Application of green synthesized silver nanoparticles in Burn therapy: a review.

Silver nanoparticles (AgNPs), owing to their unusual characteristics, have been used in various pharmaceutical, cosmetic, and healthcare products. AgNPs, with their exceptional biological potential, exhibit antibacterial, antifungal, antiviral, anti-inflammatory, anticancer, and wound healing properties and have been extensively used in burn therapy. Several studies have established the use of silver nanoparticles in the treatment of burn injuries, resulting in reduced inflammation, quick tissue regeneration, and the remarkable creation of collagen fibers. Conventional physical and chemical techniques have synthesized AgNPs, but they appear to be highly costly and hazardous. Recently, there has been considerable interest in the synthesis of AgNPs using the green chemistry approach because of its tremendous benefits, including being non-toxic, low energy consumption, pollution-free, economical, environmentally friendly, and more sustainable. This review emphasizes the green synthesis of AgNPs using bacteria, fungi, plants, and other microorganisms and the current research related to the application of green synthesized AgNPs in burn therapy, including the biological aspects of AgNPs, their mode of action, and any possible detrimental effects.

Genetic Diversity Analysis of Banana Cultivars (Musa sp.) in Saudi Arabia Based on AFLP Marker.

Banana plantation has been introduced recently to a temperate zone in the southeastern parts of Saudi Arabia (Fifa, Dhamadh, and Beesh, located in Jazan province). The introduced banana cultivars were of a clear origin without a recorded genetic background. In the current study, the genetic variability and structure of five common banana cultivars (i.e., Red, America, Indian, French, and Baladi) were analyzed using the fluorescently labeled AFLP technique. Nine different primer pairs combinations yielded 1468 loci with 88.96% polymorphism. Among all locations, high expected heterozygosity under the Hardy-Weinberg assumption was found (0.249 ± 0.003), where Dhamadh was the highest, followed by Fifa and Beesh, respectively. Based on the PCoA and Structure analysis, the samples were not clustered by location but in pairs in accordance with the cultivar's names. However, the Red banana cultivar was found to be a hybrid between the American and Indian cultivars. Based on ΦST, 162 molecular markers (i.e., loci under selection) were detected among cultivars. Identifying those loci using NGS techniques can reveal the genetic bases and molecular mechanisms involved in the domestication and selection indicators among banana cultivars.

Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice.

INTRODUCTION: The unbound brain extracelullar fluid (brainECF) to plasma steady state partition coefficient, Kp,uu,BBB, values provide steady-state information on the extent of blood-brain barrier (BBB) transport equilibration, but not on pharmacokinetic (PK) profiles seen by the brain targets. Mouse models are frequently used to study brain PK, but this information cannot directly be used to inform on human brain PK, given the different CNS physiology of mouse and human. Physiologically based PK (PBPK) models are useful to translate PK information across species. AIM: Use the LeiCNS-PK3.0 PBPK model, to predict brain extracellular fluid PK in mice. METHODS: Information on mouse brain physiology was collected from literature. All available connected data on unbound plasma, brainECF PK of 10 drugs (cyclophosphamide, quinidine, erlotonib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) from different mouse strains were used. Dosing regimen dependent plasma PK was modelled, and Kpuu,BBB values were estimated, and provided as input into the LeiCNS-PK3.0 model to result in prediction of PK profiles in brainECF. RESULTS: Overall, the model gave an adequate prediction of the brainECF PK profile for 7 out of the 10 drugs. For 7 drugs, the predicted versus observed brainECF data was within two-fold error limit and the other 2 drugs were within five-fold error limit. CONCLUSION: The current version of the mouse LeiCNS-PK3.0 model seems to reasonably predict available information on brainECF from healthy mice for most drugs. This brings the translation between mouse and human brain PK one step further.

Discovery of benzochromene derivatives first example with dual cytotoxic activity against the resistant cancer cell MCF-7/ADR and inhibitory effect of the P-glycoprotein expression levels.

