Publications by authors named "Youmna Jalkh"

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method.

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Background: Familial Mediterranean Fever (FMF) is associated with increased risk of multiple sclerosis (MS). Optimal treatment of patients with comorbid FMF and MS remains uncertain.

Case: A 28-year-old woman with FMF, treated with colchicine, had symptomatic onset of relapsing remitting MS following four simultaneous vaccines.

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Dimension reduction tools preserving similarity and graph structure such as -SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method.

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Background And Purpose: Cerebral gray matter (GM) atrophy is a proposed measure of neuroprotection in multiple sclerosis (MS). Glatiramer acetate (GA) limits clinical relapses, MRI lesions, and whole brain atrophy in relapsing-remitting MS (RRMS). The effect of GA on GM atrophy remains unclear.

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Children's understanding of unobservable scientific entities largely depends on testimony from others, especially through parental explanations that highlight the mechanism underlying a scientific entity. Mechanistic explanations are particularly helpful in promoting children's conceptual understanding, yet they are relatively rare in parent-child conversations. The current study aimed to increase parent-child use of mechanistic conversation by modeling this language in a storybook about the mechanism of electrical circuits.

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Article Synopsis
  • The study aimed to evaluate how the Bayesian Risk Estimate for MS at Onset (BREMSO) affects long-term disability in patients with Relapsing Remitting MS (RRMS).
  • Researchers followed 100 RRMS patients over two years, analyzing BREMSO scores alongside cognitive and physical performance tests, as well as brain imaging.
  • The results showed that higher BREMSO scores were linked to worse cognitive function, greater physical disability, and reduced corpus callosum volume, indicating it can be a valuable tool for predicting disease progression.
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