Clinical and Electroencephalographic Predictors of Seizures and Status Epilepticus in 12,450 Critically Ill Adults: A Retrospective Cohort Study.

OBJECTIVES: Status epilepticus (SE) is associated with significantly higher morbidity and mortality than isolated seizures. Our objective was to identify clinical diagnoses and rhythmic and periodic electroencephalogram patterns (RPPs) associated with SE and seizures. DESIGN: Retrospective cohort study. SETTING: Tertiary-care hospitals. SUBJECTS: Twelve thousand four hundred fifty adult hospitalized patients undergoing continuous electroencephalogram (cEEG) monitoring in selected participating sites in the Critical Care EEG Monitoring Research Consortium database (February 2013 to June 2021). INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: We defined an ordinal outcome in the first 72 hours of cEEG: no seizures, isolated seizures without SE, or SE (with or without isolated seizures). Composite groups included isolated seizures or SE (AnySz) and no seizure or isolated seizures. In this cohort (mean age: 60 ± 17 yr), 1,226 patients (9.8%) had AnySz and 439 patients (3.5%) had SE. In a multivariate model, factors independently associated with SE were cardiac arrest (9.2% with SE; adjusted odds ratio, 8.8 [6.3-12.1]), clinical seizures before cEEG (5.7%; 3.3 [2.5-4.3]), brain neoplasms (3.2%; 1.6 [1.0-2.6]), lateralized periodic discharges (LPDs) (15.4%; 7.3 [5.7-9.4]), brief potentially ictal rhythmic discharges (BIRDs) (22.5%; 3.8 [2.6-5.5]), and generalized periodic discharges (GPDs) (7.2%; 2.4 [1.7-3.3]). All above variables and lateralized rhythmic delta activity (LRDA) were also associated with AnySz. Factors disproportionately increasing odds of SE over isolated seizures were cardiac arrest (7.3 [4.4-12.1]), clinical seizures (1.7 [1.3-2.4]), GPDs (2.3 [1.4-3.5]), and LPDs (1.4 [1.0-1.9]). LRDA had lower odds of SE compared with isolated seizures (0.5 [0.3-0.9]). RPP modifiers did not improve SE prediction beyond RPPs presence/absence ( p = 0.8). CONCLUSIONS: Using the largest existing cEEG database, we identified specific predictors of SE (cardiac arrest, clinical seizures prior to cEEG, brain neoplasms, LPDs, GPDs, and BIRDs) and seizures (all previous and LRDA). These findings could be used to tailor cEEG monitoring for critically ill patients.

Risk factors of posthepatectomy liver failure for perihilar cholangiocarcinoma: Risk score and significance of future liver remnant volume-to-body weight ratio.

BACKGROUND: Surgery for perihilar cholangiocarcinoma (PHCC) is associated with high morbidity. This study aimed to investigate the clinical value of the future liver remnant volume-to-body weight (FLRV/BW) and propose a risk score for predicting the risk of patients with PHCC developing posthepatectomy liver failure (PHLF). METHODS: This study included 348 patients who underwent major hepatectomy with bile duct resection for PHCC during 2008-2015 at a single center in Korea and they were retrospectively analyzed. RESULTS: Clinically relevant PHLF was noted in 40 patients (11.4%). The area under the curve (AUC) for FLRV/BW was not significantly different from that for FLRV/total liver volume (P = .803) or indocyanine green clearance of the future liver remnant (P = .629) in terms of predicting PHLF. On multivariate analysis, predictors of PHLF (P < .05) were male sex, albumin less than 3.5 g/dL, preoperative cholangitis, portal vein resection, FLRV/BW less than 0.5%, and FLRV/BW 0.5% to 0.75%. These variables were included in the risk score that showed good discrimination (AUC, 0.853; 95% CI, 0.802-0.904). It will help rank patients into three risk subgroups with a predicted liver failure incidence of 4.75%, 18.73%, and 51.58%, respectively. CONCLUSIONS: FLRV/BW is a comparable risk prediction factor of PHLF and the proposed risk score can help to predict the risk of planned surgery in PHCC.

