 196. The cross-sectional areas for two groups of 3D RSFs that were obtained using these methods were semi-quantitatively analyzed and compared to evaluate the biodegradation of the implanted RSFs. RESULTS: The 3D RSFs in the SF-A group were wholly degraded at the 28th week after implantation. In contrast, the 3D RSFs in the SF-B group were completely degraded at the 16th week. Ultrasonic examination showed that the echoes of 3D RSFs in both groups gradually decreased with the increase of the implantation time. In the early stages of degradation, the echoes of the samples were higher than the echo of the muscle. In the middle of degeneration, the echoes were equal to the echo of the muscle. In the later stage, the echoes of the samples were lower than that of the muscle. The above changes in the SF-B group were earlier than those in the SF-A group. Semi-quantitative analysis of the cross-sectional areas detected using B-mode ultrasound revealed that the degradations of the two 3D RSF groups were significantly different. The degradation rate of the SF-B group was found to be higher than that of the SF-A group. This was consistent with the semi-quantitative detection results for HE staining. Regression analysis showed that the results of the B-mode ultrasound and HE staining were correlated in both groups, indicating that B-mode ultrasound is a reliable method to evaluate the SF scaffold degradation in vivo. CONCLUSIONS: This study suggests that B-mode ultrasound can clearly display the implanted SF scaffolds non-invasively and monitor the degradation of the different SF scaffolds after implantation in living organisms in real-time.

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach.

A novel analytical framework combined fuzzy learning and complex network approaches is proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded electroencephalograph (EEG) signals. Weighted visibility graph (WVG) algorithm is first applied to transform each channel EEG into network and its topological parameters were further extracted. Statistical analysis indicates that AD and normal subjects show significant difference in the structure of WVG network and thus can be used to identify Alzheimer's disease. Taking network parameters as input features, a Takagi-Sugeno-Kang (TSK) fuzzy model is established to identify AD's EEG signal. Three feature sets-single parameter from multi-networks, multi-parameters from single network, and multi-parameters from multi-networks-are considered as input vectors. The number and order of input features in each set is optimized with various feature selection methods. Classification results demonstrate the ability of network-based TSK fuzzy classifiers and the feasibility of three input feature sets. The highest accuracy that can be achieved is 95.28% for single parameter from four networks, 93.41% for three parameters from single network. In particular, multi-parameters from the multi-networks set obtained the best result. The highest accuracy, 97.12%, is achieved with five features selected from four networks. The combination of network and fuzzy learning can highly improve the efficiency of AD's EEG identification.

Altered inter-frequency dynamics of brain networks in disorder of consciousness.

OBJECTIVE: Growing evidence have linked disorders of consciousness (DOC) with the changes in frequency-specific functional networks. However, the alteration of inter-frequency dynamics in brain networks remain largely unknown. In this study, we investigated the network integration and segregation across frequency bands in a multiplex network framework. APPROACH: Resting-state EEG data were recorded and analysed from 19 patients in minimally conscious state, 35 patients in unresponsive wakefulness syndrome (UWS) and 23 healthy controls. Frequency-based multiplex (cross-frequency) networks were reconstructed by integrating the five frequency-specific networks. Multiplex graph metrics, named multiplex participation coefficient and multiplex clustering coefficient, were employed to assess the network topology of subjects with different levels of consciousness. MAIN RESULTS: Results revealed DOC networks, compared to those of healthy controls, may work at a less optimal point (closer to complete disorder) with increased integration and decreased segregation considering inter-frequency dynamics. Both metrics show increased spatial and temporal variability with the consciousness levels. Moreover, significant correlation can be found between the alteration of cross-frequency networks in DOC patients and their behavioural performance at both local and global scales. SIGNIFICANCE: These findings may contribute to the development of EEG network study and benefit our understanding of the processes of consciousness and their pathophysiology for DOC.

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease.

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

Characterization of network switching in disorder of consciousness at multiple time scales.

