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Abstract Background Due to the adverse effects of industrial chemicals and their carcinogenicity and toxicity for humans, the debates have increased on using natural preservatives. This study was conducted to investigate the inhibitory effect of pure nisin and nisin nanoparticles (nisin NPs) against Aspergillus flavus in vivo by inoculation in laboratory-manufactured Ras cheese. A novel, safe, and natural approach of nanoprecipitation using acetic acid was employed to prepare nisin nanoparticles. The prepared NPs were characterized using zeta-sizer, FTIR, and transmission electron microscopy (TEM). Furthermore, the cytotoxicity of nisin NPs on Vero cells was assessed. The minimum inhibitory concentrations (MICs) of nisin and its nanoparticles were determined in vitro against A. flavus isolates using the agar well-diffusion method. The sensory evaluation of manufactured Ras cheese was conducted over a 60-day storage period. Results The obtained results showed a strong antifungal activity of nisin NPs (0.0625 mg/mL) against A. flavus strain in comparison with pure nisin (0.5 mg/mL). Notably, the count decreased gradually by time from 2 × 108 at zero time and could not be detected at the 7th week. The count with pure nisin decreased from 2 × 108 at zero time and could not be detected at the 10th week where it’s enough time to produce aflatoxins in cheese. The MICs of nisin and nisin NPs were 0.25 and 0.0313 mg/mL, respectively. Nisin NPs used in our experiment had good biocompatibility and safety for food preservation. Additionally, the sensory parameters of the manufactured Ras cheese inoculated with nisin and nisin NPs were of high overall acceptability (OAA). Conclusions Overall, the results of this study suggested that adding more concentration (˃0.0625 mg/mL) from nisin nanoparticles during the production of Ras cheese may be a helpful strategy for food preservation against A. flavus in the dairy industry.

Brain aging is a chronic process linked to inflammation, microglial activation, and oxidative damage, which can ultimately lead to neuronal loss. Sialic acid-binding immunoglobulin-like lectin-11 (SIGLEC-11) is a human lineage-specific microglial cell surface receptor that recognizes α-2-8-linked oligo−/polysialylated glycomolecules with inhibitory effects on the microglial inflammatory pathways. Recently, the SIGLEC11 gene locus was prioritized as a top tier microglial gene with potential causality to Alzheimer’s disease, although its role in inflammation and neurodegeneration remains poorly understood. In this study, aged Siglec-11 transgenic (tg) mice, which expressed the human SIGLEC-11 receptor on microglia and tissue macrophages, were investigated. The brains of the Siglec-11 tg mice were analyzed in 6-month-old mature mice and 24-month-old aged mice using immunohistochemistry and transcriptomics. Results showed decreased density and fewer clusters of ionized calcium binding adaptor molecule 1 (Iba1)-positive microglial cells in the hippocampus and substantia nigra, as well as less lipid-laden microglia in the Siglec-11 tg in comparison to wildtype (WT) controls. Additionally, Siglec-11 tg mice exhibited less age-related neuronal loss in the substantia nigra pars compacta in comparison to WT mice. Transcriptome analysis revealed suppression of oxidative phosphorylation and inflammatory pathways in Siglec-11 tg brains at 6 months, with further suppression of complement and coagulation cascades at 24 months of age in comparison to WT mice. Gene transcript levels of the pro-inflammatory cytokines tumor necrosis factor alpha (Tnf) and interleukin 1 beta (Il-1β) as well as the oxidative stress markers cytochrome b-245 alpha and beta (Cyba and Cybb) and the nitric oxide synthase 2 (Nos2), were reduced in the brains of 24-month-old Siglec-11 tg mice relative to WT controls. Brains of 24-month-old Siglec-11 tg mice also exhibited lower gene transcription of complement components 3, 4, and integrin alpha M (C3, C4, and Itgam), along with the complement C1q subcomponents a-c (C1qa, C1qb, and C1qc). In summary, aged Siglec-11 tg mice displayed reduced brain inflammation and oxidative stress, as well as protection against age-related neuronal loss in the substantia nigra.

Sixth-generation (6G) communication advancements target massive connectivity, ultra-reliable low-latency communication (URLLC), and high data rates, essential for IoT applications. Yet, in natural disasters, particularly in dense urban areas, 6G quality of service (QoS) can falter when terrestrial networks—such as base stations—become unavailable, unstable, or strained by high user density and dynamic environments. Additionally, high-rise buildings in smart cities contribute to signal blockages. To ensure reliable, high-quality connectivity, integrating low-Earth Orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RIS) into a multilayer (ML) network offers a solution: LEO satellites provide broad coverage, UAVs reduce congestion with flexible positioning, and RIS enhances signal quality. Despite these benefits, this integration brings challenges in resource allocation, requiring path loss models that account for both line-of-sight (LOS) and non-line-of-sight (NLOS) links. To address these, a joint optimization problem is formulated focusing on resource distribution fairness. Given its complexity, a framework is proposed to decouple the problem into subproblems using the block coordinate descent (BCD) method. These subproblems include UAV placement optimization, user association, subcarrier allocation via orthogonal frequency division multiple access (OFDMA), power allocation, and RIS phase shift control. OFDMA efficiently manages shared resources and mitigates interference. This iterative approach optimizes each subproblem, ensuring convergence to a locally optimal solution. Additionally, we propose a low-complexity solution for RIS phase shift control, proving its feasibility and efficiency mathematically. The numerical results demonstrate that the proposed scheme achieves up to 43.5% higher sum rates and 80% lower outage probabilities compared to the schemes without RIS. The low complexity solution for RIS optimization achieves performance within 1.8% of the SDP approach in terms of the sum rate. This model significantly improves network performance and reliability, achieving a 16.3% higher sum rate and a 44.4% reduction in outage probability compared to joint optimization of SAT-UAV resources. These findings highlight the robustness and efficiency of the ML network model, making it ideal for next-generation communication systems in high-density urban environments.

Inorganic scale formation is a common issue in multi-stage flash (MSF) desalination plants, significantly impacting operational efficiency. To address this, acid cleaning is frequently employed, but it can lead to severe corrosion of alloy components if not properly controlled with corrosion inhibitors. This study investigates the effectiveness of toluene-2,4-diisocyanate-4-(1H-imidazole-ly) aniline (TDIA) as a corrosion inhibitor for 304L stainless steel in a simulated acid cleaning solution (1M HCl and 3.5 % NaCl). A range of tests, including electrochemical analysis, weight loss measurements, and surface characterization techniques such as AFM, EDS, and SEM, were used to assess the inhibitor's performance at temperatures of 25, 45, 65, and 90 °C. At a concentration of 50 ppm, TDIA achieved inhibition efficiencies of around 90% at 25 °C and above 80% at 90 °C, demonstrating effective protection across all temperatures studied. The adsorption behavior of TDIA followed the Langmuir adsorption model, and it acted as a mixed-type inhibitor by forming a protective layer on the metal surface, which prevents corrosive agents from accessing the steel. The dual-environment testing method, simulating conditions in desalination plants, offers valuable insights into the inhibitor's practical performance, enhancing the applicability of these findings to real-world industrial scenarios.

