3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer's disease sub-scores.

Different aspects of cognitive functions are affected in patients with Alzheimer's disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer's disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13) based on MRI and FDG-PET brain-imaging data. Analysis of the trained network showed that each key ADAS-Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG-PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG-PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.

Life Engagement Improvement Following Initiation of Brexpiprazole Treatment in Patients with MDD: A Naturalistic, Retrospective Real-World Study.

Purpose: Life engagement encompasses concepts such as life fulfillment, well-being, and participation in meaningful activities, encompassing cognitive, physical, social, and emotional dimensions. Patients with MDD experience impaired functioning across multiple domains of life engagement and have ranked concepts related to life engagement and fulfillment as important predictors of treatment success. Post-hoc analyses of three clinical trials of patients with MDD treated adjunctively with brexpiprazole have reported a significantly greater improvement in life engagement. This study investigated improvements in life engagement among patients with MDD following initiation of brexpiprazole treatment using a real-world dataset. Patients and Methods: Information was extracted from semi-structured clinical notes of the Mental Status Examination (MSE) of patients in a real-world setting to develop an outcome measure for quantifying life engagement of psychiatric patients. Measures of life engagement and its four sub-domains (emotional, physical, social, and cognitive) were calculated at each clinical visit for 624 adult patients with MDD during the 6 months following brexpiprazole initiation. Paired t-tests assessed differences between the index event and time periods within 6 months of the index event. Kaplan-Meier survival analyses were used to quantify the improvement in life engagement scores following brexpiprazole initiation. Results: The study identified 54 clinical features associated with life engagement. Statistically significant improvements were observed from as early as 1 month following brexpiprazole initiation, with 20.6%, 37.9%, and 53.9% of the patients demonstrating improved life engagement scores within 1, 3, and 6 months, respectively. The improvements were particularly apparent for the emotional and social sub-domains. Conclusion: The results of this study provide evidence of improved life engagement following brexpiprazole initiation in a real-world dataset.

Psychiatric healthcare resource utilization following initiation of aripiprazole once-monthly: a retrospective real-world study.

OBJECTIVES: This observational retrospective real-world study examined changes in healthcare resource utilization (HCRU) pre- and post-initiation of aripiprazole once-monthly (AOM 400) in patients with schizophrenia or bipolar I disorder. METHODS: Electronic health record-derived, de-identified data from the NeuroBlu Database (2013-2020) were used to identify patients ≥18 years with schizophrenia (n = 222) or bipolar I disorder (n = 129) who were prescribed AOM 400, and had visit data within 3, 6, 9, or 12 months pre- and post-initial AOM 400 prescription. Rates of inpatient hospitalization, emergency department visits, inpatient readmissions, and average length of stay were examined and compared over 3, 6, 9, and 12 months pre-/post-AOM 400 using a McNemar test. RESULTS: Statistically significant differences were seen in both schizophrenia and bipolar I disorder patient cohorts pre- and post-AOM 400 in inpatient hospitalization rates (p < .001 all time points, both cohorts) and 30-day readmission per patient rates (p < .001 all time points, both cohorts). Statistically significant improvement in mean length of stay was observed in both cohorts at all time points, except for at six months in patients with schizophrenia. Emergency department visit rates were significantly lower after AOM 400 initiation for both cohorts at all time points (p < .001). CONCLUSIONS: A reduction in the rate of hospitalizations, emergency department visits, 30-day readmissions, and average length-of-stay was observed for patients diagnosed with either schizophrenia or bipolar I disorder, which suggests a positive effect of AOM 400 treatment on HCRU outcomes and is supportive of earlier analyses from different data sources.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data.

PURPOSE: NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health. PARTICIPANTS: As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months). FINDINGS TO DATE: The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data. FUTURE PLANS: The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

Alzheimer's Disease: Key Insights from Two Decades of Clinical Trial Failures.