OBJECTIVE: Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels. APPROACH: By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain changes its community structure and traverses across defined network states (integrated and segregated states) in subjects with different consciousness levels. MAIN RESULTS: Network switching in the human brain is decreased with increasing time scale opposite to that in random systems. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in the alpha band) at short time scales. At long time scales, the opposite trend is found. Compared to healthy controls, patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales. SIGNIFICANCE: These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorders like DOC.

Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization.

Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross-frequency coupling (CFC) of Electroencephalogram (EEG) signals. The PSI matrixes for the multi-channel EEG signals are calculated from interictal to ictal period in each sliding time window. The results show that CFC changes obviously once seizure occurs between the different bands, and such alteration is earlier than the appearance of clinical symptoms in seizure. Considering the similar role of the within-frequency coupling (WFC), we further reconstruct multi-layered brain networks, including CFC networks and WFC networks. The graph metrics are applied to investigate the variation of network structure of the epileptic brain. Significant decreases/increases of the local/global efficiency are found in δ-ß, δ-α, θ-α and δ-θ bands from the CFC network, while WFC network shows a significant decline in the local efficiency in θ and α bands. These findings suggest that CFC may provide a new perspective to observe the alteration of brain structure when seizure occurs, and the investigation of functional connectivity across the full frequency spectrum can give us a deeper understanding of epileptic brains.

Spatiotemporal EEG microstate analysis in drug-free patients with Parkinson's disease.

The clinical diagnosis of Parkinson's disease (PD) is very difficult, especially in the early stage of the disease, because there is no physiological indicator that can be referenced. Drug-free patients with early PD are characterized by clinical symptoms such as impaired motor function and cognitive decline, which was caused by the dysfunction of brain's dynamic activities. The indicators of brain dysfunction in patients with PD at an early unmedicated condition may provide a valuable basis for the diagnosis of early PD and later treatment. In order to find the spatiotemporal characteristic markers of brain dysfunction in PD, the resting-state EEG microstate analysis is used to explore the transient state of the whole brain of 23 drug-free patients with PD on the sub-second timescale compared to 23 healthy controls. EEG microstates reflect a transiently stable brain topological structure with spatiotemporal characteristics, and the spatial characteristic microstate classes and temporal parameters provide insight into the brain's functional activities in PD patients. The further exploration was to explore the relation between temporal microstate parameters and significant clinical symptoms to determine whether these parameters could be used as a basis for clinically assisted diagnosis. Therefore, we used a general linear model (GLM) to explore the relevance of microstate parameters to clinical scales and multiple patient attributes, and the Wilcoxon rank sum test was used to quantify the linear relation between influencing factors and microstate parameters. Results of microstate analysis revealed that there was an unique spatial microstate different from healthy controls in PD, and several other typical microstates had significant differences compared with the normal control group, and these differences were reflected in the microstate parameters, such as longer durations and more occurrences of one class of microstates in PD compared with healthy controls. Furthermore, correlation analysis showed that there was a significant correlation between multiple microstate classes' parameters and significant clinical symptoms, including impaired motor function and cognitive decline. These results indicate that we have found multiple quantifiable feature tags that reflect brain dysfunction in the early stage of PD. Importantly, such temporal dynamics in microstates are correlated with clinical scales which represent the motor function and recognize level. The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.

Analysis of Spontaneous EEG Activity in Alzheimer's Disease Using Weighted Visibility Graph.

This study was aimed at characterizing spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) using a novel approach named weighted visibility graph (WVG). More than 10 minutes of spontaneous EEG were recorded from 15 AD patients and 15 age-matched normal controls. Two graph metrics, clustering coefficient and average weighted degree, are extracted in different frequency bands for each EEG channel based on the WVG methodology. Furthermore, statistical analysis was performed in different bands and channels for both groups. It is demonstrated that AD patients are characterized with a significant increase of clustering coefficient and degree in theta band, which can be observed in most brain regions. Our results suggest that the WVG method can be are effective to distinguish different brain states (AD and normal) and may provide further insights into the underlying brain dynamics in AD.