The effective prediction of corrosion inhibition efficiency (%IE) of modified graphene oxides (GOs); diaminohexane-modified graphene oxide (DAH-GO) and diaminooctane-modified graphene oxide (DAO-GO) is vital for advanced material applications. This study employs a dual-modelling scheme to predict the %IE, for this purpose, four stand-alone machine learning (ML) models (Multivariate Regression (MVR), Gaussian Process Regression (GPR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Neural Network (NN)), and five simple averaging (SA) ensemble paradigms (MVR-SA, GPR-SA, ANFIS-SA, NN-SA, and Decision Tree-SA (DT-SA)). Feature selection processes were carried out to develop three distinct models, leading to a comprehensive comparative analysis. The results demonstrated that the non-linear stand-alone models (GPR, ANFIS, NN) significantly outperform the linear MVR model, with the M2 model configuration yielding the highest performance across all models. Remarkably, GPR-M2 achieved perfect model tuning with zero error rates, indicating its superior predictive capabilities. Ensemble techniques further improved performance, reflecting the experimental data's complexities in %IE modelling. The hierarchical order of performance in the training phase in the testing phase is DT-SA < MVR-SA < ANFIS-SA < NN-SA < GPR-SA. The GPR-SA ensemble emerged as the most accurate technique, substantially enhancing the predictive accuracy of the ensemble models by up to 67.73% in the training phase and 50.71% in the testing phase. These findings suggest the potential of GPR-SA in boosting the performance of ensemble approaches in material science applications. The study recommended a promising future for ML in the development and application of corrosion-inhibitors.

Mahmoud A Rageh,1 Shady Mahmoud Attia Ibrahim,1 Noha Abdallah,2 Abeer Attia Tawfik2 1Department of Dermatology, Faculty of Medicine, Al-Azhar University, Cairo, Egypt; 2Department of Medical Applications of Laser, National Institute of Laser Enhanced Sciences, Cairo University, Giza, EgyptCorrespondence: Mahmoud A Rageh, Department of Dermatology, Faculty of Medicine, Al-Azhar University, Al-Darrasah, Cairo, 11633, Egypt, Email dr.mahmoudrageh@gmail.comBackground: Atrophic acne scarring is a widely prevalent condition and one of the most distressing complications of acne vulgaris. Numerous options with variable outcomes are available for the treatment of acne scarring. Laser is considered a first‐line therapy for acne scars, and recently there has been a growing interest in using stem cells and their derivatives for treating acne scars. In addition, combined therapeutic modalities often achieve more satisfactory results than a single treatment.Objective: We tried to evaluate the role of nanofat and fractional CO2 laser as a combined treatment approach for atrophic acne scarring.Methods: Twenty-five patients with atrophic acne scarring were enrolled. They received a single session of intradermal nanofat injection, at different points 1 cm apart, for acne scars. Two weeks later, they were treated with three sessions of fractional CO2 laser at monthly intervals. Patients were evaluated three months after the last session using the quantitative Goodman and Baron scoring system. Pain, side effects, and patients’ satisfaction were also evaluated.Results: There were two males and 23 females with a mean age of 25.96 years. Their skin type ranged between Fitzpatrick skin type III, IV, and V. Boxcar scars were the most common scar type in 13 patients (52%). After treatment, there was a significant reduction (p < 0.05) in the quantitative Goodman and Baron scores. The improvement was more evident in rolling scars. Seven patients reported significant improvement, and 12 reported marked improvement.Conclusion: This study showed that combining nanofat and fractional CO2 laser is a safe and effective treatment modality for atrophic acne scars.Keywords: acne scars, fractional laser, fat grafting, nanofat

Background: Machine learning in the healthcare sector represents a group of technologies in all aspects of medicine, and it appears promising, especially in emergency medicine. Hence, this study aims to utilize emergency department (ED) records to train machine learning algorithms and assess medical performance and outcomes. Methods: This is a retrospective observational cohort study utilizing emergency patient records acquired from the Emergency Department of King Faisal Specialist Hospital & Research Centre in Riyadh City. Also, different machine learning models were evaluated, including regression, instance-based, regularization, tree-based, Bayesian, dimensionality reduction, and ensemble algorithms. Results: A total of 149,513 emergency patient records were acquired. Due to many outliers and mislabeled data, clinical knowledge and a confident learning algorithm were used to preprocess the dataset. This resulted in only 84,970 patient records being kept. We observed that ensemble algorithms outperformed the others in all evaluation metrics, achieving an F-1 score and quadratic weighted kappa of 93.1% and 0.8623, respectively, in the case of CatBoost. In addition, the model never classified an emergent patient as nonurgent, nor did it classify a nonurgent ED patient as emergent. Optimizing the healthcare center workforce while ensuring that all critical patients are treated immediately is vital. Conclusion: Machine learning-based triage models are feasible, highly accurate, and provide an in-depth assessment of the patient's risk profile, which may not be found in routinely used emergency triage systems. A prospective study to evaluate the potential efficacy of machine learning-based triage models in predicting emergency visit outcomes needs to be conducted. [SJEMed 2023; 4(2.000): 112-119]

This article explores the application of deep learning techniques for sentiment analysis of patients’ drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model’s performance. Two different drug review datasets were used to train and test the models. The proposed methodology involves the implementation and evaluation of both deep learning models with the GloVe word embeddings for sentiment analysis of drug reviews. The experimental results indicate that Model A (Bi-LSTM-CNN) achieved an accuracy of 96% and Model B (Bi-LSTM-CNN) performs consistently at 87% for accuracy. Notably, the incorporation of GloVe word representations improves the overall performance of the models, as supported by Cohen’s Kappa coefficient, indicating a high level of agreement. These findings showed the efficacy of deep learning-based approaches, particularly bidirectional LSTM and bidirectional LSTM-CNN, for sentiment analysis of patients’ drug reviews.

Sialic acids, commonly found as the terminal carbohydrate on the glycocalyx of mammalian cells, are pivotal checkpoint inhibitors of the innate immune system, particularly within the central nervous system (CNS). Sialic acid-binding immunoglobulin-like lectins (SIGLECs) expressed on microglia are key players in maintaining microglial homeostasis by recognizing intact sialylation. The finely balanced sialic acid-SIGLEC system ensures the prevention of excessive and detrimental immune responses in the CNS. However, loss of sialylation and SIGLEC receptor dysfunctions contribute to several chronic CNS diseases. Genetic variants of SIGLEC3/CD33, SIGLEC11, and SIGLEC14 have been associated with neurodegenerative diseases such as Alzheimer’s disease, while sialyltransferase ST8SIA2 and SIGLEC4/MAG have been linked to psychiatric diseases such as schizophrenia, bipolar disorders, and autism spectrum disorders. Consequently, immune-modulatory functions of polysialic acids and SIGLEC binding antibodies have been exploited experimentally in animal models of Alzheimer’s disease and inflammation-induced CNS tissue damage, including retinal damage. While the potential of these therapeutic approaches is evident, only a few therapies to target either sialylation or SIGLEC receptors have been tested in patient clinical trials. Here, we provide an overview of the critical role played by the sialic acid-SIGLEC axis in shaping microglial activation and function within the context of neurodegeneration and synaptopathies and discuss the current landscape of therapies that target sialylation or SIGLECs.

This article introduces a prototype laser communication system integrated with uncrewed aerial vehicles (UAVs), aimed at enhancing data connectivity in remote healthcare applications. Traditional radio frequency systems are limited by their range and reliability, particularly in challenging environments. By leveraging UAVs as relay points, the proposed system seeks to address these limitations, offering a novel solution for real-time, high-speed data transmission. The system has been empirically tested, showcasing its ability to maintain data transmission integrity under various conditions. Results indicate a substantial improvement in connectivity, with high data transmission success rate (DTSR) scores, even amidst environmental disturbances. This study underscores the system’s potential for critical applications such as emergency response, public health monitoring, and extending services to remote or underserved areas.

