Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts

Developing a simulated intelligent instrument to measure user behavior toward cybersecurity policies

Institutions struggle to protect themselves from threats and cybercrime. Therefore, they devote much attention to improving information security infrastructures. Users’ behaviors were explored via a traditional questionnaire research instrument in a data collocate process. The questionnaire explores users’ behaviors theoretically, so the respondents’ answers to the questionnaire are insufficiently reliable, and the responses might not reflect actual behavior based on the human bias when facing theoretical problems. This study aims to solve unreliable responses to the questionnaire by developing a simulated intelligent instrument to measure users’ behaviors toward cybersecurity policies in an experimental study using gamification

Enhancing three variants of harmony search algorithm for continuous optimization problems

Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms

Optimal deep learning driven intrusion detection in SDN-Enabled IoT environment

In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed to secure the SDN-enabled IoT environment in this scenario. The current study devises a Harmony Search algorithm-based Feature Selection with Optimal Convolutional Autoencoder (HSAFS-OCAE) for intrusion detection in the SDN-enabled IoT environment. The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS (HSAFS) technique is exploited at first for feature selection. Next, the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment. Finally, the Artificial Fish Swarm Algorithm (AFSA) is used to fine-tune the hyperparameters. This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty. The proposed HSAFS-OCAE technique was experimentally validated under different aspects, and the comparative analysis results established the supremacy of the proposed model

Survival implications vs. complications: unraveling the impact of vitamin D adjunctive use in critically ill patients with COVID-19—A multicenter cohort study

BackgroundDespite insufficient evidence, vitamin D has been used as adjunctive therapy in critically ill patients with COVID-19. This study evaluates the effectiveness and safety of vitamin D as an adjunctive therapy in critically ill COVID-19 patients.MethodsA multicenter retrospective cohort study that included all adult COVID-19 patients admitted to the intensive care units (ICUs) between March 2020 and July 2021. Patients were categorized into two groups based on their vitamin D use throughout their ICU stay (control vs. vitamin D). The primary endpoint was in-hospital mortality. Secondary outcomes were the length of stay (LOS), mechanical ventilation (MV) duration, and ICU-acquired complications. Propensity score (PS) matching (1:1) was used based on the predefined criteria. Multivariable logistic, Cox proportional hazards, and negative binomial regression analyses were employed as appropriate.ResultsA total of 1,435 patients were included in the study. Vitamin D was initiated in 177 patients (12.3%), whereas 1,258 patients did not receive it. A total of 288 patients were matched (1:1) using PS. The in-hospital mortality showed no difference between patients who received vitamin D and the control group (HR 1.22, 95% CI 0.87–1.71; p = 0.26). However, MV duration and ICU LOS were longer in the vitamin D group (beta coefficient 0.24 (95% CI 0.00–0.47), p = 0.05 and beta coefficient 0.16 (95% CI −0.01 to 0.33), p = 0.07, respectively). As an exploratory outcome, patients who received vitamin D were more likely to develop major bleeding than those who did not [OR 3.48 (95% CI 1.10, 10.94), p = 0.03].ConclusionThe use of vitamin D as adjunctive therapy in COVID-19 critically ill patients was not associated with survival benefits but was linked with longer MV duration, ICU LOS, and higher odds of major bleeding

Enhancement of antifungal activity and transdermal delivery of 5-flucytosine via tailored spanlastic nanovesicles: statistical optimization, in-vitro characterization, and in-vivo biodistribution study

