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

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

Novel tetrahydrocurcumin integrated mucoadhesive nanocomposite κ-carrageenan/xanthan gum sponges: a strategy for effective local treatment of oral cancerous and precancerous lesions

AbstractOral cancer is one of the leading causes of death worldwide. Oral precancerous lesions (OPL) are the precursors of oral cancer, with varying degrees of progression. Tetrahydrocurcumin (THC) is a major metabolite of curcumin with superior anticancer properties against various types of cancer. However, THC’s clinical outcome is limited by its poor aqueous solubility. Herein, we developed novel mucoadhesive biopolymer-based composite sponges for buccal delivery of THC, exploiting nanotechnology and mucoadhesion for efficient prevention and treatment of oral cancer. Firstly, THC-nanocrystals (THC-NC) were formulated and characterized for subsequent loading into mucoadhesive composite sponges. The anticancer activity of THC-NC was assessed on a human tongue squamous carcinoma cell line (SCC-4). Finally, the chemopreventive activity of THC-NC loaded sponges (THC-NC-S) was examined in DMBA-induced hamster OPL. The selected THC-NC exhibited a particle size of 532.68 ± 13.20 nm and a zeta potential of −46.08 ± 1.12 mV. Moreover, THC-NC enhanced the anticancer effect against SCC-4 with an IC50 value of 80 µg/mL. THC-NC-S exhibited good mucoadhesion properties (0.24 ± 0.02 N) with sustained drug release, where 90% of THC was released over 4 days. Furthermore, THC-NC-S had a magnificent potential for maintaining high chemopreventive activity, as demonstrated by significant regression in the dysplasia degree and a decline in cyclin D1 (control: 40.4 ± 12.5, THC-NC-S: 12.07 ± 5.2), culminating in significant amelioration after 25 days of treatment. Conclusively, novel THC-NC-S represent a promising platform for local therapy of OPL, preventing their malignant transformation into cancer

Abstract Number ‐ 51: Automated Versus Human Hyperdense Vessel Sign Detection Using Non‐Contrast Computed Tomography Scans

Introduction Rapid detection of large vessel occlusion (LVO) is very crucial in triaging stroke patients potentially candidates for mechanical thrombectomy (MT). Hyperdense vessel sign (HDVS) is one of the earliest ischemic changes in non‐contrast CT scan (NCCT) indicating LVO stroke. Artificial intelligence emerged to detect HDVS with the advantages of faster acquisition, less variation, and a lower need for experience than the usual detection. We aimed to identify the diagnostic performance of automated software (e‐Stroke, Brainomix) in HDVS detection. Methods A prospectively collectedMT database from March 2020 to August 2021 was reviewed. Patients were included if they had intracranial internal carotid artery or middle cerebral artery M1 or M2 occlusion. Cases with HDVS were identified through the routine 2.5‐mm slice thickness NCCT scans after being correlated with patients’ clinical information and confirmed with CT angiography (CTA) scans. NCCT scans were classified according to slice thickness into two groups: 2.5‐mm scans and 0.625‐mm generated scans. All NCCT scans were read by e‐Stroke software, then deidentified and reviewed by two stroke neurologists who were blinded to any clinical, other imaging, or therapeutic information. They were required to record the presence/laterality of HDVS before and after observing other NCCT early ischemic changes like gaze deviation, loss of insular ribbon, caudate or lentiform hypodensity. ROC curve analysis was used to estimate sensitivity and specificity and the area under the curve (AUC) was compared using DeLong’s test. Inter‐rater agreement between the two readers’ final reads, e‐Stroke, and the standard read was measured using the Fleiss Kappa test. Results Among 304 patients included in the study, 37.7% had HDVS. Approximately 44% of the scans had 2.5‐mm slice thickness and 56% had 0.625‐mm slice thickness. The e‐Stroke software identified HDVS with a sensitivity of 63% and a specificity of 71% (Table 1). The mean AUC value of e‐Stroke HDVS detection (0.67[0.61‐0.74]) was similar to reader‐1 (0.68[0.62‐0.74];p = 0.87) and reader‐2 (0.63[0.57‐0.70];p = 0.56). HDVS detection improved by reader‐1(0.78[0.72‐0.83];p = 0.03) after observing other early ischemic changes on the same scans, but reader‐2 performance remained similar to e‐Stroke (0.69[0.63‐0.76];p = 0.71). AUC, sensitivity and specificity ofHDVS detection by e‐Stroke were significantly higher using 2.5‐mm compared to 0.625‐mm sliced NCCT scans (0.78[0.70‐0.86],sensitivity 70%,specificity 86%;p< 0.001) vs (0.58[0.50‐0.67],sensitivity 56%,specificity 61%;p = 0.06) respectively;p = 0.01. The readers also had higher AUC values with 2.5‐mm scans but not statistically significant, (0.74[0.66‐0.83] vs 0.64[0.56‐0.73];p = 0.18) for reader‐1 and (0.68[0.59‐0.77] vs 0.57[0.48‐0.66];p = 0.23) for reader‐2. The same after the final read, (0.85[0.78‐0.92] vs 0.75[0.67‐0.82];p = 0.08) for reader‐1 and (0.73[0.65‐0.82] vs 0.67[0.58‐0.76];p = 0.43) for reader‐2. Similarly, inter‐rater agreement was higher using 2.5‐mm sliced scans, k = 0.50(0.43‐0.75) compared to0.625‐mm scans,k = 0.27(0.21‐0.33). Conclusions Artificial intelligence (e‐Stroke software) has comparable sensitivity and specificity to human readers in HDVS detection. For e‐Stroke software, 2.5‐mm sliced CT scans are better to identifyHDVS compared to 0.625‐mm scans

Abstract Number ‐ 50: Multiplane Reconstruction Modifies The Diagnostic Performance Of CTA Imaging In Carotid Web Cases

Introduction Carotid Web (CaW) represents an important and overlooked etiology for ischemic stroke and has been associated with high rates of stroke recurrence. Computed tomography angiography (CTA) has been shown to have comparable performance to digital subtraction angiography (DSA) and has been suggested to be the non‐invasive imaging of choice for CaW detection. However, misdiagnosis has been demonstrated to be common even in specialized centers. We evaluated the impact of adding CTA multiplane reconstruction (MPR) andthree‐dimensional maximum intensity projection (3D MIP) reformat on the diagnostic performance of CTA in CaW detection. Methods After exclusion of patients aged >65 years old and patients with no available/poor quality CTA,CaW cases (n = 31 consecutive patients leading to 31 ipsilateral carotids to the stroke derived from out prospective CaW database), as well as two other groups: 1)carotid atherosclerosis (n = 27consecutivepatients from out carotid stenting database leading to 27 carotids contralateral to the index lesion) and 2) consecutive normal carotid cases (n = 49 patients with normal carotids extracted from the electronic medical records for patients imaged due to suspected blunt cerebrovascular trauma) were included. All CTA images were deidentified and reviewed independently by three stroke neurologists to record the diagnosis and level of diagnostic certainty (in form of a scale (1[lowest]‐to‐5[highest]) after evaluating the CTA axial plane alone, then after sagittal and coronal planes (MPR) reconstructions, and then after evaluation of3D MIP reformatted images.The analyses were made for the total number of observations for all readers (93 CaWs, 81 atherosclerosis cases and 147 normal carotids). Results On reviewing CTA axial projection alone, raters correctly diagnosed 44.1% of CaW, 87.7% of carotid atherosclerosis and 83% of normal carotid images. Sagittal and coronal MPR significantly increased the rate of accurate CaW diagnosis (76.3%‐Table 1) The certainty level for CaW diagnosis was lower when compared to atherosclerosis as well as normal carotid using the CTA axial projection alone (3.0[3.0‐4.0] vs 4.0[3.0‐5.0];p< 0.001 and vs 4.0[3.0‐5.0];p< 0.001) as well as after adding sagittal/coronal MPR (4.0[3.0‐5.0] vs 5.0[4.0‐5.0],p = 0.01; and vs 4.0[4.0‐5.0],p< 0.001). The certainty level became similar between CaW and atherosclerosis as well as normal carotids with the addition of 3D MIP (5.0[5.0‐5.0] vs 5.0[4.5‐5.0], p = 0.61; and vs 5.0[5.0‐5.0],p = 0.15) respectively. Inter‐rater agreement in CaW detection increased from k = 0.46(0.35‐0.57);p< 0.05usingaxial section to k = 0.80(0.69‐0.91);p< 0.05 with MPR. Axial projection alone had lower sensitivity in CaW detection (AUC = 0.69(0.62‐0.76),sensitivity = 44%,specificity = 95%,p< 0.05) compared to MPR (AUC = 0.86(0.80‐0.91),sensitivity = 76%,specificity = 96%,p< 0.05). Misdiagnosed CaW cases, after using all planes with 3D MIP (n = 23/93), were older (56[46‐61] vs 52[46‐57] years,p = 0.04) and lower length/base ratio (0.51[0.49‐0.87] vs 0.92[0.74‐1.19],p< 0.001) compared to the correctly diagnosed CaW cases (n = 70/93). Conclusions CTA axial plane alone is unreliable to detect CaW and the addition of sagittal/coronal MPR and 3D MIPs are important to increase accurate diagnosis and perceived reader diagnostic certainty

