Tay-Sachs disease (TSD) is a rare neurodegenerative disorder caused by mutations in the HEXA gene, which encodes the ɑ subunit of the enzyme β-hexosaminidase A. Lacking this key enzyme in GM2 ganglioside catabolism, individuals who are homozygous for HEXA mutations suffer from abnormal accumulation of GM2 ganglioside in brain and nerve cells, ultimately resulting in the progressive deterioration of the central nervous system. TSD is one of three disorders characterized by β-hexosaminidase deficiency; Sandhoff disease (SD) and the AB variant arise by mutations in the HEXB and GM2A genes respectively, which disrupt other points of GM2 ganglioside degradation. Characterized by developmental delay and stagnation, muscular weakness, coordination deficits, seizures, and eventual hearing and vision loss, these three disorders are clinically indistinguishable and occur in three forms defined by age of onset. While there is a much higher incidence of TSD in the Ashkenazi Jewish population, community carrier screening and counseling initiatives have reduced disease prevalence to about the equivalent of non-Jewish populations; however, such efforts have raised ethical concerns in the Jewish community that are increasingly relevant in light of scientific and medical advancements. Currently, treatments for TSD and its related disorders focus on symptom management, with gene therapies and the application of modified CRISPR-Cas-9 technology being explored. Show less

Background The evolution of multiple sclerosis (MS) is highly variable. Predicting this evolution at the beginning of the disease will help in the therapeutic management. The objective of this study was to describe the clinical characteristics and to identify early predictors of long-term disability among MS patients in Algeria. Methods We performed a descriptive and retrospective study of 400 MS patients followed over a 4-years period from July 2012 to July 2016 in the neurology department of Mustapha Bacha hospital in Algiers. The following parameters were systematically assessed for each patient: Age at onset, gender, relapses, initial demyelinating event, interval between first and second attack, residual deficit after first attack, onset of the progressive course, time of assignment of the successive scores of irreversible disability and type of disease modifying therapies. Univariate and multivariate Cox models were used to determine factors influencing time to Expanded Disability Status Scale (EDSS) 4 and 6. Results During the follow-up of this cohort of MS patients, a total of 144 (36%) and 83 (20%) patients reached EDSS scores 4 and 6 respectively. The median times from the onset of MS to assignment of a score 4 and 6 was 10 years and 19 years, respectively. Using the MSSS severity score, and after univariate analysis we identified several risk factors predictive of disease severity. These included male sex (P= 0.002), age of onset ≥ 40 years (P= 0.001), pyramidal (P= 0.0001), cerebellar (P= 0.002), and poly-symptomatic (P=0.0001) onset of the disease, incomplete recovery after the first attack (P= 0.0001), a high number of attacks during the first 2 years (P= 0.04) and the progressive form (P=0.001), and all these factors were correlated with a high MSSS score. However, prognostic factors in the multivariate binomial logistic regression analysis were limited to pyramidal onset, incomplete recovery after the first attack, short interval between the first and second attack, and progressive onset. Conclusion On the whole, the early predictive factors of disability in MS among Algerian patients were age of onset over 40 years, pyramidal onset, incomplete recovery after the first attack, short interval between the first and second attack and progressive form at onset. To increase the generalizability of findings, a national MS registry is strongly recommended, as well as long-term follow-up epidemiological studies. Show less

Exploring the dynamic dimension of functional connectivity in dementia, this article departs from traditional static studies to capture the ever-changing brain networks. Investigating temporal connectivity patterns yields valuable insights into disease progression, individualized treatment, and early intervention. Additionally, the concept of cognitive reserve, therapeutic interventions, and machine learning integration are pivotal in revolutionizing dementia research and care. Show less