Inhibiting the IRAK4/NF-κB/NLRP3 signaling pathway can reduce pyroptosis in hippocampal neurons and seizure episodes in epilepsy.

BACKGROUND: Interleukin-1 receptor-associated kinase 4 (IRAK4) plays an important role in immune modulation in various central nervous system disorders. However, IRAK4 has not been reported in epilepsy models in animal and clinical studies, nor has its involvement in regulating pyroptosis in epilepsy. METHOD: First, we performed transcriptome sequencing, quantitative real-time polymerase chain reaction, and western blot analysis on the hippocampal tissues of refractory epilepsy patients to measure the mRNA and protein levels of IRAK4 and pyroptosis-related proteins. Second, we successfully established a pentylenetetrazol (PTZ)-induced seizure mouse model. We conducted behavioral tests, electroencephalography, virus injection, and molecular biology experiments to investigate the role of IRAK4 in seizure activity regulation. RESULTS: IRAK4 is upregulated in the hippocampus of epilepsy patients and PTZ-induced seizure model mice. IRAK4 expression is observed in the hilar neurons of PTZ-induced mice. Knocking down IRAK4 in PTZ-induced mice downregulated pyroptosis-related protein expression and alleviated seizure activity. Overexpressing IRAK4 in naive mice upregulated pyroptosis-related protein expression and increased PTZ-induced abnormal neuronal discharges. IRAK4 and NF-κB were found to bind to each other in patient hippocampal tissue samples. Pyrrolidine dithiocarbamate reversed the pyroptosis-related protein expression increase caused by PTZ. PF-06650833 alleviated seizure activity and inhibited pyroptosis in PTZ-induced seizure mice. CONCLUSION: IRAK4 plays a key role in the pathological process of epilepsy, and its potential mechanism may be related to pyroptosis mediated by the NF-κB/NLRP3 signaling pathway. PF-06650833 has potential as a therapeutic agent for alleviating epilepsy.

Awareness, attitudes and first aid knowledge of epilepsy among university students - A cross-sectional study in Henan Province, China.

PURPOSE: Epilepsy is a debilitating disease that can lead to series of social and psychological issues, impairing the quality of life of people with epilepsy (PWE). This survey aimed to investigate the awareness, attitudes, and first-aid knowledge of epilepsy in university students METHOD: This cross-sectional study was conducted in Henan Province, China between January 1 and April 30, 2022. Students majored in education, medicine, science and engineering from 8 universities attended the study. The survey questionnaire comprised 28 questions covering 4 sections: demographic characteristics, awareness of epilepsy, attitudes toward PWE and knowledge of first aid for seizures. RESULTS: A total of 2376 university students completed the questionnaire. 94.7% heard of epilepsy. In the first aid knowledge section, individual question was correctly answered by at least 50% students, 9.3% students correctly answered all questions. Attitude toward PWE was independently (R2 =0.108, F=73.227, p < 0.001) associated with both awareness of epilepsy (B=0.411, p < 0.001) and first aid knowledge of epilepsy (B=0.047, p = 0.001). Among the three majors, medical students had more positive attitudes toward PWE than students majored in education, science and engineering (p < 0.05). However, medical students performed worse among the groups when answering the first aid knowledge questions. CONCLUSION: This survey showed that university students in Central China had a good awareness of epilepsy. For medical students, improvements are necessary for the awareness of the first aid knowledge for seizure.

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features.

BACKGROUND: This study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML). METHODS: Nineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel-Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method. RESULTS: The prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, ß_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV. CONCLUSIONS: The model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

The influence of resistance exercise and aerobic exercise on type 2 diabetes: a meta-analysis.

