Xin‐Hui Xing

The paper identifies NALHP/S6, a heparin derivative, as a glycan-based modulator that restores mucosal barrier function in ulcerative colitis by promoting a repair-secretory epithelial state.

The paper develops a strategy combining genome editing and biosensor-assisted sorting to identify SNVs enhancing recombinant protein secretion in Pichia pastoris, achieving a high rHSA titer.

This paper develops a probiotic system for ulcerative colitis that enhances probiotic survival and colon targeting, modulates gut microbiota and immune responses, and provides a mathematical model for release dynamics.

The paper identifies a novel tetrapeptide VAMP from hemp seed protein hydrolysates that inhibits DPP-IV and increases Akkermansia muciniphila abundance, improving glucose homeostasis in obese mice.

This study investigates how retinol affects the human skin microbiome and its role in retinol's anti-aging effects using phenomics, metagenomics, and metabolomics.

This study uses CRISPRi screening to identify transcription factors crucial for growth and carbon utilization in Clostridium ljungdahlii, improving ethanol production through deoR optimization.

This paper introduces dSort-Seq, a deep-learning-assisted Sort-Seq method for high-throughput profiling of gene expression characteristics with high precision, and demonstrates its application in studying noise and dose-response relationships in E. coli.

The paper presents a deep learning model, BERT-DPPIV, to identify DPP-IV inhibitory peptides and uncover design rules, achieving high accuracy and revealing restriction enzyme cutting sites.

The paper measures thermodynamic parameters of RNA/DNA hybrid duplexes and develops a nearest-neighbor parameter set with high accuracy for predicting their properties.

GLiDe is a web-based tool for designing CRISPRi sgRNA libraries in prokaryotes, improving off-target detection and providing a database for 1,397 species.

This paper develops a rapid, sensitive, and point-of-care FV3 detection system combining RPA-CRISPR/Cas12a with smartphone microscopy and deep learning, achieving 100% accuracy in classification.