This article introduces the Social-Emotional Nurturing and Skill Enhancement System (SENSES-ASD) as an innovative method for assisting individuals with autism spectrum disorder (ASD). Leveraging deep learning technologies, specifically convolutional neural networks (CNN), our approach promotes facial emotion recognition, enhancing social interactions and communication. The methodology involves the use of the Xception CNN model trained on the FER-2013 dataset. The designed system accepts a variety of media inputs, successfully classifying and predicting seven primary emotional states. Results show that our system achieved a peak accuracy rate of 71% on the training dataset and 66% on the validation dataset. The novelty of our work lies in the intricate combination of deep learning methods specifically tailored for high-functioning autistic adults and the development of a user interface that caters to their unique cognitive and sensory sensitivities. This offers a novel perspective on utilising technological advances for ASD intervention, especially in the domain of emotion recognition.

Abstract Background In recent years, Saudi Arabia has witnessed staggering rates of hypertension and dyslipidemia-related cardiovascular (CV) deaths, overburdening the healthcare ecosystem of the country. Appropriate public health interventions can be devised through quantitative mapping of evidence. Identification of potential data gaps can prioritize future research needs and develop a ‘best-fit’ framework for patient-centric management of hypertension and dyslipidemia. Methods This review quantified data gaps in the prevalence and key epidemiological touchpoints of the patient journey including awareness, screening, diagnosis, treatment, adherence, and control in patients with hypertension and dyslipidemia in Saudi Arabia. Studies published in English between January 2010 and December 2021 were identified through a structured search on MEDLINE, Embase, BIOSIS, and PubMed databases. An unstructured search on public and government websites, including Saudi Ministry of Health, without date limits was carried out to fill data gaps. After exclusion of studies based on predefined criteria, a total of 14 studies on hypertension and 12 studies and one anecdotal evidence for dyslipidemia were included in the final analyses. Results The prevalence of hypertension was reported to be 14.0%–41.8% while that for dyslipidemia was 12.5%–62.0%. The screening rate for hypertension was 100.0% as revealed by the nationwide surveys. Among hypertensive patients, only 27.6%–61.1% patients were aware of their condition, 42.2% patients underwent diagnosis, 27.9%–78.9% patients received antihypertensive treatment, 22.5% patients adhered to treatment medication, while blood pressure (BP) control was achieved in 27.0%–45.0% patients. Likewise, among patients with dyslipidemia, 10.5%–47.3% patients were aware of their condition, 34.6% patients were screened, and 17.8% underwent diagnosis. Although high treatment rates ranging from 40.0%–94.0% were reported, medication adherence recorded was 45.0%–77.4% among the treated patients. The overall low control rates ranged from 28.0%–41.5%. Conclusions The study findings highlight evidence gaps along key touchpoints of patient journey. Reinforcing the efforts for high-quality evidence-based research at a national level may pave a path for better resource utilization and provide guidance to practice and amend health policies for patients, healthcare practitioners (HCPs), and healthcare policy makers for better patient outcomes in Saudi Arabia.

Abstract A 6‐year‐old female spayed Great Dane was evaluated for acute onset cluster seizures. Magnetic resonance imaging (MRI) identified a mass in the olfactory bulbs with a large mucoid component caudal to the primary mass. The mass was removed via transfrontal craniotomy and histopathology revealed a tyrosine crystalline‐rich, fibrous meningioma with a high mitotic index. Repeat MRI at 6 months showed no detectable tumor regrowth. The dog is clinically normal with no seizures at the time of publication 10 months after surgery. This meningioma subtype is rare in humans. This unique meningioma occurred in a dog of younger age and uncommon breed for intracranial meningioma. Biological progression of this tumor subtype is unknown; however, growth rate might be slow despite the high mitotic index.

Drilling through shale formations can be expensive and time-consuming due to the instability of the wellbore. Further, there is a need to develop inhibitors that are environmentally friendly. Our study discovered a cost-effective solution to this problem using Gum Arabic (ArG). We evaluated the inhibition potential of an ArG clay swelling inhibitor and fluid loss controller in water-based mud (WBM) by conducting a linear swelling test, capillary suction timer test, and zeta potential, fluid loss, and rheology tests. Our results displayed a significant reduction in linear swelling of bentonite clay (Na-Ben) by up to 36.1% at a concentration of 1.0 wt. % ArG. The capillary suction timer (CST) showed that capillary suction time also increased with the increase in the concentration of ArG, which indicates the fluid-loss-controlling potential of ArG. Adding ArG to the drilling mud prominently decreased fluid loss by up to 50%. Further, ArG reduced the shear stresses of the base mud, showing its inhibition and friction-reducing effect. These findings suggest that ArG is a strong candidate for an alternate green swelling inhibitor and fluid loss controller in WBM. Introducing this new green additive could significantly reduce non-productive time and costs associated with wellbore instability while drilling. Further, a dynamic linear swelling model, based on machine learning (ML), was created to forecast the linear swelling capacity of clay samples treated with ArG. The ML model proposed demonstrates exceptional accuracy (R2 score = 0.998 on testing) in predicting the swelling properties of ArG in drilling mud.

Sialic acids are terminal sugars of the cellular glycocalyx and are highly abundant in the nervous tissue. Sialylation is sensed by the innate immune system and acts as an inhibitory immune checkpoint. Aminoglycoside antibiotics such as neomycin have been shown to activate tissue macrophages and induce ototoxicity. In this study, we investigated the systemic subcutaneous application of the human milk oligosaccharide 6’-sialyllactose (6SL) as a potential therapy for neomycin-induced ototoxicity in postnatal mice. Repeated systemic treatment of mice with 6SL ameliorated neomycin-induced hearing loss and attenuated neomycin-triggered macrophage activation in the cochlear spiral ganglion. In addition, 6SL reversed the neomycin-mediated increase in gene transcription of the pro-inflammatory cytokine interleukin-1β (Il-1b) and the apoptotic/inflammatory kinase Pik3cd in the inner ear. Interestingly, neomycin application also increased the transcription of desialylating enzyme neuraminidase 3 (Neu3) in the inner ear. In vitro, we confirmed that treatment with 6SL had anti-inflammatory, anti-phagocytic, and neuroprotective effects on cultured lipopolysaccharide-challenged human THP1-macrophages. Thus, our data demonstrated that treatment with 6SL has anti-inflammatory and protective effects against neomycin-mediated macrophage activation and ototoxicity.

In this study, a silver nanoparticle anchored transparent tape sensor was used to detect 1,4-bis(2-aminoethyl)piperazine functionalized GO (AEP-GO) adsorbed on carbon steel surface utilizing the surface-enhanced Raman scattering (SERS) technique. SERS detection enabled the extreme amplification of Raman signals emitted by inhibitor molecules in order to describe their adsorption behavior on metallic/alloy surfaces. The strong corrosion inhibition performance of AEP-GO against carbon steel corrosion in 15 % HCl solution was proven by weight loss, electrochemical measurements and surface characterization techniques in a previous study. The SERS analysis showed the Raman peaks intensity of AEP-GO on the carbon surface gradually increases with increasing AEP-GO concentration. The increasing intensity with concentration correlated well with the previously reported weight loss and electrochemical results. DFT calculation was also carried out to understand the nature of interaction between the adsorbed AEP-GO molecules and the silver nanoparticles. The AEP-GO_Ag adduct's optimized structure reveals the silver metals approached the oxygen atom at the GO epoxy group in AEP-GO rather than the oxygen atoms at the carbonyl and hydroxyl groups. With no restrictions on substrate materials, the fabricated SERS sensor created in this study can be employed as a versatile sensor to characterize corrosion adsorption processes on metal surfaces.

