Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury

Propofol Protects the Blood-Brain Barrier After Traumatic Brain Injury by Stabilizing the Extracellular Matrix via Prrx1: From Neuroglioma to Neurotrauma

The purpose of this study is to explore the shared molecular pathogenesis of traumatic brain injury (TBI) and high-grade glioma and investigate the mechanism of propofol (PF) as a potential protective agent. By analyzing the Chinese glioma genome atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases, we compared the transcriptomic data of high-grade glioma and TBI patients to identify common pathological mechanisms. Through bioinformatics analysis, in vitro experiments and in vivo TBI model, we investigated the regulatory effect of PF on extracellular matrix (ECM)-related genes through Prrx1 under oxidative stress. The impact of PF on BBB integrity under oxidative stress was investigated using a dual-layer BBB model, and we explored the protective effect of PF on tight junction proteins and ECM-related genes in mice after TBI. The study found that high-grade glioma and TBI share ECM instability as an important molecular pathological mechanism. PF stabilizes the ECM and protects the BBB by directly binding to Prrx1 or indirectly regulating Prrx1 through miRNAs. In addition, PF reduces intracellular calcium ions and ROS levels under oxidative stress, thereby preserving BBB integrity. In a TBI mouse model, PF protected BBB integrity through up-regulated tight junction proteins and stabilized the expression of ECM-related genes. Our study reveals the shared molecular pathogenesis between TBI and glioblastoma and demonstrate the potential of PF as a protective agent of BBB. This provides new targets and approaches for the development of novel neurotrauma therapeutic drugs.

RBM3 Ameliorates Acute Brain Injury-induced Inflammation and Oxidative Stress by Stabilizing GAS6 mRNA Through Nrf2 Signaling Pathway

RNA-binding motif protein 3 (RBM3), as a cold-inducible protein, exhibits neuroprotective function in brain disorders. This study was conducted to investigate the effects of RBM3 on acute brain injury (ABI) and its underlying mechanism. The cerebral injury (CI) rat model and oxygen-glucose deprivation (OGD) cell model were established. The neurological severity score, wire-grip score, morris water maze test, and Y-maze test were used to detect the neurological damage, vestibular motor, learning, and memory functions. Cerebral injury, apoptosis, oxidative stress, and inflammatory level were evaluated by hematoxylin-eosin and TUNEL staining and specific kits. Flow cytometry was used to analyze the apoptosis rate. The relationship between RBM3 and growth arrest specific (GAS) 6 was analyzed by RNA immunoprecipitation assay. The results indicated that RBM3 recovered of neurological function and behaviour impairment of CI rats. Additionally, RBM3 reversed the increased oxidative stress, inflammatory level, and apoptosis induced by CI and OGD. RBM3 interacted with GAS6 to activate the Nrf2 signaling pathway, thus playing neuroprotection on ABI. Besides, the results of RBM3 treatment were similar to those of mild hypothermia treatment. In summary, RBM3 exerted neuroprotection and ameliorated inflammatory levels and oxidative stress by stabilizing GAS6 mRNA through the Nrf2 signaling pathway, suggesting that RBM3 might be a potential therapeutic candidate for treating ABI.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Automated detection of immune effector cell-associated neurotoxicity syndrome via quantitative EEG

OBJECTIVE

To develop an automated, physiologic metric of immune effector cell-associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor-T cell therapy.

METHODS

We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor-T cell therapy with a CD19 or B-cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell-associated neurotoxicity syndrome score.

RESULTS

The EEG immune effector cell-associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R2 of 0.69 (95% CI of 0.59-0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R2 0.24, p = 0.008), minimum platelets (R2 -0.29, p = 0.001), and dexamethasone usage (R2 0.42, p < 0.0001). The score significantly correlated with duration of neurotoxicity (R2 0.31, p < 0.0001).

INTERPRETATION

The EEG immune effector cell-associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T-cell therapy.

Electrographic Seizures in the Critically Ill

Identifying and treating critically ill patients with seizures can be challenging. In this article, the authors review the available data on patient populations at risk, seizure prognostication with tools such as 2HELPS2B, electrographic seizures and the various ictal-interictal continuum patterns with their latest definitions and associated risks, ancillary testing such as imaging studies, serum biomarkers, and invasive multimodal monitoring. They also illustrate 5 different patient scenarios, their treatment and outcomes, and propose recommendations for targeted treatment of electrographic seizures in critically ill patients.