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy.

OBJECTIVES: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. DESIGN: Retrospective. SETTING: ICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. CONCLUSIONS: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance.

Randomized trial of lacosamide versus fosphenytoin for nonconvulsive seizures.

OBJECTIVE: The optimal treatment of nonconvulsive seizures in critically ill patients is uncertain. We evaluated the comparative effectiveness of the antiseizure drugs lacosamide (LCM) and fosphenytoin (fPHT) in this population. METHODS: The TRENdS (Treatment of Recurrent Electrographic Nonconvulsive Seizures) study was a noninferiority, prospective, multicenter, randomized treatment trial of patients diagnosed with nonconvulsive seizures (NCSs) by continuous electroencephalography (cEEG). Treatment was randomized to intravenous (IV) LCM 400mg or IV fPHT 20mg phenytoin equivalents/kg. The primary endpoint was absence of electrographic seizures for 24 hours as determined by 1 blinded EEG reviewer. The frequency with which NCS control was achieved in each arm was compared, and the 90% confidence interval (CI) was determined. Noninferiority of LCM to fPHT was to be concluded if the lower bound of the CI for relative risk was >0.8. RESULTS: Seventy-four subjects were enrolled (37 LCM, 37 fPHT) between August 21, 2012 and December 20, 2013. The mean age was 63.6 years; 38 were women. Seizures were controlled in 19 of 30 (63.3%) subjects in the LCM arm and 16 of 32 (50%) subjects in the fPHT arm. LCM was noninferior to fPHT (p = 0.02), with a risk ratio of 1.27 (90% CI = 0.88-1.83). Treatment emergent adverse events (TEAEs) were similar in both arms, occurring in 9 of 35 (25.7%) LCM and 9 of 37 (24.3%) fPHT subjects (p = 1.0). INTERPRETATION: LCM was noninferior to fPHT in controlling NCS, and TEAEs were comparable. LCM can be considered an alternative to fPHT in the treatment of NCSs detected on cEEG. Ann Neurol 2018;83:1174-1185.

Estimating the False Positive Rate of Absent Somatosensory Evoked Potentials in Cardiac Arrest Prognostication.

OBJECTIVES: Absence of somatosensory evoked potentials is considered a nearly perfect predictor of poor outcome after cardiac arrest. However, reports of good outcomes despite absent somatosensory evoked potentials and high rates of withdrawal of life-sustaining therapies have raised concerns that estimates of the prognostic value of absent somatosensory evoked potentials may be biased by self-fulfilling prophecies. We aimed to develop an unbiased estimate of the false positive rate of absent somatosensory evoked potentials as a predictor of poor outcome after cardiac arrest. DATA SOURCES: PubMed. STUDY SELECTION: We selected 35 studies in cardiac arrest prognostication that reported somatosensory evoked potentials. DATA EXTRACTION: In each study, we identified rates of withdrawal of life-sustaining therapies and good outcomes despite absent somatosensory evoked potentials. We appraised studies for potential biases using the Quality in Prognosis Studies tool. Using these data, we developed a statistical model to estimate the false positive rate of absent somatosensory evoked potentials adjusted for withdrawal of life-sustaining therapies rate. DATA SYNTHESIS: Two-thousand one-hundred thirty-three subjects underwent somatosensory evoked potential testing. Five-hundred ninety-four had absent somatosensory evoked potentials; of these, 14 had good functional outcomes. The rate of withdrawal of life-sustaining therapies for subjects with absent somatosensory evoked potential could be estimated in 14 of the 35 studies (mean 80%, median 100%). The false positive rate for absent somatosensory evoked potential in predicting poor neurologic outcome, adjusted for a withdrawal of life-sustaining therapies rate of 80%, is 7.7% (95% CI, 4-13%). CONCLUSIONS: Absent cortical somatosensory evoked potentials do not infallibly predict poor outcome in patients with coma following cardiac arrest. The chances of survival in subjects with absent somatosensory evoked potentials, though low, may be substantially higher than generally believed.

Combination of Clinical Exam, MRI and EEG to Predict Outcome Following Cardiac Arrest and Targeted Temperature Management.