Modulation of Spectral Power and Functional Connectivity in Human Brain by Acupuncture Stimulation.

Acupuncture, as an external stimulation, can produce clinical effects via the central nervous system. In order to investigate the modulatory efficacy of acupuncture on brain activity, multichannel EEG signals evoked by acupuncture at "Zusanli" acupoint were recorded from healthy humans in three states: pre-acupuncture, acupuncture, and post-acupuncture. Power spectral density is first used to analyze the EEG power change during acupuncture process. It is found that EEG power significantly increased in the delta and alpha bands under acupuncture and high power level remained in alpha band after acupuncture. Then, we calculated phase lag index to quantify the phase synchronization of pair-wise channels. In acupuncture state, delta and alpha bands exhibit significantly higher synchronization degree than pre-acupuncture state. Additionally, post-effect of acupuncture can be observed in alpha band as high synchronization degree remains in post-acupuncture state. Moreover, functional brain networks converted from synchronization matrix in each band are reconstructed. Acupuncture increases long-range connections between left and right hemispheres and changes the position of main nodes. Graph theory metrics are extracted to explore the change of functional connectivity in different states. The result shows the functional networks in delta and alpha bands are small world networks (SWN) and acupuncture improves the SWN efficiency of functional network.

Gamma rhythm low field magnetic stimulation alleviates neuropathologic changes and rescues memory and cognitive impairments in a mouse model of Alzheimer's disease.

INTRODUCTION: The abnormal amyloid ß (Aß) accumulation and Aß-related neural network dysfunction are considered central to the pathogenesis of Alzheimer's disease (AD) at the early stage. Deep-brain reachable low field magnetic stimulation (DMS), a novel noninvasive approach that was designed to intervene the network activity in brains, has been found to alleviate stress-related cognitive impairments. METHODS: Amyloid precursor protein/presenilin-1 transgenic mice (5XFAD) were treated with DMS, and cognitive behavior and AD-like pathologic changes in the neurochemical and electrophysiological properties in 5XFAD mice were assessed. RESULTS: We demonstrate that DMS treatment enhances cognitive performances, attenuates Aß load, upregulates postsynaptic density protein 95 level, and promotes hippocampal long-term potentiation in 5XFAD mouse brain. Intriguingly, the gamma burst magnetic stimulation reverses the aberrant gamma oscillations in the transgenic hippocampal network. DISCUSSION: This work establishes a solid foundation for the effectiveness of DMS in treating AD and proposes a future study of gamma rhythm stimulation on reorganizing rhythmic neural activity in AD brain.

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease.

The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.

The Intervention of CRAC Channels Alleviates Inflammatory Responses in Nasal Polyps.

BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with Th2-dominant inflammation. However, effective treatments for CRSwNP have not yet been found. This study aimed to investigate the expression of Orai1 in nasal polyps (NP) and the influence on them of the intervention of Ca2+ release-activated Ca2+ (CRAC) channels. MATERIALS AND METHODS: Nasal samples were obtained from normal subjects or subjects with CRSwNP. We studied the distribution of Orai1 protein in NP and normal mucosa (normal group) using immunohistochemistry. These tissues in cultures were then maintained in the absence (control group) or presence of 2-aminoethoxydiphenyl borate (2-APB) for 24 h. Orai1 was examined by means of enzyme-linked immunosorbent assay (ELISA) and real-time reverse transcription-polymerase chain reaction (RT-PCR). The Ca2+ mean fluorescence intensity (MFI) was evaluated by flow cytometry, and the inflammatory mediators, including interleukin (IL)-4, IL-5, IL-13, Dermatophagoides pteronyssinus-specific IgE, leukotriene C4 and eosinophil cation protein in cultures, were analyzed with ELISA and real-time RT-PCR. RESULTS: The expression of Orai1 was localized to the cytoplasmic membrane of inflammatory cells, and upregulated in NP compared to that in the normal group. However, Orai1 protein was decreased in polyp tissues after the 2-APB treatment. The levels of Ca2+ MFI and above inflammatory mediators were also elevated in NP, and reduced after the 2-APB administration compared to those in the control group. CONCLUSIONS: Orai1 and CRAC channels may play a crucial role in NP formation and development, and the 2-APB intervention of Orai1 protein may alleviate inflammatory responses in NP.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

In Vitro Treatment with 2-APB Inhibits the Inflammation in Nasal Polyps.

