What question did this study set out to answer?

The aim is to identify glyco-signatures that can classify survivorship in septic shock patients upon ICU admission.

November 1, 2025Molecular & Cellular ProteomicsOpen Access

Serum AGP-1-Lex glycoforms report on survivorship of patients with septic shock upon admission to intensive care unit

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Authors

TCThe Huong Chau SCSayantani Chatterjee LCLiam Caulfield

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Implication

Comparative glycomics and machine learning classify survivors and non-survivors of septic shock, indicating potential for early diagnosis and treatment.

Key Points

The aim is to identify glyco-signatures that can classify survivorship in septic shock patients upon ICU admission.
Comparative glycomics and glycoproteomics were conducted on sera from septic shock survivors and non-survivors.
Serum samples were collected longitudinally, and machine learning algorithms were applied for analysis.
A random forest model was trained using serum N-glycome data to predict survivorship outcomes.
The model accurately classified the survivorship of 94.6% of patients.
Specificity reached 100% for survivors and 75% sensitivity for non-survivors.
Glyco-signatures at ICU admission were found to distinguish between survivors and non-survivors.

Cite This Study

Chau et al. (2025) studied this question.

synapsesocial.com/papers/69254f7dc0ce034ddc35911a https://doi.org/10.1016/j.mcpro.2025.101470

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Implication

Comparative glycomics and machine learning classify survivors and non-survivors of septic shock, indicating potential for early diagnosis and treatment.

Serum AGP-1-Lex glycoforms report on survivorship of patients with septic shock upon admission to intensive care unit

Discussion

Member takes

Implication

Key Points

Cite This Study

Serum AGP-1-Lex glycoforms report on survivorship of patients with septic shock upon admission to intensive care unit

Discussion

Member takes

Implication

Key Points

Cite This Study

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