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Background Prophylaxis with a von Willebrand factor (VWF) concentrate is recommended in von Willebrand disease (VWD) patients with a history of frequent and severe bleeds. Despite nosebleeds being a frequent manifestation of VWD, few studies have investigated the efficacy of factor prophylaxis in preventing nosebleeds in patients with severe VWD. Methods This post-hoc analysis of a prospective, 12-month, phase 3 study assessed the efficacy of wilate in the prevention of nosebleeds in 33 patients aged ≥6 years with severe type 1, type 2 or type 3 VWD. All patients previously participated in a 6-month prospective study of on-demand treatment with any VWF concentrate. Prophylactic efficacy was assessed by comparing total and spontaneous annualized bleeding rates for nosebleeds (TABR and SABR, respectively) during prophylaxis with rates during on-demand treatment. Results The mean TABR and SABR for nosebleeds were reduced by 76% and 81% respectively during prophylaxis versus on-demand treatment (2.7 vs 11.0 and 2.1 vs 10.9). During the second 6 months of prophylaxis, mean TABR was 49% lower and mean SABR 57% lower versus the first 6 months. The percentage of patients with zero nosebleeds was 19% during 6 months of on-demand treatment, 55% during the first 6 months of prophylaxis, and 68% during the second 6 months of prophylaxis. The efficacy of wilate in the treatment of breakthrough nosebleeds was rated excellent in 99% of cases. Conclusion This post-hoc analysis demonstrated the efficacy of wilate prophylaxis in the prevention and treatment of nosebleeds in children and adults with severe VWD.

Abstract The blood–brain barrier (BBB) is a barrier protecting the brain and a milieu of continuous exchanges between blood and brain. There is emerging evidence that the BBB plays a major role in epileptogenesis and drug‐resistant epilepsy, through several mechanisms, such as water homeostasis dysregulation, overexpression of drug transporters, and inflammation. Studies have shown abnormal water homeostasis in epileptic tissue and altered aquaporin‐4 water channel expression in animal epilepsy models. This review focuses on abnormal water exchange in epilepsy and describes recent non‐invasive MRI methods of quantifying water exchange. Plain Language Summary Abnormal exchange between blood and brain contribute to seizures and epilepsy. The authors describe why correct water balance is necessary for healthy brain functioning and how it is impacted in epilepsy. This review also presents recent MRI methods to measure water exchange in human brain. These measures would improve our understanding of factors leading to seizures.

The aetiology of nodding syndrome remains unclear, and comprehensive genotyping and phenotyping data from patients remain sparse. Our objectives were to characterize the phenotype of patients with nodding syndrome, investigate potential contributors to disease aetiology, and evaluate response to immunotherapy. This cohort study investigated members of a single-family unit from Lamwo District, Uganda. The participants for this study were selected by the Ugandan Ministry of Health as representative for nodding syndrome and with a conducive family structure for genomic analyses. Of the eight family members who participated in the study at the National Institutes of Health (NIH) Clinical Center, three had nodding syndrome. The three affected patients were extensively evaluated with metagenomic sequencing for infectious pathogens, exome sequencing, spinal fluid immune analyses, neurometabolic and toxicology testing, continuous electroencephalography and neuroimaging. Five unaffected family members underwent a subset of testing for comparison. A distinctive interictal pattern of sleep-activated bursts of generalized and multifocal epileptiform discharges and slowing was observed in two patients. Brain imaging showed two patients had mild generalized cerebral atrophy, and both patients and unaffected family members had excessive metal deposition in the basal ganglia. Trace metal biochemical evaluation was normal. CSF was non-inflammatory and one patient had CSF-restricted oligoclonal bands. Onchocerca volvulus-specific antibodies were present in all patients and skin snips were negative for active onchocerciasis. Metagenomic sequencing of serum and CSF revealed hepatitis B virus in the serum of one patient. Vitamin B6 metabolites were borderline low in all family members and CSF pyridoxine metabolites were normal. Mitochondrial DNA testing was normal. Exome sequencing did not identify potentially causal candidate gene variants. Nodding syndrome is characterized by a distinctive pattern of sleep-activated epileptiform activity. The associated growth stunting may be due to hypothalamic dysfunction. Extensive testing years after disease onset did not clarify a causal aetiology. A trial of immunomodulation (plasmapheresis in two patients and intravenous immunoglobulin in one patient) was given without short-term effect, but longer-term follow-up was not possible to fully assess any benefit of this intervention.

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs. However, traditional GNNs are limited in their ability to characterize FC edge attributes because they typically emphasize the importance of ROI node-based brain activation data. Line GNNs convert the edges of the original graph to nodes in the transformed graph, thereby emphasizing the FC between brain regions. We hypothesize that line GNNs will outperform traditional GNNs in FC applications. We investigated the performance of two common GNN architectures (GraphSAGE and GCN) trained on line and traditional graphs predicting task-associated FC changes across two datasets. The first dataset was from the Human Connectome Project (HCP) with 205 participants, the second was a dataset with 12 participants. The HCP dataset detailed FC changes in participants during a story-listening task, while the second dataset included the FC changes in a different auditory language task. Our findings from the HCP dataset indicated that line GNNs achieved lower mean squared error compared to traditional GNNs, with the line GraphSAGE model outperforming the traditional GraphSAGE by 18% (p

Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain.Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome.Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%.Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.

Sucking abilities are critical in early infant development, and the patterns of non-nutritive suck (NNS) have been found to potentially predict neurodevelopmental issues in the future. Proper NNS assessments are essential to ensure valid conclusions. Previous studies have shown that the level of infant arousal significantly affects NNS patterns. However, the author did not find any studies that observed the influence of infant hunger levels on NNS patterns. Therefore, this study aimed to develop an NNS assessment tool to characterize NNS patterns in infants with various hunger levels. The NNS assessment was conducted using a pressure transducer connected to a pacifier. The results showed that the level of hunger significantly affected the intra-burst frequency and the sucking pressure. The more hungry the infant, the more frequent the intra-burst frequency became, while the sucking pressure tended to decrease. The intra-burst frequency of infant sucking was 2.3, 2.46, and 2.5 Hz on average for a relative hunger index of 0.67, 0.83, and 1.0, respectively. The NNS pressure of infant sucking was 6.31, 4.51, and 2.62 kPa on average for a relative hunger index of 0.67, 0.83, and 1.0, respectively. This study's results suggest that during NNS assessments, the measurement time should consider the next feeding schedule for the infant.