OBJECTIVE: Glucocorticoids are considered the main treatment option for chronic rhinosinusitis with nasal polyps (CRSwNP), but their effect rate ranges from 60.9% to 80%. Novel therapeutic means should be studied. The purpose of this study was to investigate the expression of Orai1 in nasal polyps (NPs) and the influence of intervention of Orai1 on NPs after in vitro treatment of 2-aminoethoxydiphenyl borate (2-APB). STUDY DESIGN: Prospective cross-sectional study. SETTING: University hospital. SUBJECTS AND METHODS: Nasal biopsy samples were obtained from normal subjects or subjects with CRSwNP. We studied the localization of Orai1 protein in NPs by using immunohistochemistry. Then these tissues in cultures were maintained in the absence or presence of dexamethasone (DEX) or 2-APB. Orai1 was examined by Western blot, enzyme-linked immunosorbent assay (ELISA), and real-time reverse transcription-polymerase chain reaction (RT-PCR). Inflammatory mediators including interleukin (IL)-1ß, IL-5, eosinophil cation protein (ECP), leukotriene (LT)C4, interferon (IFN)-γ, and dermatophagoides pteronyssinus (DP)-specific immunoglobulin E (sIgE) as well as mucins (MUCs) including MUC5B and MUC7 in cultures were analyzed with ELISA and real-time RT-PCR. RESULTS: The expression of Orai1 was localized to cytoplasmic membrane of inflammatory cells and submucosal glandular cells and was upregulated in NPs compared with normal nasal mucosa. Orai1 was decreased in NPs after in vitro treatment of 2-APB but not after DEX intervention. The levels of inflammatory mediators and mucins were reduced more after 2-APB treatment when compared with those after DEX treatment. CONCLUSION: Orai1 may play crucial roles in NP formation, and the intervention of Orai1 may inhibit NP development.

Compensative shuttling of merlin to phosphorylation on serine 518 in vestibular schwannoma.

BACKGROUND: Vestibular schwannoma (VS) is a benign tumor with malignant biological consequence because of its special location, and its occurrence is highly related to merlin. Cell culture studies have demonstrated that merlin acts as a molecular linker between the cytoskeleton and specific membrane proteins and is linked to cell cycle control. Therefore, we sought to detect the expression of endogenous merlin and its subcellular distribution using cyclin D1 as a cell cycle marker in VS. METHODS: To confirm endogenous merlin protein expression in abiogenous VS cells, in situ hybridization, immunohistochemistry, and Western blot was performed in tumor tissues surgically resected from patients. Merlin's subcellular distribution linked cell cycle in VS was analyzed and compared with cyclin D1 expression evaluated with confocal microscopy. RESULTS: We found that merlin appeared in 98% of the VS tissue samples, with a mean cellular positivity of 46.66 +/- 5.75%. Merlin is inversely correlated with cyclin D1 in regard to subcellular localization. Merlin locates in the cytoplasm during G0/G1 phase, moves to the nucleus at S phase, and accumulates in the cytoplasm at S phase after phosphorylation on serine 518 (S518). CONCLUSION: In line with the pathologic characteristic of VS as a benign tumor, merlin compensates for the malignant effect of S518 phosphorylation by limiting the loss of contact inhibition of growth and controlling proliferation. S518 phosphorylation can trigger re-entry into the cell cycle, failing to control transcription because of mislocalization and an inability to interact with the cytoskeleton.