BACKGROUND: Despite the widespread adoption of targeted temperature management (TTM), coma after cardiac arrest remains a common problem with a high proportion of patients suffering substantial disability. Prognostication after cardiac arrest, particularly the identification of patients with likely good outcome, remains difficult. METHODS: We performed a retrospective study of 78 patients who underwent TTM after cardiac arrest and were evaluated with both electroencephalography (EEG) and magnetic resonance imaging (MRI). We hypothesized that combining malignant versus non-malignant EEG classification with clinical exam and quantitative analysis of apparent diffusion coefficient (ADC) and fluid-attenuated inversion recovery imaging would improve prognostic ability. RESULTS: Consistent with prior literature, presence of a malignant EEG pattern was 100% specific for poor outcome. We found that decreased whole brain ADC signal intensity was associated with poor outcome (853 ± 14 vs. 950 ± 17.5 mm2/s, p < 0.0001). Less than 15% total brain volume with ADC signal intensity < 650 mm2/s was predictive of good outcome with 100% sensitivity, 51% specificity and an area under the curve of 0.787. A model combining this ADC marker with non-malignant EEG and flexor-or-better motor response was 100% sensitive and 91.1% specific for good outcome following cardiac arrest and targeted temperature management. CONCLUSION: We conclude that in the absence of malignant EEG findings, combination of physical exam and MRI findings can be a useful to identify those patients who have potential for recovery. Variability in timing of imaging and findings in different modalities argue for the need for future prospective studies of multimodal outcome prediction after cardiac arrest.

Clinical Practice Recommendations for the Management and Prevention of Cisplatin-Induced Hearing Loss Using Pharmacogenetic Markers.

Currently no pharmacogenomics-based criteria exist to guide clinicians in identifying individuals who are at risk of hearing loss from cisplatin-based chemotherapy. This review summarizes findings from pharmacogenomic studies that report genetic polymorphisms associated with cisplatin-induced hearing loss and aims to (1) provide up-to-date information on new developments in the field, (2) provide recommendations for the use of pharmacogenetic testing in the prevention, assessment, and management of cisplatin-induced hearing loss in children and adults, and (3) identify knowledge gaps to direct and prioritize future research. These practice recommendations for pharmacogenetic testing in the context of cisplatin-induced hearing loss reflect a review and evaluation of recent literature, and are designed to assist clinicians in providing optimal clinical care for patients receiving cisplatin-based chemotherapy.

Human seizures self-terminate across spatial scales via a critical transition.

Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition--slowing, increased correlation, and flickering--in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.

Efficacy of safety signals in the epilepsy monitoring unit (EMU): should we worry?

There is little consensus regarding the critical safety measures to prevent harm in epilepsy monitoring units (EMUs). We sought to determine whether the safety signals (SS) triggered during EMU events differed by seizure type and the efficacy of SS in alerting responders. We screened 468 consecutive EMU admissions from January 2008 until April 2011 for definitive events to evaluate the first 50 events of complex partial seizures (CPS), generalized tonic-clonic seizures (GTC), and psychogenic non-epileptic seizures (PNES). Response to telemetry signal was slower than to push button (PB). When there was PB alarm, response time was slower in patients with PNES. A higher proportion of PNES were triggered by PB. A greater percentage of epileptic seizures were missed compared with PNES. Future studies investigating more effective techniques to capture every epileptic seizure are warranted as 24/7 monitoring by healthcare professionals is not feasible in many settings.

Seizure onset location shapes dynamics of initiation.

OBJECTIVE: Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types. METHODS: We analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset. RESULTS: Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. CONCLUSIONS: Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. SIGNIFICANCE: To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

A standardized nomenclature for spectrogram EEG patterns: Inter-rater agreement and correspondence with common intensive care unit EEG patterns.

OBJECTIVE: To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature. METHODS: We defined seven spectrogram categories: "Solid Flames", "Irregular Flames", "Broadband-monotonous", "Narrowband-monotonous", "Stripes", "Low power", and "Artifact". Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA. RESULTS: Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was "near perfect" agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was "substantial agreement" for all other categories (κ = 74.65-79.49). CONCLUSIONS: This EEG spectrogram nomenclature has high IRA among electroencephalographers. SIGNIFICANCE: The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.