This study aims to investigate how Covid-19 has affected the microfinance institutions in Somalia. This study employed a qualitative descriptive approach by conducting a case study in Mogadishu and Hargeisa. The data of the study was collected through a questionnaire distributed to microfinance institutions in Somalia in 2021. The paper found that the number of MFIs that have been negatively affected by the epidemic was higher than the number affected positively or naturally. It also found that most MFIs witnessed high credit risks, and more than half of them have reduced lending to less than half due to Covid-19.

The recent increase in credit card fraud is rapidly has caused huge monetary losses for individuals and financial institutions. Most credit card frauds are conducted online by illegally obtaining payment credentials through data breaches, phishing, or scamming. Many solutions have been suggested to address the credit card fraud problem for online transactions. However, the high-class imbalance is the major challenge that faces the existing solutions to construct an effective detection model. Most of the existing techniques used for class imbalance overestimate the distribution of the minority class, resulting in highly overlapped or noisy and unrepresentative features, which cause either overfitting or imprecise learning. In this study, a credit card fraud detection model (CCFDM) is proposed based on ensemble learning and a generative adversarial network (GAN) assisted by Ensemble Synthesized Minority Oversampling techniques (ESMOTE-GAN). Multiple subsets were extracted using under-sampling and SMOTE was applied to generate less skewed sets to prevent the GAN from modeling the noise. These subsets were used to train diverse sets of GAN models to generate the synthesized subsets. A set of Random Forest classifiers was then trained based on the proposed ESMOTE-GAN technique. The probabilistic outputs of the trained classifiers were combined using a weighted voting scheme for decision-making. The results show that the proposed model achieved 1.9%, and 3.2% improvements in overall performance and the detection rate, respectively, with a 0% false alarm rate. Due to the massive number of transactions, even a tiny false positive rate can overwhelm the analysis team. Thus, the proposed model has improved the detection performance and reduced the cost needed for manual analysis.

Wellbore instability in oil and gas industry well drillings is a significant challenge that is linked to shale swelling when shale interacts with free water molecules in the water-based drilling fluid. Strategic design of environmentally benign, biodegradable, and effective shale hydration inhibitors is a prominent objective of contemporary exploration in well-drilling fluids as a replacement for the common KCl which is detrimental to aquatic lives. This work reports the synthesis and potential of novel green acrylic polymer-amyl ester activated carbon (-C) nanocomposite to hinder shale hydration in formations during drilling. Both less hydrophobic acrylic acid-acrylamide-activated carbon-amyl ester (AA-AAm-C-Amyl) and more hydrophobic acrylic acid-acrylamide-octadecene-activated carbon-amyl ester (AA-AAm-OD-C-Amyl) composites were synthesized, characterized, and tested with standard methods as a cleaner fluid additive for shale swelling inhibition, and their results compared with that of KCl. The polymer matrixes displayed remarkable thermal stability. Results also indicate that AA-AAm-C-Amyl and AA-AAm-OD-C-Amyl composites could stabilize wellbore effectively with 95.2% and 93.7% anti-swelling ratio, and shale recovery capacity of 97% and 95.2% respectively. The surface evaluation of the composite fluid-treated bentonite revealed that the mechanism of inhibition could be based on the collaborative action of nanopore plugging of carbon core and strong adsorption of the polymer component of the materials on clay surfaces via encapsulation and hydrogen bonding to form an impressive filter cake which could actively prevent water invasion into formation. Hence, AA-AAm-C-Amyl and AA-AAm-OD-C-Amyl composites could be a sustainable substitute for the conventional KCl as a shale inhibitor for well-drilling.

Case summary A 3-year-old male neutered Sphynx cat was referred for history of chronically increased liver enzymes and lower urinary tract signs that were first reported when the cat was 5 months old. Urine metabolic profile revealed increased amino aciduria and glucosuria despite normoglycemia, suggesting Fanconi syndrome. Urine sodium dodecyl sulfate-polyacrylamide gel electrophoresis revealed a banding pattern suggestive of primary tubular damage. Serial blood work showed non-regenerative normocytic normochromic anemia, persistently elevated liver enzymes, worsening azotemia and progressive hyperchloremic metabolic acidosis. Ultrasound revealed irregular kidneys and bilaterally hyperechoic cortices and medullae with a loss of normal corticomedullary distinction. Laparoscopic kidney biopsy revealed a moderate-to-severe chronic interstitial fibrosis with chronic lymphoplasmacytic inflammation, tubular degeneration and atrophy, mild glomerulosclerosis and mild large vascular amyloidosis. Tubular epithelial cell karyomegaly was multifocally evident throughout the kidney. The liver had moderate diffuse zone 1 hepatocellular atrophy, periportal fibrosis, biliary hyperplasia, mild perisinusoidal amyloidosis and hepatocyte karyomegaly in zones 2 and 3. The patient continued to decline and developed polyuria, polydipsia, lethargy and hyporexia irrespective of rigorous management, which failed to curtail the progressive anemia and azotemia. The patient was euthanized 8 months from the onset of clinical signs. Relevance and novel information Fanconi syndrome in cats is a rare condition, with most reports occurring secondary to chlorambucil treatment. This is the first known case of Fanconi syndrome occurring with concurrent hepatorenal epithelial karyomegaly in a young Sphynx cat.

The performance of any communication system heavily relies on the efficient routing of interventions. This article addresses the significant issue of routing protocol selection for optimal path determination in networks. Particularly, when wireless communication occurs among mobile nodes with limited resources, such as batteries, the routing problem becomes even more challenging. This article proposes the Fuzzy Control Energy Efficient (FCEE) routing protocol to overcome these challenges. The FCEE protocol combines the Ad-Hoc On-Demand Distance Vector (AODV) protocol with fuzzy logic techniques to enhance network lifetime and performance. The proposed approach introduces a memory-based channel integrated with fuzzy logic methodologies, which effectively restricts the forwarding of unnecessary broadcast packets based on the energy availability of the operating node. Through extensive simulations, demonstrate the promising capabilities of FCEE as a routing protocol for wireless mesh networks. To further assess the effectiveness of the FCEE protocol, the proposed article compares it with two existing routing protocols: AODV and Intelligent Routing AODV (IRAODV). The simulation results shows that the FCEE routing protocol significantly enhances the reliability of the conventional AODV, providing improved link connectivity and longer route lifetimes. Additionally, our proposed protocol enhances the quality of service (QoS) for mesh routing, with an average throughput of 351.374 (Kbps) compared to 90 (Kbps) for IRAODV and 39 (Kbps) for AODV. Moreover, FCEE exhibits superior energy efficiency with an average energy consumption of 14, while IRAODV and AODV consume 40 and 90 joules, respectively. In conclusion, the FCEE routing protocol demonstrates its potential to address the challenges of efficient routing in wireless mesh networks. By leveraging fuzzy logic and integrating it with AODV, FCEE enhances network lifetime, performance, and energy efficiency, making it a promising solution for future wireless communication systems.