INTRODUCTION: Diabetes is a worldwide chronic disease. The incidence rate of this disease is high, and it is a common disease in clinics. At present, the incidence rate of diabetes patients is increasing year by year due to the increasing work pressure, the accelerated pace of life, the change of diet, the reduction of labor, and the acceleration of aging. EVIDENCE ACQUISITION: The computer retrieves four databases to obtain random controlled trials on the influence of resistance exercise and aerobic exercise on type 2 diabetes. After a rigorous literature quality evaluation, data analysis was performed using RevMan 5.3 software. EVIDENCE SYNTHESIS: Ten studies were ultimately included in this meta-analysis. 10 studies reported the HbA1c of the test group and the control group, which was no significant statistical significance (SMD: -0.01; 95% CI: -0.20,0.19; P=0.959) than the control group, HOMA-IR (SMD: 0.02; 95% CI: -0.65,0.69; P=0.954), SBP (SMD: 3.92; 95% CI: -0.92,8.75; P=0.112), DBP (SMD: 0.67; 95% CI: -3.66,5.01; P=0.761), HDL (SMD: -0.08; 95% CI: -2.79,2.64; P=0.955), TG (SMD: -7.51; 95% CI: -21.25,6.22; P=0.284) and TC (SMD: 9.10; 95% CI: -13.43,31.62; P=0.428). CONCLUSIONS: The results of this study suggest that both resistance exercise and aerobic exercise may be effective on patients with type 2 diabetes, as evidenced by HbA1c, HOMA-IR, SBP, DBP, HDL, TG and TC. There is no significant difference in their impact on type 2 diabetes patients, and the above conclusions need to be verified by more high-quality studies.

Long Non-Coding RNA MALAT1 Protects Against Spinal Cord Injury via Suppressing microRNA-125b-5p Mediated Microglial M1 Polarization, Neuroinflammation, and Neural Apoptosis.

Our previous studies have discovered that long non-coding RNA (lncRNA) MALAT1 and its target microRNA-125b-5p (miR-125b-5p) are implicated in neurological diseases via regulating neuroinflammation and neuronal injury. This study aimed to further explore the relationship between lncRNA MALAT1 and miR-125b-5p, as well as their effect on microglial activation, neuroinflammation, and neural apoptosis in spinal cord injury (SCI). Primary microglia from Sprague Dawley rats were stimulated with lipopolysaccharide (LPS). Then, microglia were transfected with lncRNA MALAT1 overexpression or knock-down adenovirus-associated virus with or without miR-125b-5p mimic. The culture medium of microglia was incubated with primary neurons. SCI rats were established for in vivo validation. LncRNA MALAT1 expression was reduced by LPS treatment in a dose-dependent manner. LncRNA MALAT1 overexpression suppressed the microglial M1 polarization (decreased iNOS but increased ARG1), neuroinflammation (declined PTGS2, TNF-α, IL-1ß, and IL-6), and microglia-induced neural apoptosis (lower TUNEL positive cells and C-caspase3 but higher BCL2) under LPS treatment; its knock-down displayed the opposite trend. Moreover, lncRNA MALAT1 directly bound to and negatively regulated miR-125b-5p. MiR-125b-5p mimic promoted microglial M1 polarization, neuroinflammation, and microglia-induced neural apoptosis following LPS treatment; also, it could attenuate the effect of lncRNA MALAT1. Further in vivo study displayed that lncRNA MALAT1 overexpression elevated the Basso-Beattie-Bresnahan motor function score and improved neural injury. Also, in vivo validation indicated a similar effect of lncRNA MALAT1 on microglial polarization and neuroinflammation as in vitro. LncRNA MALAT1 improves SCI recovery via miR-125b-5p mediated microglial M1 polarization, neuroinflammation, and neural apoptosis.

A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram.

OBJECTIVE: Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG). METHODS: EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance. RESULTS: The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band. CONCLUSIONS: The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.

Flexible Switching Pressure Sensors with Fast Response and Less Bending-Sensitive Performance Applied to Pain-Perception-Mimetic Gloves.