Comparison of machine learning models for seizure prediction in hospitalized patients.

OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h). METHODS: The Critical Care EEG Monitoring Research Consortium (CCEMRC) multicenter database contains 7716 continuous EEGs (cEEG). Neural networks (NN), elastic net logistic regression (EN), and sparse linear integer model (RiskSLIM) were trained to predict seizures. RiskSLIM was used previously to generate 2HELPS2B model of seizure predictions. Data were divided into training (60% for model fitting) and evaluation (40% for model evaluation) cohorts. Performance was measured using area under the receiver operating curve (AUC), mean risk calibration (CAL), and negative predictive value (NPV). A secondary analysis was performed using Monte Carlo simulation (MCS) to normalize all EEG recordings to 48 h and use only the first hour of EEG as a "screening EEG" to generate predictions. RESULTS: RiskSLIM recreated the 2HELPS2B model. All models had comparable AUC: evaluation cohort (NN: 0.85, EN: 0.84, 2HELPS2B: 0.83) and MCS (NN: 0.82, EN; 0.82, 2HELPS2B: 0.81) and NPV (absence of seizures in the group that the models predicted to be low risk): evaluation cohort (NN: 97%, EN: 97%, 2HELPS2B: 97%) and MCS (NN: 97%, EN: 99%, 2HELPS2B: 97%). 2HELPS2B model was able to identify the largest proportion of low-risk patients. INTERPRETATION: For seizure risk stratification of hospitalized patients, the RiskSLIM generated 2HELPS2B model compares favorably to the complex NN and EN generated models. 2HELPS2B is able to accurately and quickly identify low-risk patients with only a 1-h screening EEG.

Delta neutrophil index as an early predictive marker of severe acute pancreatitis in the emergency department.

Background: Predicting severe acute pancreatitis (AP) in the early clinical stage is important for low morbidity and mortality. Delta neutrophil index (DNI) is used to detect infection and inflammation, but no previous studies have evaluated the usefulness of DNI as an early predictor of progression to severe AP (SAP). Methods: The medical records of patients who were diagnosed with AP at the emergency department (ED) of Wonju Severance Christian Hospital from January 2012 to August 2016 were retrospectively reviewed. The initial DNI obtained in the ED was compared with other inflammatory markers to predict SAP. Multivariate logistic regression was used for statistical analysis. Results: Of the 209 cases included in the analysis, 13 were classified as SAP. Compared to the DNI of the mild to moderately SAP group, that in the SAP group was considerably higher. The DNI showed a positive correlation with the Atlanta classification and bedside index of severity in AP. Using multivariate logistic regression analysis, DNI was an independent predictor of early SAP detection (odds ratio 1.122, 95% CI 1.045-1.205, p = 0.001). Among the biomarkers, DNI had the highest predictive value for SAP. Conclusions: The DNI measured in the ED at presentation is a potentially useful adjunctive marker to predict SAP.

A Simplified Mortality Score Using Delta Neutrophil Index and the Thrombotic Microangiopathy Score for Prognostication in Critically Ill Patients.