Insectivorous Old World horseshoe bats (Rhinolophus spp.) are the likely source of the ancestral SARS-CoV-2 prior to its spillover into humans and causing the COVID-19 pandemic. Natural coronavirus infections of bats appear to be principally confined to the intestines, suggesting fecal-oral transmission; however, little is known about the biology of SARS-related coronaviruses in bats. Previous experimental challenges of Egyptian fruit bats (Rousettus aegyptiacus) resulted in limited infection restricted to the respiratory tract, whereas insectivorous North American big brown bats (Eptesicus fuscus) showed no evidence of infection. In the present study, we challenged Jamaican fruit bats (Artibeus jamaicensis) with SARS-CoV-2 to determine their susceptibility. Infection was confined to the intestine for only a few days with prominent viral nucleocapsid antigen in epithelial cells, and mononuclear cells of the lamina propria and Peyer's patches, but with no evidence of infection of other tissues; none of the bats showed visible signs of disease or seroconverted. Expression levels of ACE2 were low in the lungs, which may account for the lack of pulmonary infection. Bats were then intranasally inoculated with a replication-defective adenovirus encoding human ACE2 and 5 days later challenged with SARS-CoV-2. Viral antigen was prominent in lungs for up to 14 days, with loss of pulmonary cellularity during this time; however, the bats did not exhibit weight loss or visible signs of disease. From day 7, bats had low to moderate IgG antibody titers to spike protein by ELISA, and one bat on day 10 had low-titer neutralizing antibodies. CD4+ helper T cells became activated upon ex vivo recall stimulation with SARS-CoV-2 nucleocapsid peptide library and exhibited elevated mRNA expression of the regulatory T cell cytokines interleukin-10 and transforming growth factor-β, which may have limited inflammatory pathology. Collectively, these data show that Jamaican fruit bats are poorly susceptible to SARS-CoV-2 but that expression of human ACE2 in their lungs leads to robust infection and an adaptive immune response with low-titer antibodies and a regulatory T cell-like response that may explain the lack of prominent inflammation in the lungs. This model will allow for insight of how SARS-CoV-2 infects bats and how bat innate and adaptive immune responses engage the virus without overt clinical disease.

Colorimetric methods are classical techniques that have been broadly subjected to and applied for the detection of many analytes. The method is low cost, simple, practically active, and suitable to determine the sample due to color changes which are accessed visually even at low concentrations of target analysis. Hence, different methods are applied for detecting and determining pesticides (insecticides) and phenolic compounds (bisphenol A). Moreover, researchers detected OCPs (Organochloride pesticides) in breast milk and adipose tissues, which may exhibit estrogenic, and antiestrogenic activities mostly associated with breast cancer. The common thing between two the analytes is nature because they both show endocrine-disrupting properties. Bisphenol A (phenolic compound) is commonly used in the high-volume production of monomers and plastics. Bisphenol A is used to manufacture polycarbonate plastics, used in food cans coating, baby formula bottles, milk containers, paints, etc. Pesticides and phenolic compounds identification methods can be sluggish and need extremely skilled workforces to function cultured instruments that are mostly too expensive, delicate, or immense to a position outside of a devoted laboratory facility. This review deals with the discriminatory and low-cost method for the colorimetric detection of pesticides and phenolic compounds (bisphenol A) with different bio-synthesized metals and metal-doped nanocomposites. Based on existing publications, using colorimetry with nanocomposites provides low detection limits and good reproducibility for the detection of pesticides and phenolic compounds in various samples such as food samples. These results serve as a guide for controlling pesticides and phenolic chemicals in food processing, lowering the dangers involved.

Extraskeletal osteosarcoma (EOSA) in dogs is a rare malignant mesenchymal tumor of somatic soft tissues or more commonly visceral organs with a poor prognosis. In dogs, EOSAs have been described as arising from multiple locations, but differently from humans, never from a main vessel. In this report, we describe the first case of an EOSA arising from the post-hepatic caudal vena cava in a 7-year-old male neutered mix breed dog. This report focuses on the description of the diagnostic challenges to obtain a preoperative diagnosis, highlights the importance of histopathology for a correct diagnosis, and introduces a new differential diagnosis for an animal presenting with a suspected thrombus of the vena cava.

The following article investigates the Metaverse and its potential to bolster digital accessibility for persons with disabilities. Through qualitative analysis, we examine responses from eleven experts in digital accessibility, Metaverse development, disability advocacy, and policy formulation. This exploration uncovers key insights into the Metaverse’s current state, its inherent principles, and the challenges and opportunities it presents in terms of accessibility. The findings reveal a mixed state of inclusivity within the Metaverse, highlighting significant advancements along with notable gaps, especially in integrating assistive technologies and ensuring interoperability across different virtual environments. This study emphasizes the Metaverse’s potential to revolutionize experiences for individuals with disabilities, provided that accessibility is embedded in its foundational design. Ethical and legal considerations, such as privacy, non-discrimination, and evolving legal frameworks, are identified as critical factors that shape an inclusive Metaverse. We propose a comprehensive framework that emphasizes technological adaptation and innovation, user-centric design, universal access, social and economic considerations, and global standards. This framework aims to guide future research and policy interventions to foster an inclusive digital environment in the Metaverse. This paper contributes to the emerging discourse on the Metaverse and digital accessibility, offering a nuanced understanding of its complexities and a roadmap for future exploration and development. This underscores the necessity of a multi-faceted approach that incorporates technological innovation, user-centered design, ethical considerations, legal compliance, and continuous research to create an inclusive and accessible Metaverse.

Background: Nosocomial infection (NCI) is a problem with global concern due to increasing morbidity and mortality in hospitalized patients. Chronic liver disease increases the chance of NCI by suppressing cell and antibody mediated immunity. Aim: We aimed in this study to visualize the problem of NCI in hepatic patients regarding organisms causing it, pattern of resistance and possible device associated with its existence. Methods: End stage chronic hepatitis C patients who admitted to the National Liver Institute Hospital, Menoufia University, were enrolled in this observational study. Different samples from alleged sites further processed by conventional culture and sensitivity techniques and confirmed by VITEK2C2 system. Antibiotic resistance pattern of isolates was assessed. Pan drug resistance (PDR) Acinetobacter baumannii to Omp A, bap, and Csu E virulence genes was further processed by Multiplex PCR. Results: Gram- negative pathogens were significantly higher in CAUTI. The most predominant nosocomial organism was Pseudomonas aeruginosa which represent 19% (32 from 168), followed by Staph aureus that account for 17.86% (30 from 168), Acinetobacter baumannii signify 14.2%, Klebsiella pneumonia form 11.9%. MDR represents 123 isolates from 168 with 73.2% percentage, while XDR represents 23.8% of total isolates. Pan drug resistance Acinetobacter baumannii represents 5 from 24 isolates. It was associated with CAUTI and CLBSI but non-significant. 4 PDR isolates show Prescence of Omp A, Csu E, and bap biofilm forming genes. Conclusions: MDR NCI in hepatic patients need more attention regarding rational use of antibiotics especially with appearance of PDR Acinetobacter baumannii carrying Omp A, Csu E, and bap biofilm forming genes.

