Sepsis remains one of the most prevalent conditions necessitating admission to intensive care units and is associated with high mortality. Unfortunately, r

OBJECTIVE: To determine the impact of using dynamic measures of fluid responsiveness in guiding the resuscitation of adult patients with sepsis and septic shock. DATA SOURCE: We searched MEDLINE, Embase, and unpublished sources from inception to February 3, 2025. STUDY SELECTION: We included randomized controlled trials (RCTs) that evaluated the use of dynamic measures of fluid responsiveness to guide resuscitation compared with any other method in patients with sepsis and septic shock. DATA EXTRACTION: We collected data regarding study and patient characteristics, definitions of fluid responsiveness, modality for assessing fluid responsiveness, and outcome data. We performed a random-effects meta-analysis and rated the certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework. DATA SYNTHESIS: We included nine eligible RCTs (n = 698 patients). The use of dynamic measures of fluid responsiveness to guide IV fluid (IVF) administration of patients with septic shock probably reduces 28-day mortality (relative risk] 0.61; 95% CI, 0.42–0.90, moderate certainty), may reduce the risk of acute kidney injury (AKI) (RR 0.66; 95% CI, 0.44–0.98, low certainty), and cumulative fluid balance on day 3 (mean difference –1.57L; 95% CI, –2.44 L to –0.69 L, low certainty). The use of dynamic measures of fluid responsiveness has an uncertain effect on ICU mortality, ICU and hospital length of stay, need for and duration of mechanical ventilation, need for renal replacement therapy, vasoactive medication administration, duration of vasopressor use, and IVF administration on day 1. CONCLUSIONS: In adult patients with sepsis and septic shock, using dynamic measures of fluid responsiveness may improve survival and reduce the risk of AKI. Future studies should evaluate the impact of this intervention on other important clinical outcomes and determine the comparative efficacy of specific modalities for assessing fluid responsiveness.

Vascular diseases (VDs) and cardiovascular diseases (CVDs) are the leading causes of morbidity and mortality worldwide. Current diagnostic and therapeutic approaches are limited by insufficient resolution and a lack of mechanistic understanding at the cellular level. Traditional imaging and clinical assays do not fully capture the dynamic molecular and structural complexities underlying vascular pathology. Recent technological innovations, including single-cell and spatial transcriptomics, super-resolution and photoacoustic imaging, microfluidic organ-on-chip platforms, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-based gene editing, and artificial intelligence (AI), have created new opportunities for investigating the cellular and molecular basis of VDs. These techniques enable high-resolution mapping of cellular heterogeneity and functional alterations, facilitating the integration of large-scale data for biomarker discovery, disease modeling, and therapeutic development. This review focuses on evaluating the translational readiness, limitations, and potential clinical applications of these emerging technologies. Understanding the cellular and molecular mechanisms of VDs is essential for developing targeted therapies and precise diagnostics. Integrating single-cell and multiomics approaches highlights disease-driving cell types and gene programs. Optogenetics and organ-on-chip platforms allow for controlled manipulation and physiologically relevant modeling, while AI enhances data integration, risk prediction, and clinical interpretability. Future efforts should prioritize multi-center, large-scale validation studies, harmonization of assay protocols, and integration with clinical datasets and human samples. Multi-omics approaches and computational modeling hold promise for unraveling disease complexity, while advances in regulatory science and digital simulation (such as digital twins) may further accelerate personalized medicine in vascular disease research and treatment.

Sepsis remains one of the most complex and lethal syndromes in pediatric critical care, driven by dysregulated and heterogeneous host responses to infection. Despite decades of biomarker research, few biomarkers have been translated into routine clinical use for diagnosis or prognostication. This is largely because single marker approaches cannot capture the systemic complexity of sepsis pathobiology with high sensitivity and specificity. This review explores how the convergence of multi-omic technologies and computational biology is transforming biomarker discovery, from isolated molecular signals into integrated, systems-level understanding of disease, with an emphasis on pediatric sepsis. Recent omic studies reveal dysregulation across immune, endothelial, metabolic, and microbial networks in sepsis. Advances in bioinformatics and artificial intelligence now enable characterization of complex biological patterns that link molecular profiles into interpretable clinical phenotypes and outcomes. Multi-omic integration represents a paradigm shift in pediatric sepsis research, uniting biomarker discovery with clinical application through biologically coherent, computationally derived signatures. As these approaches mature, they promise to transform pediatric sepsis care from empiric treatment to precision medicine guided by the molecular pathways that define each patient’s pathobiology.

Critical care has produced hundreds of neutral randomized trials, in part because therapies have been tested in biologically incoherent populations that di

Sepsis triggers a complex and heterogeneous host response, yet decades of biomarker studies have yielded few targeted therapeutics that improve patient out