RIL-seq reveals extensive involvement of small RNAs in virulence and capsule regulation in hypervirulent Klebsiella pneumoniae.

Hypervirulent Klebsiella pneumoniae (hvKp) can infect healthy individuals, in contrast to classical strains that commonly cause nosocomial infections. The recent convergence of hypervirulence with carbapenem-resistance in K. pneumoniae can potentially create 'superbugs' that are challenging to treat. Understanding virulence regulation of hvKp is thus critical. Accumulating evidence suggest that posttranscriptional regulation by small RNAs (sRNAs) plays a role in bacterial virulence, but it has hardly been studied in K. pneumoniae. We applied RIL-seq to a prototypical clinical isolate of hvKp to unravel the Hfq-dependent RNA-RNA interaction (RRI) network. The RRI network is dominated by sRNAs, including predicted novel sRNAs, three of which we validated experimentally. We constructed a stringent subnetwork composed of RRIs that involve at least one hvKp virulence-associated gene and identified the capsule gene loci as a hub target where multiple sRNAs interact. We found that the sRNA OmrB suppressed both capsule production and hypermucoviscosity when overexpressed. Furthermore, OmrB base-pairs within kvrA coding region and partially suppresses translation of the capsule regulator KvrA. This agrees with current understanding of capsule as a major virulence and fitness factor. It emphasizes the intricate regulatory control of bacterial phenotypes by sRNAs, particularly of genes critical to bacterial physiology and virulence.

Global Mapping of Small RNA-Target Interactions in Bacteria.

Small RNAs (sRNAs) associated with the RNA chaperon protein Hfq are key posttranscriptional regulators of gene expression in bacteria. Deciphering the sRNA-target interactome is an essential step toward understanding the roles of sRNAs in the cellular networks. We developed a broadly applicable methodology termed RIL-seq (RNA interaction by ligation and sequencing), which integrates experimental and computational tools for in vivo transcriptome-wide identification of interactions involving Hfq-associated sRNAs. By applying this methodology to Escherichia coli we discovered an extensive network of interactions involving RNA pairs showing sequence complementarity. We expand the ensemble of targets for known sRNAs, uncover additional Hfq-bound sRNAs encoded in various genomic regions along with their trans encoded targets, and provide insights into binding and possible cycling of RNAs on Hfq. Comparison of the sRNA interactome under various conditions has revealed changes in the sRNA repertoire as well as substantial re-wiring of the network between conditions.

In vivo cleavage rules and target repertoire of RNase III in Escherichia coli.

Bacterial RNase III plays important roles in the processing and degradation of RNA transcripts. A major goal is to identify the cleavage targets of this endoribonuclease at a transcriptome-wide scale and delineate its in vivo cleavage rules. Here we applied to Escherichia coli grown to either exponential or stationary phase a tailored RNA-seq-based technology, which allows transcriptome-wide mapping of RNase III cleavage sites at a nucleotide resolution. Our analysis of the large-scale in vivo cleavage data substantiated the established cleavage pattern of a double cleavage in an intra-molecular stem structure, leaving 2-nt-long 3' overhangs, and refined the base-pairing preferences in the cleavage site vicinity. Intriguingly, we observed that the two stem positions between the cleavage sites are highly base-paired, usually involving at least one G-C or C-G base pair. We present a clear distinction between intra-molecular stem structures that are RNase III substrates and intra-molecular stem structures randomly selected across the transcriptome, emphasizing the in vivo specificity of RNase III. Our study provides a comprehensive map of the cleavage sites in both intra-molecular and inter-molecular duplex substrates, providing novel insights into the involvement of RNase III in post-transcriptional regulation in the bacterial cell.

Acetazolamide therapy for metabolic alkalosis in critically ill pediatric patients.

