Publications by authors named "L I N Qing"

The accurate quantification of multicomponents using LC-MS is pivotal for ensuring the quality control of herbal medicine, as well as the investigation of their analysis of biological tissue distribution. However, two significant challenges persist: the scarcity of authentic standards and the selection of appropriate internal standards. In this study, we present a highly sensitive isotope-coded equivalent reporter ion assay (iERIA) that combines equivalently quantitative ion and isotope-coded derivatization strategies.

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Introduction: Reconstruction for various regions of the foot and ankle in pediatric patients remains a challenging topic. This study presents the circumflex scapular artery perforator (CSAP) flap and its surgical refinements for addressing soft tissue defects in various regions of the foot and ankle in pediatrics.

Patients And Methods: Forty-seven patients underwent CSAP flap and its surgical refinements for the reconstruction of foot and ankle defects from 2010 to 2022.

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Background: Qingre Huoxue Decoction (QRHX) is a classical Chinese herbal prescription widely used in clinical practice for the treatment of atherosclerosis (AS). Our previous study demonstrated its efficacy in stabilizing plaque and improving prognosis, as well as its ability to regulate macrophage polarization. This study aimed to further investigate the effects of QRHX on AS and explore the underlying mechanisms.

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The relentless pursuit of innovative hydrophobic tags remains a formidable challenge within the realm of targeted protein degradation. Herein, we have uncovered the remarkable potential of D-ring-contracted artemisinin as a potent hydrophobic tag that demonstrates exceptional degradation efficiency. We have crafted a series of conjugates by fusing D-ring-contracted artemisinin with raloxifene, and among these, has emerged as a promising candidate for degrading estrogen receptor α (ERα).

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Objective: To identify lifting actions and count the number of lifts performed in videos based on robust class prediction and a streamlined process for reliable real-time monitoring of lifting tasks.

Background: Traditional methods for recognizing lifting actions often rely on deep learning classifiers applied to human motion data collected from wearable sensors. Despite their high performance, these methods can be difficult to implement on systems with limited hardware resources.

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