Abstract Introduction Carcinoid heart disease is a rare disease affecting the right sided heart valves (1) while anthracycline cardiotoxicity is a dreaded sequelae of a common chemotherapeutic agent that can occur early or even years after exposure (2). Activated fibroblasts, a key factor in these diseases, can be imaged with 68Gallium-fibroblast activation protein inhibitor (68Ga-FAPI). Method 12 patients with carcinoid syndrome, 12 patients with previous anthracycline exposure and 10 healthy volunteers underwent hybrid 68Ga-FAPI positron emission tomography and magnetic resonance imaging. Areas of fibroblast activation were quantified as mean and maximum target-to-background ratios (TBRmean and TBRmax). Results Carcinoid syndrome: 2 of the 12 patients had carcinoid heart disease [mean age 70, 50% female] (CHD) (Figure 1A). 68Ga-FAPI uptake was seen in the liver lesions of all patients with known carcinoid liver tumours. Of the 10 patients without cardiac involvement, 6 had significant tricuspid valve 68Ga-FAPI uptake (CHD- FAPI+) (Figure 1B) and 4 did not (CHD- FAPI-). Median tricuspid TBRmax was significantly higher in CHD + [2.38 (IQR 2.35-2.42)], than CHD- patients [CHD- FAPI+: 1.68 (IQR 1.65-1.73), CHD- FAPI-: 1.55 (IQR 1.53-1.65)], although this was higher than healthy volunteers [1.45 (IQR 1.43- 1.61; p<0.05). CHD- FAPI+ patients had significantly elevated urinary 5-HIAA levels compared to CHD- FAPI- patients [Median =188 mg/24hr (IQR 151-196) in CHD- FAPI+ vs 32mg/24hr ( IQR 12-73); p=0.03]. Anthracycline chemotherapy: 3 of the 12 patients had cardiotoxicity [mean age 61, 67% female]. Anthracycline patients had higher left ventricular myocardial 68Ga-FAPI uptake than healthy volunteers with cardiotoxicity patients having the highest. [TBRmean: Cardiotoxicity 1.46 (IQR 1.36- 1.53), No cardiotoxicity 1.37 (IQR 1.19- 1.49), Healthy volunteers 0.73 (IQR 0.66 - 0.81); p<0.01 (Figure 1E). Across the cohort a moderate negative correlation was observed between Left ventricular ejection fraction and 68Ga-FAPI activity (LV TBRmean r =-0.46 and TBRmax (r= -0.66) Discussion 68 Ga-FAPI provides us the ability to assess fibroblast activation and for the first time demonstrated the key role of these cells in both these cardio-oncology conditions. Increased fibroblast activation is observed around the tricuspid valve in patients with carcinoid syndrome both in those with and without overt clinical disease. Similarly increased myocardial fibroblast activation is observed in patients exposed to anthracyclines even many years after exposure and where there is no overt evidence of cardiotoxicity. Conclusion 68Ga-FAPI imaging holds promise in evaluating the role of activated fibroblasts in the pathology of both carcinoid heart disease and cardiotoxicity, providing information beyond the current resolution of current clinical approaches.