Hydrated cement is one of the complex composite systems due to the presence of multi-scale phases with varying morphologies. Calcium silicate hydrate, which is the principal binder phase in the hydrated cement, is responsible for the stiffness, strength, and durability of Portland cement concrete. To understand the mechanical and durability behavior of concrete, it is important to investigate the interactions of hydrated cement phases with other materials at the nanoscale. In this regard, the molecular simulation of cement-based materials is an effective approach to study the properties and interactions of the cement system at the fundamental scale. Recently, many studies have been published regarding atomistic simulations to investigate the cement phases to define/explain the microscopic physical and chemical properties, thereby improving the macroscopic performance of hardened binders. The research in molecular simulation of cementitious systems involves researchers with multidisciplinary backgrounds, mainly in two areas: ① cement chemistry, where the hydration reactions govern most of the chemical and physical properties at the atomic scale; and ② computational materials science and engineering, where the bottom-up approach is required. The latter approach is still in its infancy, and as such, a study of the prevailing knowledge is useful, namely through an exhaustive literature review. This state-of-the-art report provides a comprehensive survey on studies that were conducted in this area and cites the important findings.

Case summary This report describes an indoor-only cat with a rare form of sino-orbital aspergillosis (SOA) with cervical lymphadenopathy causing local obstruction. Extensive work-up on initial presentation failed to identify the underlying etiology and the diagnosis was not determined until the disease progressed during a prolonged course of glucocorticoid therapy. Relevance and novel information SOA caused by Aspergillus viridinutans complex is increasingly recognized as a significant cause of mortality in cats in recent years, with most cases reported in Australia, Europe and Asia. Feline SOA carries a poor prognosis owing to its invasive nature and resistance to antifungal therapy. This case demonstrates the importance of clinical awareness of SOA as a differential for cats with chronic nasal signs and exophthalmos in the USA. Moreover, it demonstrates a rare form of presentation and potential difficulty in achieving a correct diagnosis.

Abstract Necrosis seen in histopathology Whole Slide Images is a major criterion that contributes towards scoring tumour grade which then determines treatment options. However conventional manual assessment suffers from inter-operator reproducibility impacting grading precision. To address this, automatic necrosis detection using AI may be used to assess necrosis for final scoring that contributes towards the final clinical grade. Using deep learning AI, we describe a novel approach for automating necrosis detection in Whole Slide Images, tested on a canine Soft Tissue Sarcoma (cSTS) data set consisting of canine Perivascular Wall Tumours (cPWTs). A patch-based deep learning approach was developed where different variations of training a DenseNet-161 Convolutional Neural Network architecture were investigated as well as a stacking ensemble. An optimised DenseNet-161 with post-processing produced a hold-out test F1-score of 0.708 demonstrating state-of-the-art performance. This represents a novel first-time automated necrosis detection method in the cSTS domain as well specifically in detecting necrosis in cPWTs demonstrating a significant step forward in reproducible and reliable necrosis assessment for improving the precision of tumour grading.

As part of the study of soil water erosion in the Beni Boufrah watershed, which forms part of the northern slopes of the Central Rif, generally dipping towards the Mediterranean Sea, we carried out soil analyses to determine certain physicochemical characteristics of the soil. These analyses, which consisted of measuring pH, estimating residual moisture content, organic matter and carbonates, and measuring magnetic susceptibility in the horizons of eight profiles sampled from different soils in the Beni Boufrah watershed, are of great interest in understanding the behaviour of these soils with regard to water erosion. The results obtained show that the soils in this catchment area are alkaline, and that their residual moisture and organic matter content are remarkably low, especially in the surface horizons. The carbonate content is only significant in soils resulting from the alteration of parent rocks belonging to the Bokkoya limestone ridge. Magnetic susceptibility measurements show that the soils studied are disturbed and reworked.

The water erosion of soil in northern Morocco raises significant concerns due to its potential impact on the sustainability and productivity of the land. The objective of our study is to evaluate water erosion by utilizing magnetic susceptibility of soil particles in the watershed of the Oued Ghiss Dam. Magnetic susceptibility measurements were taken on 25cm deep profiles collected from six distinct areas, distributed based on pedology, slope, and land use. The results reveal significant variations in susceptibility. Transects 5 and 6 stand out with significantly higher magnetic susceptibility values compared to other transects, indicating relative soil stability under dense forest cover. Transect 2 also exhibits a range of magnetic susceptibility from 21.05 to 51.67 x 10-8 m³/kg, suggesting relative stability despite slight changes in magnetic susceptibility due to vegetation density and morphometric characteristics. Transects 1, 3, and 4 display lower magnetic susceptibility ranges, indicating signs of active erosion in bare soils. Magnetic signatures of soil particles provide crucial insights into soil dynamics in the Central Rif region of Morocco, emphasizing the impact of geodynamic factors and land use on the degree of evolution and degradation.

This article deals with the hygrothermal behavior of round perforated brick including hysteresis effects. It aims at examining the effect of temperature on sorption isotherms and hysteresis of this product, reducing the energy consumption of the buildings, reinforcing the constructional economy and the inhabitant’s comfort as well as offering a hygrothermal description of this product that has never been previously investigated. To achieve this, we have carried out an experimental study to determine the sorption isotherms and the hysteresis in five different temperatures (10, 23, 30, 40, and 50 °C). Twelve equations have been tested against the experimental isotherms in order to find the optimal one. After that, three models were tested in order to develop a personal model; the latter explains the real effect of the hysteresis phenomenon on hygrothermal behavior of the studied product. To validate the mathematical model, two methods were applied to calculate the isosteric heat. The obtained results revealed a significant influence of the increase in temperature on the hygrothermal behavior in which a decrease in the equivalent water content was found. Moreover, the developed experimental model is based on Oswin’s model describing the dependence of temperature on the sorption characteristics of round perforated bricks. Furthermore, the Huang model was found to be the best compared with the other models with a regression factor (R2) of 0.990. The validation of the results using the isosteric heat parameter has confirmed that the suggested models demonstrated a perfect presentation of the sorption isotherms and the hysteresis phenomenon of the round perforated brick.

Background and Objectives: The percentage of Saudi older adults (SOA) is increasing over time. With advanced age, the prevalence of chronic diseases and multiple disabilities are increasing. This leads to increase utilization of multiple medications. The objectives of this study were to describe medication utilization, determine the prevalence of polypharmacy (PP) and factors associated with it among SOA. Methods: This cross-sectional study was conducted among community-dwelling SOA aged ≥ 60 years old using the Saudi National Survey for Elderly Health (SNSEH). The survey was conducted between 2006 and 2007 by the Ministry of Health on a nationally representative sample of SOA. The data included demographics, socioeconomic and health information such as diseases and medications. Polypharmacy was defined as the concurrent use of medications from ≥ 5 therapeutic classes. A modified Poisson multivariable regression was used to study factors associated with PP controlling for confounders. All analyses were done using STATA 14. Results: The study included 2,946 SOA; 50.4% were males, 60.9% were 60–70 years old, and 69.6% were illiterate. The most common medications used among SOA were: Paracetamol (67%), joint pain medications and NSAIDs (50% each), anti-diabetic and multivitamins and minerals (47% each). PP was identified in (51.5%) of participants. The most medication associated with PP were: Paracetamol (79.9%), multivitamins and minerals (71.6%), steroid and DMARDs (70.1%), NSAIDs (66.4%), anti-diabetic and anti-hypertensive (61.3%). Higher risk of PP was associated with diabetes (RR: 1.863; 95% CI: 1.686–2.059), hypertension (RR: 1.829; 95% CI: 1.624–2.060), having pain (RR: 2.282; 95% CI: 1.918–2.713), urinary incontinence (RR: 1.389; 95% CI: 1.238–1.560; ref: no urinary incontinence) or suggestive depression (RR: 1.379; 95% CI: 1.259–1.512). Similarly, compared to low income (