Given the acknowledged lack of success in Alzheimer's disease (AD) drug development over the past two decades, the objective of this review was to derive key insights from the myriad failures to inform future drug development. A systematic and exhaustive review was performed on all failed AD compounds for dementia (interventional phase II and III clinical trials from ClinicalTrials.gov) from 2004 to the present. Starting with the initial ∼2,700 AD clinical trials, ∼550 trials met our initial criteria, from which 98 unique phase II and III compounds with various mechanisms of action met our criteria of a failed compound. The two recent reported phase III successes of aducanumab and oligomannate are very encouraging; however, we are awaiting real-world validation of their effectiveness. These two successes against the 98 failures gives a 2.0% phase II and III success rate since 2003, when the previous novel compound was approved. Potential contributing methodological factors for the clinical trial failures were categorized into 1) insufficient evidence to initiate the pivotal trials, and 2) pivotal trial design shortcomings. Our evaluation found that rational drug development principles were not always followed for AD therapeutics development, and the question remains whether some of the failed compounds may have shown efficacy if the principles were better adhered to. Several recommendations are made for future AD therapeutic development. The whole database of the 98 failed compounds is presented in the Supplementary Material.

Factors That Affect Patient Attrition in Buprenorphine Treatment for Opioid Use Disorder: A Retrospective Real-World Study Using Electronic Health Records.

PURPOSE: To describe attrition patterns of opioid use disorder (OUD) patients treated with buprenorphine (BUP) and to assess how clinical, sociodemographic, or BUP medication dosing features are associated with attrition. PATIENTS AND METHODS: Electronic health records of adults (16+ year-olds) with OUD treated with BUP from 23 different substance use or mental health care programs across 11 US states were examined for one year following BUP initiation in inpatient (IP), intensive outpatient (IOP), or outpatient (OP) settings. Treatment attrition was declared at >37 days following the last recorded visit. Survival analyses and predictive modelling were used. RESULTS: Retention was consistently 2-3 times higher following BUP initiation in OP (n = 2409) than in IP/IOP (n = 2749) settings after 2 (50% vs 25%), 6 (27% vs 9%) and 12 months (14% vs 4%). Retention was higher for females, whites (vs blacks), and those with less severe OUD, better global function, or not using non-psychotropic medications. Comorbid substance use, other psychiatric disorders, and the number of psychotropic medications were variously related to retention depending on the setting in which BUP was initiated. Predictive modelling revealed that a higher global assessment of functioning and a smaller OUD severity based on the Clinical Global Impression - Severity led to longer retentions, a higher initial BUP dose led to higher retention in a few cases, an OP setting of BUP initiation led to longer retentions, and a lower total number of psychotropic and non-psychotropic medications led to longer retentions. These were the most important parameters in the model, which identified 75.2% of patients who left BUP treatment within three months post-initiation, with a precision of 90.5%. CONCLUSION: Of all the OUD patients who began BUP, 50-75% left treatment within three months, and most could be accurately identified. This could facilitate patient-centered management to better retain OUD patients in BUP treatment.

CKD subpopulations defined by risk-factors: A longitudinal analysis of electronic health records.

Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make this a formidable task. We have developed a hybrid semimechanistic modeling methodology to investigate CKD progression. The model is represented as a system of ordinary differential equations with embedded neural networks and takes into account complex disease progression pathways, feedback loops, and effects of 53 medications to generate time trajectories of eight clinical biomarkers that capture CKD progression due to various risk factors. The model was applied to real world data of US patients with CKD to map the available longitudinal information onto a set of time-invariant patient-specific parameters with a clear biological interpretation. These parameters describing individual patients were used to segment the cohort using a clustering approach. Model-based simulations were conducted to investigate cluster-specific treatment strategies. The model was able to reliably reproduce the variability in biomarkers across the cohort. The clustering procedure segmented the cohort into five subpopulations - four with enhanced sensitivity to a specific risk factor (hypertension, hyperlipidemia, hyperglycemia, or impaired kidney) and one that is largely insensitive to any of the risk factors. Simulation studies were used to identify patient-specific strategies to restrain or prevent CKD progression through management of specific risk factors. The semimechanistic model enables identification of disease progression phenotypes using longitudinal data that aid in prioritizing treatment strategies at individual patient level.

Building a Longitudinal National Integrated Cardiovascular Database　- Lessons Learnt From SingCLOUD.

Background: Real world data on clinical outcomes and quality of care for patients with coronary artery disease (CAD) are fragmented. We describe the rationale and design of the Singapore Cardiovascular Longitudinal Outcomes Database (SingCLOUD). Methods and Results: We designed a health data grid to integrate clinical, administrative, laboratory, procedural, prescription and financial data from all public-funded hospitals and primary care clinics, which provide 80% of health care in Singapore. Here, we explain our approach to harmonize real-world data from diverse electronic medical and non-medical platforms to develop a robust and longitudinal dataset. We present pilot data on patients with myocardial infarction (MI) treated with percutaneous coronary intervention (PCI) between 2012 and 2014. The initial data set had 53,395 patients. Of these, 35,203 had CAD confirmed on coronary angiography, of whom 21,521 had PCI. Eventually, limiting to 2012-2014, 3,819 patients had MI with PCI, while 5,989 had MI. Compared with the quality improvement registry, Singapore Cardiac Data Bank, which had 189 fields for analysis, the SingCLOUD platform generated an additional 313 additional data fields, and was able to identify an additional 250 heart failure events, 664 major adverse cardiovascular events at 2 years, and low-density lipoprotein levels to 1 year for 3,747 patients. Conclusions: By integrating multiple incongruent data sources, SINGCLOUD enables in-depth analysis of real-world cardiovascular "big data".

Whole-body iron transport and metabolism: Mechanistic, multi-scale model to improve treatment of anemia in chronic kidney disease.

Iron plays vital roles in the human body including enzymatic processes, oxygen-transport via hemoglobin and immune response. Iron metabolism is characterized by ~95% recycling and minor replenishment through diet. Anemia of chronic kidney disease (CKD) is characterized by a lack of synthesis of erythropoietin leading to reduced red blood cell (RBC) formation and aberrant iron recycling. Treatment of CKD anemia aims to normalize RBC count and serum hemoglobin. Clinically, the various fluxes of iron transport and accumulation are not measured so that changes during disease (e.g., CKD) and treatment are unknown. Unwanted iron accumulation in patients is known to lead to adverse effects. Current whole-body models lack the mechanistic details of iron transport related to RBC maturation, transferrin (Tf and TfR) dynamics and assume passive iron efflux from macrophages. Hence, they are not predictive of whole-body iron dynamics and cannot be used to design individualized patient treatment. For prediction, we developed a mechanistic, multi-scale computational model of whole-body iron metabolism incorporating four compartments containing major pools of iron and RBC generation process. The model accounts for multiple forms of iron in vivo, mechanisms involved in iron uptake and release and their regulation. Furthermore, the model is interfaced with drug pharmacokinetics to allow simulation of treatment dynamics. We calibrated our model with experimental and clinical data from peer-reviewed literature to reliably simulate CKD anemia and the effects of current treatment involving combination of epoietin-alpha and iron dextran. This in silico whole-body model of iron metabolism predicts that a year of treatment can potentially lead to 90% downregulation of ferroportin (FPN) levels, 15-fold increase in iron stores with only a 20% increase in iron flux from the reticulo-endothelial system (RES). Model simulations quantified unmeasured iron fluxes, previously unknown effects of treatment on FPN-level and iron stores in the RES. This mechanistic whole-body model can be the basis for future studies that incorporate iron metabolism together with related clinical experiments. Such an approach could pave the way for development of effective personalized treatment of CKD anemia.

A long-term mechanistic computational model of physiological factors driving the onset of type 2 diabetes in an individual.

A computational model of the physiological mechanisms driving an individual's health towards onset of type 2 diabetes (T2D) is described, calibrated and validated using data from the Diabetes Prevention Program (DPP). The objective of this model is to quantify the factors that can be used for prevention of T2D. The model is energy and mass balanced and continuously simulates trajectories of variables including body weight components, fasting plasma glucose, insulin, and glycosylated hemoglobin among others on the time-scale of years. Modeled mechanisms include dynamic representations of intracellular insulin resistance, pancreatic beta-cell insulin production, oxidation of macronutrients, ketogenesis, effects of inflammation and reactive oxygen species, and conversion between stored and activated metabolic species, with body-weight connected to mass and energy balance. The model was calibrated to 331 placebo and 315 lifestyle-intervention DPP subjects, and one year forecasts of all individuals were generated. Predicted population mean errors were less than or of the same magnitude as clinical measurement error; mean forecast errors for weight and HbA1c were ~5%, supporting predictive capabilities of the model. Validation of lifestyle-intervention prediction is demonstrated by synthetically imposing diet and physical activity changes on DPP placebo subjects. Using subject level parameters, comparisons were made between exogenous and endogenous characteristics of subjects who progressed toward T2D (HbA1c > 6.5) over the course of the DPP study to those who did not. The comparison revealed significant differences in diets and pancreatic sensitivity to hyperglycemia but not in propensity to develop insulin resistance. A computational experiment was performed to explore relative contributions of exogenous versus endogenous factors between these groups. Translational uses to applications in public health and personalized healthcare are discussed.

Trauma in silico: Individual-specific mathematical models and virtual clinical populations.

Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1ß inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.

Computational modeling and analysis of iron release from macrophages.

A major process of iron homeostasis in whole-body iron metabolism is the release of iron from the macrophages of the reticuloendothelial system. Macrophages recognize and phagocytose senescent or damaged erythrocytes. Then, they process the heme iron, which is returned to the circulation for reutilization by red blood cell precursors during erythropoiesis. The amount of iron released, compared to the amount shunted for storage as ferritin, is greater during iron deficiency. A currently accepted model of iron release assumes a passive-gradient with free diffusion of intracellular labile iron (Fe2+) through ferroportin (FPN), the transporter on the plasma membrane. Outside the cell, a multi-copper ferroxidase, ceruloplasmin (Cp), oxidizes ferrous to ferric ion. Apo-transferrin (Tf), the primary carrier of soluble iron in the plasma, binds ferric ion to form mono-ferric and di-ferric transferrin. According to the passive-gradient model, the removal of ferrous ion from the site of release sustains the gradient that maintains the iron release. Subcellular localization of FPN, however, indicates that the role of FPN may be more complex. By experiments and mathematical modeling, we have investigated the detailed mechanism of iron release from macrophages focusing on the roles of the Cp, FPN and apo-Tf. The passive-gradient model is quantitatively analyzed using a mathematical model for the first time. A comparison of experimental data with model simulations shows that the passive-gradient model cannot explain macrophage iron release. However, a facilitated-transport model associated with FPN can explain the iron release mechanism. According to the facilitated-transport model, intracellular FPN carries labile iron to the macrophage membrane. Extracellular Cp accelerates the oxidation of ferrous ion bound to FPN. Apo-Tf in the extracellular environment binds to the oxidized ferrous ion, completing the release process. Facilitated-transport model can correctly predict cellular iron efflux and is essential for physiologically relevant whole-body model of iron metabolism.

A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine.

OBJECTIVE: To gain insights into individual variations in acute inflammation and physiology. DESIGN: Large-animal study combined with mathematical modeling. SETTING: Academic large-animal and computational laboratories. SUBJECTS: Outbred juvenile swine. INTERVENTIONS: Four swine were instrumented and subjected to endotoxemia (100 µg/kg), followed by serial plasma sampling. MEASUREMENTS AND MAIN RESULTS: Swine exhibited various degrees of inflammation and acute lung injury, including one death with severe acute lung injury (PaO(2)/FIO(2) ratio µ200 and static compliance µ10 L/cm H(2)O). Plasma interleukin-1ß, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, high mobility group box-1, and NO(2)/NO(3) were significantly (p µ .05) elevated over the course of the experiment. Principal component analysis was used to suggest principal drivers of inflammation. Based in part on principal component analysis, an ordinary differential equation model was constructed, consisting of the lung and the blood (as a surrogate for the rest of the body), in which endotoxin induces tumor necrosis factor-α in monocytes in the blood, followed by the trafficking of these cells into the lung leading to the release of high mobility group box-1, which in turn stimulates the release of interleukin-1ß from resident macrophages. The ordinary differential equation model also included blood pressure, PaO(2), and FIO(2), and a damage variable that summarizes the health of the animal. This ordinary differential equation model could be fit to both inflammatory and physiologic data in the individual swine. The predicted time course of damage could be matched to the oxygen index in three of the four swine. CONCLUSIONS: The approach described herein may aid in predicting inflammation and physiologic dysfunction in small cohorts of subjects with diverse phenotypes and outcomes.

Translational systems approaches to the biology of inflammation and healing.