A strategy is proposed herein for preparing a flexible switching piezoresistive pressure sensor, which has a bridge-like structure and inverted micropyramids (IMPs) on its lower conductive substrate. The sensor substrates were prepared by injection compression molding (an industrial manufacturing process) using thermoplastic polyurethane (TPU; an industrial grade polymer). The designed bridge-like structure enables the sensor to obtain a pressure threshold. The flexibility of upper and lower TPU substrates allows them to contact quickly when pressed, and so the sensor exhibits a fast response (as short as 2 ms) and can respond to both static force and dynamic force (up to 50 Hz frequency), which are prominent for the sensor made from TPU. The sensor exhibits less bending-sensitive performance, which is attributed to the conformality of the upper and lower substrates and lower strain on the lower substrate with the IMP under bending. The sensor can amplify signal response at the monitoring limit (the relative resistance change is up to 46%). It can achieve a higher sensitivity in different low-pressure ranges by changing the gap of the bridge-like structure. Moreover, the sensor can obviously and steadily respond to an additional very low pressure under preloading and exhibits good durability performance. As the sensor has a pressure threshold similar to the human pain perception process, a pain-perception-mimetic glove that can identify the external mechanical stimuli but reduces the interference of finger bending is prepared, displaying potential applications of the flexible switching sensor in intelligent wearable protectors.

COVID-19 vaccination for patients with epilepsy: A Chinese expert consensus.

Coronavirus disease-2019 (COVID-19) first emerged in late 2019 and has since spread worldwide. More than 600 million people have been diagnosed with COVID-19, and over 6 million have died. Vaccination against COVID-19 is one of the best ways to protect humans. Epilepsy is a common disease, and there are approximately 10 million patients with epilepsy (PWE) in China. However, China has listed "uncontrolled epilepsy" as a contraindication for COVID-19 vaccination, which makes many PWE reluctant to get COVID-19 vaccination, greatly affecting the health of these patients in the COVID-19 epidemic. However, recent clinical practice has shown that although a small percentage of PWE may experience an increased frequency of seizures after COVID-19 vaccination, the benefits of COVID-19 vaccination for PWE far outweigh the risks, suggesting that COVID-19 vaccination is safe and recommended for PWE. Nonetheless, vaccination strategies vary for different PWE, and this consensus provides specific recommendations for PWE to be vaccinated against COVID-19.

Delayed 18F-FDG PET imaging provides better metabolic asymmetry in potential epileptogenic zone in temporal lobe epilepsy.

Objective: To investigate the value of 18F-FDG positron emission tomography/computed tomography (PET/CT) two time point imaging for the identification of the potential epileptogenic zone (EZ) in temporal lobe epilepsy (TLE). Methods: Fifty-two patients with TLE were prospectively enrolled in the 18F-FDG PET/CT two time point imaging study. The early imaging was obtained approximately 40 min (43.44 ± 18.04 min) after 18F-FDG injection, and the delayed imaging was obtained about 2 to 3 h (160.46 ± 28.70 min) after the injection. Visual and semi-quantitative analysis of 18F-FDG uptake were performed at the two time points in EZ and contralateral symmetrical region. The mean standardized uptake value (SUVmean) of EZ and contralateral symmetrical region was calculated to determine the asymmetry index (AI) of the early and delayed images, as well as in the MRI positive and negative patient groups. Results: Semi-quantitative analysis demonstrated that AI of the early and delayed 18F-FDG PET/CT images was 13.47 ± 6.10 and 16.43 ± 6.66, respectively. The ΔAI was 2.95 ± 3.05 in 52 TLE patients between the two time points. The AI of the EZ was significantly elevated in delayed images compared to the early images (p < 0.001). The AI of delayed imaging was also significantly elevated compared to the early imaging in both MRI positive (ΔAI = 2.81 ± 2.54, p < 0.001) and MRI negative (ΔAI = 3.21 ± 3.91, p < 0.003) groups, and more pronounced in MRI negative group. Visual analysis also showed that the delayed imaging appeared to be superior to the early imaging for identification of potential EZ. Conclusion: Delayed 18F-FDG PET imaging provided significantly better than the early imaging in the identification of potential EZ, which can be valuable during epilepsy pre-surgical evaluation in patients with TLE.

