RESUMEN
Anti-immunoglobulin-like cell adhesion molecule 5 (IGLON5), a disease first described in 2014 by Sabater et al., is characterised by dysphonia, dysphagia, stridor and dysautonomia. We discuss the case of a patient presenting to the emergency department with anti-IGLON5 associated airway compromise following progressive reduced vocal cord movement requiring a surgical tracheostomy. We discuss the outpatient and emergency presentation of this case along with the available literature on anti-IGLON5. We aim to remind ENT practitioners to look beyond the common diagnoses and consider the diagnosis of anti-IGLON5 disease when faced with the symptoms listed above.
RESUMEN
Radiological imaging plays a vital role in clinically diagnosing TSC. TSC prognosis is largely determined by the severity and the extent of the systems affected by it. TSC patients are symptomatically managed, since no cure is present. Healthcare professionals must frequently check-up TSC patients who have a lifelong disorder.
RESUMEN
The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist anti-phishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based meta-learning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its meta-learner variants for phishing website detection and applicable cybersecurity attacks are recommended.