OBJECTIVE: Despite a paucity of supporting literature, acetazolamide is commonly used in critically ill children with metabolic alkalosis (elevated plasma bicarbonate [pHco-3] and pH). The objective of this study was to assess the change in 18 hours after initiation of acetazolamide therapy. DESIGN: Retrospective study. SETTING: PICU of an urban, tertiary-care children's hospital. PATIENTS: Mechanically ventilated children (≤ 17 yr) with metabolic alkalosis (pHco-3 ≥ 35 mmol/L). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 153 consecutively screened patients, 61 patients (29 female patients) were enrolled: 18 cardiac patients (after congenital heart disease repair) and 43 noncardiac patients. The cardiac patients were younger than the noncardiac patients (median [interquartile range] age, 0.6 mo [0.3-2.5 mo] vs 7.4 mo [2.8-39.9 mo]; p < 0.00001) and had higher preacetazolamide baseline diuretic scores and urine output. The pHco-3 levels 18 hours after initiation of acetazolamide were reduced in the cohort as a whole (40.2 ± 4.8 to 36.2 ± 5.6 mmol/L; p < 0.001) and in the noncardiac patients, but they were unchanged in the cardiac patients. The PCO2 remained unchanged after acetazolamide in both subgroups. Because young age and presence of cardiac disease were potential confounders, the 20 noncardiac patients who are 6 months old or younger were compared with the cardiac subgroup and demonstrated reduced pHco-3 after acetazolamide and lower preacetazolamide baseline diuretic score and urine output. CONCLUSION: Acetazolamide reduces pHco-3 concentration in critically ill, mechanically ventilated children overall, but it did not do so in cardiac patients in our cohort, even in comparison with noncardiac patients of a similar age. These findings do not support the current use of acetazolamide for metabolic alkalosis in critically ill children with congenital heart disease. Further study is required to determine why these cardiac patients respond differently to acetazolamide than noncardiac patients and whether this response impacts important clinical outcomes, for example, weaning mechanical ventilation.

Mixed-order phase transition in a one-dimensional model.

We introduce and analyze an exactly soluble one-dimensional Ising model with long range interactions that exhibits a mixed-order transition, namely a phase transition in which the order parameter is discontinuous as in first order transitions while the correlation length diverges as in second order transitions. Such transitions are known to appear in a diverse classes of models that are seemingly unrelated. The model we present serves as a link between two classes of models that exhibit a mixed-order transition in one dimension, namely, spin models with a coupling constant that decays as the inverse distance squared and models of depinning transitions, thus making a step towards a unifying framework.

TRS: a method for determining transcript termini from RNAtag-seq sequencing data.

In bacteria, determination of the 3' termini of transcripts plays an essential role in regulation of gene expression, affecting the functionality and stability of the transcript. Several experimental approaches were developed to identify the 3' termini of transcripts, however, these were applied only to a limited number of bacteria and growth conditions. Here we present a straightforward approach to identify 3' termini from widely available RNA-seq data without the need for additional experiments. Our approach relies on the observation that the RNAtag-seq sequencing protocol results in overabundance of reads mapped to transcript 3' termini. We present TRS (Termini by Read Starts), a computational pipeline exploiting this property to identify 3' termini in RNAtag-seq data, and show that the identified 3' termini are highly reliable. Since RNAtag-seq data are widely available for many bacteria and growth conditions, our approach paves the way for studying bacterial transcription termination in an unprecedented scope.

Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography.