Clinical features and brain MRI volumetric changes in anti-mGluR5 encephalitis.

BACKGROUND: Anti-metabotropic glutamate receptor 5 (mGluR5) encephalitis is a rare and under-recognized autoimmune encephalitis. This study is conducted to characterize its clinical and neuroimaging features. METHODS: Twenty-nine patients with anti-mGluR5 encephalitis (15 new cases identified in this study and 14 previously reported cases) were included in this study and their clinical features were characterized. Brain MRI volumetric analysis using FreeSurfer software was performed in 9 new patients and compared with 25 healthy controls at both early (≤6 months of onset) and chronic (>1 year of onset) disease stages. RESULTS: The common clinical manifestations of anti-mGluR5 encephalitis included cognitive deficits (n = 21, 72.4%), behavioral and mood disturbances (n = 20, 69%), seizures (n = 16, 55.2%), and sleep disorder (n = 13, 44.8%). Tumors were observed in 7 patients. Brain MRI T2/FLAIR signal hyperintensities were observed predominantly in mesiotemporal and subcortical regions in 75.9% patients. MRI volumetric analysis demonstrated significant amygdala enlargement in both early and chronic disease stages compared to healthy controls (P < 0.001). Twenty-six patients had complete or partial recovery, one remained stable, one died and one was lost to follow-up. CONCLUSION: Our findings demonstrated that cognitive impairment, behavioral disturbance, seizures, and sleep disorder are the prominent clinical manifestations of anti-mGluR5 encephalitis. Most patients showed a good prognosis with full recovery, even in the paraneoplastic disease variants. The amygdala enlargement in the early and chronic disease stages is a distinct MRI feature, which exploratively offer a valuable perspective for the study of the disease processes.

Generative AI for brain image computing and brain network computing: a review.

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.

Computation-Efficient Fault Detection Framework for Partially Known Nonlinear Distributed Parameter Systems.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Scalable generation of sensory neurons from human pluripotent stem cells.

Development of new non-addictive analgesics requires advanced strategies to differentiate human pluripotent stem cells (hPSCs) into relevant cell types. Following principles of developmental biology and translational applicability, here we developed an efficient stepwise differentiation method for peptidergic and non-peptidergic nociceptors. By modulating specific cell signaling pathways, hPSCs were first converted into SOX10+ neural crest, followed by differentiation into sensory neurons. Detailed characterization, including ultrastructural analysis, confirmed that the hPSC-derived nociceptors displayed cellular and molecular features comparable to native dorsal root ganglion (DRG) neurons, and expressed high-threshold primary sensory neuron markers, transcription factors, neuropeptides, and over 150 ion channels and receptors relevant for pain research and axonal growth/regeneration studies (e.g., TRPV1, NAV1.7, NAV1.8, TAC1, CALCA, GAP43, DPYSL2, NMNAT2). Moreover, after confirming robust functional activities and differential response to noxious stimuli and specific drugs, a robotic cell culture system was employed to produce large quantities of human sensory neurons, which can be used to develop nociceptor-selective analgesics.

The potential of the Lempel-Ziv complexity of the EEG in diagnosing cognitive impairment in patients with temporal lobe epilepsy.

AIM: To analyze whether the Lempel-Ziv Complexity (LZC) in quantitative electroencephalogram differs between the temporal lobe epilepsy (TLE) patients with or without cognitive impairment (CI) and explore the diagnostic value of LZC for identifying CI in TLE patients. METHODS: Twenty-two clinical features and 20-min EEG recordings were collected from 48 TLE patients with CI and 27 cognitively normal (CON) TLE patients. Seventy-six LZC features were calculated for 19 leads in four frequency bands (alpha, beta, delta, and theta). The clinical and LZC features were compared between the two groups. A support vector machine (SVM) was subsequently constructed using the leave-one-out method of cross-validation for LZC features with statistical differences. RESULTS: Regarding the clinical features, the level of education (p < .001), hippocampal atrophy and sclerosis (p = .029), and depression (p = .037) were statistically different between the two groups. For the LZC features, there were statistically significant differences in the alpha (Fp1, Fz, Cz, Pz, C3, C4, T3, T4, T5, T6, F3, F4, F7, F8, O1, and O2), beta (Fp2), and theta (F7) oscillations. The mean LZC in the alpha band was higher in the TLE-CI group than that in the CON group, and there were no differences in the remaining bands. The SVM model showed 74.51% accuracy, 79.63% sensitivity, 84.30% F1 score, 68.75% specificity, and .85 area under the curve scores. CONCLUSIONS: The LZC in the alpha band might have the potential to be used as a biomarker for the diagnosis of TLE combined with CI. The TLE-CI group, on the other hand, exhibited a higher degree of complexity in alpha oscillations, which were widespread and occurred in all brain regions.

