Articles: sepsis.
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Observational Study
Identification of key regulatory genes in the pathogenesis of COVID-19 and sepsis: An observational study.
Patients with severe COVID-19 and those with sepsis have similar clinical manifestations. We used bioinformatics methods to identify the common hub genes in these 2 diseases. Two RNA-seq datasets from the Gene Expression Omnibus were used to identify common differentially expressed genes (DEGs) in COVID-19 and sepsis. ⋯ We used bioinformatics tools to identify common DEGs, miRNAs, and transcription factors for COVID-19 and sepsis. The 5 identified hub genes had higher expression in validation cohorts of COVID-19 and sepsis. These genes had good or excellent diagnostic performance based on ROC analysis, and therefore have potential use as novel markers or therapeutic targets.
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Observational Study
The association of arterial partial oxygen pressure with mortality in critically ill sepsis patients: a nationwide observational cohort study.
Although several trials were conducted to optimize the oxygenation range in intensive care unit (ICU) patients, no studies have yet reached a universal recommendation on the optimal a partial pressure of oxygen in arterial blood (PaO2) range in patients with sepsis. Our aim was to evaluate whether a relatively high arterial oxygen tension is associated with longer survival in sepsis patients compared with conservative arterial oxygen tension. ⋯ In critically ill patients with sepsis, higher PaO2 (≥ 80 mm Hg) during the first three ICU days was associated with a lower 28-day mortality compared with conservative PaO2.
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We present a case of bilateral endogenous endophthalmitis with an extremely rare etiology of Capnocytophaga canimorsus. A 42-year-old asplenic patient with bilateral deterioration of visual acuity presented to the Emergency Department. The sudden deterioration of visual acuity, which prompted the patient to visit the ophthalmologist, was the first sign of the onset of sepsis. ⋯ Immunocompromised patients are particularly susceptible to C. canimorsus infection. Endophthalmitis of this etiology has a very aggressive course, both ophthalmic and systemic. Therefore, quick diagnosis and initiation of adequate therapy are crucial.
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Review
Biological basis of critical illness subclasses: from the bedside to the bench and back again.
Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. ⋯ In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.
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Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored. ⋯ Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.