Myeloid tissue factor does not modulate lung inflammation or permeability during experimental acute lung injury

Tissue factor (TF) is a critical mediator of direct acute lung injury (ALI) with global TF deficiency resulting in increased airspace inflammation, alveolar-capillary permeability, and alveolar hemorrhage after intra-tracheal lipopolysaccharide (LPS). In the lung, TF is expressed diffusely on the lung epithelium and intensely on cells of the myeloid lineage. We recently reported that TF on the lung epithelium, but not on myeloid cells, was the major source of TF during intra-tracheal LPS-induced ALI. Because of a growing body of literature demonstrating important pathophysiologic differences between ALI caused by different etiologies, we hypothesized that TF on myeloid cells may have distinct contributions to airspace inflammation and permeability between direct and indirect causes of ALI. To test this, we compared mice lacking TF on myeloid cells (TF∆mye, LysM.Cre+/−TFflox/flox) to littermate controls during direct (bacterial pneumonia, ventilator-induced ALI, bleomycin-induced ALI) and indirect ALI (systemic LPS, cecal ligation and puncture). ALI was quantified by weight loss, bronchoalveolar lavage (BAL) inflammatory cell number, cytokine concentration, protein concentration, and BAL procoagulant activity. There was no significant contribution of TF on myeloid cells in multiple models of experimental ALI, leading to the conclusion that TF in myeloid cells is not a major contributor to experimental ALI

Regulation of Alveolar Procoagulant Activity and Permeability in Direct Acute Lung Injury by Lung Epithelial Tissue Factor

Tissue factor (TF) initiates the extrinsic coagulation cascade in response to tissue injury, leading to local fibrin deposition. Low levels of TF in mice are associated with increased severity of acute lung injury (ALI) after intratracheal LPS administration. However, the cellular sources of the TF required for protection from LPS-induced ALI remain unknown. In the current study, transgenic mice with cell-specific deletions of TF in the lung epithelium or myeloid cells were treated with intratracheal LPS to determine the cellular sources of TF important in direct ALI. Cell-specific deletion of TF in the lung epithelium reduced total lung TF expression to 39% of wild-type (WT) levels at baseline and to 29% of WT levels after intratracheal LPS. In contrast, there was no reduction of TF with myeloid cell TF deletion. Mice lacking myeloid cell TF did not differ from WT mice in coagulation, inflammation, permeability, or hemorrhage. However, mice lacking lung epithelial TF had increased tissue injury, impaired activation of coagulation in the airspace, disrupted alveolar permeability, and increased alveolar hemorrhage after intratracheal LPS. Deletion of epithelial TF did not affect alveolar permeability in an indirect model of ALI caused by systemic LPS infusion. These studies demonstrate that the lung epithelium is the primary source of TF in the lung, contributing 60–70% of total lung TF, and that lung epithelial, but not myeloid, TF may be protective in direct ALI

Low levels of tissue factor lead to alveolar hemorrhage, potentiating murine acute lung injury and oxidative stress

Systemic blockade of Tissue Factor (TF) attenuates acute lung injury (ALI) in animal models of sepsis but the effects of global TF deficiency are unknown

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Circulating microparticle levels are reduced in patients with ARDS

Abstract Background It is unclear how to identify which patients at risk for acute respiratory distress syndrome (ARDS) will develop this condition during critical illness. Elevated microparticle (MP) concentrations in the airspace during ARDS are associated with activation of coagulation and in vitro studies have demonstrated that MPs contribute to acute lung injury, but the significance of MPs in the circulation during ARDS has not been well studied. The goal of the present study was to test the hypothesis that elevated levels of circulating MPs could prospectively identify critically ill patients who will develop ARDS and that elevated circulating MPs are associated with poor clinical outcomes. Methods A total of 280 patients with platelet-poor plasma samples from the prospective Validating Acute Lung Injury biomarkers for Diagnosis (VALID) cohort study were selected for this analysis. Demographics and clinical data were obtained by chart review. MP concentrations in plasma were measured at study enrollment on intensive care unit (ICU) day 2 and on ICU day 4 by MP capture assay. Activation of coagulation was measured by plasma recalcification (clot) times. Results ARDS developed in 90 of 280 patients (32%) in the study. Elevated plasma MP concentrations were associated with reduced risk of developing ARDS (odds ratio (OR) 0.70 per 10 μM increase in MP concentration, 95% CI 0.50–0.98, p = 0.042), but had no significant effect on hospital mortality. MP concentration was greatest in patients with sepsis, pneumonia, or aspiration as compared with those with trauma or receiving multiple blood transfusions. MP levels did not significantly change over time. The inverse association of MP levels with ARDS development was most striking in patients with sepsis. After controlling for age, presence of sepsis, and severity of illness, higher MP concentrations were independently associated with a reduced risk of developing ARDS (OR 0.69, 95% CI 0.49–0.98, p = 0.038). MP concentration was associated with reduced plasma recalcification time. Conclusions Elevated levels of circulating MPs are independently associated with a reduced risk of ARDS in critically ill patients. Whether this is due to MP effects on systemic coagulation warrants further investigation

