A Case Report on Multiple Sclerosis Associated With Atrial Fibrillation and Neurogenic Hypertension: Area Postrema Syndrome?

Multiple sclerosis (MS) is an autoimmune demyelinating neurological disorder primarily manifesting with a range of neurological symptoms, with cardiovascular autonomic involvement being a rare occurrence. We report a case where a patient initially presented with Bell's palsy, without other notable symptoms or signs, and subsequently developed atrial fibrillation, hypertension, and hemiparesis. Magnetic resonance imaging (MRI) revealed extensive demyelination in the cerebral hemispheres, brainstem, and notably, the area postrema. The anatomy of the area postrema and its connections, in relation to neurogenic hypertension, are discussed. The demyelination in the area postrema was thought to be the cause of our patient's arrhythmias and acute hypertension. Furthermore, we discuss the cerebral origins of cardiac arrhythmias, with a focus on MS and other neurological conditions. This case underscores the rarity of isolated cranial neuropathies, such as Bell's palsy, as an initial sign of MS, marking the onset of a relapse.

Dental procedure induced cerebellar haemorrhage with visual tilt and unsuspected CADASIL.

A man in his late 60s had vertigo and vision tilt following a dental procedure. A cerebellar haemorrhage and cerebral microbleeds (CMBs) were diagnosed on imaging. Subsequent testing revealed CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy). The role of the dental procedure as a trigger for intracerebral haemorrhage (ICH) is discussed. The incidence of CMBs and ICH in CADASIL is discussed. A summary of the causes and pathology associated with visual tilt is documented.

Osmotic demyelination syndrome: novel risk factors and proposed pathophysiology.

BACKGROUND: Osmotic demyelination syndrome (ODS) is non-inflammatory demyelination in response to an osmotic challenge. It can be pontine or extrapontine in presentation. AIMS: To retrospectively review cases involving ODS and define the spectrum of causes, risk factors, clinical and radiological presentations, and functional outcomes. RESULTS: The study utilised data from 15 patients with a mean age of 53.6 years. Malnutrition (9; 60%) and chronic alcoholism (10; 66.7%) were the most common associated disorders. Two (13.3%) patients had severe hyponatraemia (<120 mmol/L). The average highest single-day change was 5.1 mmol/L. Radiologically, 14 (93.3%) had pontine and 6 (40%) had extra-pontine lesions. Hypokalaemia (14; 93.3%) and hypophosphataemia (9; 60%) were commonly associated. Common clinical manifestations include altered consciousness/encephalopathy (9; 60%), dysphagia (4; 26.7%) and limb weakness (4; 26.7%). At 3 months, two (14.3%) had died and six (40%) were functionally independent (modified Rankin scale 0-2). CONCLUSION: We found that ODS occurred despite appropriate correction rates of hyponatraemia. Factors such as malnutrition, chronic alcoholism, hypokalaemia and hypophosphataemia are thought to play a role in its pathogenesis. Approximately half of the patients survived and became functionally independent.

A Smartphone application to provide real-time cardiopulmonary resuscitation quality feedback.

BACKGROUND: Quality of cardiopulmonary resuscitation (CPR) contributes significantly to outcomes. Key determinants of CPR quality pertaining to chest compressions are compression rate, compression depth, duration of interruptions, and chest recoil. Several studies have demonstrated that real-time audiovisual feedback improves CPR quality. We hypothesize that a mobile application using sensor data from built-in accelerometers in smartphones can provide accurate chest compression quality feedback in real time. This study aims to develop and validate an application for smartphone which can provide real-time audiovisual and haptic feedback on determinants of CPR quality. METHODS: A mobile application was developed to detect the compression depth and compression rate in real time using data captured from a smartphone's intrinsic accelerometer. The mobile device was placed on an adult manikin's chest at the point of compressions. In a simulated environment, data obtained using the application was compared directly to data obtained from a validated standard CPR quality tool. RESULTS: CPR quality parameters were obtained from the application and industry standard for 60, 30s-long sessions. Bland-Altman plot analysis for compression depth showed agreement between the app measurements and standard within ±4 mm (<10% error). The interclass correlation for agreement in the measurement of compression count was 0.92 (95% CI: 0.88-0.95), indicative of very strong agreement. CONCLUSIONS: Smart device applications using acceleration sensor data derived from smart phones can accurately provide real-time CPR quality feedback. With further development and validation, they can provide a ubiquitously available CPR feedback tool valuable for out-of-hospital arrests and in-hospital arrests in under-privileged areas.

Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG.

Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture-the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system's latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.

Prognostic Significance of Magnetic Resonance Imaging in Patients with Severe Nonpenetrating Traumatic Brain Injury Requiring Decompressive Craniectomy.

BACKGROUND: Diffuse axonal injury (DAI) detected on magnetic resonance imaging (MRI) may be useful to predict outcome after traumatic brain injury (TBI). METHODS: This study compared the ability of the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic model with DAI on MRI to predict the 18-month neurologic outcome in 56 patients who had required decompressive craniectomy after TBI. RESULTS: Of the 56 patients included in the study (19 scans occurred within 14 days; median time for all patients 24 days, interquartile range 14-42), 18 (32%) had evidence of DAI on the MRI scans. The presence of DAI on the MRI diffusion-weighted (DW) T2*-weighted gradient echo and susceptibility-weighted (SWI) sequences was associated with an increased risk of unfavorable outcome at 18 months compared with patients without DAI (44% vs. 17%, difference = 27%, 95% confidence interval 2.4-46.7%; P = 0.032), particularly when the brainstem was involved. However, neither the grading (I to IV) nor the number of brain regions with DAI was as good as the IMPACT model in discriminating between patients with unfavorable and favorable outcomes (area under the receiver operating characteristic curve: 0.625 and 0.621 vs. 0.918, respectively; P < 0.001 for both comparisons). After adjustment for the IMPACT prognostic risks, DAI in different brain regions and the grading of DAI were also not independently associated with unfavorable outcome. CONCLUSIONS: The prognostic significance of DAI on MRI may, in part, be captured by the IMPACT prognostic model. More research is needed before MRI should be routinely used to prognosticate the outcomes in patients with TBI requiring decompressive craniectomy.