BACKGROUND: This study was conducted to assess the prognostic value of a simplified mortality score (SMS) using the delta neutrophil index (DNI) and thrombotic microangiopathy (TMA) score, both easily obtained from the complete blood count, to identify critically ill patients at high risk of death. METHODS: This was a retrospective study performed in the medical ICU at Yonsei University College of Medicine from June 2015 to February 2016. The primary end point was 28-day all-cause mortality. Participants were divided into two groups: a training (n = 232) and a test (n = 57) set. We used Cox proportional-hazards analysis, Harrell's C index, and Kaplan-Meier survival analysis to derive the SMS and test its internal validity. RESULTS: We enrolled 289 patients. The 28-day mortality rate was 31.1% (n = 90). Nonsurvivors had higher APACHE II, SOFA, and TMA scores, and DNI. The SMS, derived by Cox proportional-hazards analysis, consisted of age, sex, DNI, and TMA score. We assigned a weighted point to each variable in the SMS, as follows: age + 11 if male + (2 × DNI) + (61 [TMA = 1], 76 [TMA = 2], 74 [TMA = 3], 26 [TMA = 4], 99 [TMA = 5]). Nonsurvivors had a higher median SMS than survivors, and the Harrell's C index was 0.660. Analysis of survival by risk group according to SMS (low, intermediate, high risk) showed a significant difference among these three groups (P < 0.001). We then investigated this SMS in the test set to determine internal validity; the results were similar to those of the training set. CONCLUSIONS: The SMS is a more rapid, simple prognostic score for predicting 28-day mortality and stratifying risk than the APACHE II or SOFA scores. However, external validation using a larger sample is needed.

Sensitivity of quantitative EEG for seizure identification in the intensive care unit.

OBJECTIVE: To evaluate the sensitivity of quantitative EEG (QEEG) for electrographic seizure identification in the intensive care unit (ICU). METHODS: Six-hour EEG epochs chosen from 15 patients underwent transformation into QEEG displays. Each epoch was reviewed in 3 formats: raw EEG, QEEG + raw, and QEEG-only. Epochs were also analyzed by a proprietary seizure detection algorithm. Nine neurophysiologists reviewed raw EEGs to identify seizures to serve as the gold standard. Nine other neurophysiologists with experience in QEEG evaluated the epochs in QEEG formats, with and without concomitant raw EEG. Sensitivity and false-positive rates (FPRs) for seizure identification were calculated and median review time assessed. RESULTS: Mean sensitivity for seizure identification ranged from 51% to 67% for QEEG-only and 63%-68% for QEEG + raw. FPRs averaged 1/h for QEEG-only and 0.5/h for QEEG + raw. Mean sensitivity of seizure probability software was 26.2%-26.7%, with FPR of 0.07/h. Epochs with the highest sensitivities contained frequent, intermittent seizures. Lower sensitivities were seen with slow-frequency, low-amplitude seizures and epochs with rhythmic or periodic patterns. Median review times were shorter for QEEG (6 minutes) and QEEG + raw analysis (14.5 minutes) vs raw EEG (19 minutes; p = 0.00003). CONCLUSIONS: A panel of QEEG trends can be used by experts to shorten EEG review time for seizure identification with reasonable sensitivity and low FPRs. The prevalence of false detections confirms that raw EEG review must be used in conjunction with QEEG. Studies are needed to identify optimal QEEG trend configurations and the utility of QEEG as a screening tool for non-EEG personnel. CLASSIFICATION OF EVIDENCE REVIEW: This study provides Class II evidence that QEEG + raw interpreted by experts identifies seizures in patients in the ICU with a sensitivity of 63%-68% and FPR of 0.5 seizures per hour.

A coding variant in RARG confers susceptibility to anthracycline-induced cardiotoxicity in childhood cancer.

Anthracyclines are used in over 50% of childhood cancer treatment protocols, but their clinical usefulness is limited by anthracycline-induced cardiotoxicity (ACT) manifesting as asymptomatic cardiac dysfunction and congestive heart failure in up to 57% and 16% of patients, respectively. Candidate gene studies have reported genetic associations with ACT, but these studies have in general lacked robust patient numbers, independent replication or functional validation. Thus, the individual variability in ACT susceptibility remains largely unexplained. We performed a genome-wide association study in 280 patients of European ancestry treated for childhood cancer, with independent replication in similarly treated cohorts of 96 European and 80 non-European patients. We identified a nonsynonymous variant (rs2229774, p.Ser427Leu) in RARG highly associated with ACT (P = 5.9 × 10(-8), odds ratio (95% confidence interval) = 4.7 (2.7-8.3)). This variant alters RARG function, leading to derepression of the key ACT genetic determinant Top2b, and provides new insight into the pathophysiology of this severe adverse drug reaction.

New-onset refractory status epilepticus: Etiology, clinical features, and outcome.

