Neuropsychological performances, quality of life, and psychological issues in pediatric onset multiple sclerosis: a narrative review

Multiple sclerosis (MS) is primarily a disease diagnosed in young and middle-aged adults. Although MS is a rare condition in pediatric age, an increasing rate of patients is diagnosed under the age of 18. The disabling nature of the disease cannot be reduced only to physical symptoms. Several additional symptoms such as cognitive impairment, fatigue, and psychological symptoms are common features of pediatric MS. The reviewed literature suggests that, despite the lower physical disability, children and adolescents diagnosed with MS are vulnerable to cognitive impairment even in the early stage of the disease. The neuropsychological profile of pediatric MS may resemble that of adult MS, including an impairment in attention/information processing speed, learning, verbal, and visuospatial memory. However, cognitive difficulties in children and adolescents are more likely to involve also general intelligence and linguistic abilities, presumably due to patients’ younger age and cognitive growth stage. Cognitive difficulties, beyond physical disability and relapses, may have a considerable impact on learning and school achievement. Depression and fatigue are other highly prevalent disturbances in pediatric MS and may contribute to patients’ low functional outcomes. Overall, these manifestations may cause considerable functional impairment on daily activities and quality of life that may require individualized rehabilitative treatment and extensive psychosocial care. Additional neuropsychological research evaluating larger samples, using more homogenous methods, and exploring the role of MS treatment on cognitive and psychological development is required

Safety of SARS-CoV2 vaccination and COVID-19 short-term outcome in pediatric acquired demyelinating disorders of central nervous system: A single center experience

IntroductionConcern of a correlation between disease relapse in patients with acquired demyelinating disorders of central nervous system (CNS) and SARS-CoV2 vaccines has been raised. In this single center study, we retrospectively evaluated safety of SARS-CoV2 vaccination and COVID-19 short-term outcome in pediatric acquired demyelinating disorders of CNS. Materials and methodsPatients with multiple sclerosis (MS), myelin oligodendrocyte glycoprotein antibody associated disease (MOGAD) and neuromyelitis optica spectrum disorder (NMOSD) with disease onset before 18 years of age were included. Demographic and clinical data, and information regarding previous SARS-CoV-2 infection and vaccination were collected. ResultsWe included nine patients with MOGAD. Six patients received SARS-CoV2 vaccination and complained pain at injection site while only one had fever and fatigue. Median follow-up was 28 weeks (range 20-48). Seven patients had COVID-19 occurring with mild flu-like symptoms and median follow-up was 28 weeks (range 24-34). Nobody had disease relapse. Five patients with NMOSD were included. All patients received SARS-CoV2 vaccination (BNT162b2-Pfizer-BioNTech). The median follow-up was 20 weeks (range 14-24) and only two patients complained pain at injection site, fever and fatigue. Three patients had also COVID-19 with mild flu-like symptoms, despite two of them being under immunosuppressive treatment. Lastly, forty-three patients with MS were included. 35 out of 43 received SARS-CoV2 vaccination with a median follow-up of 24 weeks (range 8-36). Fourteen patients had no side effects, while 21 complained mild side effects (mainly pain at injection site) and one experienced a disease relapse with complete recovery after steroid therapy. At vaccination, all but one were under treatment. Sixteen patients had COVID-19 occurring with mild symptoms. DiscussionCOVID-19 outcome was good although many patients were under immunosuppressive treatment. Vaccine-related side effects were frequent but were mild and self-limited. Only one MS patient had a post-vaccination relapse with complete recovery after steroid therapy. In conclusion, our data support the safety of SARS-CoV-2 vaccines in pediatric MS, MOGAD and NMOSD

Lower Urinary Tract Dysfunction in Pediatric Patients with Multiple Sclerosis: Diagnostic and Management Concerns

background: multiple sclerosis (MS) is increasing in the pediatric population and, as in adults, symptoms vary among patients. in children the first manifestations can sometimes overlap with acute neurological symptoms. urological symptoms have not been much studied in childhood. we shared our experience with MS urological manifestation in children. methods: this article is a retrospective evaluation of all children with MS, according to the Krupp criteria, who also present with urological symptoms. we collected demographic and clinical history, the MR localization of demyelinating lesions, urological symptoms, and exams. results: we report on six MS pediatric cases with urological manifestation. urinary symptoms, characterized by urinary incontinence in five patients and urinary retention in one patient, appeared in a different time frame from MS diagnosis. urodynamic exams showed both overactive and underactive bladder patterns. treatment was defined according to lower urinary tract dysfunction, using clean intermittent catheterization, oxybutynin, and intradetrusor onabotulinum toxin-a injection. a low acceptance rate of invasive evaluation and urological management was observed. conclusions: the MS diagnosis was traumatic for all our patients. we believe it is important to address urological care in young people from the time of diagnosis for prompt management; it could be useful to include a pediatric urologist in multidisciplinary teams

Early Immunotherapy and Longer Corticosteroid Treatment Are Associated With Lower Risk of Relapsing Disease Course in Pediatric MOGAD