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Low levels of tissue factor lead to alveolar haemorrhage, potentiating murine acute lung injury and oxidative stress

BACKGROUND: Systemic blockade of Tissue Factor (TF) attenuates acute lung injury (ALI) in animal models of sepsis but the effects of global TF deficiency are unknown. HYPOTHESIS: We used mice with complete knockout of mouse TF and low levels (~1%) of human TF (LTF mice) to test the hypothesis that global TF deficiency attenuates lung inflammation in direct lung injury. METHODS: LTF mice were treated with 10 μg of lipopolysaccharide (LPS) or vehicle administered by direct intratracheal (IT) injection and studied at 24 hours. RESULTS: Contrary to our hypothesis, LTF mice had increased lung inflammation and injury as measured by bronchoalveolar lavage cell count (3.4 × 10(5) WT LPS versus 3.3 × 10(5) LTF LPS, p=0.947) and protein (493 μg/ml WT LPS versus 1014 μg/ml LTF LPS, p=0.006), proinflammatory cytokines (TNF-α, IL-10, IL-12, p<0.035 WT LPS versus LTF LPS) and histology compared to wild type mice. LTF mice also had increased hemorrhage and free hemoglobin in the airspace accompanied by increased oxidant stress as measured by lipid peroxidation products (F(2)-Isoprostanes and Isofurans). CONCLUSIONS: These findings indicate that global TF deficiency does not confer protection in a direct lung injury model. Rather, TF deficiency causes increased intra-alveolar hemorrhage following LPS leading to increased lipid peroxidation. Strategies to globally inhibit tissue factor may be deleterious in patients with ALI

Regulation of Alveolar Procoagulant Activity and Permeability in Direct Acute Lung Injury by Lung Epithelial Tissue Factor

Tissue factor (TF) initiates the extrinsic coagulation cascade in response to tissue injury, leading to local fibrin deposition. Low levels of TF in mice are associated with increased severity of acute lung injury (ALI) after intratracheal LPS administration. However, the cellular sources of the TF required for protection from LPS-induced ALI remain unknown. In the current study, transgenic mice with cell-specific deletions of TF in the lung epithelium or myeloid cells were treated with intratracheal LPS to determine the cellular sources of TF important in direct ALI. Cell-specific deletion of TF in the lung epithelium reduced total lung TF expression to 39% of wild-type (WT) levels at baseline and to 29% of WT levels after intratracheal LPS. In contrast, there was no reduction of TF with myeloid cell TF deletion. Mice lacking myeloid cell TF did not differ from WT mice in coagulation, inflammation, permeability, or hemorrhage. However, mice lacking lung epithelial TF had increased tissue injury, impaired activation of coagulation in the airspace, disrupted alveolar permeability, and increased alveolar hemorrhage after intratracheal LPS. Deletion of epithelial TF did not affect alveolar permeability in an indirect model of ALI caused by systemic LPS infusion. These studies demonstrate that the lung epithelium is the primary source of TF in the lung, contributing 60–70% of total lung TF, and that lung epithelial, but not myeloid, TF may be protective in direct ALI

Association of Neutralizing Antispike Monoclonal Antibody Treatment With Coronavirus Disease 2019 Hospitalization and Assessment of the Monoclonal Antibody Screening Score

Objective: To test the hypothesis that the Monoclonal Antibody Screening Score performs consistently better in identifying the need for monoclonal antibody infusion throughout each “wave” of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant predominance during the coronavirus disease 2019 (COVID-19) pandemic and that the infusion of contemporary monoclonal antibody treatments is associated with a lower risk of hospitalization. Patients and Methods: In this retrospective cohort study, we evaluated the efficacy of monoclonal antibody treatment compared with that of no monoclonal antibody treatment in symptomatic adults who tested positive for SARS-CoV-2 regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients with a diagnosis of COVID-19 from November 19, 2020, through May 12, 2022. Results: Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (eg, relative risk, 0.15; 95% CI, 0.14-0.17). Conclusion: Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic