Comparative Antioxidant and Anti-gout Activities of Citrullus colocynthis loaded Fruit Silver nanoparticles with its Ethanolic extract.

Background: The biological synthesis of silver nanoparticles (AgNPs) using plant materials is a rapidly developing method with several alternative medical applications. This comparative study of ethanolic fruit extract of Citrullus colocynthis (C. colocynthis) (EFECC) and synthesized silver nanoparticles (CC-AgNPs) were carried out for antioxidants and anti-gout arthritic activities. Methods: The AgNPs were synthesized using C. colocynthis fruit and its characterization was done by UV-visible spectroscopy, TEM, XRD and FT-IR. The 90% ethanol was used for extract preparation. Antioxidant activity was analyzed by DPPH and the Hydrogen Peroxide (H2O2) method. In vitro anti-arthritic activity was tested by xanthine oxidase inhibition, protein denaturation and HRBC membrane stabilization assay. Results: The synthesized CC-AgNPs were confirmed by UV-vis spectroscopy and TEM images displayed spherical shapes with 10-45 nm size range. Furthermore, the functional groups and crystalline structure of CC-AgNPs were determined by FT-IR and XRD analysis. The biosynthesized CC-AgNPs exhibited an excellent free radical scavenging ability than EFECC. In anti-arthritic activity, the CC-AgNPs showed effective inhibition of xanthine oxidase production, protein denaturation, and damaged RBC membranes compared to EFECC. Conclusion: The antioxidant activities and in vitro anti-arthritic assays revealed that CC-AgNPs are better anti-gout agents than EFECC. This research suggested that biosynthesized silver nanoparticles from C. colocynthis fruit are an important target in the field of anti-gout drug discovery.

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method.

INTRODUCTION: Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE). METHODS: We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ's real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists. RESULTS: Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%. DISCUSSION: Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity. CONCLUSIONS: Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ's high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

The interictal mesial temporal lobe epilepsy network.

OBJECTIVE: Identification of patient-specific epileptogenic networks is critical to designing successful treatment strategies. Multiple noninvasive methods have been used to characterize epileptogenic networks. However, these methods lack the spatiotemporal resolution to allow precise localization of epileptiform activity. We used intracranial recordings, at much higher spatiotemporal resolution, across a cohort of patients with mesial temporal lobe epilepsy (MTLE) to delineate features common to their epileptogenic networks. We used interictal rather than seizure data because interictal spikes occur more frequently, providing us greater power for analyzing variances in the network. METHODS: Intracranial recordings from 10 medically refractory MTLE patients were analyzed. In each patient, hour-long recordings were selected for having frequent interictal discharges and no ictal events. For all possible pairs of electrodes, conditional probability of the occurrence of interictal spikes within a 150-millisecond bin was computed. These probabilities were used to construct a weighted graph between all electrodes, and the node degree was estimated. To assess the relationship of the highly connected regions in this network to the clinically identified seizure network, logistic regression was used to model the regions that were surgically resected using weighted node degree and number of spikes in each channel as factors. Lastly, the conditional spike probability was normalized and averaged across patients to visualize the MTLE network at group level. RESULTS: We generated the first graph of connectivity across a cohort of MTLE patients using interictal activity. The most consistent connections were hippocampus to amygdala, anterior fusiform cortex to hippocampus, and parahippocampal gyrus projections to amygdala. Additionally, the weighted node degree and number of spikes modeled the brain regions identified as seizure networks by clinicians. SIGNIFICANCE: Apart from identifying interictal measures that can model patient-specific epileptogenic networks, we also produce a group map of network connectivity from a cohort of MTLE patients.

Network-based brain stimulation selectively impairs spatial retrieval.

