Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
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Traumatic brain injury (TBI) is a kind of disease with high morbidity, mortality, and disability, and its pathogenesis is still unclear. Research shows that nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3) activation in neurons and astrocytes is involved in neuroinflammatory cascades after TBI. What is more, polydatin (PD) has been shown to have a protective effect on TBI-induced neuroinflammation, but the mechanisms remain unclear. ⋯ More importantly, PD could inhibit the level of SOD2 Ac-K122, NLRP3, and cleaved caspase-1 and promote the expression of SOD2 after TBI both in vivo and in vitro. Polydatin also inhibited mtROS accumulation and MMP collapse after stretching injury. These results indicated that PD inhibited SOD2 acetylation to alleviate NLRP3 inflammasome activation, thus acting a protective role against TBI neuroinflammation.
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The unacceptable high mortality of severe infections and sepsis led over the years to understand the need for adjunctive immunotherapy to modulate the dysregulated host response of the host. However, not all patients should receive the same type of treatment. The immune function may largely differ from one patient to the other. ⋯ ImmunoSep is a first-in-class paradigm of precision medicine for sepsis. Other approaches need to consider classification by sepsis endotypes, targeting T cell and application of stem cells. Basic principle for any trial to be successful is the delivery of appropriate antimicrobial therapy as standard-of-care taking into consideration not just the likelihood for resistant pathogens but also the pharmacokinetic/pharmacodynamic mode of action of the administered antimicrobial.
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Circulatory shock from trauma and hemorrhage remains a clinical challenge with mortality still high within the first hours after impact. It represents a complex disease involving the impairment of a number of physiological systems and organs and the interaction of different pathological mechanisms. ⋯ Recently, novel targets and models with complex multiscale interaction of data from different sources have been identified which offer new windows of opportunity. Future works needs to consider patient-specific conditions and outcomes to mount shock research onto the next higher level of precision and personalized medicine.
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Multicenter Study
A Preventative Tool for Predicting Blood Stream Infections in Children with Burns.
Introduction: Despite significant advances in pediatric burn care, bloodstream infections (BSIs) remain a compelling challenge during recovery. A personalized medicine approach for accurate prediction of BSIs before they occur would contribute to prevention efforts and improve patient outcomes. Methods: We analyzed the blood transcriptome of severely burned (total burn surface area [TBSA] ≥20%) patients in the multicenter Inflammation and Host Response to Injury ("Glue Grant") cohort. ⋯ Conclusions: The multibiomarker panel model yielded a highly accurate prediction of BSIs before their onset. Knowing patients' risk profile early will guide clinicians to take rapid preventive measures for limiting infections, promote antibiotic stewardship that may aid in alleviating the current antibiotic resistance crisis, shorten hospital length of stay and burden on health care resources, reduce health care costs, and significantly improve patients' outcomes. In addition, the biomarkers' identity and molecular functions may contribute to developing novel preventive interventions.
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Background: Acute kidney injury (AKI) is a prevalent and serious complication among patients with sepsis-associated acute respiratory distress syndrome (ARDS). Prompt and accurate prediction of AKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict AKI via thorough analysis of data derived from electronic medical records. ⋯ In addition, a novel shiny application based on the XGBoost model was established to predict the probability of developing AKI among patients with sepsis-associated ARDS. Conclusions: Machine learning models could be used for predicting AKI in patients with sepsis-associated ARDS. Accordingly, a user-friendly shiny application based on the XGBoost model with reliable predictive performance was released online to predict the probability of developing AKI among patients with sepsis-associated ARDS.