A series of 1H-benzo[f]chromene moieties (4a-z) were synthesised under Ultrasonic irradiation and confirmed with spectral analyses. Derivative 4i solely possessed an X-ray single crystal. The anti-proliferative efficacy of the desired molecules has been explored against three cancer cells: MCF-7, HCT-116, and HepG-2 with the cytotoxically active derivatives screened against MCF-7/ADR and normal cells HFL-1 and WI-38. Furthermore, compounds 4b-d, 4k, 4n, 4q, and 4w, which possessed good potency against MCF-7/ADR, were tested as permeability glycoprotein (P-glycoprotein [P-gp]) expression inhibitors. The attained data confirmed that 4b-d, 4q, and 4w exhibited strong expression inhibition against the P-gp alongside its cytotoxic effect on MCF-7/ADR. The western blot results and Rho123 accumulation assays showed that compounds 4b-d, 4q, and 4w effectively inhibited the P-gp expression and efflux function. Meanwhile, 4b-d, 4q, and 4w induced apoptosis and accumulation of the treated MCF-7/ADR cells in the G1 phase and 4k and 4n in the S phase of the cell cycle.

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks.

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

DIETARY LIFESTYLE, HYPOALBUMINAEMIA AND ANAEMIA AMONG OLDER ADULTS ATTENDING A PRIMARY CARE CLINIC IN NIGERIA: A CROSS-SECTIONAL STUDY.

Introduction: Anaemia has been extensively studied in children and women of reproductive age. However, there are few data on anaemia and related conditions in the elderly from developing countries. Objectives: To determine the prevalence and severity pattern of anaemia, and assess the relationship between dietary lifestyle, hypoalbuminaemia, and anaemia in older persons. Methodology: A cross-sectional study involving 378 patients aged≥60 years who presented at the General Outpatient Clinic. Dietary lifestyle was assessed using a structured questionnaire. Samples were collected for packed cell volume and serum albumin. Inferential statistical analyses were used to determine the associations between variables. Results: A total of 348 respondents completed the study. The mean age of respondents was 67.83 ±7.53 years with female (60.9%) predominance. The prevalence of anaemia and hypoalbuminaemia were 42.2% and 17.8% respectively. Hypoalbuminaemia (ß=0.335, 95%CI=0.131-0.229, P<0.001), long duration of co-morbidities (ß= - 0.179, 95%CI= -0.165-0.047, P<0.001), one full meal/day (ß=0.130, 95%CI=0.224-1.879, P=0.013), and low monthly income (ß=0.122, 95%CI=0.179-1.543, P=0.026) were the predictors of anaemia among the elderly in this study. Conclusion: Data on the predictors of anaemia from this study will be useful in developing guidelines and strategies for managing the condition in primary care settings and other similar sites.

The Extension of the LeiCNS-PK3.0 Model in Combination with the "Handshake" Approach to Understand Brain Tumor Pathophysiology.

Micrometastatic brain tumor cells, which cause recurrence of malignant brain tumors, are often protected by the intact blood-brain barrier (BBB). Therefore, it is essential to deliver effective drugs across not only the disrupted blood-tumor barrier (BTB) but also the intact BBB to effectively treat malignant brain tumors. Our aim is to predict pharmacokinetic (PK) profiles in brain tumor regions with the disrupted BTB and the intact BBB to support the successful drug development for malignant brain tumors. LeiCNS-PK3.0, a comprehensive central nervous system (CNS) physiologically based pharmacokinetic (PBPK) model, was extended to incorporate brain tumor compartments. Most pathophysiological parameters of brain tumors were obtained from literature and two missing parameters of the BTB, paracellular pore size and expression level of active transporters, were estimated by fitting existing data, like a "handshake". Simultaneous predictions were made for PK profiles in extracellular fluids (ECF) of brain tumors and normal-appearing brain and validated on existing data for six small molecule anticancer drugs. The LeiCNS-tumor model predicted ECF PK profiles in brain tumor as well as normal-appearing brain in rat brain tumor models and high-grade glioma patients within twofold error for most data points, in combination with estimated paracellular pore size of the BTB and active efflux clearance at the BTB. Our model demonstrated a potential to predict PK profiles of small molecule drugs in brain tumors, for which quantitative information on pathophysiological alterations is available, and contribute to the efficient and successful drug development for malignant brain tumors.

Drug Distribution in Brain and Cerebrospinal Fluids in Relation to IC50 Values in Aging and Alzheimer's Disease, Using the Physiologically Based LeiCNS-PK3.0 Model.