Society is increasingly dependent upon the use of distributed cyber-physical systems (CPSs), such as energy networks, chemical processing plants and transport systems. Such CPSs typically have multiple layers of protection to prevent harm to people or the CPS. However, if both the control and protection systems are vulnerable to cyber-attacks, an attack may cause CPS damage or breaches of safety. Such weaknesses in the combined control and protection system are described here as hazardous vulnerabilities (HVs). Providing assurance that a complex CPS has no HVs requires a rigorous process that first identifies potential hazard scenarios and then searches for possible ways that a cyber-attacker could cause them. This article identifies the attributes that a rigorous hazardous vulnerability analysis (HVA) process would require and compares them against related works. None fully meet the requirements for rigour. A solution is proposed, HVA_CPS, which does have the required attributes. HVA_CPS applies a novel combination of two existing analysis techniques: control signal analysis and attack path analysis. The former identifies control actions that lead to hazards, known as hazardous control actions (HCAs); the latter models the system and searches the model for sequences of attack steps that can cause the HCAs. Both analysis techniques have previously been applied alone on different CPSs. The two techniques are integrated by extending the formalism for attack path analysis to capture HCAs. This converts the automated search for attack paths to a selected asset into an exhaustive search for HVs. The integration of the two techniques has been applied using HCAs from an actual CPS. To preserve confidentiality, the application of HVA_CPS is described on a notional electricity generator and its connection to the grid. The value of HVA_CPS is that it delivers rigorous analysis of HVs at system design stage, enabling assurance of their absence throughout the remaining system lifecycle.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the virus that causes coronavirus disease-19 (COVID-19), emerged in late 2019 in Wuhan, China and its rapid global spread has resulted in millions of deaths. An important public health consideration is the potential for SARS-CoV-2 to establish endemicity in secondary animal reservoirs outside of Asia or acquire adaptations that result in new variants with the ability to evade the immune response and reinfect the human population. Previous work has shown that North American deer mice (Peromyscus maniculatus) are susceptible and can transmit SARS-CoV-2 to naïve conspecifics, indicating its potential to serve as a wildlife reservoir for SARS-CoV-2 in North America. In this study, we report experimental SARS-CoV-2 susceptibility of two additional subspecies of the North American deer mouse and two additional deer mouse species, with infectious virus and viral RNA present in oral swabs and lung tissue of infected deer mice and neutralizing antibodies present at 15 days post-challenge. Moreover, some of one species, the California mouse (P. californicus) developed clinical disease, including one that required humane euthanasia. California mice often develop spontaneous liver disease, which may serve as a comorbidity for SARS-CoV-2 severity. The results of this study suggest broad susceptibility of rodents in the genus Peromyscus and further emphasize the potential of SARS-CoV-2 to infect a wide array of North American rodents.

IntroductionImmunocompetent and immunocompromised murine models have been instrumental in answering important questions regarding ZIKV pathogenesis and vertical transmission. However, mimicking human congenital zika syndrome (CZS) characteristics in these murine models has been less than optimal and does not address the potential viral effects on the human immune system.MethodsHere, we utilized neonatal humanized Rag2-/-γc-/- mice to model CZS and evaluate the potential viral effects on the differentiation of human hematopoietic stem cells in vivo. Newborn Rag2-/-γc-/- mice were engrafted with ZIKV-infected hematopoietic stem cells (HSC) and monitored for symptoms and lesions.ResultsWithin 13 days, mice displayed outward clinical symptoms that encompassed stunted growth, hunched posture, ruffled fur, and ocular defects. Striking gross pathologies in the brain and visceral organs were noted. Our results also confirmed that ZIKV actively infected human CD34+ hematopoietic stem cells and restricted the development of terminally differentiated B cells. Histologically, there was multifocal mineralization in several different regions of the brain together with ZIKV antigen co-localization. Diffuse necrosis of pyramidal neurons was seen with collapse of the hippocampal formation.DiscussionOverall, this model recapitulated ZIKV microcephaly and CZS together with viral adverse effects on the human immune cell ontogeny thus providing a unique in vivo model to assess the efficacy of novel therapeutics and immune interventions.

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.

Mesoporous nanocapsules originated from silica encapsulated ZnS doped with metal oxide (Cu or Mn) were assembled and characterized. The morphological characterization was performed using Fourier-transform infrared spectroscopy (FTIR), UV–visible absorption spectra, and transmission electron microscopy (TEM). The analyses findings proved the structures of the synthesized metal oxide nanoparticles. It was verified that those metal nanoparticles are doped in the silica mesopores with an average particle size of 10–50, 30, 40, and 200 nm for ZnS, Zn doped Cu+2, Zn doped Mn+2, and silica, respectively. The morphological characterization of the cotton fabrics treated with the prepared mesoporous nanocapsules has been carried out by scanning electron microscopy (SEM). The mechanical and physical properties, antibacterial activity, and UPF measurements were also explored. The analyses results confirmed that the silica-encapsulated ZnS doped with metal ions imparts excellent antibacterial activity and ultraviolet protection to the cotton fabric that is distracted by physical or mechanical properties.

Novel fluorescent nanoparticles were prepared via the encapsulation of perylene diimide into silica nanoparticles. The fluorescent nanoparticles were characterized by various analytical techniques, including transmission electron microscopy (TEM), scanning electron microscopy (SEM), Fourier-transform infrared spectra (FT-IR), and fluorescent optical microscopy (FOM). The diameters of the perylene-labeled silica nanoparticles were detected in the range of 40–180 nm. The emission properties of the prepared perylene-labeled silica nanoparticles were explored. The perylene-labeled silica nanoparticles displayed good photo and thermal properties. Depending on the type of the perylene salt, the composite nanoparticles showed tunable colors ranging between yellow, orange, and red. The diluted solutions of perylenes in aqueous media were highly fluorescent, displaying different colors between green and yellow, with a maximum emission at 510 nm and 650 nm, respectively. Those fluorescent perylene-labeled silica nanoparticles pave the road for the preparation of promising fluorescent substrates, including photonics, smart textiles, electronic displays, and optoelectronic devices.

Schistosomiasis is one of the neglected tropical diseases that affect millions of people worldwide. Globally, it affects economically poor countries, typically due to a lack of proper sanitation systems, and poor hygiene conditions. Currently, no vaccine is available against schistosomiasis, and the preferred treatment is chemotherapy with the use of praziquantel. It is a common anti-schistosomal drug used against all known species of Schistosoma. To date, current treatment primarily the drug praziquantel has not been effective in treating Schistosoma species in their early stages. The drug of choice offers low bioavailability, water solubility, and fast metabolism. Globally drug resistance has been documented due to overuse of praziquantel, Parasite mutations, poor treatment compliance, co-infection with other strains of parasites, and overall parasitic load. The existing diagnostic methods have very little acceptability and are not readily applied for quick diagnosis. This review aims to summarize the use of nanotechnology in the treatment, diagnosis, and prevention. It also explored safe and effective substitute approaches against parasitosis. At this stage, various nanomaterials are being used in drug delivery systems, diagnostic kits, and vaccine production. Nanotechnology is one of the modern and innovative methods to treat and diagnose several human diseases, particularly those caused by parasite infections. Herein we highlight the current advancement and application of nanotechnological approaches regarding the treatment, diagnosis, and prevention of schistosomiasis.