Abstract Introduction The new 2022 ESC Guidelines in Cardio-Oncology focused on cancer therapy-related cardiac dysfunction (CT-RCD), with the formal introduction of biomarkers in the classification and risk stratification of CTR-CD in addition to structural imaging . We here, investigated how these changes affect the classification of patients in the Cardiac CARE study and the value of high sensitivity troponin I (hSTrop-I). Methods The Cardiac CARE study prospectively recruited 191 patients who underwent anthracycline therapy for breast cancer or non-Hodgkin’s lymphoma. All patients underwent regular hSTrop-I levels throughout their anthracycline therapy as well as a baseline and 6-month cardiac magnetic resonance scans including assessment of left ventricular ejection fraction and global longitudinal strain. A post-hoc analysis assessed the association between hSTrop-I and the development of cardiac dysfunction (defined as a reduction in left ventricular ejection fraction <50% or a reduction in GLS by >15%) using logistic regression with data corrected for age and gender. In addition, receiver operator curves (ROC) were drawn and area under the curve (AUC) analysis was performed to assess the value of hSTrop-I as an addition to the classification model. Finally, to compare our data with the new guidelines, a confusion matrix was performed. Results 11 patients were diagnosed as having cardiotoxicity in the Cardiac CARE study but using the 2022 ESC guidelines in Cardio-Oncology, 53 patients were re-classified into the ‘asymptomatic mild cardiotoxicity’ group (95% CI of 0.5162 and 0.6823; kappa value 0.157). Using a logistic regression model, a difference in hSTrop-I measured at the first cycle of anthracycline therapy had an odds ratio of 1.37 (95% CI 0.37-5.10) which when corrected for age dropped to 1.18 (95% CI of 0.28-5.01) and with combined age and sex correction, fell further, to 0.83 (95% CI of 0.12 and 5.56). A ROC analysis looking at the performance of hSTrop-I at predicting cardiac dysfunction demonstrated an area under the curve of 0.605 and when corrected for age and sex, was 0.640. Conclusion The addition of cardiac biomarkers to the new classification, significantly reclassified 53 of 191 (27.7%) of patients who were previously normal, to having asymptomatic mild cancer therapy-related cardiac dysfunction, mostly due to the inclusion of hSTrop I. HSTrop-I prior to anthracycline treatment was not a robust predictor of the development of anthracycline-induced cardiotoxicity in our cohort of patients. Of note, the Cardiac CARE patients are a low-risk group of patients and follow-up data is limited to 6 months.

Fluoropyrimidines are used widely to treat cancer but their cardiotoxicity, which remains incompletely understood, has serious consequences for some patients. The fluoropyrimidine S-1 (Teysuno®) has recently been approved for the management of colorectal cancer in patients who develop cardiovascular toxicity with other fluoropyrimidines. This was a single-centre phase II prospective randomised open-label blinded endpoint trial that investigated the cardiotoxicity of capecitabine and S-1 in patients treated between May 2014 and March 2020. Patients were randomized 1:1 to receive oral capecitabine or S-1, alone or with oxaliplatin, for days 1-14 of a 21-day cycle. Baseline computed tomography coronary angiogram (CTCA) was performed. Myocardial ischaemia was quantified by ST-segment analysis of continuous 12-lead electrocardiography before and during days 3-5 of oral chemotherapy, by a blinded analyst. Alpha-fluoro-beta-alanine (FBAL) was measured using a validated liquid chromatography tandem mass spectrometry method. Fifty-nine patients (29 capecitabine; 30 S-1; mean age 64 years (range 40-83)) were enrolled, 56 started treatment and 72% (capecitabine) and 70% (S-1) received oxaliplatin. CTCA was performed in 53 patients, coronary artery disease (CAD) was seen in 30 (capecitabine, n=14/26; S-1, n=16/27). Duration of ST change was not significantly different between groups (p = 0.2198) but change in mean daily ischaemic burden (p=0.0442) was significantly higher for capecitabine compared to S-1. Ischaemic burden was unrelated to the presence of underlying CAD. FBAL (precursor to cardiotoxic fluoroacetate) was significantly increased with capecitabine compared to S-1 during treatment (Table). Table: 2157PMean FBAL (ng/mL) during fluoropyrimidine treatmentWeekCapecitabine (n=23)S-1 (n=23)P value1250795<0.000122632103<0.000131230.0083 Open table in a new tab In this prospective randomised trial, capecitabine was associated with significantly higher ischaemic burden and higher levels of FBAL than S-1 and this was unrelated to presence of CAD.

Aims/hypothesisTo identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role.MethodsOne hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool.ResultsMeasures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals.ConclusionsIndividuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.

BACKGROUND:Individuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.METHODS:Using data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.FINDINGS:The model predicts the progression to diabetic kidney disease with accuracy: 0.96 ± 0.25 and 0.96 ± 0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75 ± 0.14 and 0.79 ± 0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.INTERPRETATION:With further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.FUNDING:This study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.