Introdução: Na doença falciforme (DF), uma mutação pontual no gene da beta-globina resulta na produção de hemoglobina S (HbS), que pode polimerizar-se após desoxigenação. A polimerização distorce a membrana dos glóbulos vermelhos e gera eritrócitos falciformes, contribuindo para oclusões microvasculares que podem manifestar-se através de crises vaso-oclusivas agudas e dolorosas (CVOs). Para pacientes (pts) com DF 70% das visitas às emergências e 90% das hospitalizações estão relacionadas a CVOs. Apesar de tratamentos existentes para prevenção de CVO, morbidade e mortalidade permanecem consideráveis; o tratamento da CVO aguda é limitado a cuidados de suporte. Dados não clínicos relacionam a desregulação do complemento na fisiopatologia da DF, incluindo vasco-oclusão, hemólise, inflamação, trombogenicidade, ativação endotelial e lesões de órgãos. Crovalimabe (Crova) é um anticorpo monoclonal anti complemento C5 que permite adm SC de baixo volume após dose IV inicial. Descrevemos 2 estudos randomizados, controlados por placebo avaliando Crova em pts com DF: CROSSWALK-a (fase 1b; NCT04912869) e CROSSWALK-c (fase 2a; NCT05075824) com centros abertos atualmente no Brasil. Objetivos: CROSSWALK-a avalia segurança de Crova no manejo de CVOs agudas não-complicadas. CROSSWALK-c avalia eficácia e segurança de Crova como terapia adjuvante na prevenção de CVOs. Métodos: Pts 12-55 anos, pesando ≥40 kg, diagnosticados com DF (HbSS homozigótica ou talassemia heterozigótica HbS/β0), vacinados contra N. meningitidis, H. influenzae tipo B e S. pneumoniae são elegíveis. Terapias concomitantes para DF são permitidas. CROSSWALK-a: Pts com CVO aguda não complicada necessitando hospitalização e opióides parenterais, histórico de ≤10 CVOs nos 12 meses pré-randomização. Pts elegíveis serão randomizados 2:1 para dose única IV peso-dependente de Crova ou placebo, monitorados na hospitalização e durante observação por 84 dias, seguido de acompanhamento de segurança por 322 dias (10,5 meses). O objetivo princial do CROSSWALK-a é avaliar a segurança até Dia 84, incluindo: Incidência e gravidade de eventos adversos, Incidência e gravidade de reações relacionadas à infusão e hipersensibilidade, Alterações em sinais vitais e resultados de exames laboratoriais específicos. Serão avaliados também eficácia, farmacocinética (PK), farmacodinâmica (PD), imunogenicidade e biomarcador exploratório. CROSSWALK-c: São elegíveis Pts com 2-10 CVOs nos 12 meses pré-randomização e serão randomizados 1:1 para receber doses diferenciadas, peso-dependentes de Crova ou placebo, consistindo em doses iniciais (IV: Dia 1; SC: Dia 2 e semanalmente nas Semanas 2-4) e doses mensais de manutenção SC nas Semanas 5-49 em 48 semanas de tratamento. O objetivo principal do CROSSWALK-c é avaliar eficácia de Crova vs placebo até 48 semanas, com base na Taxa anual de CVOs manejadas em ambientes hospitalares. Principais objetivos secundários de eficácia são: Taxas anuais de CVOs domiciliares, síndrome torácica aguda, dias de hospitalização para manejo de CVOs, Fadiga relatada por pacientes adultos. Parâmetros de segurança, farmacocinética, farmacodinâmica, imunogenicidade e biomarcador exploratório também serão avaliados. Resultados iniciais serão publicados após o readout do estudo.

In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

The sensory cortices of many mammals consist of modules in the form of cortical columns. By analyzing functional connectivity and neural responses to visual stimuli, the authors show that this organization may extend to the human temporal lobe.

Abstract We investigated the association between human herpesvirus 6 (HHV‐6) and mesial temporal sclerosis (MTS) in 87 patients who had surgery for drug‐resistant epilepsy. Fifty‐four had MTS, 22 focal cortical dysplasia (FCD), four tumors, three vascular malformations, and three a history of encephalitis. We extracted DNA from fresh brain tissue immediately after surgery and performed viral detection with quantitative real‐time polymerase chain reaction (PCR) or digital droplet PCR specific for HHV‐6A and HHV‐6B. Tissue was studied with standard clinical techniques, including hematoxylin and eosin, glial fibrillary acidic protein, and NeuN stains. Twenty‐nine of 54 patients with MTS, six of 23 with focal cortical dysplasia (FCD), and one of three with a history of encephalitis were positive for HHV‐6 (P

[This corrects the article DOI: 10.1371/journal.pone.0161345.].

