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Yu Deng

Publications and source records attributed to Yu Deng.

2 recordsLinked to original sources
PubMed2026

Application of metagenomic next-generation sequencing in children with pneumonia of unknown etiology.

OBJECTIVE: To investigate the pathogen spectrum and clinical application value of metagenomic next-generation sequencing (mNGS) in lower respiratory tract specimens from children with pneumonia of unknown etiology. METHODS: A retrospective analysis was conducted on children hospitalized in the intensive care unit (ICU) and respiratory department ward of Children's Hospital of Chongqing Medical University from January 2025 to December 2025. All enrolled cases presented negative results for conventional respiratory pathogen tests and received mNGS testing of lower respiratory tract specimens for etiological identification. The mNGS findings and clinical data of the included children were analyzed. RESULTS: A total of 92 children were enrolled, including 54 males and 38 females, with ages ranging from 2 months to 13 years and 8 months. Causative pathogens were detected in 77 cases (83.7%). The clinically adjudicated etiological diagnosis rates of bacteria, viruses, fungi and atypical pathogens were 75.0% (69/92), 37.0% (34/92), 13.0% (12/92) and 5.4% (5/92), respectively. Thirty-eight cases were complicated with polymicrobial infection, among which bacterial-viral infection was predominant, accounting for 23.1% (24/92). Children with immunocompromised conditions exhibited higher incidences of clinically adjudicated bacterial, fungal and polymicrobial infection than immunocompetent patients. The most common clinically confirmed causative pathogens in immunocompromised children were Streptococcus pneumoniae, human cytomegalovirus, Haemophilus influenzae, Stenotrophomonas maltophilia and Enterococcus faecalis. Treatment regimens were adjusted in 58 cases (63.0%) based on mNGS findings, switching to pathogen-targeted anti-infective therapy. CONCLUSION: For pediatric pneumonia with negative conventional etiological tests, mNGS of lower respiratory tract specimens significantly enhances pathogen detection rates, effectively identifies polymicrobial infection and opportunistic pathogens. Immune status serves as a critical stratification factor influencing pathogen spectrum and infection patterns, with immunocompromised children being more susceptible to opportunistic infections. Adjustment of anti-infective regimens based on mNGS results can effectively facilitate personalized anti-infective therapy.

Humans↗
PubMed2025

Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.

Core promoters are essential regulatory elements that control transcription initiation, but accurately predicting and designing their strength remains challenging due to complex sequence-function relationships and the limited generalizability of existing AI-based approaches. To address this, we developed a modular platform integrating rational library design, predictive modelling, and generative optimization into a closed-loop workflow for end-to-end core promoter engineering. Conserved and spacer region of core promoters exert distinct effects on transcriptional strength, with the former driving large-scale variation and the latter enabling finer gradation. Based on this insight, Mutation-Barcoding-Reverse Sequencing approach was used and constructed a synthetic promoter library comprising 112 955 variants with minimal redundancy and a 16 226-fold expression range. A Transformer-based model trained on this dataset achieved a Pearson correlation of 0.87 with experimentally measured promoter strengths. When combined with a conditional diffusion model, the system enabled de novo generation of promoter sequences with defined strengths, achieving a design-to-measurement correlation of 0.95 and maintaining high accuracy (R = 0.93) across varied sequence contexts. The designed promoters consistently preserved their intended strength gradients, demonstrating robust plug-and-play functionality. This work establishes a scalable and extensible platform (www.yudenglab.com) for deep learning-guided programmable design of Escherichia coli core promoters, enabling precise transcriptional control.

Promoter Regions, Genetic↗