Potential ecological and socio-economic effects of a novel megaherbivore introduction: the hippopotamus in Colombia

Introduced species can have strong ecological, social and economic effects on their non-native environment. Introductions of megafaunal species are rare and may contribute to rewilding efforts, but they may also have pronounced socio-ecological effects because of their scale of influence. A recent introduction of the hippopotamus Hippopotamus amphibius into Colombia is a novel introduction of a megaherbivore onto a new continent, and raises questions about the future dynamics of the socio-ecological system into which it has been introduced. Here we synthesize current knowledge about the Colombian hippopotamus population, review the literature on the species to predict potential ecological and socio-economic effects of this introduction, and make recommendations for future study. Hippopotamuses can have high population growth rates (7–11%) and, on the current trajectory, we predict there could be 400–800 individuals in Colombia by 2050. The hippopotamus is an ecosystem engineer that can have profound effects on terrestrial and aquatic environments and could therefore affect the native biodiversity of the Magdalena River basin. Hippopotamuses are also aggressive and may pose a threat to the many inhabitants of the region who rely upon the Magdalena River for their livelihoods, although the species could provide economic benefits through tourism. Further research is needed to quantify the current and future size and distribution of this hippopotamus population and to predict the likely ecological, social and economic effects. This knowledge must be balanced with consideration of social and cultural concerns to develop appropriate management strategies for this novel introduction

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artifcial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expertlevel performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identifed eight loci associated with delta age (p ≤ 5 × 10−8), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine

Studying accelerated cardiovascular ageing in Russian adults through a novel deep-learning ECG biomarker

Background: A non-invasive, easy-to-access marker of accelerated cardiac ageing would provide novel insights into the mechanisms and aetiology of cardiovascular disease (CVD) as well as contribute to risk stratification of those who have not had a heart or circulatory event. Our hypothesis is that differences between an ECG-predicted and chronologic age of participants (δage) would reflect accelerated or decelerated cardiovascular ageing Methods: A convolutional neural network model trained on over 700,000 ECGs from the Mayo Clinic in the U.S.A was used to predict the age of 4,542 participants in the Know Your Heart study conducted in two cities in Russia (2015-2018). Thereafter, δage was used in linear regression models to assess associations with known CVD risk factors and markers of cardiac abnormalities. Results: The biomarker δage (mean: +5.32 years) was strongly and positively associated with established risk factors for CVD: blood pressure, body mass index (BMI), total cholesterol and smoking. Additionally, δage had strong independent positive associations with markers of structural cardiac abnormalities: N-terminal pro b-type natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT) and pulse wave velocity, a valid marker of vascular ageing. Conclusion: The difference between the ECG-age obtained from a convolutional neural network and chronologic age (δage) contains information about the level of exposure of an individual to established CVD risk factors and to markers of cardiac damage in a way that is consistent with it being a biomarker of accelerated cardiovascular (vascular) ageing. Further research is needed to explore whether these associations are seen in populations with different risks of CVD events, and to better understand the underlying mechanisms involved

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction

Objective - To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. Background - LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. Methods - We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35–69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. Results - Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. Conclusions - The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.

Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age ("delta age") to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ([Formula: see text]), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine

Machine-learning-derived heart and brain age are independently associated with cognition

BACKGROUND AND PURPOSE: A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated. METHODS: Using 12-lead electrocardiograms, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.3% men). Associations between heart delta age (HDA) and cognitive test scores were studied adjusted for cardiovascular risk factors. In addition, the relationship between HDA, brain delta age (BDA) and cognitive test scores was investigated in mediation analysis. RESULTS: Significant associations between HDA and the Word test, Digit Symbol Coding Test and tapping test scores were found. HDA was correlated with BDA (Pearson's r = 0.12, p = 0.0001). Moreover, 13% (95% confidence interval 3-36) of the HDA effect on the tapping test score was mediated through BDA. DISCUSSION: Heart delta age, representing the cumulative effects of life-long exposures, was associated with brain age. HDA was associated with cognitive function that was minimally explained through BDA

Machine-learning-derived heart and brain age are independently associated with cognition

Background and purpose: A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated. Methods: Using 12-lead electrocardiograms, heart age was estimated for a population-based sample (N = 7779, age 40–85 years, 45.3% men). Associations between heart delta age (HDA) and cognitive test scores were studied adjusted for cardiovascular risk factors. In addition, the relationship between HDA, brain delta age (BDA) and cognitive test scores was investigated in mediation analysis. Results: Significant associations between HDA and the Word test, Digit Symbol Coding Test and tapping test scores were found. HDA was correlated with BDA (Pearson's r = 0.12, p = 0.0001). Moreover, 13% (95% confidence interval 3–36) of the HDA effect on the tapping test score was mediated through BDA. Discussion: Heart delta age, representing the cumulative effects of life-long exposures, was associated with brain age. HDA was associated with cognitive function that was minimally explained through BDA

Effect of synthesis parameters on the performance of alkali-activated non-conformant EN 450 pulverised fuel ash

The fly ash reported in this paper is coarser than conventional pulverised fuel ash (PFA), with loss on ignition (LOI) exceeding 10.8%. Consequently, it is precluded from being used as a supplementary cementitious material (SCM) according to EN 450 and disposed in landfills. Alkali-activation of such PFAs is considered here. Three concentrations of sodium hydroxide (NaOH) were separately blended with water glass at different ratios to modify the silica modulus. Heat of reaction, setting time, compressive strength and drying shrinkage were investigated as a function of activator composition. Specimens were either cured at room temperature or hydro-thermally treated at 75 °C for five hours. The results show that by optimizing the activator composition, a binder with a 28 day compressive strength of 25 MPa can be synthesised from such PFAs even at room temperature. Among the activator parameters, the alkali content was observed to be most influential

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Improving physical health and reducing substance use in psychosis - randomised control trial (IMPACT RCT): study protocol for a cluster randomised controlled trial

The National Institute for Health Research funds the IMPACT programme at King’s College London and South London and Maudsley NHS Foundation Trust (ref: RP-PG-0606-1049)