Aim and background: This current study aimed to load 5-flucytosine (5-FCY) into spanlastic nanovesicles (SPLNs) to make the drug more efficient as an antifungal and also to load the 5-FCY into a hydrogel that would allow for enhanced transdermal permeation and improved patient compliance.Methods: The preparation of 5-FCY-SPLNs was optimized by using a central composite design that considered Span 60 (X1) and the edge activator Tween 80 (X2) as process variables in achieving the desired particle size and entrapment efficiency. A formulation containing 295.79 mg of Span 60 and 120.00 mg of Tween 80 was found to meet the prerequisites of the desirability method. The optimized 5-FCY-SPLN formulation was further formulated into a spanlastics gel (SPG) so that the 5-FCY-SPLNs could be delivered topically and characterized in terms of various parameters.Results: As required, the SPG had the desired elasticity, which can be credited to the physical characteristics of SPLNs. An ex-vivo permeation study showed that the greatest amount of 5-FCY penetrated per unit area (Q) (mg/cm2) over time and the average flux (J) (mg/cm2/h) was at the end of 24 h. Drug release studies showed that the drug continued to be released until the end of 24 h and that the pattern was correlated with an ex-vivo permeation and distribution study. The biodistribution study showed that the 99mTc-labeled SFG that permeated the skin had a steadier release pattern, a longer duration of circulation with pulsatile behavior in the blood, and higher levels in the bloodstream than the oral 99mTc-SPNLs. Therefore, a 5-FCY transdermal hydrogel could possibly be a long-acting formula for maintenance treatment that could be given in smaller doses and less often than the oral formula

Evaluation of inhaled nitric oxide (iNO) treatment for moderate-to-severe ARDS in critically ill patients with COVID-19: A multicenter cohort study

Background: Inhaled nitric oxide (iNO) is used as rescue therapy in patients with refractory hypoxemia due to severe COVID-19 acute respiratory distress syndrome (ARDS) despite the recommendation against the use of this treatment. To date, the effect of iNO on the clinical outcomes of critically ill COVID-19 patients with moderate-to-severe ARDS remains arguable. Therefore, this study aimed to evaluate the use of iNO in critically ill COVID-19 patients with moderate-to-severe ARDS. Methods: This multicenter, retrospective cohort study included critically ill adult patients with confirmed COVID-19 treated from March 01, 2020, until July 31, 2021. Eligible patients with moderate-to-severe ARDS were subsequently categorized into two groups based on inhaled nitric oxide (iNO) use throughout their ICU stay. The primary endpoint was the improvement in oxygenation parameters 24 h after iNO use. Other outcomes were considered secondary. Propensity score matching (1:2) was used based on the predefined criteria. Results: A total of 1598 patients were screened, and 815 were included based on the eligibility criteria. Among them, 210 patients were matched based on predefined criteria. Oxygenation parameters (PaO2, FiO2 requirement, P/F ratio, oxygenation index) were significantly improved 24 h after iNO administration within a median of six days of ICU admission. However, the risk of 30-day and in-hospital mortality were found to be similar between the two groups (HR: 1.18; 95% CI: 0.77, 1.82; p = 0.45 and HR: 1.40; 95% CI: 0.94, 2.11; p= 0.10, respectively). On the other hand, ventilator-free days (VFDs) were significantly fewer, and ICU and hospital LOS were significantly longer in the iNO group. In addition, patients who received iNO had higher odds of acute kidney injury (AKI) (OR (95% CI): 2.35 (1.30, 4.26), p value = 0.005) and hospital/ventilator-acquired pneumonia (OR (95% CI): 3.2 (1.76, 5.83), p value = 0.001). Conclusion: In critically ill COVID-19 patients with moderate-to-severe ARDS, iNO rescue therapy is associated with improved oxygenation parameters but no mortality benefits. Moreover, iNO use is associated with higher odds of AKI, pneumonia, longer LOS, and fewer VFDs

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

Authorisation management in business process environments: An authorisation model and a policy model

This thesis provides two main contributions. The first one is BP-TRBAC, a unified authorisation model that can support legacy systems as well as business process systems. BP-TRBAC supports specific features that are required by business process environments. BP-TRBAC is designed to be used as an independent enterprise-wide authorisation model, rather than having it as part of the workflow system. It is designed to be the main authorisation model for an organisation. The second contribution is BP-XACML, an authorisation policy language that is designed to represent BPM authorisation policies for business processes. The contribution also includes a policy model for BP-XACML. Using BP-TRBAC as an authorisation model together with BP-XACML as an authorisation policy language will allow an organisation to manage and control authorisation requests from workflow systems and other legacy systems