Automated Models for the Classification of Magnetic Resonance Brain Tumour Images

Brain tumours are the second largest cause of cancer death in children under 15 and young adults until age 34. Also, among people over 65, these tumours are the second-fastest growing cause of cancer death. Computer-assisted tumour diagnosis is challenging, and efforts to increase the accuracy of tumour classification and generalisation are continually being made despite the plethora of studies conducted. This study of automated multi-class brain tumour classification utilising Magnetic Resonance Images aims to design and develop three automatic brain tumour classification approaches to categorise the brain tumours as glioma, meningioma, and pituitary tumours, which assist clinicians in making brain tumour diagnoses and developing further treatment plans to save patient’s life. This research proposes a transfer learning approach using ResNet 50, handcrafted features with machine learning classifiers, and a hybrid firefly-optimised multi-class classifier for tumour classification. The hybrid methodology yields the highest classification accuracy of 99% using the Figshare dataset. Furthermore, using the Figshare dataset, the hybrid technique yields the highest sensitivity (recall) of 99% for meningioma and pituitary tumours, the highest precision of 100% for pituitary tumours, and the highest F1- measure of 99% for pituitary tumours

Identification and Characterization of Genetic Determinants of Isoniazid and Rifampicin Resistance in Mycobacterium tuberculosis in Southern India

Abstract: Drug-resistant tuberculosis (TB), one of the leading causes of death worldwide, arises mainly from spontaneous mutations in the genome of Mycobacterium tuberculosis. There is an urgent need to understand the mechanisms by which the mutations confer resistance in order to identify new drug targets and to design new drugs. Previous studies have reported numerous mutations that confer resistance to anti-TB drugs, but there has been little systematic analysis to understand their genetic background and the potential impacts on the drug target stability and/or interactions. Here, we report the analysis of whole-genome sequence data for 98 clinical M. tuberculosis isolates from a city in southern India. The collection was screened for phenotypic resistance and sequenced to mine the genetic mutations conferring resistance to isoniazid and rifampicin. The most frequent mutation among isoniazid and rifampicin isolates was S315T in katG and S450L in rpoB respectively. The impacts of mutations on protein stability, protein-protein interactions and protein-ligand interactions were analysed using both statistical and machine-learning approaches. Drug-resistant mutations were predicted not only to target active sites in an orthosteric manner, but also to act through allosteric mechanisms arising from distant sites, sometimes at the protein-protein interface

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

A novel fast hybrid face recognition approach using convolutional Kernel extreme learning machine with HOG feature extractor

The ability to identify a person's face from a digitized photo or video frame against a database of faces is known as facial recognition. In the past few years, algorithms that use deep learning to recognize faces have become more popular. The majority of them are predicated on extremely accurate but complicated Convolutional Neural Networks (CNNs), which require a lot of computational power, storage space, and a number of training epochs before they provide satisfying results, and are notably difficult to implement. In an effort to reduce the training time by reducing the number of epochs and increase accuracy, this paper introduces a novel fast hybrid face recognition approach HOG-CKELM, based on CNN that makes use of Kernel based Extreme Learning Machines (KELM) and Histogram of Oriented Gradients (HOG) as facial feature extractor. The effectiveness of the proposed hybrid face recognition technique is evaluated using AT &amp; T, Yale, and JAFFE datasets. When compared to traditional HOG-CNN based techniques, the experimental evaluation indicates that the proposed method for face recognition is capable of achieving excellent performance in terms of accuracy and training time

Re-construction of original face images from plastic surgery images using harmony search algorithm and IRBFN model

Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images

Immunogenicity of recombinant fragments of Plasmodium falciparum acidic basic repeat antigen produced in Escherichia coli

The acidic basic repeat antigen (ABRA) of Plasmodium falciparum is a potential vaccine candidate against erythrocytic stages of malaria. We report, for the first time, the immunological characteristics of recombinant ABRA constructs. The recombinant proteins representing different fragments of ABRA were expressed in Escherichia coli, either as fusions with maltose binding protein or as 6X histidine tagged molecules, and purified by affinity chromatography. Immunogenicity studies with these constructs in rabbits and mice indicated that the N-terminal region is the least immunogenic part of ABRA. T-cell proliferation experiments in mice immunized with these constructs revealed that the T-cell epitopes were localized in the middle portion of the protein. More importantly, the purified immunoglobulin G specific to middle and C-terminal fragments prevented parasite growth at levels approaching 80-90%. We found that these proteins were also recognized by sera from P. falciparum-infected patients from Rourkela, a malaria endemic zone of India. Our immunogenicity results suggest that potential of ABRA as a vaccine candidate antigen should be investigated further

Impact of protein supplementation and care and support on body composition and CD4 count among HIV-infected women living in rural India: results from a randomized pilot clinical trial.

Body composition in HIV-infected individuals is subject to many influences. We conducted a pilot 6-month randomized trial of 68 women living with AIDS (WLA) from rural India. High protein intervention combined with education and supportive care delivered by HIV-trained village women (activated social health activist [Asha] life [AL]) was compared to standard protein with usual care delivered by village community assistants (usual care [UC]). Measurements included CD4 counts, ART adherence, socio-demographics, disease characteristics (questionnaires); and anthropometry (bioimpedance analyzer). Repeated measures analysis of variance modeled associations. AL significantly gained in BMI, muscle mass, fat mass, ART adherence, and CD4 counts compared to UC, with higher weight and muscle mass gains among ART adherent (≥66%) participants who had healthier immunity (CD4 ≥450). BMI of WLA improved through high protein supplementation combined with education and supportive care. Future research is needed to determine which intervention aspect was most responsible

Impact of protein supplementation and care and support on body composition and CD4 count among HIV-infected women living in rural India: results from a randomized pilot clinical trial.

Body composition in HIV-infected individuals is subject to many influences. We conducted a pilot 6-month randomized trial of 68 women living with AIDS (WLA) from rural India. High protein intervention combined with education and supportive care delivered by HIV-trained village women (activated social health activist [Asha] life [AL]) was compared to standard protein with usual care delivered by village community assistants (usual care [UC]). Measurements included CD4 counts, ART adherence, socio-demographics, disease characteristics (questionnaires); and anthropometry (bioimpedance analyzer). Repeated measures analysis of variance modeled associations. AL significantly gained in BMI, muscle mass, fat mass, ART adherence, and CD4 counts compared to UC, with higher weight and muscle mass gains among ART adherent (≥66%) participants who had healthier immunity (CD4 ≥450). BMI of WLA improved through high protein supplementation combined with education and supportive care. Future research is needed to determine which intervention aspect was most responsible

Household Food Insecurity as Mediator of the Association Between Internalized Stigma and Opportunistic Infections.

Internalized HIV stigma can affect health outcomes, but the mechanism underlying this relationship is poorly understood. We investigated the potential pathways for the association between internalized stigma and opportunistic infections (OIs) among women living with HIV in rural India. We conducted a cross-sectional study involving in-person interviews with 600 participants. We modeled two outcome variables, total number of OIs and fungal dermatoses, which was the most frequently reported OI. Causal mediation analysis was performed to estimate the total effect, direct effect, and indirect effect through mediators while controlling for confounders. Food insecurity was a strong mediator of the association between internalized stigma and the number of OIs (70% of the total effect) and fungal dermatoses (83% of the total effect), while the indirect effect of stigma through adherence was minimal for both outcomes. Household food insecurity may be an important mediator of the impact of HIV-related stigma on opportunistic infections