Inflammation is a complex, non-linear process central to many of the diseases that affect both developed and emerging nations. A systems-based understanding of inflammation, coupled to translational applications, is therefore necessary for efficient development of drugs and devices, for streamlining analyses at the level of populations, and for the implementation of personalized medicine. We have carried out an iterative and ongoing program of literature analysis, generation of prospective data, data analysis, and computational modeling in various experimental and clinical inflammatory disease settings. These simulations have been used to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals for in silico studies associated with the clinical settings of sepsis, trauma, acute liver failure, and wound healing to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation; and to gain insight into host-pathogen interactions in malaria, necrotizing enterocolitis, and sepsis. These simulations have converged with other systems biology approaches (e.g., functional genomics) to aid in the design of new drugs or devices geared towards modulating inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ impacts via tissue damage/dysfunction. This framework has now allowed us to suggest how to modulate acute inflammation in a rational, individually optimized fashion. This plethora of computational and intertwined experimental/engineering approaches is the cornerstone of Translational Systems Biology approaches for inflammatory diseases.

Mathematical modeling of posthemorrhage inflammation in mice: studies using a novel, computer-controlled, closed-loop hemorrhage apparatus.

Hemorrhagic shock (HS) elicits a global acute inflammatory response, organ dysfunction, and death. We have used mathematical modeling of inflammation and tissue damage/dysfunction to gain insight into this complex response in mice. We sought to increase the fidelity of our mathematical model and to establish a platform for testing predictions of this model. Accordingly, we constructed a computerized, closed-loop system for mouse HS. The intensity, duration, and time to achieve target MAP could all be controlled using a software. Fifty-four male C57/black mice either were untreated or underwent surgical cannulation. The cannulated mice were divided into 8 groups: (a) 1, 2, 3, or 4 h of surgical cannulation alone and b) 1, 2, 3, or 4 h of cannulation + HS (25 mmHg). MAP was sustained by the computer-controlled reinfusion and withdrawal of shed blood within +/-2 mmHg. Plasma was assayed for the cytokines TNF, IL-6, and IL-10 as well as the NO reaction products NO2-/NO3-. The cytokine and NO2-/NO3- data were compared with predictions from a mathematical model of post-hemorrhage inflammation, which was calibrated on different data. To varying degrees, the levels of TNF, IL-6, IL-10, and NO2/NO3 predicted by the mathematical model matched these data closely. In conclusion, we have established a hardware/software platform that allows for highly accurate, reproducible, and mathematically predictable HS in mice.

Blockade of receptor for advanced glycation end product attenuates pulmonary reperfusion injury in mice.

OBJECTIVE: The receptor for advanced glycation end products (RAGE) is expressed at high levels in the lung, particularly in type 1 alveolar cells, and has been shown to amplify injury triggered by acute stress. Previous studies suggest serum concentrations of soluble RAGE increase during pulmonary reperfusion injury after transplantation. RAGE blockade has been shown to suppress hepatic and cardiac ischemia and reperfusion injury in mice. Thus we tested the hypothesis that RAGE mediates tissue-injury mechanisms in ischemia and reperfusion injury in the lung. METHODS: C57BL/6 mice were subjected to 30 minutes of pulmonary ischemia by clamping the left hilum, followed by 60 minutes of reperfusion. Lung function was assessed by means of blood gas analysis, and capillary leak was assessed by injecting fluorescein isothiocyanate-labeled albumin and comparing fluorescence in bronchial lavage fluid with that in serum. Histologic analysis of the lung was performed by a pathologist naive to the experimental conditions. RESULTS: In animals subjected to RAGE blockade, significant increases in Po(2) (108 vs 73 mm Hg, P = .0094) and more than 3-fold decrease in capillary leak Relative Fluorescent Units (RFU, 6.12 vs 1.75; P = .001) were observed. Histologic examination revealed significant injury reduction in soluble RAGE-treated animals versus control animals. RAGE knockout mice exhibited a protected phenotype when exposed to pulmonary ischemia and reperfusion. Additionally, interleukin 8 production and nuclear factor kappaB activation were increased in control mice. CONCLUSION: Abrogation of RAGE signaling attenuates pulmonary ischemia and reperfusion injury. This study suggests that RAGE might play a central role in pulmonary reperfusion injury and in transplantation and that blockade of RAGE might offer a potential target to abrogate pulmonary reperfusion injury in clinical transplantation.

