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Red blood cells (RBCs), traditionally recognized for their role in transporting oxygen, play a pivotal role in the body's immune response by expressing TLR9 and scavenging excess host cell-free DNA. DNA capture by RBCs leads to accelerated RBC clearance and triggers inflammation. Whether RBCs can also acquire microbial DNA during infections is unknown. Murine RBCs acquire microbial DNA in vitro and bacterial-DNA-induced macrophage activation was augmented by WT but not Tlr9-deleted RBCs. In a mouse model of polymicrobial sepsis, RBC-bound bacterial DNA was elevated in WT but not in erythroid Tlr9-deleted mice. Plasma cytokine analysis in these mice revealed distinct sepsis clusters characterized by persistent hypothermia and hyperinflammation in the most severely affected subjects. RBC-Tlr9 deletion attenuated plasma and tissue IL-6 production in the most severe group. Parallel findings in human subjects confirmed that RBCs from septic patients harbored more bacterial DNA compared to healthy individuals. Further analysis through 16S sequencing of RBC-bound DNA illustrated distinct microbial communities, with RBC-bound DNA composition correlating with plasma IL-6 in patients with sepsis. Collectively, these findings unveil RBCs as overlooked reservoirs and couriers of microbial DNA, capable of influencing host inflammatory responses in sepsis.

Inflammation induced by lung infection is a double-edged sword, moderating both anti-viral and immune pathogenesis effects; the mechanism of the latter is not fully understood. Previous studies suggest the vasculature is involved in tissue injury. Here, we report that expression of Sparcl1, a secreted matricellular protein, is upregulated in pulmonary capillary endothelial cells (EC) during influenza-induced lung injury. Endothelial overexpression of SPARCL1 promotes detrimental lung inflammation, with SPARCL1 inducing 'M1-like' macrophages and related pro-inflammatory cytokines, while SPARCL1 deletion alleviates these effects. Mechanistically, SPARCL1 functions through TLR4 on macrophages in vitro, while TLR4 inhibition in vivo ameliorates excessive inflammation caused by endothelial Sparcl1 overexpression. Finally, SPARCL1 expression is increased in lung ECs from COVID-19 patients when compared with healthy donors, while fatal COVID-19 correlates with higher circulating SPARCL1 protein levels in the plasma. Our results thus implicate SPARCL1 as a potential prognosis biomarker for deadly COVID-19 pneumonia and as a therapeutic target for taming hyperinflammation in pneumonia., (© 2024. The Author(s).)