Use-Dependent Relief of Inhibition of Nav1.8 Channels by A-887826.

Sodium channel inhibitors used as local anesthetics, antiarrhythmics, or antiepileptics typically have the property of use-dependent inhibition, whereby inhibition is enhanced by repetitive channel activation. For targeting pain, Nav1.8 channels are an attractive target because they are prominent in primary pain-sensing neurons, with little or no expression in most other kinds of neurons, and a number of Nav1.8-targeted compounds have been developed. We examined the characteristics of Nav1.8 inhibition by one of the most potent Nav1.8 inhibitors so far described, A-887826, and found that when studied with physiologic resting potentials and physiologic temperatures, inhibition had strong "reverse use dependence", whereby inhibition was relieved by repetitive short depolarizations. This effect was much stronger with A-887826 than with A-803467, another Nav1.8 inhibitor. The use-dependent relief from inhibition was seen in both human Nav1.8 channels studied in a cell line and in native Nav1.8 channels in mouse dorsal root ganglion (DRG) neurons. In native Nav1.8 channels, substantial relief of inhibition occurred during repetitive stimulation by action potential waveforms at 5 Hz, suggesting that the phenomenon is likely important under physiologic conditions. SIGNIFICANCE STATEMENT: Nav1.8 sodium channels are expressed in primary pain-sensing neurons and are a prime current target for new drugs for pain. This work shows that one of the most potent Nav1.8 inhibitors, A-887826, has the unusual property that inhibition is relieved by repeated short depolarizations. This "reverse use dependence" may reduce inhibition during physiological firing and should be selected against in drug development.

PR-957 Suppresses Th1 and Th17 Cell Differentiation via Inactivating PI3K/AKT Pathway in Alzheimer's Disease.

PR-957 [low molecular mass polypeptide (LMP)-7 selective inhibitor] regulates T helper (Th) cell differentiation and inflammatory response in multiple neurological diseases. Hence, this study aimed to explore the effect of PR-957 on Th1/Th2/Th17 cell differentiation, therapeutic efficacy and its potential mechanisms in Alzheimer's disease (AD). The LMP7 expressions in peripheral blood mononuclear cells from 30 AD patients and 30 healthy controls (HC) were detected. PR-957 was added for the incubation of naive cluster of differentiation (CD)4+ T cells from AD patients, then SC79 [phosphorylated protein kinase B (pAKT) agonist] was added. LMP7, Th1 cells, and Th17 cells were upregulated, while Th2 cells were downregulated in AD patients compared to HC. Also, LMP7 was positively related to Th1 cells and Th17 cells, but it did not correlate with Th2 cells in AD patients. PR-957 treatment downregulated Th1 cells, Th17 cells, and their secreted cytokines as well as phosphorylated phosphoinositide 3-kinase (pPI3K)/PI3K and pAKT/AKT expressions in AD CD4+ T cells. SC79 addition upregulated pAKT/AKT expression, Th1 cells, and Th17 cells, while downregulated Th2 cells; also SC79 could alleviate the effect of PR-957 on regulating PI3K/AKT pathway and Th1, Th2, and Th17 cell differentiation in AD CD4+ T cells. Furthermore, PR-957 attenuated cognitive impairment and neurofibrillary tangle; also it inhibited Th17 cell differentiation and PI3K/AKT pathway in the brain and spleen of AD mice. In conclusion, PR-957 suppresses Th1 and Th17 cell differentiation, attenuates neural injury and improves cognitive function via inactivating PI3K/AKT pathway in AD.

