Repositioning of anti-infective compounds against monkeypox virus core cysteine proteinase: a molecular dynamics study.

Monkeypox virus (MPXV) core cysteine proteinase (CCP) is one of the major drug targets used to examine the inhibitory action of chemical moieties. In this study, an in silico technique was applied to screen 1395 anti-infective compounds to find out the potential molecules against the MPXV-CCP. The top five hits were selected after screening and processed for exhaustive docking based on the docked score of ≤ -9.5 kcal/mol. Later, the top three hits based on the exhaustive-docking score and interaction profile were selected to perform MD simulations. The overall RMSD suggested that two compounds, SC75741 and ammonium glycyrrhizinate, showed a highly stable complex with a standard deviation of 0.18 and 0.23 nm, respectively. Later, the MM/GBSA binding free energies of complexes showed significant binding strength with ΔGTOTAL from -21.59 to -15 kcal/mol. This report reported the potential inhibitory activity of SC75741 and ammonium glycyrrhizinate against MPXV-CCP by competitively inhibiting the binding of the native substrate.

Antifungal drug discovery for targeting Candida albicans morphogenesis through structural dynamics study.

In response to the escalating threat of drug-resistant fungi to human health, there is an urgent need for innovative strategies. Our focus is on addressing this challenge by exploring a previously untapped target, yeast casein kinase (Yck2), as a potential space for antifungal development. To identify promising antifungal candidates, we conducted a thorough screening of the diverse-lib drug-like molecule library, comprising 99,288 molecules. Five notable drug-like compounds with diverse-lib IDs 24334243, 24342416, 17516746, 17407455, and 24360740 were selected based on their binding energy scores surpassing 11 Kcal/mol. Our investigation delved into the interaction studies and dynamic stability of these compounds. Remarkably, all selected molecules demonstrated acceptable RMSD values during the 200 ns simulation, indicating their stable nature. Further analysis through Principal Component Analysis (PCA)-based Free Energy Landscape (FEL) revealed minimal energy transitions for most compounds, signifying dynamic stability. Notably, the two compounds exhibited slightly different behaviour in terms of energy transitions. These findings mark a significant breakthrough in the realm of antifungal drugs against C. albicans by targeting the Yck2 protein. However, it is crucial to note that additional experimental validation is imperative to assess the efficacy of these molecules as potential antifungal candidates. This study serves as a promising starting point for further exploration and development in the quest for effective antifungal solutions.Communicated by Ramaswamy H. Sarma.

Antiviral actions of natural compounds against dengue virus RNA dependent RNA polymerase: insights from molecular dynamics and Gibbs free energy landscape.

Dengue fever, a major global health challenge, affects nearly half the world's population and lacks effective treatments or vaccines. Addressing this, our study focused on natural compounds that potentially inhibit the dengue virus's RNA-dependent RNA polymerase (RdRp), a crucial target in the viral replication cycle. Utilizing the MTiOpenScreen webserver, we screened 1226 natural compounds from the NP-lib database. This screening identified four promising compounds ZINC000059779788, ZINC0000044404209, ZINC0000253504517 and ZINC0000253499146), each demonstrating high negative binding energies between -10.4 and -9.9 kcal/mol, indicative of strong potential as RdRp inhibitors. These compounds underwent rigorous validation through re-docking and a detailed 100 ns molecular dynamics (MD) simulation. This analysis affirmed the dynamic stability of the protein-ligand complexes, a critical factor in the effectiveness of potential drug candidates. Additionally, we conducted essential dynamics and free energy landscape calculations to understand the structural transitions in the RdRp protein upon ligand binding, providing valuable insights into the mechanism of inhibition. Our findings present these natural molecules as promising therapeutic agents against the dengue virus. By targeting the allosteric site of RdRp, these compounds offer a novel approach to hinder the viral replication process. This research significantly contributes to the search for effective anti-dengue treatments, positioning natural compounds as potential key players in dengue virus control strategies.Communicated by Ramaswamy H. Sarma.

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework.

The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O's algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques.

Micro aneurysm detection using optimized residual-based temporal attention Convolutional Neural Network with Inception-V3 transfer learning.

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non-proliferative diabetic retinopathy depending on deep learning approaches (DRD-CNN-NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN-AMD-MAFP-Net) respectively. RESEARCH HIGHLIGHTS: Micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT-CNN algorithm successfully identifies the micro aneurysms using EOA.