BACKGROUND: Direct brain stimulation via electrodes implanted for intracranial electroencephalography (iEEG) permits the modulation of endogenous electrical signals with significantly greater spatial and temporal specificity than non-invasive approaches. It also allows for the stimulation of deep brain structures important to memory, such as the hippocampus, that are difficult, if not impossible, to target non-invasively. Direct stimulation studies of these deep memory structures, though, have produced mixed results, with some reporting improvement, some impairment, and others, no consistent changes. OBJECTIVE/HYPOTHESIS: We hypothesize that to modulate cognitive function using brain stimulation, it is essential to modulate connected nodes comprising a network, rather than just alter local activity. METHODS: iEEG data collected while patients performed a spatiotemporal memory retrieval task were used to map frequency-specific, coherent oscillatory activity between different brain regions associated with successful memory retrieval. We used these to identify two target nodes that exhibited selectively stronger coupling for spatial vs. temporal retrieval. In a subsequent session, electrical stimulation - theta-bursts with a fixed phase-lag (0° or 180°) - was applied to the two target regions while patients performed spatiotemporal retrieval. RESULTS: Stimulation selectively impaired spatial retrieval while not affecting temporal retrieval, and this selective impairment was associated with theta decoupling of the spatial retrieval network. CONCLUSION: These findings suggest that stimulating tightly connected nodes in a functional network at the appropriate phase-lag may effectively modulate the network function, and while in this case it impaired memory processes, it sets a foundation for further network-based perturbation studies.

Role of CA3 theta-modulated interneurons during the transition to spontaneous seizures.

Multiple studies have observed heterogeneous neuronal firing patterns as a local network transitions to spontaneous seizures. We demonstrated that separately examining interneurons and pyramidal cells during this transition in a rat model of temporal lobe epilepsy elucidates some of this heterogeneity. Recently, it was demonstrated that classifying cells into specific theta-related subtypes further clarified the heterogeneity. Moreover, changes in neuronal synchrony with the local field potential were identified and determined to be specific to interneurons during the transition to seizures. To extend our understanding of the chronic changes in epileptic networks, we examined field potentials and single neuron activity in the CA3 hippocampus of pilocarpine-treated rats during interictal periods and compared these to neuronal activity in healthy controls and during preictal periods. Neurons were classified into theta-subtypes based on changes in firing patterns during theta periods. As previously reported, we find a high probability of theta oscillations before seizure onset and a selective increase in theta-on interneuron firing rate immediately preceding seizure onset. However, we also find overall slower theta rhythm and a general decrease in subtype-specific firing during interictal periods compared to that in control animals. The decrease in subtype specific interneuron activity is accompanied by increases in synchrony. Exceptionally, theta-on interneurons, that selectively increase their firing rate at seizure onset, maintain similar firing rates and synchrony as controls during interictal period. These data suggest that increased synchrony during interictal periods may compensate for low firing rates creating instability during theta that is prone to seizure initiation via a transition to hyper-synchronous activation of theta-on interneurons.

Neuronal synchrony and the transition to spontaneous seizures.

The role of inhibitory neuronal activity in the transition to seizure is unclear. On the one hand, seizures are associated with excessive neuronal activity that can spread across the brain, suggesting run-away excitation. On the other hand, recent in vitro studies suggest substantial activity of inhibitory interneurons prior to the onset of evoked seizure-like activity. Yet, little is known about the behavior of interneurons before and during spontaneous seizures in chronic temporal lobe epilepsy. Here, we examined the relationship between the on-going local field potential (LFP) and the activity of populations of hippocampal neurons during the transition to spontaneous seizures in the pilocarpine rat model of epilepsy. Pilocarpine treated rats that exhibited spontaneous seizures were implanted with drivable tetrodes including an LFP electrode and recordings were obtained from the CA3 region. For each recorded seizure, identified single units were classified into putative interneurons or pyramidal cells based on average firing rate, autocorrelation activity and waveform morphology. The onset of sustained ictal spiking, a consistent seizure event that occurred within seconds after the clinically defined seizure onset time, was used to align data from each seizure to a common reference point. Ictal spiking, in this paper, refers to spiking activity in the low-pass filtered LFP during seizures and not the neuronal action potentials. Results show that beginning minutes before the onset of sustained ictal spiking in the local field, subpopulations of putative interneurons displayed a sequence of synchronous behaviors. This includes progressive synchrony with local field oscillations at theta, gamma, and finally ictal spiking frequencies, and an increased firing rate seconds before the onset of ictal spiking. Conversely, putative pyramidal cells did not exhibit increased synchrony or firing rate until after ictal spiking had begun. Our data suggest that the transition to spontaneous seizure in this network is not mediated by increasing excitatory activity, but by distinct changes in the dynamical state of putative interneurons. While these states are not unique for seizure onset, they suggest a series of state transitions that continuously increase the likelihood of a seizure. These data help to interpret the link between in vitro studies demonstrating interneuron activation at the transition to seizure, and human studies demonstrating heterogeneous neuronal firing at this time.