Sphingosine 1-phosphate (S1P) inhibits monocyte–endothelial cell interaction by regulating of RhoA activity

AbstractRecent studies suggest that sphingosine 1-phosphate (S1P) protects against atherosclerosis. We assessed the effects of S1P on monocyte–endothelial interaction in the presence of inflammatory mediators. Pretreatment of THP-1 cells with S1P abolished Phorbol 12 myristate 13-acetate (PMA)-induced THP-1 cell adhesion to human umbilical vein endothelial cells (HUVECs). S1P inhibited PMA-induced activation of RhoA, but not PKCs. S1P activated p190Rho GTPase activation protein (GAP) only in the presence of PMA, suggesting an inhibitory effect of S1P and PMA to suppress RhoA. In conclusion, S1P inhibited monocyte–endothelial interactions by inhibiting RhoA activity which may explain its anti-atherogenic effects

Coupling between infraslow activities and high-frequency oscillations precedes seizure onset

Hashimoto H., Khoo H.M., Yanagisawa T., et al. Coupling between infraslow activities and high-frequency oscillations precedes seizure onset. Epilepsia Open 5, 501 (2020); https://doi.org/10.1002/epi4.12425.Infraslow activities and high-frequency oscillations (HFOs) are observed in seizure-onset zones. However, the relation between them remains unclear. In this study, we investigated phase-amplitude coupling between infraslow phase (0.016-1 Hz) and HFOs' amplitude of focal impaired awareness seizures followed by focal to bilateral tonic-clonic seizures, in a 28-year-old right-handed man with a dysembryoplastic neuroepithelial tumor. We recorded five habitual seizures. After the time of seizure onset, a significant increase in the power of HFOs was observed, and the power was significantly coupled with θ (4-8 Hz) phase. In contrast, coupling of infraslow activities and HFOs surged a few minutes before the seizure-onset time, and ictal HFOs discharged after that. Collectively, our results show that coupling of infraslow activities and HFOs precedes the seizure-onset time. We infer that such coupling may be a potential biomarker for seizure prediction

Phase-amplitude coupling of ripple activities during seizure evolution with theta phase

Hashimoto H., Khoo H.M., Yanagisawa T., et al. Phase-amplitude coupling of ripple activities during seizure evolution with theta phase. Clinical Neurophysiology 132, 1243 (2021); https://doi.org/10.1016/j.clinph.2021.03.007.Objective: High-frequency activities (HFAs) and phase-amplitude coupling (PAC) are key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures. Methods: We used intracranial electrodes to record seizures of focal epilepsy (12 focal-to-bilateral tonic-clonic seizures and three focal-aware seizures in seven patients). The synchronization index, representing PAC, was used to analyze the coupling between the amplitude of ripples (80–250 Hz) and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were scaled linearly to the power changes of ripple. Results: The main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The ripple power increased simultaneously with rhythmic fluctuations from the seizure onset zone, and spread to other regions. Conclusions: Ripple activities during seizure evolution were modulated by the θ phase. The PAC phenomenon was visualized as rhythmic fluctuations. Significance: Ripple power associated with seizure evolution increased and spread with fluctuations. The θ oscillations related to the fluctuations might represent the common neurophysiological processing involved in seizure generation

Phase-amplitude coupling between infraslow and high-frequency activities well discriminates between the preictal and interictal states

Hashimoto H., Khoo H.M., Yanagisawa T., et al. Phase-amplitude coupling between infraslow and high-frequency activities well discriminates between the preictal and interictal states. Scientific Reports 11, 17405 (2021); https://doi.org/10.1038/s41598-021-96479-1.Infraslow activity (ISA) and high-frequency activity (HFA) are key biomarkers for studying epileptic seizures. We aimed to elucidate the relationship between ISA and HFA around seizure onset. We enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement. We comparatively analyzed the ISA, HFA, and ISA-HFA phase-amplitude coupling (PAC) in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. We recorded 15 seizures. HFA and ISA were larger in the ictal states than in the interictal or preictal state. During seizures, the HFA and ISA of the SOZ were larger and occurred earlier than those of nSOZ. In the preictal state, the ISA-HFA PAC of the SOZ was larger than that of the interictal state, and it began increasing at approximately 87 s before the seizure onset. The receiver-operating characteristic curve revealed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. This study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures. Our findings indicate that ISA-HFA PAC could be a useful biomarker for discriminating between the preictal and interictal states

Frequency band coupling with high-frequency activities in tonic-clonic seizures shifts from θ to δ band

Hashimoto H., Khoo H.M., Yanagisawa T., et al. Frequency band coupling with high-frequency activities in tonic-clonic seizures shifts from θ to δ band. Clinical Neurophysiology 137, 122 (2022); https://doi.org/10.1016/j.clinph.2022.02.015.Objective: To clarify variations in the relationship between high-frequency activities (HFAs) and low-frequency bands from the tonic to the clonic phase in focal to bilateral tonic-clonic seizures (FBTCS), using phase-amplitude coupling. Methods: This retrospective study enrolled six patients with drug-resistant focal epilepsy who underwent intracranial electrode placement at Osaka University Hospital (July 2018–July 2019). We recorded 11 FBTCS. The synchronization index (SI) and receiver-operating characteristic (ROC) analysis were used to analyze the coupling between HFA amplitude (80–250 Hz) and lower frequencies phase. Results: In the tonic phase, the θ (4–8 Hz)-HFA coupling peaked, and the HFA power occurred at baseline (0 μV) of θ oscillations. In contrast, in the clonic phase, the δ (2–4 Hz)-HFA coupling peaked, and the HFA power occurred at the trough of δ oscillations. ROC analysis indicated that the δ-HFA SI discriminated well the clonic from the tonic phase. Conclusions: The main low-frequency band modulating the HFA shifted from the θ band in the tonic phase to the δ band in the clonic phase. Significance: Neurophysiological key frequency bands were implied to be the θ band and δ band in tonic and clonic seizures, respectively, which improves our understanding of FBTCS

Behavior-level Analysis of a Successive Stochastic Approximation Analog-to-Digital Conversion System for Multi-channel Biomedical Data Acquisition

In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog frontends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximationregister ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method