Characterisation of neonatal seizures and their treatment using continuous EEG monitoring: a multicentre experience.

OBJECTIVE: The aim of this multicentre study was to describe detailed characteristics of electrographic seizures in a cohort of neonates monitored with multichannel continuous electroencephalography (cEEG) in 6 European centres. METHODS: Neonates of at least 36 weeks of gestation who required cEEG monitoring for clinical concerns were eligible, and were enrolled prospectively over 2 years from June 2013. Additional retrospective data were available from two centres for January 2011 to February 2014. Clinical data and EEGs were reviewed by expert neurophysiologists through a central server. RESULTS: Of 214 neonates who had recordings suitable for analysis, EEG seizures were confirmed in 75 (35%). The most common cause was hypoxic-ischaemic encephalopathy (44/75, 59%), followed by metabolic/genetic disorders (16/75, 21%) and stroke (10/75, 13%). The median number of seizures was 24 (IQR 9-51), and the median maximum hourly seizure burden in minutes per hour (MSB) was 21 min (IQR 11-32), with 21 (28%) having status epilepticus defined as MSB>30 min/hour. MSB developed later in neonates with a metabolic/genetic disorder. Over half (112/214, 52%) of the neonates were given at least one antiepileptic drug (AED) and both overtreatment and undertreatment was evident. When EEG monitoring was ongoing, 27 neonates (19%) with no electrographic seizures received AEDs. Fourteen neonates (19%) who did have electrographic seizures during cEEG monitoring did not receive an AED. CONCLUSIONS: Our results show that even with access to cEEG monitoring, neonatal seizures are frequent, difficult to recognise and difficult to treat. OBERSERVATION STUDY NUMBER: NCT02160171

Computer-assisted assessment of the Human Epidermal Growth Factor Receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls

<p>Abstract</p> <p>Background</p> <p>Breast cancers that overexpress the human epidermal growth factor receptor 2 (HER2) are eligible for effective biologically targeted therapies, such as trastuzumab. However, accurately determining HER2 overexpression, especially in immunohistochemically equivocal cases, remains a challenge. Manual analysis of HER2 expression is dependent on the assessment of membrane staining as well as comparisons with positive controls. In spite of the strides that have been made to standardize the assessment process, intra- and inter-observer discrepancies in scoring is not uncommon. In this manuscript we describe a pathologist assisted, computer-based continuous scoring approach for increasing the precision and reproducibility of assessing imaged breast tissue specimens.</p> <p>Methods</p> <p>Computer-assisted analysis on HER2 IHC is compared with manual scoring and fluorescence in situ hybridization results on a test set of 99 digitally imaged breast cancer cases enriched with equivocally scored (2+) cases. Image features are generated based on the staining profile of the positive control tissue and pixels delineated by a newly developed Membrane Isolation Algorithm. Evaluation of results was performed using Receiver Operator Characteristic (ROC) analysis.</p> <p>Results</p> <p>A computer-aided diagnostic approach has been developed using a membrane isolation algorithm and quantitative use of positive immunostaining controls. By incorporating internal positive controls into feature analysis a greater Area Under the Curve (AUC) in ROC analysis was achieved than feature analysis without positive controls. Evaluation of HER2 immunostaining that utilized membrane pixels, controls, and percent area stained showed significantly greater AUC than manual scoring, and significantly less false positive rate when used to evaluate immunohistochemically equivocal cases.</p> <p>Conclusion</p> <p>It has been shown that by incorporating both a membrane isolation algorithm and analysis of known positive controls a computer-assisted diagnostic algorithm was developed that can reproducibly score HER2 status in IHC stained clinical breast cancer specimens. For equivocal scoring cases, this approach performed better than standard manual evaluation as assessed by ROC analysis in our test samples. Finally, there exists potential for utilizing image-analysis techniques for improving HER2 scoring at the immunohistochemically equivocal range.</p

Astroglial Inhibition of NF-κB Does Not Ameliorate Disease Onset and Progression in a Mouse Model for Amyotrophic Lateral Sclerosis (ALS)

Motor neuron death in amyotrophic lateral sclerosis (ALS) is considered a “non-cell autonomous” process, with astrocytes playing a critical role in disease progression. Glial cells are activated early in transgenic mice expressing mutant SOD1, suggesting that neuroinflammation has a relevant role in the cascade of events that trigger the death of motor neurons. An inflammatory cascade including COX2 expression, secretion of cytokines and release of NO from astrocytes may descend from activation of a NF-κB-mediated pathway observed in astrocytes from ALS patients and in experimental models. We have attempted rescue of transgenic mutant SOD1 mice through the inhibition of the NF-κB pathway selectively in astrocytes. Here we show that despite efficient inhibition of this major pathway, double transgenic mice expressing the mutant SOD1G93A ubiquitously and the dominant negative form of IκBα (IκBαAA) in astrocytes under control of the GFAP promoter show no benefit in terms of onset and progression of disease. Our data indicate that motor neuron death in ALS cannot be prevented by inhibition of a single inflammatory pathway because alternative pathways are activated in the presence of a persistent toxic stimulus

<i>C9ORF72</i> repeat expansion causes vulnerability of motor neurons to Ca²⁺-permeable AMPA receptor-mediated excitotoxicity

Funded by The Wellcome Trust (Grant 092742/Z/10/Z), MNDA (Miles/Oct14/878-792), MRC, Euan MacDonald Centre, UK DRI, DBT-India, ISSF (WT/UoE), Royal Society of Edinburgh (CRF), and Biogen/UoE Joint Discovery Research Collaboration. RNA-Seq raw reads were generated by Edinburgh Genomics, The University of Edinburgh. Edinburgh Genomics is partly supported through core grants from NERC (R8/H10/56), MRC (MR/K001744/1), and BBSRC (BB/J004243/1).Mutations in C9ORF72 are the most common cause of familial amyotrophic lateral sclerosis (ALS). Here, through a combination of RNA-seq and electrophysiological studies on induced pluripotent stem cell (iPSC) derived motor neuron (MNs), we show that increased expression of GluA1 AMPA receptor (AMPAR) subunit occurs in MNs with C9ORF72 mutations that leads to increased Ca2+-permeable AMPAR expression and results in enhanced selective MN vulnerability to excitotoxicity. These deficits are not found in iPSC-derived cortical neurons and are abolished by CRISPR/Cas9-mediated correction of the C9ORF72 repeat expansion in MNs. We also demonstrate that MN-specific dysregulation of AMPAR expression is also present in C9ORF72 patient post mortem material. We therefore present multiple lines of evidence for the specific upregulation of GluA1 subunits in human mutant C9ORF72 MNs that could lead to a potential pathogenic excitotoxic mechanism in ALS.Publisher PDFPeer reviewe

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial

Background: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR). / Methods: This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non-algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780. / Findings: Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25·0%) of 128 neonates in the algorithm group and 38 (29·2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81·3% (95% CI 66·7–93·3) in the algorithm group and 89·5% (78·4–97·5) in the non-algorithm group; specificity was 84·4% (95% CI 76·9–91·0) in the algorithm group and 89·1% (82·5–94·7) in the non-algorithm group; and the false detection rate was 36·6% (95% CI 22·7–52·1) in the algorithm group and 22·7% (11·6–35·9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non-algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66·0%; 95% CI 53·8–77·3] of 268 h vs 177 [45·3%; 34·5–58·3] of 391 h; difference 20·8% [3·6–37·1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37·5% [95% CI 25·0 to 56·3] vs 31·6% [21·1 to 47·4]; difference 5·9% [–14·0 to 26·3]). / Interpretation: ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required