A Combination of Curcuma longa and Diazepam Attenuates Seizures and Subsequent Hippocampal Neurodegeneration

Epilepsy is one of the most common neurological disorders, which occurs due to the instability in the inhibitory and excitatory synaptic transmissions in the brain. However, many patients develop resistance to the available drugs, which results in cell degeneration caused due to inadequate control of the seizures. Curcumin, Curcuma longa, is known to be effective for the treatment of organic disorders and may prevent seizures, reduce oxidative stress, and decrease brain damage. Given this, the present study evaluated the antiepileptic effects of C. longa in comparison with both the diazepam and the combined application of these two substances, in terms of their effects on the brain activity and the potential histopathological changes in the hippocampus. This study used male Wistar rats (age: 10–12 weeks; weight: 260 ± 20 g), which were pretreated for 4 days with either saline, C. longa, diazepam, or C. longa + diazepam; and on the fifth day, pentylenetetrazol (PTZ) was administered to induce the seizure. In the C. longa group, a significant increase was observed in the latency of the onset of seizure-related behavior. Surprisingly, however, the combined treatment resulted in the best control of the seizure-related behavior, with the greatest latency of the onset of spasms and isolated clonic seizures. This group also obtained the best results in the electroencephalographic trace and seizure control, with a reduction in the frequency and amplitude of the spike-waves. In the saline group, PTZ significantly reduced the number of cells present in the CA1 and CA3 regions of the hippocampus, while the combined treatment obtained the best results in terms of the preservation of the neuron-like cells. These findings indicate that C. longa may contribute to the control of both seizures and the cell damage induced by PTZ, and that its association with diazepam may be a potentially effective option for the treatment of epilepsy in the future.

Optogenetics for Understanding and Treating Brain Injury: Advances in the Field and Future Prospects

Optogenetics is emerging as an ideal method for controlling cellular activity. It overcomes some notable shortcomings of conventional methods in the elucidation of neural circuits, promotion of neuroregeneration, prevention of cell death and treatment of neurological disorders, although it is not without its own limitations. In this review, we narratively review the latest research on the improvement and existing challenges of optogenetics, with a particular focus on the field of brain injury, aiming at advancing optogenetics in the study of brain injury and collating the issues that remain. Finally, we review the most current examples of research, applying photostimulation in clinical treatment, and we explore the future prospects of these technologies.

Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S)

OBJECTIVES

Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

DESIGN

Retrospective cohort study.

SETTING

Single-center tertiary academic medical center.

PATIENTS

Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity.

CONCLUSIONS

The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

Systematic evaluation of rationally chosen multitargeted drug combinations: a combination of low doses of levetiracetam, atorvastatin and ceftriaxone exerts antiepileptogenic effects in a mouse model of acquired epilepsy

Epileptogenesis, the gradual process that leads to epilepsy after brain injury or genetic mutations, is a complex network phenomenon, involving a variety of morphological, biochemical and functional brain alterations. Although risk factors for developing epilepsy are known, there is currently no treatment available to prevent epilepsy. We recently proposed a multitargeted, network-based approach to prevent epileptogenesis by rationally combining clinically available drugs and provided first proof-of-concept that this strategy is effective. Here we evaluated eight novel rationally chosen combinations of 14 drugs with mechanisms that target different epileptogenic processes. The combinations consisted of 2-4 different drugs per combination and were administered systemically over 5 days during the latent epileptogenic period in the intrahippocampal kainate mouse model of acquired temporal lobe epilepsy, starting 6 h after kainate. Doses and dosing intervals were based on previous pharmacokinetic and tolerability studies in mice. The incidence and frequency of spontaneous electrographic and electroclinical seizures were recorded by continuous (24/7) video linked EEG monitoring done for seven days at 4 and 12 weeks post-kainate, i.e., long after termination of drug treatment. Compared to vehicle controls, the most effective drug combination consisted of low doses of levetiracetam, atorvastatin and ceftriaxone, which markedly reduced the incidence of electrographic seizures (by 60%; p<0.05) and electroclinical seizures (by 100%; p<0.05) recorded at 12 weeks after kainate. This effect was lost when higher doses of the three drugs were administered, indicating a synergistic drug-drug interaction at the low doses. The potential mechanisms underlying this interaction are discussed. We have discovered a promising novel multitargeted combination treatment for modifying the development of acquired epilepsy.

Early detection of brainstem herniation using electroencephalography monitoring - case report

Background

Continuous electroencephalography (cEEG) is an important neuromonitoring tool in brain injured patients. It is commonly used for detection of seizure but can also be used to monitor changes in cerebral blood flow. One such event that can cause a change in cerebral blood flow is imminent, cerebral herniation. cEEG monitoring and quantitative electroencephalography (QEEG) can be used as neurotelemetry to detect cerebral herniation prior to onset of clinical signs.

Case presentation

We discuss two cases highlighting the use of cEEG in cerebral herniation accompanied by clinical examination changes. The first case is a patient with multiorgan failure and intracerebral hemorrhage (ICH). Given his coagulopathy status, his ICH expanded. The second case is a patient with intraventricular hemorrhage and worsening obstructive hydrocephalus. In both cases, the cEEG showed increasing regional/lateralized slowing. The Quantitative electroencephalography (QEEG) showed a decrease in frequencies, worsening asymmetry, decreasing amplitude and increasing burst suppression ratio corresponding with the ongoing herniation. Clinically, these changes on cEEG preceded the bedside neurological changes by up to 1 h.

Conclusions

The use of cEEG to monitor patients at high risk for herniation syndromes may identify changes earlier than bedside clinical exam. This earlier identification may allow for an earlier opportunity to intervene.