Automated External Defibrillators

Sensitivity, Specificity, and Accuracy [13 (link)] are commonly used to measure gene-finder performance relative to some standard, usually a reference annotation that is well supported by experimental evidence. Sensitivity (SN) is the fraction of the reference feature predicted, whereas Specificity (SP) is the fraction of the prediction overlapping the reference feature. Both measures can be calculated for any feature class, e.g. transcripts, exons or introns; and the calculations can be preformed at the nucleotide level, or, if greater stringency is desired, the fraction of the features predicted exactly [23 (link)]. SN and SP are often combined into a single measure called Accuracy (AC). Several formulations of accuracy are in use (see [13 (link)]). Some of these take true negatives into account; others do not. In practice, it can be difficult to determine the scope of true negatives for genome annotations, as these can be considered as limited to some flanking region around the gene in question, the entire intergenic region or even the rest of the genome. Including true negatives in the accuracy calculation also complicates inter-genome comparisons. For example, gene-prediction accuracy will tend to be higher for those genomes with large introns and intergenic regions. For these reasons we have used a simple average, (SN +SP)/2, to measure accuracy.
Although SN, SP and AC are normally thought of as measures of agreement between a prediction and a reference annotation, there is no inherent requirement for a reference annotation. The measures can also be used to compare two annotations to one another. Reformulating SN in terms of sets makes this clear (see Figure 6). SN for example is usually given as SN = tp/(tp + fn), where tp is number of true positives and fn false negatives. But SN can also be thought of as the fraction of annotation i overlapping annotation j. Substituting tp and fn for their set-theoretic equivalents (Figure 6), SN = |i∩j|/(|i∩j| + |j\i|), where |i∩j| is the number of overlapping nucleotides (tp), and |j\i| the number of nucleotides in j not annotated in i, or fn. Since, by definition |j| = |i∩j| + |j\i|, SN = |i∩j|/|j|, or the fraction of j overlapping i. Likewise, SP can be thought of as the fraction of i overlapping j, and Accuracy (AC) as the average of these two fractional overlaps – a bi-directional measure of Congruency between two annotations that we denote as C. The incongruence or distance, D, between annotation versions i and j then becomes D = 1-C.
So long as both versions of the annotation contain only a single annotated transcript, AED, is easily calculated. Alternative splicing, however, complicates matters somewhat. The problem lies in how best to pair the transcripts of one version of the annotation with those of another. Several different procedures can be envisioned; we have chosen one that will always give the minimal distance. The procedure is shown in Figure 7. First, pairwise incongruencies, or 1-C, between each possible pairing of annotation i's transcripts with those of j are calculated. Each transcript is then paired with its closest partner from the other annotation. In cases where a transcript has multiple equidistant partners, one of these is chosen randomly. In cases where the two annotations have different numbers of transcripts, two transcripts from one version can share the same partner in the other annotation. The pairwise distances are then summed (Figure 7, panel D). The result is a (minimum) measure of distance between two versions of the gene, which we term Annotation Edit Distance, or AED. This value can also normalized by the number of transcript-pairs to give the average AED/transcript-pair, a number useful for analyses of alternatively spliced annotations. AED is a general measure, not restricted to transcripts. This makes it possible to compute multiple, feature-specific AEDs for purposes of better annotation management. For example, changes to UTRs between releases can be analyzed independently of changes to other features. Moreover, AED is genome independent; this means that the magnitude of release-to-release revisions can be compared across different genomes as we have done in Figure 2.

All patients with good grade SAH, who either failed to reach extubation due to persisting neurological impairment or clinical deterioration, which could not be adequately explained on CT-A or CT-P imaging, underwent serial electroencephalography (EEG).
The diagnosis of isolated seizure was based on clinical symptoms by the treating physicians. The diagnosis of ncSE was based on consciousness impairment and positive findings on EEG and cEEG [17 (link),18 (link)]. The diagnosis of convulsive status epilepticus (CSE) was defined as prolonged generalized convulsive seizures (>5 min) [19 (link)].
When epileptic seizures were detected, anti-epileptic monotherapy with levetiracetam was initiated. In case of persistent poor neurological status, continuous EEG (cEEG) was used for extended monitoring. The results of the cEEGs were evaluated on daily basis by a multidisciplinary board including certified neurologist, anesthesiologist and neurosurgeon.
In patients with persisting ncSE despite the initial monotherapy, we subsequently added lacosamide and brivaracetam to the anti-epileptic drug (AED) therapy. In case of further persistence of ncSE despite therapy escalation, we induced deep sedation state with isoflurane and aimed to achieve burst suppression according to our intensive-care algorithm [4 (link),20 (link)]. Subsequently, we widened the AED therapy according to the multidisciplinary consensus for each patient individually. In the present cohort, up to 5 AEDs were used in combination to treat super-refractory ncSE.

All AEDs (purity ≥98%) were purchased from Sigma-Aldrich. Lamotrigine and valproic acid were dissolved in dimethyl sulfoxide (DMSO) to produce a 1 M stock solution. The stock solution was stored at −20 °C and freshly diluted in the bath solution to the desired concentration just before use. The concentration of DMSO in the final solution was less than 0.33% and this does not affect cardiac ion channels [18 (link),19 (link)]. Pregabalin, gabapentin, and levetiracetam were freshly dissolved in the bath solution to the desired concentration just before use. The Na_v1.5 current was measured at baseline conditions and after the wash-in of AEDs at concentrations of 1, 10, 30, 100, 300, or 1000 µM; these concentration ranges surrounded the therapeutic concentrations of the AEDs, as indicated by the yellow parts in Figure 1B–F [20 (link),21 (link),22 (link),23 (link),24 (link),25 (link),26 (link),27 ].