Advanced deep learning approaches for biosingnals applications

University of Technology Sydney. Faculty of Engineering and Information Technology.A wide gap exists between clinical application results and those from laboratory observations concerning hand rehabilitation devices. In most instances, laboratory observations show superior outcomes the real-time applications demonstrate poor consequences. The robust nature of the electromyography signal and limited laboratory applications are the principal reasons for the gap. This thesis aims to introduce and develop a deep learning model that is capable of learning features from biosignals. The deep learning model is expected to tame the variable nature of the electromyography signal which will lead to the best available outcomes. Furthermore, the suggested deep learning scheme will be trained to be skilled in learning the best features that match the biosignal application regardless of the number of classes. Moreover, traditional feature extraction is time consuming and extremely reliant on the user’s experience and the application. The objective of this research is accomplished via the following four implemented models. 1. Developing a deep learning model via implementing a two-stage autoencoder along with applying different signal representations like spectrogram, wavelet and wavelet packet to tame variations of the electromyography signal. Support vector machine, extreme learning machine with two activation functions (sigmoid and radial basis function) and softmax layer were used for classifications. Moreover, the classifier fusion layer achieved testing accuracy of more than 92% and training attained more than 98%. The same dataset was implemented for superimposed signal representations for two stages autoencoder and softmax layer, support vector machine, k-nearest neighbor and discriminant analysis for classification besides the classifier fusion which led to testing accuracy of more than 90%. 2. Presenting principal component analysis and independent component analysis for feature learning purposes after applying different signal representations algorithms such as spectrogram, wavelet and wavelet packet. Discriminant analysis, extreme learning machine and support vector machine were used for classification. Furthermore, the two proposed models showed acceptable accuracy along with shorter simulation time. The testing accuracy achieved more than 90% by implementing a classifier fusion layer. Manhattan index was estimated for all features and only the top 50 Manhattan index features were included to decrease the simulation time while attaining acceptable accuracy values. 3. Introducing a self-organising map for deep learning whereby the biosignal was represented by spectrograms, wavelet and wavelet packet. The presented biosignal was introduced to a layer of self- organising map then the suggested system performance was evaluated by extreme learning machine, self-adaptive evolutionally extreme learning machine, discriminant analysis and support vector machine for classification. Adding a classifier fusion layer increased the testing accuracy to 96.60% for ten-finger movements and 99.73% for training. The proposed system showed superior behavior regarding accuracy and simulation time. 4. Presenting a deep learning model where 1) the data was augmented after representing the biosignal by a spectrogram, 2) the augmented signal was represented by a tensor, and finally 3) The signal was introduced to the two-stage autoencoder. The same dataset was used with traditional pattern recognition for comparison purposes. Classifier fusion layer was executed in deep learning scheme whereby the ten-finger movements achieved 90.25% and 87.11% attained by pattern recognition. Besides, the six finger movement dataset was acquired from amputee participants and accomplished 91.85% for deep learning and reached 89.64% for traditional pattern recognition. Furthermore, different datasets for different applications were tested using the recommended deep learning model. Eventually, feeding the deep learning model with various datasets for different applications afforded the model with higher fidelity, combined with real outcomes and generalization

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

Liver fatty acid binding protein: A potential urinary and tissue biomarker for lupus nephritis

Aim of the work: To assess urinary liver fatty acid binding protein (uL-FABP) levels and tissue expression (tL-FABP) in renal biopsies of active and inactive lupus nephritis (LN) patients and examine their relationship with disease characteristics. Patients and methods: uL-FABP levels and tL-FABP expression were assessed in 75 systemic lupus erythematosus (SLE) patients; 25 active LN, 25 inactive LN and 25 SLE without LN as well as 10 matched healthy control. Results: Mean age was 33.9 ± 6.7 years, disease duration 4.6 ± 2.4 years and were 66 females and 9 males. Patients with active LN had higher uL-FABP higher than patients with inactive LN and without LN. uL-FABP in patients with active and inactive LN significantly correlated with renal SLEDAI (r = 0.96, r = 0.92 respectively and p < 0.0001) and 24-h urinary protein (r = 0.97, r = 0.68 respectively and p < 0.0001) but negatively correlated with the estimated Glomerular Filtration Rate (r = −0.97, r = −0.84 respectively and p < 0.0001). uL-FABP significantly correlated with grade of renal biopsy in active and inactive LN (F = 155.6 and 40.7 respectively, p < 0.0001). L-FABP was highly expressed in renal tissue of LN patients; the tubules seemed to be the main location for tL-FABP staining. The uL-FABP levels significantly correlated with the chronicity index score of renal pathology (F = 17.6, p < 0.0001) and the expression of tL-FABP in active and inactive LN (F = 21.4 and 42.2 respectively, p < 0.0001). Conclusion: Urinary and tissue L-FABP levels were associated with active renal disease. Urinary levels of L-FABP might be a potential non invasive marker for the presence of renal involvement in patients with SLE alternative to renal biopsy

Clinical characteristics of human fascioliasis in Egypt

Abstract There is a lack of epidemiological data on fascioliasis in Egypt regarding disease characteristics and treatment outcomes across different governorates. We aimed to identify the demographic, epidemiologic, clinical, laboratory, and radiological characteristics and treatment outcomes of patients diagnosed with fascioliasis in Egypt. Data on human fascioliasis were collected retrospectively from patients’ medical records in the period between January 2018 and January 2020. The study included 261 patients. More than 40% of enrolled patients were in the age group of 21–40 years old. Geographically, 247 (94.6%) were from Assiut Governorate with 69.3% were from rural areas. The most frequent symptoms were right upper quadrant pain (96.9%), and fever (80.1%). Eosinophilia was found in 250 cases (95.8%). Hepatic focal lesions were detected in 131 (50.2%); out of them 64/131 (48.9%) had a single lesion. All patients received a single dose of 10 mg/kg of triclabendazole, 79.7% responded well to a single dose, while in 20.3% a second ± a third dose of treatment was requested. After therapy, there was a reduction in leucocytes, Fasciola antibodies titer, eosinophilic count, bilirubin, and liver enzymes with an increase in hemoglobin level. According to our findings, a high index of suspicion should be raised in cases with fever, right upper abdominal pain, and peripheral eosinophilia, and further imaging workup is mandated to detect hepatic focal lesions. Prompt treatment by triclabendazole can serve as a standard-of-care regimen even for suspected cases