Incidence of Cardiovascular Instability in Patients With Guillain-Barré Syndrome: A Retrospective Study.

Introduction Guillain-Barré syndrome (GBS) is an autoimmune disease affecting radicles and peripheral nerves resulting in acute flaccid paralysis. Respiratory failure, autonomic dysfunction, and secondary complications such as pneumonia, and venous thromboembolism are the major causes of death and disability in GBS. Cardiovascular complications play a major role in the prognosis of GBS patients. The aim is to determine the incidence of cardiovascular instability in GBS patients and to see if there are any specific risk groups associated with the development of cardiovascular instability. Methodology This is a retrospective descriptive study conducted in a tertiary care center in South India. Data on 50 consecutive GBS patients were collected from hospital records including case sheets, death summaries, and discharge summaries. Patients with evidence of sepsis, blood loss, heavy alcohol consumption, and chronic liver disease were excluded from the study. Baseline demographic data, symptom onset to admission time, baseline Erasmus Guillain-Barré Syndrome Respiratory Insufficiency Score (EGRIS), and baseline liver function tests were documented. The presence of heart rate and blood pressure fluctuations was noted from the records. Frequency data were calculated from the categorical variables. Analysis of non-parametric variables by chi-square test was done using IBM SPSS Statistics for Windows, Version 25.0 (Released 2017; IBM Corp., Armonk, New York, United States). Results Cardiovascular instability was present in 15 (30%) patients in the study population. It was present in all patients (100%) who require mechanical ventilation. The incidence of cardiovascular instability was higher in patients who had lesser onset to admission times (41.9% vs 10.5%; p=0.019), EGRIS≥4 (40.6% vs 11.1%; p=0.029), and lower cranial nerve involvement (40% vs 6.7%; p=0.018). Conclusion Of patients with GBS, 30% developed cardiovascular instability during their disease course. Patients with lesser onset to admission times, EGRIS ≥4, and those with lower cranial nerve involvement had a greater incidence of cardiovascular instability.

Myasthenia-Like Presentation of Imidacloprid Toxicity.

Imidacloprid is a neonicotinoid insecticide highly specific to nicotinic acetylcholine receptors in insects and other invertebrates. Nicotinic receptors in mammalian species have a low affinity to neonicotinoids. However, cross-reactivity with mammalian nicotinic receptors is a major concern especially due to the propensity of this commonly used agent to persist in environmental water sources for an extended period of time. Here, we present a case report of a patient who presented to the emergency department with features suggestive of neuromuscular junction dysfunction, following exposure to imidacloprid.

CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.

Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the prevalence of AS is projected to rise, resulting in a progressively significant healthcare and economic burden. While surgical aortic valve replacement (SAVR) has been the gold standard approach, the less invasive transcatheter aortic valve replacement (TAVR) is poised to become the dominant method for high- and medium-risk interventions. Computational simulations using patient-specific models, have opened new research avenues for optimizing emerging devices and predicting clinical outcomes. The traditional techniques of generating digital replicas of patients' aortic root, native valve, and calcification are time-consuming and labor-intensive processes requiring specialized tools and expertise in anatomy. Alternatively, deep learning models, such as the U-Net architecture, have emerged as reliable and fully automated methods for medical image segmentation. Two-dimensional U-Nets have been shown to produce comparable or more accurate results than trained clinicians' manual segmentation while significantly reducing computational costs. In this study, we have developed a fully automatic AI tool capable of reconstructing the digital twin geometry and analyzing the calcification distribution on the aortic valve. The developed automatic segmentation package enables the modeling of patient-specific anatomies, which can then be used to simulate virtual interventional procedures, optimize emerging prosthetic devices, and predict clinical outcomes.

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming.

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.

Pneumoperitoneum from pneumomediastinum in paraquat poisoning.

Pneumomediastinum is not uncommon in paraquat poisoning and usually results from oesophageal perforation or alveolar rupture in fibrotic lung disease. However, the combined presentation of pneumomediastinum and pneumoperitoneum is a rarity. We recently managed a young patient with paraquat ingestion who developed spontaneous pneumomediastinum. His chest radiograph also showed free air under the right hemidiaphragm. This pneumoperitoneum caused no clinical symptom and resolved spontaneously within a few days without any surgical intervention.