Platelet activation in the postoperative period after lung transplantation.

OBJECTIVE: During lung transplantation, cells in the pulmonary parenchyma are subjected to ischemia, hypothermic storage, and reperfusion injury. Platelets, whose granular contents include adhesion receptors, chemokines, and coactivating substances that activate inflammatory and coagulant cascades, likely play a critical role in the lung allograft response to ischemia and reperfusion. The platelet response to the pulmonary allograft, however, has never been studied. Here we report significant platelet activation immediately after lung transplantation. METHODS: We performed a prospective cohort study comparing markers of platelet activation in patients undergoing lung transplantation and patients undergoing nontransplant thoracotomy. Plasma levels of soluble P-selectin, soluble CD40 ligand, and platelet-leukocyte conjugates were measured before surgery, after skin closure, and at 6 postoperative hours. RESULTS: Both soluble P-selectin and soluble CD40 ligand levels increased significantly after lung transplantation but not after thoracotomy. Additionally, platelet-monocyte conjugate fluorescence was significantly higher after lung transplantation than after thoracotomy alone. CONCLUSION: These findings suggest that platelet activation is significantly increased after lung transplantation beyond that expected from the postoperative state. The increase in circulating platelet-monocyte conjugates suggests an important interaction between platelets and inflammatory cells. Further research should examine whether platelet activation affects early graft function after lung transplantation.

Unexpected role of ceruloplasmin in intestinal iron absorption.

Ferroxidases are essential for normal iron homeostasis in most organisms. The paralogous vertebrate ferroxidases ceruloplasmin (Cp) and hephaestin (Heph) are considered to have nonidentical functions in iron transport: plasma Cp drives iron transport from tissue stores while intestinal Heph facilitates iron absorption from the intestinal lumen. To clarify the function of Cp, we acutely bled Cp-/- mice to stress iron homeostasis pathways. Red cell hemoglobin recovery was defective in stressed Cp-/- mice, consistent with low iron availability. Contrary to expectations, iron was freely released from spleen and liver stores in Cp-/- mice, but intestinal iron absorption was markedly impaired. Phlebotomy of wild-type mice caused a striking shift of Cp from the duodenal epithelium to the underlying lamina propria, suggesting a critical function of Cp in basolateral iron transport. Regulated relocalization of intestinal Cp may represent a fail-safe mechanism in which Cp shares with Heph responsibility for iron absorption under stress.

Role of ceruloplasmin in macrophage iron efflux during hypoxia.

The reticuloendothelial system has a central role in erythropoiesis and iron homeostasis. An important function of reticuloendothelial macrophages is phagocytosis of senescent red blood cells. The iron liberated from heme is recycled for delivery to erythrocyte precursors for a new round of hemoglobin synthesis. The molecular mechanism by which recycled iron is released from macrophages remains unresolved. We have investigated the mechanism of macrophage iron efflux, focusing on the role of ceruloplasmin (Cp), a copper protein with a potent ferroxidase activity that converts Fe2+ to Fe3+ in the presence of molecular oxygen. As shown by others, Cp markedly increased iron binding to apotransferrin at acidic pH; however, the physiological significance of this finding is uncertain because little stimulation was observed at neutral pH. Introduction of a hypoxic atmosphere resulted in marked Cp-stimulated binding of iron to apotransferrin at physiological pH. The role of Cp in cellular iron release was examined in U937 monocytic cells induced to differentiate to the macrophage lineage. Cp added at its normal plasma concentration increased the rate of 55Fe release from U937 cells by about 250%. The stimulation was absolutely dependent on the presence of apotransferrin and hypoxia. Cp-stimulated iron release was confirmed in mouse peritoneal macrophages. Stimulation of iron release required an intracellular "labile iron pool" that was rapidly depleted in the presence of Cp and apotransferrin. Ferroxidase-mediated loading of iron into apotransferrin was critical for iron release because ferroxidase-deficient Cp was inactive and because holotransferrin could not substitute for apotransferrin. The extracellular iron concentration was critical as shown by inhibition of iron release by exogenous free iron, and marked enhancement of release by an iron chelator. Together these data show that Cp stimulates iron release from macrophages under hypoxic conditions by a ferroxidase-dependent mechanism, possibly involving generation of a negative iron gradient.