PURPOSE: The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis. MATERIALS AND METHODS: Training, testing and validation data were acquired from a dataset of 82,690 chest and abdomen CT examinations performed at 17 different institutions. Free text in DICOM metadata was utilized as weak labels for semi-supervised classification training. Contrast phase identification was approached as a classification task, using a 12-layer CNN and ResNet18 with four contrast-phase output. The model was reformulated to fit a regression task aimed to predict actual seconds from time of IV contrast administration to series image acquisition. Finally, transfer learning was used to optimize the model to predict contrast presence on CT Chest. RESULTS: By training based on labels inferred from noisy, free text DICOM information, contrast phase was predicted with 93.3% test accuracy (95% CI: 89.3%, 96.6%) . Regression analysis resulted in delineation of early vs late arterial phases and a nephrogenic phase in between the portal venous and delayed excretory phase. Transfer learning applied to Chest CT achieved an AUROC of 0.776 (95% CI: 0.721, 0.832) directly using the model trained for abdomen CT and 0.999 (95% CI: 0.998, 1.000) by fine-tuning. CONCLUSIONS: The presence and phase of contrast on CT examinations of the Abdomen-pelvis accurately and automatically be ascertained by a machine learning algorithm. Transfer learning applied to CT Chest achieves high precision with as little as 100 labeled samples.

Widespread compensatory evolution conserves DNA-encoded nucleosome organization in yeast.

Evolution maintains organismal fitness by preserving genomic information. This is widely assumed to involve conservation of specific genomic loci among species. Many genomic encodings are now recognized to integrate small contributions from multiple genomic positions into quantitative dispersed codes, but the evolutionary dynamics of such codes are still poorly understood. Here we show that in yeast, sequences that quantitatively affect nucleosome occupancy evolve under compensatory dynamics that maintain heterogeneous levels of A+T content through spatially coupled A/T-losing and A/T-gaining substitutions. Evolutionary modeling combined with data on yeast polymorphisms supports the idea that these substitution dynamics are a consequence of weak selection. This shows that compensatory evolution, so far believed to affect specific groups of epistatically linked loci like paired RNA bases, is a widespread phenomenon in the yeast genome, affecting the majority of intergenic sequences in it. The model thus derived suggests that compensation is inevitable when evolution conserves quantitative and dispersed genomic functions.

Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA-RNA Interaction Data.

The genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryotes. Bacterial sRNA-encoding genes were initially identified in intergenic regions, but recent evidence suggest that they can be encoded within other, well-defined, genomic elements. This notion was strongly supported by data generated by RIL-seq, a RNA-seq-based methodology we recently developed for deciphering chaperon-dependent sRNA-target networks in bacteria. Applying RIL-seq to Hfq-bound RNAs in Escherichia coli, we found that ∼64% of the detected RNA pairs involved known sRNAs, suggesting that yet unknown sRNAs may be included in the ∼36% remaining pairs. To determine the latter, we first tested and refined a set of quantitative features derived from RIL-seq data, which distinguish between Hfq-dependent sRNAs and "other RNAs". We then incorporated these features in a machine learning-based algorithm that predicts novel sRNAs from RIL-seq data, and identified high-scoring candidates encoded in various genomic regions, mostly intergenic regions and 3' untranslated regions, but also 5' untranslated regions and coding sequences. Several candidates were further tested and verified by northern blot analysis as Hfq-dependent sRNAs. Our study reinforces the emerging concept that sRNAs are encoded within various genomic elements, and provides a computational framework for the detection of additional sRNAs in Hfq RIL-seq data of E. coli grown under different conditions and of other bacteria manifesting Hfq-mediated sRNA-target interactions.

The impact of Hfq-mediated sRNA-mRNA interactome on the virulence of enteropathogenic Escherichia coli.

Small RNAs (sRNAs) exert their regulation posttranscriptionally by base pairing with their target mRNAs, often in association with the RNA chaperone protein Hfq. Here, integrating RNA-seq­based technologies and bioinformatics, we deciphered the Hfq-mediated sRNA-target interactome of enteropathogenic Escherichia coli (EPEC). The emerging network comprises hundreds of sRNA-mRNA pairs, including mRNAs of virulence-associated genes interacting with known sRNAs encoded within the core genome, as well as with newly found sRNAs encoded within pathogenicity islands. Some of the sRNAs affect multiple virulence genes, suggesting they function as hubs of virulence control. We further analyzed one such sRNA hub, MgrR, and one of its targets identified here, the major virulence-associated chaperon, cesT. We show that MgrR adjusts the level of EPEC cytotoxicity via regulation of CesT expression. Our results reveal an elaborate sRNA-mRNA interactome controlling the pathogenicity of EPEC and reinforce a role for sRNAs in the control of pathogen-host interaction.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

PURPOSE: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm. METHODS: Between January 2010 and September 2014, 610 CT studies of the abdomen and pelvis were used to develop spinal column and L1 to L4 multiclass segmentation. DEXA simulation training and validation used 1,843 pairs of CT studies accompanied by DEXA results obtained within a 6-month interval from the same individual. Machine learning-based regression was used to determine correlation between calculated grade (on the basis of vertebrae L1-L4) and DEXA t score. RESULTS: Analysis of the t score equivalent, generated by the algorithm, revealed true positives in 1,144 patients, false positives in 92 patients, true negatives in 245 patients, and false negatives in 212 patients, resulting in an accuracy of 82%. Sensitivity for the detection of osteoporosis or osteopenia was 84.4% (95% confidence interval, 82.3%-86.2%), and specificity was 72.7% (95% confidence interval, 67.7%-77.2%). CONCLUSIONS: The presented algorithm can identify osteoporosis and osteopenia with a high degree of accuracy (82%) and a small proportion of false positives. Efforts to cull greater information using machine-learning algorithms from pre-existing data have the potential to have a marked impact on population health efforts such as bone mineral density screening for osteoporosis, in which gaps in screening currently exist.

Mapping the small RNA interactome in bacteria using RIL-seq.

Small RNAs (sRNAs) are major post-transcriptional regulators of gene expression in bacteria. To enable transcriptome-wide mapping of bacterial sRNA-target pairs, we developed RIL-seq (RNA interaction by ligation and sequencing). RIL-seq is an experimental-computational methodology for capturing sRNA-target interactions in vivo that takes advantage of the mutual binding of the sRNA and target RNA molecules to the RNA chaperone protein Hfq. The experimental part of the protocol involves co-immunoprecipitation of Hfq and bound RNAs, ligation of RNAs, library preparation and sequencing. The computational pipeline maps the sequenced fragments to the genome, reveals chimeric fragments (fragments comprising two ligated independent fragments) and determines statistically significant overrepresented chimeric fragments as interacting RNAs. The statistical filter is aimed at reducing the number of spurious interactions resulting from ligation of random neighboring RNA fragments, thus increasing the reliability of the determined sRNA-target pairs. A major advantage of RIL-seq is that it does not require overexpression of sRNAs; instead, it simultaneously captures the in vivo targets of all sRNAs in the native state of the cell. Application of RIL-seq to bacteria grown under different conditions provides distinctive snapshots of the sRNA interactome and sheds light on the dynamics and rewiring of the post-transcriptional regulatory network. As RIL-seq needs no prior information about the sRNA and target sequences, it can identify novel sRNAs, along with their targets. It can be adapted to detect protein-mediated RNA-RNA interactions in any bacterium with a sequenced genome. The experimental part of the RIL-seq protocol takes 7-9 d and the computational analysis takes ∼2 d.

Exact extreme-value statistics at mixed-order transitions.

We study extreme-value statistics for spatially extended models exhibiting mixed-order phase transitions (MOT). These are phase transitions that exhibit features common to both first-order (discontinuity of the order parameter) and second-order (diverging correlation length) transitions. We consider here the truncated inverse distance squared Ising model, which is a prototypical model exhibiting MOT, and study analytically the extreme-value statistics of the domain lengths The lengths of the domains are identically distributed random variables except for the global constraint that their sum equals the total system size L. In addition, the number of such domains is also a fluctuating variable, and not fixed. In the paramagnetic phase, we show that the distribution of the largest domain length l_{max} converges, in the large L limit, to a Gumbel distribution. However, at the critical point (for a certain range of parameters) and in the ferromagnetic phase, we show that the fluctuations of l_{max} are governed by novel distributions, which we compute exactly. Our main analytical results are verified by numerical simulations.

