Publications by authors named "L K Chen"

Leptomeningeal metastasis (LM) is a challenging complication of non-small cell lung cancer (NSCLC). Cerebrospinal fluid (CSF) cell-free DNA (cfDNA) analysis using next-generation sequencing (NGS) offers insights into resistance mechanisms and potential treatment strategies. We conducted a study from February 2022 to April 2023 involving patients from five hospitals in Taiwan who had recurrent or advanced NSCLC with LM.

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Background: This study investigated RANBP2 mutations in children with acute necrotizing encephalopathy (ANE) and conducted a systematic review of the differences in clinical characteristics between with or without RANBP2 mutations.

Methods: Whole-exome sequencing was performed on 19 pediatric ANE patients at Beijing Children's Hospital affiliated to Capital Medical University between 2017 and 2020. A systematic literature review was also conducted on the clinical characteristics and spectrum analysis of RANBP2 mutations.

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The cerebrospinal fluid (CSF) border accommodates diverse immune cells that permit peripheral cell immunosurveillance. However, the intricate interactions between CSF immune cells and infiltrating cancer cells remain poorly understood. Here we use fate mapping, longitudinal time-lapse imaging and multiomics technologies to investigate the precise origin, cellular crosstalk and molecular landscape of macrophages that contribute to leptomeningeal metastasis (LM) progression.

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Inflammasomes represent a crucial component of the innate immune system, which respond to threats by recognizing different molecules. These are known as pathogen-associated molecular patterns (PAMPs) or host-derived damage-associated molecular patterns (DAMPs). In neurodegenerative diseases and neuroinflammation, the accumulation of misfolded proteins, such as beta-amyloid and alpha-synuclein, can lead to inflammasome activation, resulting in the release of interleukin (IL)-1β and IL-18.

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Background: Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

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