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction

Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones

Background and aim: Machine learning methods are examined by many researchers to identify weeds in crop images captured by drones. However, metaheuristic optimization is rarely used in optimizing the machine learning models used in weed classification. Therefore, this research targets developing a new optimization algorithm that can be used to optimize machine learning models and ensemble models to boost the classification accuracy of weed images. Methodology: This work proposes a new approach for classifying weed and wheat images captured by a sprayer drone. The proposed approach is based on a voting classifier that consists of three base models, namely, neural networks (NNs), support vector machines (SVMs), and K-nearest neighbors (KNN). This voting classifier is optimized using a new optimization algorithm composed of a hybrid of sine cosine and grey wolf optimizers. The features used in training the voting classifier are extracted based on AlexNet through transfer learning. The significant features are selected from the extracted features using a new feature selection algorithm. Results: The accuracy, precision, recall, false positive rate, and kappa coefficient were employed to assess the performance of the proposed voting classifier. In addition, a statistical analysis is performed using the one-way analysis of variance (ANOVA), and Wilcoxon signed-rank tests to measure the stability and significance of the proposed approach. On the other hand, a sensitivity analysis is performed to study the behavior of the parameters of the proposed approach in achieving the recorded results. Experimental results confirmed the effectiveness and superiority of the proposed approach when compared to the other competing optimization methods. The achieved detection accuracy using the proposed optimized voting classifier is 97.70%, F-score is 98.60%, specificity is 95.20%, and sensitivity is 98.40%. Conclusion: The proposed approach is confirmed to achieve better classification accuracy and outperforms other competing approaches

Endovascular versus medical therapy in posterior cerebral artery stroke: role of baseline NIHSS and occlusion site.

Background: Acute ischemic stroke (AIS) with isolated posterior cerebral artery occlusion (iPCAO) lacks management evidence from randomized trials. We aimed to evaluate whether the association between endovascular treatment (EVT) and outcomes in iPCAO-AIS is modified by initial stroke severity (baseline NIHSS) and arterial occlusion site. Methods: Based on the multicenter, retrospective, case-control study of consecutive iPCAO-AIS patients (PLATO study), we assessed the heterogeneity of EVT outcomes compared to medical management (MM) for iPCAO, according to baseline NIHSS (≤6 vs. >6) and occlusion site (P1 vs. P2), using multivariable regression modelling with interaction terms. The primary outcome was the favorable shift of 3-month mRS. Secondary outcomes included excellent outcome (mRS 0-1), functional independence (mRS 0-2), symptomatic intracranial hemorrhage (sICH) and mortality. Results: From 1344 patients assessed for eligibility, 1,059 were included (median age 74 years, 43.7% women, 41.3% had intravenous thrombolysis), 364 receiving EVT and 695 MM. Baseline stroke severity did not modify the association of EVT with 3-month mRS distribution (pint=0.312), but did with functional independence (pint=0.010), with a similar trend on excellent outcome (pint=0.069). EVT was associated with more favorable outcomes than MM in patients with baseline NIHSS>6 (mRS 0-1: 30.6% vs. 17.7%, aOR=2.01, 95%CI=1.22-3.31; mRS 0-2: 46.1% vs. 31.9%, aOR=1.64, 95%CI=1.08-2.51), but not in those with NIHSS≤6 (mRS 0-1: 43.8% vs. 46.3%, aOR=0.90, 95%CI=0.49-1.64; mRS 0-2: 65.3% vs. 74.3%, aOR=0.55, 95%CI=0.30-1.0). EVT was associated with more sICH regardless of baseline NIHSS (pint=0.467), while the mortality increase was more pronounced in patients with NIHSS≤6 (pint=0.044, NIHSS≤6: aOR=7.95,95%CI=3.11-20.28, NIHSS>6: aOR=1.98,95%CI=1.08-3.65). Arterial occlusion site did not modify the association of EVT with outcomes compared to MM. Conclusion: Baseline clinical stroke severity, rather than the occlusion site, may be an important modifier of the association between EVT and outcomes in iPCAO. Only severely affected patients with iPCAO (NIHSS>6) had more favorable disability outcomes with EVT than MM, despite increased mortality and sICH