Autonomic arousal index: an automated detection based on peripheral arterial tonometry.

Arousals from sleep are associated with increased sympathetic activation and are therefore associated with peripheral vasoconstriction. We hypothesized that digital vasoconstrictions as measured by peripheral arterial tonometery (PAT), combined with an increase in pulse rate, would accurately reflect arousals from sleep, and can provide an autonomic arousal index (AAI). Based on a previously studied group of 40 sleep apnea patients simultaneously recorded by both polysomnography (PSG) and PAT systems, an automated algorithm using the PAT signal (and pulse rate derived from it) was developed for detection of arousals from sleep. This was further validated in a separate group of 96 subjects (85 patients referred with suspected obstructive sleep apnea and 11 healthy volunteers mean age 46.2+/-14.4 years, BMI 28.5+/-5.4 kg/m2). All underwent a whole night PSG with simultaneous PAT recording. The PSG recordings were blindly manually analyzed for arousals based on American Academy of Sleep Medicine (AASM) criteria, while PAT was scored automatically. There was a significant correlation between PSG and PAT arousals (R=0.82, p<0.0001) with a good agreement across a wide range of values, with a ROC curve having an area under the curve (AUC) of 0.88. We conclude that automated analysis of the peripheral arterial tonometry signal can detect EEG arousals from sleep, in a relatively quick and reproducible fashion.

Sleep characteristics following adenotonsillectomy in children with obstructive sleep apnea syndrome.

OBJECTIVE: To compare the effect of adenotonsillectomy on rapid eye movement (REM)- and non-REM-related respiratory and sleep architecture characteristics in children with obstructive sleep apnea syndrome (OSAS). STUDY DESIGN: This prospective study evaluated 36 children (median age, 6.9 years; range, 1.8 to 12.6 years) with OSAS using polysomnography before and a few months after adenotonsillectomy. Primary outcomes included the number of obstructive apnea and hypopnea and arousals per hour of sleep. RESULTS: At 4.6 months (range, 1 to 16 months) after adenotonsillectomy, there was a significant improvement of all respiratory parameters. The median respiratory disturbance index (RDI) decreased from 4.1/h (range, 0 to 85/h) to 0.9/h (range, 0 to 13/h) after adenotonsillectomy (p < 0.0001). The median non-REM RDI decreased from 3.0/h (range, 0 to 89/h) to 0.4/h (range, 0 to 13/h) [p < 0.001] as compared with REM RDI, which decreased from 7.8/h (range, 0 to 69/h) to 2.3/h (range, 0 to 54/h) after adenotonsillectomy (p < 0.01). Median arousal index decreased following adenotonsillectomy from 17.5/h (range, 7 to 57/h) to 14.0/h (range, 6 to 47/h) [p < 0.03]. CONCLUSIONS: Adenotonsillectomy resulted in a greater improvement in non-REM RDI as compared with REM-RDI, and a decrease in the number of arousals.

Evaluation of a portable device based on peripheral arterial tone for unattended home sleep studies.