A full-scale, experimental landfarm was tested for the capacity to biodegrade oil-polluted soil under high-Arctic tundra conditions in northeast Greenland at the military outpost 9117 Station Mestersvig. Soil contaminated with Arctic diesel was transferred to the landfarm in August 2012 followed by yearly addition of fertilizer and plowing and irrigation to optimize microbial diesel biodegradation. Biodegradation was determined from changes in total petroleum hydrocarbons (TPH), enumeration of specific subpopulations of oil-degrading microorganisms (MPN), and changes in selected classes of alkylated isomers and isomer ratios. Sixty-four percent of the diesel was removed in the landfarm within the first year, but a recalcitrant fraction (18%) remained after five years. n-alkanes and naphthalenes were biodegraded as demonstrated by changing isomer ratios. Dibenzothiophenes and phenanthrenes showed almost constant isomer ratios indicating that their removal was mostly abiotic. Oil-degrading microorganisms were present for the major components of diesel (n-alkanes, alkylbenzenes and alkylnaphthalenes). The degraders showed very large population increases in the landfarm with a peak population of 1.2 × 109 cells g-1 of total diesel degraders. Some diesel compounds such as cycloalkanes, hydroxy-PAHs and sulfur-heterocycles had very few or no specific degraders, these compounds may consequently be degraded only by slow co-metabolic processes or not at all.

Hypoglycemia is a major limiting factor in achieving recommended glycemic target for patients in insulin treatment and can indirectly lead to diabetic complications, morbidity and mortality. While counterregulatory hormonal responses have been studied extensively in patients with type 1 diabetes, a more comprehensive assessment of the metabolic responses has not been done previously. This post-hoc study explored potential responses of the metabolome to hypoglycemia. Twenty-one patients with type 1 diabetes were examined using a hypoglycemic clamp at day one, and 24 hours later, at day two. Blood samples were taken during normoglycemia (5.0-6.0 mmol/L) and hypoglycemia (2.0-2.5 mmol/L). Non-targeted plasma metabolomic analyses was conducted using two-dimensional gas chromatography/time-of-flight mass spectroscopy. Metabolites were analyzed by a linear mixed effect model, adjusting for age and sex. P-values were adjusted for multiple testing using the Benjamini-Hochberg method. A total of 79 metabolites were identified. At day one, two amino acids decreased in response to hypoglycemia, leucine (β±SE: -0.78±0.18, p = 0.004) and isoleucine (β±SE: -0.72±0.16, p = 0.002). At day two, five amino acids including all three branched-chained amino acids decreased: Leucine (β±SE: -0.62±0.13, p = 0.002), isoleucine (β±SE: -0.59±0.19, p = 0.026), valine (β±SE: -0.52±0.12, p=0.002), methionine (β±SE: -0.62±0.19, p = 0.026) and phenylalanine (β±SE: -0.65±0.20, p = 0.026). Two fatty acids were elevated, oleic acid (β±SE: 0.50±0.14, p = 0.016) and tetradecanoic acid (β±SE: 0.63±0.17, p = 0.013). No significant changes were observed between the two days. In conclusion, this study found that the metabolome alternates in response to insulin-induced hypoglycemia resulting in decrement of several amino acids and increment of two fatty acids, contributing to the knowledge of human physiology in individuals with type 1 diabetes. Disclosure R. She: None. N. Al-sari: None. A. Sejling: Employee; Self; Novo Nordisk A/S. I. Mattila: None. P. Henriksen: None. J. Pedersen: None. C. Legido-quigley: None. U. Pedersen-bjergaard: Advisory Panel; Self; Novo Nordisk A/S, Sanofi-Aventis, Consultant; Self; Abbott, Speaker’s Bureau; Self; Novo Nordisk A/S.

