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Related Concept Videos

Formation of Lipopolysaccharides01:19

Formation of Lipopolysaccharides

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Lipopolysaccharides (LPS) are crucial components of the outer membrane of Gram-negative bacteria, serving both structural and functional roles. It contributes to membrane stability and protects bacteria from host immune responses. LPS is composed of three major regions—lipid A, a core oligosaccharide, and an O antigen. The biosynthesis and assembly of LPS involve a highly coordinated set of enzymatic reactions and transport mechanisms. Additionally, LPS is recognized as an endotoxin,...
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The bulk modulus is a scientific term used to describe a material's resistance to uniform compression. It is the proportionality constant that links a change in pressure to the resulting relative volume change.
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Bulk density refers to the mass of aggregate particles that would fill a unit volume. The concept of bulk density originates from the inability to pack aggregate particles in a manner that completely eliminates void spaces. Hence, the term bulk refers to the volume that encompasses both the aggregates and the voids. This measurement is crucial when aggregates are batched by volume and is used to convert quantities by mass to volume.
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Updated: Feb 8, 2026

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells
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Bulk Microarray and Single-Cell Transcriptomic Analyses Reveal Bacterial Lipopolysaccharide-Related Biomarkers in

Haili Zhang1, Xiaoying Wu1, Lixue Wu1

  • 1Department of Emergency Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.

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Summary
This summary is machine-generated.

Researchers identified key lipopolysaccharide (LPS)-related genes for sepsis diagnosis and prognosis. This LPS biomarker model integrates bulk and single-cell data, offering new insights for sepsis management.

Keywords:
biomarkersbulk microarraylipopolysaccharidescRNA‐seqsepsis

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Area of Science:

  • Biochemistry and Molecular Biology
  • Immunology
  • Computational Biology

Background:

  • Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection.
  • Identifying reliable biomarkers for sepsis, particularly those related to lipopolysaccharide (LPS), is crucial for effective diagnosis and treatment.
  • Current understanding of LPS-related mechanisms in sepsis requires further exploration.

Purpose of the Study:

  • To identify and validate lipopolysaccharide (LPS)-related genes (LRGs) associated with sepsis.
  • To develop and assess diagnostic and prognostic biomarker models for sepsis using integrated transcriptomic data.
  • To elucidate cell-type-specific LRG expression and intercellular communication networks in sepsis.

Main Methods:

  • Integrated bulk microarray and single-cell RNA sequencing (scRNA-seq) datasets from the Gene Expression Omnibus.
  • Applied cell clustering, annotation, AUCell scoring, and cell-cell communication analysis using scRNA-seq.
  • Utilized machine learning algorithms (LASSO, SVM-RFE, XGBoost) for biomarker selection and validated gene expression via qRT-PCR.

Main Results:

  • Identified seven key LPS-related genes (LRGs) robustly associated with sepsis.
  • Developed diagnostic and prognostic models with high accuracy (AUC > 0.89) in independent validation cohorts.
  • Observed increased myeloid-derived suppressor cell and macrophage infiltration, activated inflammatory pathways, and distinct communication networks in high-risk sepsis patients.

Conclusions:

  • Established a validated LPS-related biomarker model integrating bulk and single-cell transcriptomics for sepsis.
  • The model demonstrates significant potential for sepsis diagnosis and prognosis.
  • Findings offer novel insights into sepsis pathogenesis and potential therapeutic targets, particularly within myeloid cell populations.