BACKGROUND: Very little knowledge exists on the impact of Alzheimer's disease on the CNS target site pharmacokinetics (PK). AIM: To predict the CNS PK of cognitively healthy young and elderly and of Alzheimer's patients using the physiologically based LeiCNS-PK3.0 model. METHODS: LeiCNS-PK3.0 was used to predict the PK profiles in brain extracellular (brainECF) and intracellular (brainICF) fluids and cerebrospinal fluid of the subarachnoid space (CSFSAS) of donepezil, galantamine, memantine, rivastigmine, and semagacestat in young, elderly, and Alzheimer's patients. The physiological parameters of LeiCNS-PK3.0 were adapted for aging and Alzheimer's based on an extensive literature search. The CNS PK profiles at plateau for clinical dose regimens were related to in vitro IC50 values of acetylcholinesterase, butyrylcholinesterase, N-methyl-D-aspartate, or gamma-secretase. RESULTS: The PK profiles of all drugs differed between the CNS compartments regarding plateau levels and fluctuation. BrainECF, brainICF and CSFSAS PK profile relationships were different between the drugs. Aging and Alzheimer's had little to no impact on CNS PK. Rivastigmine acetylcholinesterase IC50 values were not reached. Semagacestat brain PK plateau levels were below the IC50 of gamma-secretase for half of the interdose interval, unlike CSFSAS PK profiles that were consistently above IC50. CONCLUSION: This study provides insights into the relations between CNS compartments PK profiles, including target sites. CSFSAS PK appears to be an unreliable predictor of brain PK. Also, despite extensive changes in blood-brain barrier and brain properties in Alzheimer's, this study shows that the impact of aging and Alzheimer's pathology on CNS distribution of the five drugs is insignificant.

Tunisian camel casein gene characterization reveals similarities and differences with Sudanese and Nigerian populations.

Milk is a primary protein source that has always played a role in mammalian health. Despite the intensification of research projects on dromedary and the knowledge of the genetic diversity at the casein loci, the genetic structure of the Tunisian camel population still needs exploration. This study sought to determine the genetic diversity of 3 casein gene variants in 5 Tunisian camel ecotypes: c.150G>T at CSN1S1 (αS1-casein), g.2126A>G at CSN2 (ß-casein), and g.1029T>C at CSN3 (κ-casein). The obtained results were compared with data published on Sudanese and Nigerian camels to establish the level of differentiation within and between populations. A total of 159 blood samples were collected from 5 Tunisian camel ecotypes and the extracted DNA was genotyped by PCR-RFLP. A streamlined genotyping protocol was also developed for CSN3. Results indicated that allele T was quite rare (0.06) at CSN1S1 for all ecotypes. Minor allele frequency was found for G (0.462) in CSN2 except for Ardhaoui Medenine ecotype who deviated from the average CSN2 allele frequency of the total population. Allele C showed minor allele frequency of 0.384 in CSN3. Among the Tunisian population, GAT (0.343) was the most represented haplotype in all ecotypes except for Ardhaoui Medenine, where GGC (0.322) was the most frequent one. Significant differences in heterozygosity and local inbreeding were observed across the Tunisian, Sudanese, and Nigerian populations, although the global fixation index indicated that only 2.2% of the genetic variance is related to ecotype differences. Instead, phylogenetic analysis revealed a closer link between the Tunisian and Sudanese populations through a clade subdivision with 3 main branches among the ecotypes. This study represents the first attempt to understand casein gene variability in Tunisian camels; with further study, milk traits and genetic differentiation among populations can be associated with the history of camel domestication.

Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT.

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

Synthesis of 9-Hydroxy-1H-Benzo[f]chromene Derivatives with Effective Cytotoxic Activity on MCF7/ADR, P-Glycoprotein Inhibitors, Cell Cycle Arrest and Apoptosis Effects.

ß-Enaminonitriles bearing 9-hydroxy-1H-benzo[f]chromene moiety was synthesized. The targeted compounds were evaluated for their anti-proliferative activity against three human tumor cell lines, PC-3, SKOV-3 and HeLa, and the active cytotoxic compounds were further evaluated against cancer cells, MCF-7/ADR, and two normal cell lines, HFL-1 and WI-38. Few compounds were assigned to be the most potent derivatives against PC-3, SKOV-3 and HeLa cell lines in comparison with Vinblastine and Doxorubicin. Several compounds possessed a relatively good potency against MCF-7/ADR cells as compared with Doxorubicin and were tested as a P-gp inhibitor. Moreover, the halogenated substituents, 2,4-F2, 2,3-Cl2, 2,5-Cl2 and 3,4-Cl2; have good potency against P-gp-mediated MDR in MCF-7/ADR as compared with Doxorubicin. Meanwhile, Rho123 accumulation assays revealed that few compounds effectively inhibited P-pg and efflux function. In addition, certain derivatives induced apoptosis and an accumulation of the treated MCF-7/ADR cells in the G1, S and G1/S phases.

