Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis

Liu, Ruoqi; Hunold, Katherine M.; Caterino, Jeffrey M.; Zhang, Ping

doi:10.1038/s42256-023-00638-0

Cited by 6 publications

(1 citation statement)

References 41 publications

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“…MI is also used to make antibiotic recommendations for sepsis. After training and validation with two intensive care testing sets ( the MIMIC-III and AmsterdamUMCdb), the antibiotic treatment strategy recommended by the T4 model is proven to be effective in decreasing the mortality rate ( 135 ). The COMPOSER model was trained for the early identification of patients with a high risk of sepsis and abandoned the prediction of false predictions to improve accuracy (AUC = 0.925–0.953) ( 136 ).…”

Section: Artificial Intelligence and Biomarkers In Sepsismentioning

confidence: 99%

The potential immunological mechanisms of sepsis

Zhang,

Yuan

et al. 2024

Front. Immunol.

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Sepsis is described as a life-threatening organ dysfunction and a heterogeneous syndrome that is a leading cause of morbidity and mortality in intensive care settings. Severe sepsis could incite an uncontrollable surge of inflammatory cytokines, and the host immune system's immunosuppression could respond to counter excessive inflammatory responses, characterized by the accumulated anti-inflammatory cytokines, impaired function of immune cells, over-proliferation of myeloid-derived suppressor cells and regulatory T cells, depletion of immune effector cells by different means of death, etc. In this review, we delve into the underlying pathological mechanisms of sepsis, emphasizing both the hyperinflammatory phase and the associated immunosuppression. We offer an in-depth exploration of the critical mechanisms underlying sepsis, spanning from individual immune cells to a holistic organ perspective, and further down to the epigenetic and metabolic reprogramming. Furthermore, we outline the strengths of artificial intelligence in analyzing extensive datasets pertaining to septic patients, showcasing how classifiers trained on various clinical data sources can identify distinct sepsis phenotypes and thus to guide personalized therapy strategies for the management of sepsis. Additionally, we provide a comprehensive summary of recent, reliable biomarkers for hyperinflammatory and immunosuppressive states, facilitating more precise and expedited diagnosis of sepsis.

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Section: Artificial Intelligence and Biomarkers In Sepsismentioning

confidence: 99%

The potential immunological mechanisms of sepsis

Zhang,

Yuan

et al. 2024

Front. Immunol.

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Oxygen‐Vacancy‐Rich Monolayer BiO₂₋_X Nanosheets for Bacterial Sepsis Management via Dual Physically Antibacterial and Chemically Anti‐inflammatory Functions

Liu,

Zhang,

et al. 2024

Adv Healthcare Materials

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Sepsis is defined as a life‐threatening organ dysfunction caused by a dysregulated host response to infection. Effective treatment of bacterial sepsis remains challenging due to the rapid progression of infection and the systemic inflammatory response. In this study, we successfully synthesized monolayer BiO2‐X nanosheets (BiO2‐X NSs) with oxygen‐rich vacancies through sonication‐assisted liquid‐phase exfoliation. Herein, the BiO2‐X NSs exhibited a novel nanozyme‐enabled intervention strategy for the management of bacterial sepsis, based on its pH dependent dual antibacterial and anti‐inflammatory functions. BiO2‐X NSs exhibit effective antibacterial by utilizing oxidase (OXD)‐like activity. Additionally, BiO2‐X NSs can scavenge multiple reactive oxygen species (ROS) and mitigate systemic hyperinflammation by mimicking superoxide dismutase (SOD) and catalase (CAT). These dual capabilities of BiO2‐X NSs allow them to address bacterial infection, proinflammatory cytokines secretion and ROS burst collaboratively, effectively reversing the progression of bacterial sepsis. In vivo experiments have demonstrated that BiO2‐X NSs significantly reduce bacterial burden, attenuate systemic hyperinflammation, and rapidly rescued organ damage. Importantly, no obvious adverse effects were observed at the administered dose of BiO2‐X NSs. This study presents a novel defect engineering strategy for the rational design of high‐performance nanozymes and development of new nanomedicines for managing bacterial sepsis.This article is protected by copyright. All rights reserved

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Causal machine learning for predicting treatment outcomes

Feuerriegel,

Frauen,

Melnychuk

et al. 2024

Nat Med

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Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis

Cited by 6 publications

References 41 publications

The potential immunological mechanisms of sepsis

The potential immunological mechanisms of sepsis

Oxygen‐Vacancy‐Rich Monolayer BiO₂₋_X Nanosheets for Bacterial Sepsis Management via Dual Physically Antibacterial and Chemically Anti‐inflammatory Functions

Causal machine learning for predicting treatment outcomes

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Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis

Cited by 6 publications

References 41 publications

The potential immunological mechanisms of sepsis

The potential immunological mechanisms of sepsis

Oxygen‐Vacancy‐Rich Monolayer BiO2−X Nanosheets for Bacterial Sepsis Management via Dual Physically Antibacterial and Chemically Anti‐inflammatory Functions

Causal machine learning for predicting treatment outcomes

Contact Info

Product

Resources

About

Oxygen‐Vacancy‐Rich Monolayer BiO₂₋_X Nanosheets for Bacterial Sepsis Management via Dual Physically Antibacterial and Chemically Anti‐inflammatory Functions