OBJECTIVE: Our aim was to apply state-of-the-art machine learning algorithms to predict the risk of future progression to diabetes complications, including diabetic kidney disease ([≥]30% decline in eGFR) and diabetic retinopathy (mild, moderate or severe). RESEARCH DESIGN AND METHODS: Using data in a cohort of 537 adults with type 1 diabetes we predicted diabetes complications emerging during a median follow-up of 5.4 years. Prediction models were computed first with clinical risk factors at baseline (17 measures) and then with clinical risk factors and blood-derived metabolomics and lipidomics data (965 molecular features) at baseline. Participants were first classified into two groups: type 1 diabetes stable (n=195) or type 1 diabetes with progression to diabetes complications (n=190). Furthermore, progression of diabetic kidney disease ([≥]30% decline in eGFR; n=79) and diabetic retinopathy (mild, moderate or severe; n=111) were predicted in two complication-specific models. Models were compared by 5-fold cross-validated area under the receiver operating characteristic (AUROC) curves. The Shapley additive explanations algorithm was used for feature selection and for interpreting the models. Accuracy, precision, recall, and F-score were used to evaluate clinical utility. RESULTS: During a median follow-up of 5.4 years, 79 (21 %) of the participants (mean+-SD: age 54.8 +- 13.7 years) progressed in diabetic kidney disease and 111 (29 %) of the participants progressed to diabetic retinopathy. The predictive models for diabetic kidney disease progression were highly accurate with clinical risk factors: the accuracy of 0.95 and AUROC of 0.92 (95% CI 0.857;0.995) was achieved, further improved to the accuracy of 0.98 and AUROC of 0.99 (95% CI 0.876;0.997) when omics-based predictors were included. The predictive panel composition was: albuminuria, retinopathy, estimated glomerular filtration rate, hemoglobin A1c, and six metabolites (five identified as ribitol, ribonic acid, myo-inositol, 2,4- and 3,4-dihydroxybutanoic acids). Models for diabetic retinopathy progression were less predictive with clinical risk predictors at, AUROC of 0.81 (95% CI 0.754;0.958) and with omics included at AUROC of 0.87 (95% CI 0.781;0.996) curve. The final retinopathy-panel included: hemoglobin A1c, albuminuria, mild degree of retinopathy, and seven metabolites, including one ceramide and the 3,4-dihydroxybutanoic acid). CONCLUSIONS: Here we demonstrate the application of machine learning to effectively predict five-year progression of complications, in particular diabetic kidney disease, using a panel of known clinical risk factors in combination with blood small molecules. Further replication of this machine learning tool in a real-world context or a clinical trial will facilitate its implementation in the clinic.

Abnormal gut microbiota and blood metabolome profiles have been reported both in children and adults with uncomplicated type 1 diabetes as well as in adults with type 1 diabetes and advanced stages of diabetic nephropathy. In this study we aimed to investigate the gut microbiota and a panel of targeted plasma metabolites in individuals with type 1 diabetes of long duration without and with different levels of albuminuria. In a cross-sectional study we included 161 individuals with type 1 diabetes and 50 healthy control individuals. Individuals with type 1 diabetes were categorised into three groups according to historically measured albuminuria: (1) normoalbuminuria (<3.39 mg/mmol); (2) microalbuminuria (3.39–33.79 mg/mmol); and (3) macroalbuminuria (≥33.90 mg/mmol). From faecal samples, the gut microbiota composition at genus level was characterised by 16S rRNA gene amplicon sequencing and in plasma a targeted profile of 31 metabolites was analysed with ultra HPLC coupled to MS/MS. Study participants were aged 60 ± 11 years (mean ± SD) and 42% were women. The individuals with type 1 diabetes had had diabetes for a mean of 42 ± 15 years and had an eGFR of 75 ± 25 ml min−1 (1.73 m)−2. Measures of the gut microbial beta diversity differed significantly between healthy controls and individuals with type 1 diabetes, either with micro- or macroalbuminuria. Taxonomic analyses showed that 79 of 324 genera differed in relative abundance between individuals with type 1 diabetes and healthy controls and ten genera differed significantly among the three albuminuria groups with type 1 diabetes. For the measured plasma metabolites, 11 of 31 metabolites differed significantly between individuals with type 1 diabetes and healthy controls. When individuals with type 1 diabetes were stratified by the level of albuminuria, individuals with macroalbuminuria had higher plasma concentrations of indoxyl sulphate and l-citrulline than those with normo- or microalbuminuria and higher plasma levels of homocitrulline and l-kynurenine compared with individuals with normoalbuminuria. Whereas plasma concentrations of tryptophan were lower in individuals with macroalbuminuria compared with those with normoalbuminuria. We demonstrate that individuals with type 1 diabetes of long duration are characterised by aberrant profiles of gut microbiota and plasma metabolites. Moreover, individuals with type 1 diabetes with initial stages of diabetic nephropathy show different gut microbiota and plasma metabolite profiles depending on the level of albuminuria.