Robust Adaptive Fault-Tolerant Control for a Riser-Vessel System With Input Hysteresis and Time-Varying Output Constraints.

Recently, with the development of the marine economy, marine risers have garnered increasing attention as they present facile and reliable methods for oil and gas transportation. However, these risers are susceptible to vibrations, which can lead to system performance degradation and fatigue damage. Therefore, effective vibration control strategies are required to address this issue. In this study, a novel adaptive fault-tolerant control (FTC) strategy is adopted to suppress the vibrations of a 3-D riser-vessel system against the effects of actuator failures, backlash-like hysteresis, and external disturbances. A barrier-based Lyapunov function is merged to eliminate the time-varying output constraints of the system. Adaptive FTC laws with projection mapping operators are designed to compensate for parameter uncertainties and consider input nonlinearities to improve system robustness. Finally, a rigorous Lyapunov analysis and numerical simulations are performed to verify the validity of the proposed controller and guarantee uniformly bounded stability of the system.

The performance evaluation of the state-of-the-art EEG-based seizure prediction models.

The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.

Efficacy and Safety of the Ketogenic Diet for Mitochondrial Disease With Epilepsy: A Prospective, Open-labeled, Controlled Study.

Background: The ketogenic diet (KD) is increasingly used to treat drug-resistant epilepsy because of its favorable effect on seizure reduction. Patients with mitochondrial diseases tend to experience seizures. Therefore, this study aimed to test the efficacy of the KD on participants with mitochondrial diseases in a controlled trial. Methods: Participants from fourteen clinical centers who were diagnosed with mitochondrial disease were semi-randomized to either the intervention (KD) or control group. The KD group followed a 3-month KD intervention, while the control group received a 1-month normal diet initially and then a 3-month KD intervention. The primary outcome measure was seizure reduction. Biomarker changes, cognitive impairments, and side effects were also recorded, if available. Result: A total of 33 participants were assigned to the KD (n = 22) and control groups (n = 11). In the KD group, 31.8% (7/22) of participants achieved ≥50% seizure reduction after 1 month of diet intervention, which increased to 40.9% (9/22) at 3 months. In the control group, only 18.2% (2/11) of the participants had ≥50% seizure reduction during the normal diet period. After the control group was transferred to the KD, 63.6% (7/11) of participants had >50% seizure reduction, and this rate increased to 72.7% (8/11) at 3 months. The KD also showed high efficacy in participants with mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) or pathogenic variants in mitochondrial DNA (mtDNA) (90% and 93.3% response rates, respectively). The most frequent side effects reported at the 3-month review were vomiting, cold, hyperlipidemia, and bloating. Conclusion: The KD is a safe and effective therapy for seizure control in mitochondrial diseases, especially MELAS and pathogenic variants of mtDNA. KD intervention can be considered in the management of these patients.

Uncovering a new route to pain therapy.

High-voltage-activated calcium channels (HVACCs) are promising targets for developing analgesics given their roles in controlling synaptic transmission, neuronal excitability and neuropeptide release in primary nociceptive neurons. Despite previous efforts in developing HVACCs inhibitors of various drug modalities, it remains undetermined whether targeting HVACCs directly by a gene therapy approach could lead to pain alleviation in vivo. To test this, Sun and colleagues adopted a post-translational ubiquitination-based knockdown method targeting HVACCs in primary sensory neurons. They showed ablation of HVACC currents in a subset of primary sensory neurons, dampened hyperexcitability of sensory neurons after nerve injury and reduced pain behavior with no apparent adverse effects [1]. The results open the possibility of targeting ion channels with ubiquitination-based knockdown as a promising gene therapy candidate for pain treatment in future clinical studies.