Mobile ad-hoc networks (MANETs) and wireless mesh networks (WMNs) are used in a variety of research areas, including the military, industry, healthcare, agriculture, the Internet of Things (IoT), transportation, and smart cities. The swift advancement in MANET technology is the driving force behind this rising adoption rate. Routing over MANET is a critical problem due to the dynamic nature of the link qualities, even when nodes are static. A key challenge in MANETs is the need for an efficient routing protocol that establishes a route according to certain performance metrics related to the link quality. The routing protocols utilised by the nodes in WMNs and MANETs are distinct. Nodes in both types of networks exchange data packets through the routing protocols. For this highly mobile network, the ad-hoc On-Demand Distance Vector (AODV) routing protocol has been suggested as a possible solution. Recent years have attracted researchers’ attention to AODV since it is a routing technique for ad-hoc networks that prevents looping. The architecture of this routing protocol considers several factors, including the mobility of nodes, the failure of connection links, and the loss of packets. In this systematic review, one of the key focuses is bringing attention to the classic AODV, which was developed after discussing the recent development of several versions of AODV. The AODV routing protocol performs a path strength check to generate a more reliable and secure route between the source and destination nodes. In AODV, investigations demonstrate advances in both the format protocol approach and the network simulation-2 (NS-2), and these improvements were made in the same scenario used to revitalise AODV. It has been discovered that the AODV is more effective in several aspects, such as throughput, end-to-end delay, packet delivery ratio (PDR), energy consumption, jitter, packet loss ratio, and network overhead. Furthermore, this paper presents this systematic review based on AODV modifications in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It also provides a methodological framework for the papers’ selection.

Blockchain technology is rapidly gaining the attention of financial institutions around the world, especially among decision makers in Islamic financial institutions who are planning to apply this innovative technology into their institutions activities which could be an upright solution for their issues and challenges. This paper contributes to the emerging literature on financial technology by discussing some of the innovative applications of blockchain in Islamic financial institutions. In addition, this paper sheds light on the main opportunities and challenges facing application of Blockchain Technology in the Islamic financial industry and highlight the way forward to address these issues. Relying on the previous literature and analysis; this paper confirms the enormous opportunities for utilizing blockchain technology in various Islamic financial applications such as Waqf, Zakat and Sukuk. On the other hand, the main challenges towards implementing blockchain in this industry are the complexity of Islamic finance products added to the opacity of its application which end up with unclear regulations and lack of standards.

Inflation has many important macroeconomic issues in many countries like Turkey. The uncertainty resulting from high inflation is one of the negativities that closely affect the financial sector. The purpose of this paper is to explore the feasibility of inflation targeting monetary policy in the dual banking system. The study investigates the long-run relationship between inflation rate, interest rate, real effective exchange rate, narrow money, and profit rate. The data collected were monthly and the period is from 2007 to 2017. The result of the study showed that interest rate and narrow money have a positive correlation with the inflation rate. Inflation targeting may not be a proper monetary policy for the financial system of Turkey. Because of the high sensitivity, it is not always easy to achieve targeted inflation. Instead of inflation targeting, interest rate targeting may help monetary policies to exert more influence on the financial sector. Keeping the interest rate under control may be a more useful monetary policy. In this way, the effects of monetary policies will be reflected more directly in the financial sector. If the monetary policies implemented are become more effective for both types of finance, their contribution to the economy will be enhanced, and controlling the macroeconomic targets will be more easily fulfilled. Thus, controlling the interest rate will give the authorities more control over Islamic finance in the sector.

Abstract Background The anatomical variation of the frontal sinus and its intimate relation to the skull base and orbit makes its surgery demanding. The extended endoscopic frontal sinus surgery allows wide better drainage and preventing the recurrence of the disease. Fourteen patients underwent EEFSS from May 2017 to May 2019. These patients are nine patients presented by chronic recurrent frontal sinusitis, three patients presented by chronic recurrent fronto ethmoidal mucocele and two patients with chronic recurrent external frontal fistula. Draff III done for ten patients of them and Draff IIB done for four patients of them. This study is designed for evaluating the efficacy of the extended endoscopic frontal sinus surgery (E E F S S) in management of chronic and recurrent frontal sinus diseases. Results The neo opening of the restored frontal sinus was remained opened with Draff III with high success rate; two patients from four patients with Draff IIb were with closed nasofrontal duct. The main follow-up was 12 months; the patients were followed up post-operatively for many office visits without any other manifestations. Conclusion The chronic recurrent frontal sinus diseases can be treated successfully with extended endoscopic frontal sinus surgery (E E F S S). The extended endoscopic frontal sinus surgery (Draff III) provides good results with low morbidity and less post-operative care.

Rotenone is a naturally occurring insecticide that inhibits mitochondrial complex I and leads to neurochemical and neuropathological deficits closely resembling those in Parkinson's disease (PD). Deficits include loss of dopaminergic neurons (DAn) in the substantia nigra pars compacta (SNpc), decreased dopamine levels and aggregation of misfolded alpha-synuclein (p129). In rat models of rotenone-induced parkinsonism, the progression of neuronal injury has been associated with activation of microglia and astrocytes. However, these neuroinflammatory changes have been challenging to study in mice, in part because the systemic rotenone exposure model utilized in rats is more toxic to mice. To establish a reproducible murine model of rotenone-induced PD, we therefore investigated the progression of neuroinflammation, protein aggregation and DAn loss in C57Bl/6 mice by exposing animals to 2.5 mg/kg/day rotenone for 14 days, followed by a two-week period where neuroinflammation is allowed to progress. Our results indicate that initial cellular dysfunction leads to increased formation of proteinase K-resistant p129 aggregates in the caudate-putamen and SNpc. Clearance of these aggregates was region- and cell type-specific, with the early appearance of reactive astrocytes coinciding with accumulation of p129 in the SNpc. Phagocytic microglial cells containing p129 aggregates were observed proximal to p129+ DAn in the SNpc. The majority of neuronal loss in the SNpc occurred during the two-week period after rotenone exposure, subsequent to the peak of microglia and astrocyte activation, as well as the peak of p129 aggregation. A secondary peak of p129 coincided with neurodegeneration at later timepoints. These data indicate that systemic exposure to rotenone in C57Bl/6 mice causes progressive accumulation and regional spread of p129 aggregates that precede maximal loss of DAn. Thus, activation of glial cells and aggregation of p129 appear to drive neuronal loss following neurotoxic stress imposed by exposure to rotenone.

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

The current paper focuses on the use of Cynomorium Coccineum peels extract for cotton fibers dyeing in an easy and effective way without the use of mordants. Prepared extracts were analyzed using UV-visible, FT-IR spectroscopy, Total Contents both Phenolic (TPC) and Flavanoid (TFC) and their ability to reduce reactive species by the 2,2-diphenyl-1-picrylhydrazyl radical scavenging motion experiments. The effect of the most important dyeing conditions, including dyebath pH, temperature, extract concentration, and time contact on the performance of cotton fibers dyeing was discussed. Data were assessed by the measurement of the color strength (K/S). A surface design was adopted to assess the influential factors and the interaction effects of the studied parameters. The optimal conditions for dyeing were to be: pH = 5.5, T = 72.5°C, extract concentration = 0.275% and dyeing duration = 25 min. As also observed, changing the concentration of the electrolyte NaCl, the color strength can be improved. Overall, the friendly extracted dye exhibited excellent textile dyeing characteristics with uniform morphology as proved by SEM analysis and good fastness to washing, perspiration, and rubbing.