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs. However, traditional GNNs are limited in their ability to characterize FC edge attributes because they typically emphasize the importance of ROI node-based brain activation data. Line GNNs convert the edges of the original graph to nodes in the transformed graph, thereby emphasizing the FC between brain regions. We hypothesize that line GNNs will outperform traditional GNNs in FC applications. We investigated the performance of two common GNN architectures (GraphSAGE and GCN) trained on line and traditional graphs predicting task-associated FC changes across two datasets. The first dataset was from the Human Connectome Project (HCP) with 205 participants, the second was a dataset with 12 participants. The HCP dataset detailed FC changes in participants during a story-listening task, while the second dataset included the FC changes in a different auditory language task. Our findings from the HCP dataset indicated that line GNNs achieved lower mean squared error compared to traditional GNNs, with the line GraphSAGE model outperforming the traditional GraphSAGE by 18% (p < 0.0001). When applying the same models to the second dataset, both line GNNs also showed statistically significant improvements over their traditional counterparts with little to no overfitting. We believe this shows that line GNN models demonstrate promising utility in FC studies. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors contributing to and affecting consumer behavior toward renewable energy by following the theories of reasoned action and planned behavior. For this reason, a quantitative research method was employed and primary data were collected via a questionnaire, resulting in a random sample of 450 respondents. Structural equation modeling (SEM) revealed that concern for environmental commitment positively affects attitudes toward solar energy (β = 0.272, p < 0.001), positive subjective norms toward environmental commitment positively influence attitudes (β = 0.092, p < 0.001), perceptions of solar energy values significantly shape attitudes (β = 0.533, p < 0.001), social influence also plays a role in shaping attitudes (β = 0.047, p < 0.001), and that regulations (behavioral control) have a negative impact on attitudes (β = −0.204, p < 0.001). A positive attitude toward solar energy strongly predicts purchase intention (β = 0.944, p < 0.001). The overall model highlights the direct influence of attitude on buying intention and underscores the roles of environmental concern and functional utility in shaping consumer attitudes. This study contributes to the existing literature on renewable energy adoption by providing empirical evidence on the factors influencing consumers' attitudes toward solar energy systems. By identifying key predictors, the study offers valuable insights into how these elements shape consumer attitudes and subsequent purchase intentions. Additionally, the study enhances the understanding of the role of positive attitudes in driving investment in solar energy, thereby contributing to both theoretical frameworks and practical applications in energy policy and marketing strategies. [ABSTRACT FROM AUTHOR]

Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG. [ABSTRACT FROM AUTHOR]

Haque et al. demonstrate that the episodic memory of a single visual scene is sufficient for humans to recognize if a visual scene has subsequently changed. A prediction error signal first arises in the visual association cortex when individuals recognize these changes.

Abstract Objective To study the effects of human herpes virus 6 (HHV‐6) on the hippocampal volume in patients with mesial temporal sclerosis (MTS). Background HHV‐6 may play an etiologic role in MTS. Previous studies found a possible association with febrile status epilepticus. Several investigators have reported a higher prevalence of HHV‐6 in MTS resections compared to other epilepsy etiologies. Design/Methods We used FreeSurfer to segment cortical structures and obtain whole hippocampal and subfield volumes in 41 patients with intractable epilepsy. In addition, an investigator blinded to other data traced hippocampi manually on each slice. The main study outcome measure was the asymmetry index (AI) between hippocampal volumes ipsilateral and contralateral to seizure foci compared between HHV‐6 positive and negative patients. Viral DNA was isolated from fresh brain tissue obtained at temporal lobectomy. For 25 patients, viral detection was performed using quantitative real‐time PCR specific for HHV‐6A and HHV‐6B. For 16 patients, viral DNA detection was performed using digital droplet PCR specific for HHV‐6A and HHV‐6B. Results Twenty‐two patients were positive (14 of 25 tested with real‐time PCR, and 8 of 16 with digital droplet PCR), and 19 negatives for HHV‐6. HHV‐6 negative patients had significantly greater AI and lower total hippocampal volume ipsilateral to seizure foci than HHV‐6 positive patients. Epilepsy duration and age of onset did not affect results. Interpretation Our data suggest multiple potential etiologies for MTS. HHV‐6 may have a less severe effect on the hippocampus than other etiologies.

Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.