BACKGROUND: Diagnosis of obstructive sleep apnea syndrome (OSAS) by ambulatory systems is a growing practice in view of the large number of patients awaiting correct diagnosis. The Watch PAT100 (WP100) [Itamar Medical; Caesarea, Israel] is a portable device based on the peripheral arterial tone (PAT) signal, and is designed for unattended home sleep studies. OBJECTIVES: To evaluate the efficacy, reliability, and reproducibility of the WP100 device for the diagnosis of OSAS as compared to in-laboratory, standard polysomnographic-based manual scoring. DESIGN AND METHODS: One hundred two subjects (78 men; 69 patients with OSAS and 33 normal volunteers; mean +/- SD age, 41.4 +/- 15.2 years; body mass index, 26.8 +/- 5.5) underwent in-laboratory full polysomnography simultaneously with WP100 recording. Fourteen subjects also underwent two additional unattended home sleep studies with the WP100 alone. The polysomnography recordings were blindly scored for apnea/hypopnea according to the American Academy of Sleep Medicine criteria (1999), and the polysomnography respiratory disturbance index (RDI) [PSG-RDI] was calculated. The WP100 data were analyzed automatically for the PAT RDI (PRDI) by a proprietary algorithm that was previously developed on an independent group of subjects. RESULTS: Across a wide range of RDI levels, the PRDI was highly correlated with the PSG-RDI (r = 0.88, p < 0.0001), with an area under the receiver operating characteristic curve of 0.82 and 0.87 for thresholds of 10 events per hour and 20 events per hour, respectively. The PRDI scores were also highly reproducible, showing high correlation between home and in-laboratory sleep studies (r = 0.89, p < 0.001). CONCLUSION: The WP100 may offer an accurate, robust, and reliable ambulatory method for the detection of OSAS, with minimal patient discomfort.

An automatic ambulatory device for detection of AASM defined arousals from sleep: the WP100.

OBJECTIVES AND BACKGROUND: Arousals from sleep are associated with increased sympathetic activation and therefore with peripheral vasoconstriction. Sleep fragmentation in the form of multiple arousals is associated with daytime somnolence and cognitive impairment; however, manual scoring of arousal is time consuming and problematic due to relatively high inter-scorer variability. We have recently shown that automated analysis of in-lab recorded peripheral arterial tone (PAT) signal and the pulse rate derived from it can accurately assess arousals from sleep as defined by the American Academy of Sleep Medicine (AASM). In the current study we sought to extend these findings to the Watch_PAT100 (WP100), an ambulatory device measuring PAT, oximetry and actigraphy. METHODS: Sixty-eight subjects (61 patients referred to the sleep lab with suspected obstructive sleep apnea and seven healthy volunteers, mean age 46.3+/-14.2 years) underwent a whole night polysomnography (PSG) with simultaneous recording of PAT signal by the ambulatory WP100 device. The PSG recordings were blindly manually analyzed for arousals based on AASM criteria, while PAT was scored automatically based on the algorithm developed previously. RESULTS: There was a significant correlation between AASM arousals derived from the PSG and PAT autonomic arousals derived from the WP100 (R=0.87, P<0.001), with a good agreement across a wide range of values. The sensitivity and specificity of PAT in detecting patients with at least 20 arousals per hour of sleep were 0.80 and 0.79, respectively, with a receiver operating characteristic curve having an area under the curve of 0.87. CONCLUSIONS: We conclude that automatic analysis of peripheral arterial tonometry signal derived from the ambulatory device Watch_PAT100 can accurately identify arousals from sleep in a simple and time saving fashion.

A 12-year-old African American girl with subacute bilateral ophthalmoplegia.

A twelve-year-old African-American female presented with two week history of progressively worsening headache and fatigue, and vision difficulties for the past week. The physical examination was normal. The neurological evaluation was normal, except for cranial nerves (CN) testing, which showed bilateral restriction of adduction (CN III) and up gaze (CN IV) motions, vertical nystagmus, and left side facial paresis of central origin (CN VII). The bilateral exotropia and ophthalmoplegia are characteristics of WEBINO (Wall-Eyed Bilateral Intranuclear Ophthalmoplegia) syndrome, associated to a brain stem structural lesion. The following causes were evaluated and ruled out: tumor, infection, ischemic stroke, non-infectious inflammation. Pediatric Acquired Demyelinating Syndromes were then considered. Neuromyelitis Optica was ruled out in the absence of neuritis and normal spinal cord MRI. The differential diagnosis between Clinically Isolated Syndrome and Acute Demyelinating Encephalomyelitis, causing an isolated brain stem syndrome, is discussed.