Publications by authors named "Q I Huang"

We describe in this work an operationally facile and generally applicable -nitration of boronic esters by Fe(NO)·9HO in hexafluoroisopropanol (HFIP), allowing us fast access to various nitroarenes that are currently difficult to obtain via traditional electrophilic C-H nitrations. In contrast to previous deborylative -nitrations, this new protocol utilized less reactive and more stable organoboron reagents and therefore had significantly improved substrate scope and functional group tolerance, which was exemplified in the late-stage -nitration of various natural products, pharmaceuticals, and biologically active molecules.

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Magnetic miniature robots have shown great potential in biomedical applications in recent years. However, a challenge remains in which it is difficult for magnetic miniature robots to achieve balanced capabilities for multimodal locomotion and fluidic manipulation in various environments. Here, we report a magnetic shaftless propeller-like millirobot (MSPM) that possesses the capabilities of rotating-based multimodal 3-dimensional motion and cargo transportation with untethered manipulation.

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InfoScan is a novel bioinformatics tool designed for the comprehensive analysis of full-length single-cell RNA sequencing (scRNA-seq) data. It enables the identification of unannotated transcripts and rare cell populations, providing a powerful platform for transcriptome characterization. In this study, InfoScan was applied to glioblastoma multiforme (GBM), identifying a rare "neoplastic-stemness" subpopulation exhibiting cancer stem cell-like features.

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The cytokinin (CK) type B response regulator () gene is involved in the CK signaling pathway and performs a key function for mediating reactions to amounts of abiotic stresses. Nevertheless, the gene family remains to be characterized in Poaceae (also known as Gramineae or grasses). Here, we performed a comprehensive analysis encompassing phylogenetic relationships, evolutionary pressures, and expression patterns of the gene family in six Poaceae species, including rice, , , , maize, and wheat.

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Background: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial.

Objective: This study aimed to review the accuracy of ML in predicting in-hospital mortality risk in MI patients and to provide evidence-based advices for the development or updating of clinical tools.

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