Background and Objectives We sought to identify early factors associated with relapse and outcome in paediatric-onset myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD). Methods In a multicenter retrospective cohort of pediatric MOGAD (≤18 years), onset features and treatment were compared in patients with monophasic vs relapsing disease (including cases with follow-up ≥12 months after onset or relapse at any time) and in patients with final Expanded Disability Status Scale (EDSS) 0 vs ≥1 at last follow-up (including cases with followup >3 months after last event or EDSS0 at any time). Multivariable logistic regression models were used to evaluate factors associated with relapsing disease course and EDSS ≥ 1 at final follow-up. Results Seventy-five children were included (median onset age 7 years; median 30 months of follow-up). Presentation with acute disseminated encephalomyelitis was more frequent in children aged 8 years or younger (66.7%, 28/42) than in older patients (30.3%, 10/33) (p = 0.002), whereas presentation with optic neuritis was more common in children older than 8 years (57.6%, 19/33) than in younger patients (21.4%, 9/42) (p = 0.001). 40.0% (26/65) of patients relapsed. Time to first relapse was longer in children aged 8 years or younger than in older patients (median 18 vs 4 months) (p = 0.013). Factors at first event independently associated with lower risk of relapsing disease course were immunotherapy <7 days from onset (6.7-fold reduced odds of relapsing course, OR 0.15, 95% CI 0.03–0.61, p = 0.009), corticosteroid treatment for ≥5 weeks (6.7-fold reduced odds of relapse, OR 0.15, 95% CI 0.03–0.80, p = 0.026), and abnormal optic nerves on onset MRI (12.5-fold reduced odds of relapse, OR 0.08, 95% CI 0.01–0.50, p = 0.007). 21.1% (15/71) had EDSS ≥ 1 at final follow-up. Patients with a relapsing course had a higher proportion of final EDSS ≥ 1 (37.5%, 9/24) than children with monophasic disease (12.8%, 5/39) (p = 0.022, univariate analysis). Each 1-point increment in worst EDSS at onset was independently associated with 6.7-fold increased odds of final EDSS ≥ 1 (OR 6.65, 95% CI 1.33–33.26, p = 0.021). Discussion At first attack of pediatric MOGAD, early immunotherapy, longer duration of corticosteroid treatment, and abnormal optic nerves on MRI seem associated with lower risk of relapse, whereas higher disease severity is associated with greater risk of final disability (EDSS ≥ 1)

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

Cyclic vomiting syndrome in children: a nationwide survey of current practice on behalf of the Italian Society of Pediatric Gastroenterology, Hepatology and Nutrition (SIGENP) and Italian Society of Pediatric Neurology (SINP)

Background: Cyclic Vomiting Syndrome (CVS) is a rare functional gastrointestinal disorder, which has a considerable burden on quality of life of both children and their family. Aim of the study was to evaluate the diagnostic modalities and therapeutic approach to CVS among Italian tertiary care centers and the differences according to subspecialties, as well as to explore whether potential predictive factors associated with either a poor outcome or a response to a specific treatment. Methods: Cross-sectional multicenter web-based survey involving members of the Italian Society of Pediatric Gastroenterology, Hepatology and Nutrition (SIGENP) and Italian Society of Pediatric Neurology (SINP). Results: A total of 67 responses were received and analyzed. Most of the respondent units cared for less than 20 patients. More than half of the patients were referred after 3 to 5 episodes, and a quarter after 5 attacks. We report different diagnostic approaches among Italian clinicians, which was particularly evident when comparing gastroenterologists and neurologists. Moreover, our survey demonstrated a predilection of certain drugs during emetic phase according to specific clinic, which reflects the cultural background of physicians. Conclusion: In conclusion, our survey highlights poor consensus amongst clinicians in our country in the diagnosis and the management of children with CVS, raising the need for a national consensus guideline in order to standardize the practice

theta-Subsumption and Resolution: A New Algorithm

Efficiency of the first-order logic proof procedure is a major issue when deduction systems are to be used in real environments, both on their own and as a component of larger systems (e.g., learning systems). This paper proposes a new #-subsumption algorithm that is able to return the set of all substitutions by which such a relation holds between two clauses without performing backtracking. Di#erently from others proposed in the literature, it can be extended to perform resolution, also in theories containing recursive clauses

Ensembles of density estimators for positive-unlabeled learning

Positive-Unlabeled (PU) learning works by considering a set of positive samples, and a (usually larger) set of unlabeled ones. This challenging setting requires algorithms to cleverly exploit dependencies hidden in the unlabeled data in order to build models able to accurately discriminate between positive and negative samples. We propose to exploit probabilistic generative models to characterize the distribution of the positive samples, and to label as reliable negative samples those that are in the lowest density regions with respect to the positive ones. The overall framework is flexible enough to be applied to many domains by leveraging tools provided by years of research from the probabilistic generative model community. In addition, we show how to create mixtures of generative models by adopting a well-known bagging method from the discriminative framework as an effective and cheap alternative to the classical Expectation Maximization. Results on several benchmark datasets show the performance and flexibility of the proposed approach