Mph1 is a member of the conserved FANCM family of DNA motor proteins that play key roles in genome maintenance processes underlying Fanconi anemia, a cancer predisposition syndrome in humans. Here, we identify Mte1 as a novel interactor of the Mph1 helicase in Saccharomyces cerevisiae. In vitro, Mte1 (Mph1-associated telomere maintenance protein 1) binds directly to DNA with a preference for branched molecules such as D loops and fork structures. In addition, Mte1 stimulates the helicase and fork regression activities of Mph1 while inhibiting the ability of Mph1 to dissociate recombination intermediates. Deletion of MTE1 reduces crossover recombination and suppresses the sensitivity of mph1Δ mutant cells to replication stress. Mph1 and Mte1 interdependently colocalize at DNA damage-induced foci and dysfunctional telomeres, and MTE1 deletion results in elongated telomeres. Taken together, our data indicate that Mte1 plays a role in regulation of crossover recombination, response to replication stress, and telomere maintenance.

DNA replication stress is a source of genomic instability. Here we identify changed mutation rate 1 (Cmr1) as a factor involved in the response to DNA replication stress in Saccharomyces cerevisiae and show that Cmr1--together with Mrc1/Claspin, Pph3, the chaperonin containing TCP1 (CCT) and 25 other proteins--define a novel intranuclear quality control compartment (INQ) that sequesters misfolded, ubiquitylated and sumoylated proteins in response to genotoxic stress. The diversity of proteins that localize to INQ indicates that other biological processes such as cell cycle progression, chromatin and mitotic spindle organization may also be regulated through INQ. Similar to Cmr1, its human orthologue WDR76 responds to proteasome inhibition and DNA damage by relocalizing to nuclear foci and physically associating with CCT, suggesting an evolutionarily conserved biological function. We propose that Cmr1/WDR76 plays a role in the recovery from genotoxic stress through regulation of the turnover of sumoylated and phosphorylated proteins.

Purpose: Myocardial inflammation following infarction or coronary bypass graft (CABG) surgery exerts detrimental effects on ventricular remodelling and function. “Ultrasmall superparamagnetic particles of iron-oxide” (USPIOs) are internalised by inflammatory cells, and shorten T2* decay times during magnetic resonance imaging (MRI). The aim of the study was to assess whether USPIOs could be used to measure in vivo myocardial cellular inflammation after ST-elevation myocardial infarction (STEMI) or CABG surgery. Methods: Patients underwent MRI 2-4 days post STEMI (baseline), and 24-hours after intravenous USPIO infusion (4 mg/kg Ferumoxytol; n = 10) or no infusion (n = 6). The same protocol was performed in 47 patients 5-28 days following on-pump CABG and 10 healthy volunteers, with all subjects receiving USPIO. Standard Cardiac MRI was performed at 3T. USPIO imaging was performed using T2*-weighted multi-gradient-echo sequences. Four echoes (TE = 2.13, 4.27, 6.41, 8.55 ms) were combined to create R2* (1/T2*) maps, that were used to evaluate USPIO uptake (Figure 1). Plasma troponin I concentrations were determined by high sensitivity assay in patients undergoing CABG surgery. Results (Figure 2): Following USPIO administration, R2* values increased in the liver and spleen consistent with uptake by the reticuloendothelial system. In patients with STEMI, there was a large increase in R2* in infarcted myocardium (41 ± 12 to 155 ± 45 s−1; p<0.001) indicative of macrophage accumulation in this region. There was only a small increase in R2* in the myocardium remote from the infarct (39 ± 3 to 80 ± 15 s−1; p<0.001), similar to the increase observed in the myocardium of healthy volunteers (44 ± 6 to 88 ± 12 s−1). This represented USPIO laden blood pooling. In contrast, R2* signal increased further in the myocardium of CABG surgery patients (44 ± 6 to 114 ± 21 s−1; p<0001) and this was greater than in the myocardium of healthy volunteers (p<0.05), but lower than the infarcted myocardium (p<0.001). High sensitivity troponin I increased 1340 fold from baseline (8 ± 9 pg/ml) to a peak at 6 hours (4809 ± 6176 pg/ml), and correlated with R2* increase (r = 0.34, p = 0.03). R2* map Results