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19., Design: Prospective observational cohort study., Setting: Two hospitals in the United States., Patients: One hundred sixty-seven hospitalized adults with COVID-19., Intervention: None., Measurements and Main Results: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids ( p = 0.006)., Conclusions: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia., Competing Interests: Dr. Wherry has consulting agreements with and/or is on the scientific advisory board for Merck, Roche, Pieris, Elstar, and Surface Oncology. He is a founder of Surface Oncology and Arsenal Biosciences. EJW has a patent licensing agreement on the PD-1 pathway with Roche/Genetech. He is an inventor on U.S. patent number 10,270,446 submitted by Emory University that covers the use of programmed death ligand 1 blockade to treat infections and cancer. Dr. Meyer receives funding to her institution from Quantum Leap Healthcare Consortium, Athersys, Inc, Biomarck Inc, and the Marcus Foundation for work unrelated to this article. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Purpose: Although dozens of studies have associated vancomycin + piperacillin-tazobactam with increased acute kidney injury (AKI) risk, it is unclear whether the association represents true injury or a pseudotoxicity characterized by isolated effects on creatinine secretion. We tested this hypothesis by contrasting changes in creatinine concentration after antibiotic initiation with changes in cystatin C concentration, a kidney biomarker unaffected by tubular secretion., Methods: We included patients enrolled in the Molecular Epidemiology of SepsiS in the ICU (MESSI) prospective cohort who were treated for ≥ 48 h with vancomycin + piperacillin-tazobactam or vancomycin + cefepime. Kidney function biomarkers [creatinine, cystatin C, and blood urea nitrogen (BUN)] were measured before antibiotic treatment and at day two after initiation. Creatinine-defined AKI and dialysis were examined through day-14, and mortality through day-30. Inverse probability of treatment weighting was used to adjust for confounding. Multiple imputation was used to impute missing baseline covariates., Results: The study included 739 patients (vancomycin + piperacillin-tazobactam n = 297, vancomycin + cefepime n = 442), of whom 192 had cystatin C measurements. Vancomycin + piperacillin-tazobactam was associated with a higher percentage increase of creatinine at day-two 8.04% (95% CI 1.21, 15.34) and higher incidence of creatinine-defined AKI: rate ratio (RR) 1.34 (95% CI 1.01, 1.78). In contrast, vancomycin + piperacillin-tazobactam was not associated with change in alternative biomarkers: cystatin C: - 5.63% (95% CI - 18.19, 8.86); BUN: - 4.51% (95% CI - 12.83, 4.59); or clinical outcomes: dialysis: RR 0.63 (95% CI 0.31, 1.29); mortality: RR 1.05 (95%CI 0.79, 1.41)., Conclusions: Vancomycin + piperacillin-tazobactam was associated with creatinine-defined AKI, but not changes in alternative kidney biomarkers, dialysis, or mortality, supporting the hypothesis that vancomycin + piperacillin-tazobactam effects on creatinine represent pseudotoxicity., (© 2022. The Author(s).)

Competing Interests: The authors disclose no conflict of interest.

Competing Interests: Competing interests: None declared.

Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability calibration, has limited their use for decision support. These models tend to perform worse in patients at a high risk for mortality. We hypothesized that a sequential modeling approach wherein an initial regression model assigns risk and all patients deemed high risk then have their risk quantified by a second, high-risk-specific, regression model would result in a model with superior calibration across the risk spectrum. We compared this approach to a logistic regression model and a sophisticated machine learning approach, the gradient boosting machine. The sequential approach did not have an effect on the receiver operating characteristic curve or the precision-recall curve but resulted in improved reliability curves. The gradient boosting machine achieved a small improvement in discrimination performance and was similarly calibrated to the sequential models., Competing Interests: Competing interestsO.B. is employed by Philips Healthcare. The other authors declare no competing interests.

As big data, machine learning , and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine., Competing Interests: Declaration of conflicting interests:The author(s) received no financial support for the research, authorship, and/or publication of this article.

Multidimensional tunneling calculations are carried out for 13 reactions, to test the scope of heavy-atom tunneling in organic chemistry, and to check the accuracy of one-dimensional tunneling models. The reactions include pericyclic, cycloaromatization, radical cyclization and ring opening, and S N 2. When compared at the temperatures that give the same effective rate constant of 3×10 -5  s -1 , tunneling accounts for 25-95 % of the rate in 8 of the 13 reactions. Values of transmission coefficients predicted by Bell's formula, κ Bell  , agree well with multidimensional tunneling (canonical variational transition state theory with small curvature tunneling), κ SCT . Mean unsigned deviations of κ Bell vs. κ SCT are 0.08, 0.04, 0.02 at 250, 300 and 400 K. This suggests that κ Bell is a useful first choice for predicting transmission coefficients in heavy-atom tunnelling., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

DFT and CASSCF calculations for the cyclization of (3Z)-cyclodec-3-en-1,5-diyne were carried out to investigate heavy-atom tunneling. At 37 °C, tunneling was computed to enhance the rate by 38-40% over the transition-state theory rate. Intramolecular (12)C/(13)C kinetic isotope effects were predicted to be substantial, with a steep temperature dependence. These results are discussed in relation to recent experimental findings that show heavy-atom tunneling at moderate temperatures. The calculations point to the possibility of a simple computational test for the likelihood of heavy-atom tunneling using standard quantum-chemical information.