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

Pangolins in global camera trap data: Implications for ecological monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (<0.05) for all species. Occupancy was associated with distance from rivers for M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

Pangolins in Global Camera Trap Data: Implications for Ecological Monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Range expansion: Servals spotted in the Kalahari

Servals Leptailurus serval have a widespread distribution across sub-Saharan Africa with two large gaps: one in the tropical forest block of central Africa and one in the arid western block of southern Africa. We present new camera trap records of servals that fall within a large portion of the latter gap, including records from Khutse Game Reserve and Ghanzi that are more than 100 km outside the known range of the serval and may suggest a Kalahari-wide distribution

Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa

Conservation of large carnivores, such as the African lion, requires preservation of extensive core habitat areas, linkages between them, and mitigation of human-wildlife conflict. However, there are few rigorous examples of efforts that prioritized conservation actions for all three of these critical components. We used an empirically optimized resistance surface to calculate resistant kernel and factorial least cost path predictions of population connectivity and conflict risk for lions across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) and surrounding landscape. We mapped and ranked the relative importance of (1) lion dispersal areas outside National Parks, (2) corridors between the key areas, and (3) areas of highest human-lion conflict risk. Spatial prioritization of conservation actions is critical given extensive land use redesignations that are reducing the extent and increasing the fragmentation of lion populations. While our example focuses on lions in southern Africa, it provides a general approach for rigorous, empirically based comprehensive conservation planning based on spatial prioritization

Evaluating the Potential Anticancer Properties of <i>Salvia triloba</i> in Human-Osteosarcoma U2OS Cell Line and Ovarian Adenocarcinoma SKOV3 Cell Line

Salvia triloba (S. triloba) is an herb inherently linked to traditional medicine systems in the Eastern Mediterranean region. There is minimal experimental evidence however, regarding the anticancer effects of S. triloba in both osteosarcoma and ovarian cancer. In this study, we investigated the effects of crude (macerated) S. triloba ethanol and acetone leaf extracts on viability, migratory ability, and the expression of genes regulating these activities in U2OS and SKOV3 cells using MTT assay, scratch-wound healing/trans-well migration assay, and RT-qPCR respectively. MTT assay results indicated that the acetone extract significantly reduced both U2OS and SKOV3 cell viability with half-maximal inhibitory concentrations (IC50) of 54.51 ± 1.10 µg/mL and 75.96 ± 1.0237 µg/mL respectively; these concentrations further displayed negligible hemolytic activity. The combination of acetone extract (19 µg/mL) and paclitaxel (0.787 µg/mL) displayed synergy and reduced SKOV3 cell viability by over 90%. Additionally, the trans-well migration assay illustrated that the acetone extract (IC50) inhibited both U2OS and SKOV3 cell migration by more than 50%. Moreover, S. triloba acetone extract significantly downregulated the steady-state mRNA expression of key genes involved in driving select cancer hallmarks. Four fractions were generated from the acetone extract by thin layer chromatography (TLC), and the obtained retention factors (Rf) (ranging from 0.2 to 0.8) suggested a mixture of high and moderately polar compounds whose bioactivities require further investigation. In addition, FTIR measurements of the extract revealed peaks corresponding to OH, aliphatic CH, and ester groups suggesting the presence of phenolic compounds, terpenes, and polysaccharides. Altogether, these results suggest that S. triloba possesses potential therapeutic compounds that inhibit cell proliferation and migration, and modulate several genes involved in osteosarcoma and ovarian carcinoma progression

Robust ecological analysis of camera trap data labelled by a machine learning model

Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time-consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case-study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n&#xA0;=&#xA0;227 camera stations, n&#xA0;=&#xA0;23,868 images), and outperforms humans in several respects (e.g. detecting &#x2018;invisible&#x2019; animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n&#xA0;=&#xA0;4 species tested) and occupancy (n&#xA0;=&#xA0;4 species tested) derived from machine learning labels with the same estimates derived from expert labels. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n&#xA0;=&#xA0;4 species tested) and estimating occupancy (n&#xA0;=&#xA0;3 of 4 species tested) in a large, completely out-of-sample test dataset. Simple thresholding using the Softmax values (i.e. excluding &#x2018;uncertain&#x2019; labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user-community with a multi-platform, multi-language graphical user interface that can be used to run our model offline