The derivation, performance, sensitivity and inherent uncertainty of ecological quality indicators have become major topics in developing tools for the management of marine, transitional and coastal waters. In reviewing the advances in these waters, related to an ecological status assessment, we show the future challenges to be addressed within the European Water Framework Directive (WFD). Using new analyses carried out under the research project ‘Water Bodies in Europe: Integrative Systems to Assess Ecological status and Recovery’, we provide a complete set of assessments for the biological quality elements (BQEs) (phytoplankton, macroalgae-seagrasses, macroinvertebrates and fish) to be assessed, as well as the validation of existing indicators and multimetric indices and, in some cases, the development of new assessment indices. We show that these indices respond differently to different human pressures and they each have challenges in defining reference conditions against which future changes are judged. In investigating good ecological potential, as the response to heavily modified water bodies, we show that there are flaws in the Directive, not least in its definitions. Our analyses have also focussed on uncertainty in using the indices and we emphasise the problems of defining ecological class boundaries based on indices which themselves may be combined indices (multimetrics). The analysis shows that some of those multimetrics are redundant and/or are inter-correlated and thus may reduce the sensitivity in defining ecological class boundaries. If this is related to the drivers-pressures-state change-impacts-response approach then there are lessons for management measures aimed at achieving good ecological status and even the potential for legal challenges to decisions based on uncertain indices under the WFD. Hence, we conclude the continued need for advances in assessing pressures and gradients, and defining reference conditions for state change, index development, impact assessment and the validation of indices for each BQE.

Characterisation of phytoplankton communities is important for classification of the ecological status of marine waters. In order to design a monitoring programme, it is important to know what degree of variation in the measurements occur at each level (water body, station and sample), so that resources can be spent in a way that maximise the precision of the measured parameters. Seven European water bodies were sampled to assess the variation in pigment concentrations and population densities attributed to water body, station and sample levels. It was found that the main proportion of the variation between pigment measurements was explained by the variation between stations (12–91% of variation) followed by the variation between water bodies (0–89% of variation). For measurements of population density, the main proportion of the variation between densities of cells recorded was explained by the variation between the taxonomists counting the samples (61%), whilst the main proportion of the variation between numbers of taxa recorded was explained by the variation between water bodies (83%). When the cell density of the nine dominant classes were analysed separately, the main proportion of variation was explained at the water body level for all but two class.

Background Inflammation following myocardial infarction has detrimental effects on reperfusion, myocardial remodelling and left ventricular function. MRI using ultrasmall superparamagnetic particles of iron oxide (USPIO) can be used to detect cellular inflammation in tissues. Methods 15 patients were recruited up to 5 days after ST-segment elevation myocardial infarction. Nine patients underwent cardiac MRI (3 Tesla) at baseline, and at 24 and 48 h following infusion of USPIO (4 mg/kg; Ferumoxytol, AMAG). Six control patients underwent the same scanning protocol without infusion of USPIO. T2*-weighted multi-gradient-echo sequences were acquired and R2* maps (inverse of T2*) were generated to assess USPIO accumulation. Baseline scans were registered to subsequent 24 and 48 h scans and the infarct zone was defined on Gadolinium-enhanced T2-weighted images. An “object map” was created that defined corresponding regions of interest (ROI) on all scans for each subject. The ROIs included infarct zone, peri-infarct zone, remote myocardium, liver, blood pool and skeletal muscle. The R2* values for each ROI was calculated. Results In the control group, the R2* value in the infarct zone remained constant: baseline, 0.047 s−1 (95% CI 0.034 to 0.059); 24 h, 0.043 s−1 (95% CI 0.035 to 0.052) and 48 h, 0.040 s−1 (95% CI 0.024 to 0.056). In the infarct zone, the R2* value increased from a baseline of 0.041 s−1 (95% CI 0.029 to 0.053) to 0.164 s−1 (95% CI 0.125 to 0.204) at 24 h and 0.128 s−1 (95% CI 0.097 to 0.158) at 48 h following USPIO administration (p<0.01; non-parametric repeated measure one-way ANOVA, Dunn9s post test comparison). Conclusion USPIO are taken up into the infarcted myocardium following acute myocardial infarction and can be quantified by MRI. This approach appears to image infarct-related cellular inflammation and represents an important novel method of assessing recovery following acute myocardial infarction.