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting.

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

Genetic Variation of Taenia Saginata Cyst Isolates from Iraq Based on Mitochondrial COX1 Sequences.

The zoonotic parasite disease of economic and public health relevance is bovine cysticercosis, resulting from the larval stage of Taenia saginata. The presented research aims to identify intraspecific variation in T. saginata isolated from cattle in Iraq's Sulaymaniyah province using the mitochondrial cytochrome c oxidase subunit 1 (COX1) gene. Sequence analysis of the COX1 gene revealed that five distinct haplotypes were identified in 37 T. saginata specimens from Iraq. Four of the five T. saginata haplotypes may have been identified for the first time in the world. Phylogenetic research revealed that all T. saginata haplotypes had been clustered in a single clade, with Korean and Iranian isolates sharing a high degree of closeness. In addition, individual haplotypes related to COX1 had a pairwise evolutionary divergence of 0.005- 0.013, whereas the overall evolutionary divergence regarding all five haplotypes ranged between 0.000-0.018. It was concluded that added newly recorded data on T. saginata genetic variation could have substantial implications for taeniasis epidemiology and control.

The outcome of V vs. S shaped nasal deformity in preservation rhinoplasty; A comparative study.

BACKGROUND: Preservation rhinoplasty resulted in a major change in the philosophy of rhinoplasty surgery, it include 3 main elements: elevation of a sleeve of skin from the subperichondrial-subperiosteal plane, preservation of the osseo-cartilaginous dorsum, and maintaining the alar cartilages with minimum excision while achieving the desired shape using sutures. PATIENTS AND METHODS: This is prospective study included 113 patients who underwent dorsal preservation rhinoplasty for nasal hump treatment. Patients were followed for 15months after surgery. AIM OF STUDY: To compare the different outcomes between V-shaped and S-shaped dorsum regarding the cosmetic and functional outcomes after preservation rhinoplasty. RESULTS: The mean age of our patients was 27.19years, females constituted 61.1%. Most patients (77.9%) had no complications after surgery, residual hump was the commonest in 13.3% of the patients, other complications were dorsal indentation in 5.3%, bleeding in 2.7%, and granuloma at the dorsal osteotomy site in 0.9% of patients. About 75.22% had S-shaped deformity, and the rest 24.78% had V-shaped deformity. The correlation was not significant between the types of the nasal hump deformity and the gender (P-value 0.395), age of patients (P-value 0.064), the overall postoperative SCHNOS for obstructive symptoms (P-value 0.19), the overall postoperative SCHNOS for aesthetic outcome (P-value 0.33), the postoperative complications (P-value 0.531), and the type of complications (0.705), while it was very significant with different postoperative SCHNOS scores for obstructive symptoms (P-value 0.000), postoperative SCHNOS different aesthetic scores (P-value 0.001), operation time and lowering the dorsal hump (P-values 0.000) for both. CONCLUSION: Preservation rhinoplasty has low risk for postoperative complications. V-shaped deformity has a better overall outcome than S-shaped one.

Comparison of multimodal analgesia with thoracic epidural after transthoracic oesophagectomy.

