Mucopolysaccharidoses and the blood–brain barrier is a research paper published in Fluids and Barriers of the CNS (2022). On theSindex it has a DataRank of 0.551. It has been cited 15 times, with 15 citing works in its 1-hop citation network.
AbstractMucopolysaccharidoses comprise a set of genetic diseases marked by an enzymatic dysfunction in the degradation of glycosaminoglycans in lysosomes. There are eight clinically distinct types of mucopolysaccharidosis, some with various subtypes, based on which lysosomal enzyme is deficient and symptom severity. Patients with mucopolysaccharidosis can present with a variety of symptoms, including cognitive dysfunction, hepatosplenomegaly, skeletal abnormalities, and cardiopulmonary issues. Additionally, the onset and severity of symptoms can vary depending on the specific disorder, with symptoms typically arising during early childhood. While there is currently no cure for mucopolysaccharidosis, there are clinically approved therapies for the management of clinical symptoms, such as enzyme replacement therapy. Enzyme replacement therapy is typically administered intravenously, which allows for the systemic delivery of the deficient enzymes to peripheral organ sites. However, crossing the blood–brain barrier (BBB) to ameliorate the neurological symptoms of mucopolysaccharidosis continues to remain a challenge for these large macromolecules. In this review, we discuss the transport mechanisms for the delivery of lysosomal enzymes across the BBB. Additionally, we discuss the several therapeutic approaches, both preclinical and clinical, for the treatment of mucopolysaccharidoses.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.416
From this paper's citation signal
Citation Network Contribution
0.135
From 10 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 75% comes from its base citations and 25% from the citation network (10 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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