OBJECTIVES: The aims of this study were to determine the etiology, clinical features, and predictors of outcome of new-onset refractory status epilepticus. METHODS: Retrospective review of patients with refractory status epilepticus without etiology identified within 48 hours of admission between January 1, 2008, and December 31, 2013, in 13 academic medical centers. The primary outcome measure was poor functional outcome at discharge (defined as a score >3 on the modified Rankin Scale). RESULTS: Of 130 cases, 67 (52%) remained cryptogenic. The most common identified etiologies were autoimmune (19%) and paraneoplastic (18%) encephalitis. Full data were available in 125 cases (62 cryptogenic). Poor outcome occurred in 77 of 125 cases (62%), and 28 (22%) died. Predictors of poor outcome included duration of status epilepticus, use of anesthetics, and medical complications. Among the 63 patients with available follow-up data (median 9 months), functional status improved in 36 (57%); 79% had good or fair outcome at last follow-up, but epilepsy developed in 37% with most survivors (92%) remaining on antiseizure medications. Immune therapies were used less frequently in cryptogenic cases, despite a comparable prevalence of inflammatory CSF changes. CONCLUSIONS: Autoimmune encephalitis is the most commonly identified cause of new-onset refractory status epilepticus, but half remain cryptogenic. Outcome at discharge is poor but improves during follow-up. Epilepsy develops in most cases. The role of anesthetics and immune therapies warrants further investigation.

An electrochemical glucose biosensor exploiting a polyaniline grafted multiwalled carbon nanotube/perfluorosulfonate ionomer-silica nanocomposite.

A glucose biosensor was fabricated with loading of glucose oxidase (GOx) into a new organic-inorganic hybrid nanocomposite. The preparation involves formation of silica network into a Nafion (perfluorosulfonate ionomer) and subsequent loading of polyaniline grafted multiwalled carbon nanotubes (MWNT-g-PANI) onto Nafion-silica nanocomposite. Field emission scanning electron microscopy (FE-SEM) of Nafion-silica/MWNT-g-PANI composite reveals the presence of spherical silica particles (sizes in the range 250 nm-1 microm) and tubular MWNT-g-PANI particles. Chronoamperometry and cyclic voltammetry were used to evaluate the performance of biosensor towards glucose. The Nafion-silica/MWCNT-g-PANI/GOx biosensor exhibited a linear response to glucose in the concentration range of 1-10 mm with a correlation coefficient of 0.9972, good sensitivity (5.01 microA/mm), a low response time (approximately 6s), repeatability (R.S.D value of 2.2%) and along-term stability. The presence of silica network within Nafion and MWNT-g-PANI synergistically contributes to the performance of the biosensor towards the electrochemical detection of glucose.

Expressional and mutational analysis of pro-apoptotic Bcl-2 member PUMA in hepatocellular carcinomas.

Deregulation of apoptosis is involved in mechanisms of cancer development. PUMA is a pro-apoptotic member of the Bcl-2 family and mediates p53-dependent and -independent apoptosis. The aim of this study was to investigate whether alterations of PUMA protein expression and somatic mutations of PUMA gene are characteristics of human hepatocellular carcinoma (HCC). We analyzed expression of PUMA protein in 20 HCCs using immunohistochemistry. Also, we analyzed mutation of the Bcl-2 homology 3 (BH3) domain of PUMA gene, which is an important domain in apoptosis function of PUMA by single-strand conformation polymorphism (SSCP) in 69 HCCs. PUMA protein expression was detected in both HCC cells and non-tumor hepatocytes in all of the 20 HCCs. In 10 of these HCCs, cancer cells showed higher PUMA expression than non-tumor (cirrhotic) hepatocytes of the same patients; whereas in the remaining 10, cancer cells and non-tumor hepatocytes showed similar levels. Mutational analysis revealed no PUMA BH3 domain mutation in the 69 HCCs, suggesting that PUMA BH3 domain mutation is not a direct target of inactivation in hepatocellular cancer development. The increased expression of PUMA in malignant hepatocellular cells relative to that in non-tumor hepatocytes suggests that PUMA expression may play a role in HCC development.