BACKGROUND: Thoracic epidural analgesia (TEA) has been regarded as the standard of care after oesophagectomy for pain control, but has several side-effects. Multimodal (intrathecal diamorphine, paravertebral and rectus sheath catheters) analgesia (MA) may facilitate postoperative mobilization by reducing hypotensive episodes and the need for vasopressors, but uncertainty exists about whether it provides comparable analgesia. This study aimed to determine whether MA provides comparable analgesia to TEA following transthoracic oesophagectomy. METHODS: Consecutive patients undergoing oesophagectomy for cancer between January 2015 and December 2018 were grouped according to postoperative analgesia regimen. Propensity score matching (PSM) was used to account for treatment selection bias. Pain scores at rest and on movement, graded from 0 to 10, were used. The incidence of hypotensive episodes and the requirement for vasopressors were evaluated. RESULTS: The study included 293 patients; 142 (48.5 per cent) received TEA and 151 (51.5 per cent) MA. After PSM, 100 patients remained in each group. Mean pain scores were significantly higher at rest in the MA group (day 1: 1.5 versus 0.8 in the TEA group, P = 0.017; day 2: 1.7 versus 0.9 respectively, P = 0.014; day 3: 1.2 versus 0.6, P = 0.047). Fewer patients receiving MA had a hypotensive episode (25 per cent versus 45 per cent in the TEA group; P = 0.003) and fewer required vasopressors (36 versus 53 per cent respectively; P = 0.016). There was no significant difference in the overall complication rate (71.0 versus 61.0 per cent; P = 0.136). CONCLUSION: MA is less effective than TEA at controlling pain, but this difference may not be clinically significant. However, fewer patients experienced hypotension or required vasopressor support with MA; this may be beneficial within an enhanced recovery programme.

Improved ANFIS model for forecasting Wuhan City Air Quality and analysis COVID-19 lockdown impacts on air quality.

In this study, we propose an improved version of the adaptive neuro-fuzzy inference system (ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid optimization method to improve ANFIS performance, called PSOSMA, using a new modified meta-heuristics (MH) algorithm, Slime mould algorithm (SMA), which is improved by using the particle swarm optimizer (PSO). The proposed PSOSMA-ANFIS has been trained with air quality index time series data of three years and has been applied to forecast the fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), and nitrogen dioxide (NO2) for one year. We also compared the proposed PSOSMA to other MH algorithms used to train ANFIS. We found that the modified ANFIS using PSOSMA achieved better performance than compared algorithms. Moreover, we analyzed the impacts of the lockdown of Wuhan City on the concentrations of PM2.5, NO2, CO2, and SO2. We compared the correspondence period with previous years, and we concluded that there are significant decreases in the concentrations of PM2.5, CO2, SO2, and NO2.

Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: insights from LeiCNS-PK3.0 simulations.

Predicting brain pharmacokinetics is critical for central nervous system (CNS) drug development yet difficult due to ethical restrictions of human brain sampling. CNS pharmacokinetic (PK) profiles are often altered in CNS diseases due to disease-specific pathophysiology. We previously published a comprehensive CNS physiologically-based PK (PBPK) model that predicted the PK profiles of small drugs at brain and cerebrospinal fluid compartments. Here, we improved this model with brain non-specific binding and pH effect on drug ionization and passive transport. We refer to this improved model as Leiden CNS PBPK predictor V3.0 (LeiCNS-PK3.0). LeiCNS-PK3.0 predicted the unbound drug concentrations of brain ECF and CSF compartments in rats and humans with less than two-fold error. We then applied LeiCNS-PK3.0 to study the effect of altered cerebrospinal fluid (CSF) dynamics, CSF volume and flow, on brain extracellular fluid (ECF) pharmacokinetics. The effect of altered CSF dynamics was simulated using LeiCNS-PK3.0 for six drugs and the resulting drug exposure at brain ECF and lumbar CSF were compared. Simulation results showed that altered CSF dynamics changed the CSF PK profiles, but not the brain ECF profiles, irrespective of the drug's physicochemical properties. Our analysis supports the notion that lumbar CSF drug concentration is not an accurate surrogate of brain ECF, particularly in CNS diseases. Systems approaches account for multiple levels of CNS complexity and are better suited to predict brain PK.

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System.

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions.

Classification of COVID-19 X-ray images to determine the patient's health condition is a critical issue these days since X-ray images provide more information about the patient's lung status. To determine the COVID-19 case from other normal and abnormal cases, this work proposes an alternative method that extracted the informative features from X-ray images, leveraging on a new feature selection method to determine the relevant features. As such, an enhanced cuckoo search optimization algorithm (CS) is proposed using fractional-order calculus (FO) and four different heavy-tailed distributions in place of the Lévy flight to strengthen the algorithm performance during dealing with COVID-19 multi-class classification optimization task. The classification process includes three classes, called normal patients, COVID-19 infected patients, and pneumonia patients. The distributions used are Mittag-Leffler distribution, Cauchy distribution, Pareto distribution, and Weibull distribution. The proposed FO-CS variants have been validated with eighteen UCI data-sets as the first series of experiments. For the second series of experiments, two data-sets for COVID-19 X-ray images are considered. The proposed approach results have been compared with well-regarded optimization algorithms. The outcomes assess the superiority of the proposed approach for providing accurate results for UCI and COVID-19 data-sets with remarkable improvements in the convergence curves, especially with applying Weibull distribution instead of Lévy flight.

