Innovative and non-invasive method for the diagnosis of dyschromatopsia and the re-education of the eyes

Objective Dyschromatopsia is a pathology that afflicts many people even if, in most cases, they are not aware of it. The pathology, in fact, is not disabling in everyday life even if it is limiting from some points of view. Once diagnosed, dyschromatopsia is generally not investigated further: it is not known exactly how it manifests itself and with what extent. Furthermore, since it is a genetic pathology, it is “condemned” not to be resolvable. Biological neural networks have shown the capability to readapt their structure in order to overcome sensory malfunctions or neuronal damage. We propose a diagnostic algorithm capable of qualitatively and quantitatively assessing the degree of visual impairment due to the presence of congenital or acquired dyschromatopsia. The algorithm can also be easily integrated for its possible therapeutic use. Methods The application of a novel approach based on an innovative algorithm for the diagnosis of dyschromatopsia and plastic reeducation training of the eye is proposed. Results Our algorithm provides an accurate measure of the degree of dyschromatopsia severity in patients quickly and noninvasively. In addition, it can be used for a reeducational training process. Conclusions Dyschromatopsia is an increasingly common disease in the world. The method we developed can diagnose dyschromatopsia. The algorithm also develops a metric scale for recognizing the degree of severity. The algorithm can be used independently by specilized and non-specilized people. In addition, the algorithm can be integrated with Machine Learning techniques to create a customized eye retrainer based on the plasticity and adaptability of neural tissue

Disease Reactivation in Secondary Progressive Multiple Sclerosis Patients Switching from Fingolimod to Siponimod: A Case Series

Siponimod, a selective modulator of sphingosine 1-phosphate receptors 1 (S1P1) and 5 (S1P5), has recently been marketed for patients with Secondary Progressive Multiple Sclerosis (SPMS). Herein, we report three SPMS patients presenting disease reactivation in the first three months after switching from fingolimod to siponimod. Fingolimod binds to S1P1, S1P3, S1P4 and S1P5 receptors. S1P3 holds a central role in eliciting central proinflammatory responses, thus it has been hypothesized that upregulation of S1P3 may be the mechanism behind relapses after switching from fingolimod to siponimod. Further studies are needed to investigate the safety and efficacy of this treatment sequencing

Health related quality of life in the domain of physical activity predicts confirmed disability progression in people with relapsing remitting multiple sclerosis

Introduction: The diagnosis of the progression phase of Multiple Sclerosis (MS) is still retrospective and based on the objectivation of clinical disability accumulation. Objectives: To assess whether the Patient Reported Outcomes Measures (PROMs) scores predict the occurrence of disease progression within three years of follow-up. Methods: Observational prospective multicenter study. Stable Relapsing-Remitting MS (RRMS) patients were enrolled. At enrollment, patients completed the following PROMs: Beck Depression Inventory- II, The Treatment Satisfaction Questionnaire for Medications, Medical Outcomes Study Short Form 36- Item (SF36), Fatigue Severity Scale. EDSS was assessed at enrollment and three years later. The outcome measure was defined as the occurrence of confirmed disability progression (CDP) within three years of follow-up. Univariable and multivariable logistic regression models were performed to study the association between the final score of each test and the outcome. Results: SF36-Physical Functioning (SF36-PF) was the only independent variable associated with the outcome. The ROC curve analysis determined a score of 77.5 at SF36-PF as the cut-off point identifying patients experiencing CDP within three years of follow-up [AUC: 0.66 (95% CI: 0.56-0.75)]. Conclusions: RRMS patients scoring higher (>77.5) at SF36-PF subscale have a higher likelihood to experience CDP within the next three years