Missed Elevated Blood Pressure in Triaged Outpatients: A Preliminary Cross-Sectional Multicentre Study in Northern Nigeria.

BACKGROUND: Hypertension is a common medical condition but largely undiagnosed, untreated, poorly controlled, and undertreated in low- and middle-income countries. Studies have reported missed hypertension during triage in Emergency Departments; however, little is known about missed elevated blood pressure (EBP) during triage in primary care settings. This study assessed the prevalence and predictors of missed EBP among triaged patients attending General Outpatient Clinics in Northern Nigeria. METHODS: This was a descriptive cross-sectional multi-centre study involving 187 adults randomly selected from patients triaged in four General Outpatient Clinics in northern Nigeria. An investigator-administered questionnaire was employed to obtain data regarding participants' socio-demographic, triage blood pressure (BP) measurement, physician-led BP measurement, and physicians' clinical decision characteristics. Chi-square test and logistic regression analysis were used to determine factors associated with and predictors of missed EBP, respectively. RESULTS: Participants' mean age was 40.6±14.7years. They were predominantly females (122, 65.2%). EBP was missed in 28 (15%) participants at the triage. Participants' age (>40years) (Odds ratio [OR]=3.82, 95% Confidence interval [CI]=1.43-10.22, P=0.008), study site (OR=0.32, 95% CI=0.12-0.82, P=0.018) and perceived receipt of explanation on BP measurement procedure (OR=2.97, 95% CI=1.25-7.07, P=0.014) were predictors of missed EBP among participants. CONCLUSION: The prevalence of missed EBP was low but remarkable because of its huge public health implications. Age, study site, perceived receipt of explanation on BP measurement procedure predicted missed EBP. Effective interventions are needed to curtail missed EBP in these and similar settings.

RÉSUMÉ: L'hypertension est une condition médicale courante mais en grande partie non diagnostiquée, non traitée, mal contrôlée et sous-traitée dans les pays à revenu faible et intermédiaire. Des études ont signalé une hypertension manquée lors du triage dans les services d'urgence; Cependant, on sait peu de choses sur l'hypertension artérielle (EBP) manquée lors du triage dans les établissements de soins primaires. Cette étude a évalué la prévalence et les prédicteurs de l'EBP manquée parmi les patients triés fréquentant les cliniques externes générales dans le nord du Nigeria. MÉTHODES: Il s'agissait d'une étude multicentrique transversale descriptive portant sur 187 adultes choisis au hasard parmi des patients triés dans quatre cliniques ambulatoires générales dans le nord du Nigéria. Un questionnaire administré par l'investigateur a été utilisé pour obtenir des données concernant les données sociodémographiques des participants, la mesure de la pression artérielle (PA) au triage, la mesure de la PA dirigée par un médecin et les caractéristiques de décision clinique des médecins. Un test du chi carré et une analyse de régression logistique ont été utilisés pour déterminer les facteurs associés et les prédicteurs de l'EBP manquée, respectivement. RÉSULTATS: L'âge moyen des participants était de 40,6 ± 14,7 ans. Il s'agissait majoritairement de femmes (122, 65,2 %). L'EBP a été manquée chez 28 (15 %) participants au triage. Âge des participants (> 40 ans) (rapport de cotes [OR] = 3,82, intervalle de confiance à 95 % [IC] = 1,43 à 10,22, P = 0,008), site d'étude (OR = 0,32, IC à 95 % = 0,12 à 0,82, P = 0,018) et la réception perçue d'explications sur la procédure de mesure de la PA (OR=2,97, IC à 95 %=1,25-7,07, P=0,014) étaient des prédicteurs d'EBP manquée chez les participants. CONCLUSION: La prévalence de l'EBP manquée était faible mais remarquable en raison de ses énormes implications pour la santé publique. L'âge, le site d'étude, la réception perçue d'explications sur la procédure de mesure de la PA prédisaient une EBP manquée. Des interventions efficaces sont nécessaires pour réduire l'EBP manquée dans ces contextes et similaires. MOTS CLÉS: tension artérielle élevée, hypertension, absence, consultations externes, soins primaires, triage.