Bioprospecting of Meliaceae family phytomolecules for the treatment of monkeypox virus infection: a QSAR modeling and MD simulation approach.

Recent monkeypox virus (MPXV) infections show the risk of MPXV transmission that persists today and the significance of surveillance and quick response methods to stop the virus's spread. Currently, the monkeypox virus infection is not specifically treated. In this study, QSAR models were designed using known inhibitors of cysteine proteinase from the vaccinia virus, where the Random Forest model and Ridge model had showed the best correlation between predicted and observed EC50. These models were used to screen Maliaceae family phytochemicals against MPXV cysteine proteinase. The compound, IMPHY010637 was detected in top 5 from both the QSAR screening models and showed best docked score (-8.6 kcal/mol) and thus selected for further investigation. Further, the IMPHY010637 showed interaction with the catalytic residue His241 of the protein as reported in earlier studies. The ADMET analysis of the compound showed the acceptable drug-like properties of IMPHY010637. However, these properties could be improved after experimental validation of protein-ligand binding. Both docked complex and poses created in 100 ns MD simulation of the protein-ligand complex showed the presence of multiple hydrogen bonds. RMSD and conformation analysis showed stable binding of IMPHY010637 with the cysteine proteinase of MPXV at its active site. Compared to the known inhibitor, IMPHY010637 showed better binding with the protein as observed by the PCA and MM/GBSA analysis. This study concluded IMPHY010637 as a potential inhibitor for the cysteine proteinase of MPXV using computational methods that could be tested in in-vitro experiments.Communicated by Ramaswamy H. Sarma.

Exploration of phytochemical compounds against Marburg virus using QSAR, molecular dynamics, and free energy landscape.

Marburg virus disease (MVD) is caused by the Marburg virus, a one-of-a-kind zoonotic RNA virus from the genus Filovirus. Thus, this current study employed AI-based QSAR and molecular docking-based virtual screening for identifying potential binders against the target protein (nucleoprotein (NP)) of the Marburg virus. A total of 2727 phytochemicals were used for screening, out of which the top three compounds (74977521, 90470472, and 11953909) were identified based on their predicted bioactivity (pIC50) and binding score (< - 7.4 kcal/mol). Later, MD simulation in triplicates and trajectory analysis were performed which showed that 11953909 and 74977521 had the most stable and consistent complex formations and had the most significant interactions with the highest number of hydrogen bonds. PCA (principal component analysis) and FEL (free energy landscape) analysis indicated that these compounds had favourable energy states for most of the conformations. The total binding free energy of the compounds using the MM/GBSA technique showed that 11953909 (ΔGTOTAL = - 30.78 kcal/mol) and 74977521 (ΔGTOTAL = - 30 kcal/mol) had the highest binding affinity with the protein. Overall, this in silico pipeline proposed that the phytochemicals 11953909 and 74977521 could be the possible binders of NP. This study aimed to find phytochemicals inhibiting the protein's function and potentially treating MVD.

Postrenal transplant anemia and its effects on patients and graft outcomes: Seven years follow-up.

Introduction: Post-transplant anemia (PTA) is a common serious complication following kidney transplantation. It affects graft and patient survival. Anemia that presents within six months post-transplantation is defined as an early PTA. Late PTA is when anemia occurs more than six months following transplantation. Despite this, there are limited studies on the long-term impact of anemia on patient survival and graft function in kidney transplants. We conducted a retrospective study with long-term follow-up to investigate the effect of early and late PTA on patient and graft function within seven years and to estimate the prevalence of PTA at six months, two, four, and seven years postrenal transplantation along with the associated risk factors. Method: A retrospective chart review of 145 adult patients who had kidney transplants from January 1 to December 31, 2015, and were followed up until December 31, 2022. Anemia was defined according to the World Health Organization (WHO) criteria of hemoglobin<12 gm/dl in females and<13 mg/dl in males. Pretransplant, six months, two, four, and seven years postrenal transplantation medications and laboratory data were obtained. Patients were excluded if they were pediatrics or had missing data. Result: 180 patients were screened, and 145 patients met the inclusion criteria. The prevalence of early PTA was 8.3%. Trimethoprim-sulfamethoxazole was significantly associated with anemia within six months. The prevalence of anemia increased at two, four-, and seven-years post-transplant (24.8%, 24.8%, and 27.6%, respectively). Graft failure was significantly associated with late PTA at four and seven years (p-value < 0.001). P < 0.005, respectively). Death was reported for 3 patients, and it was significantly associated with late transplant anemia (p-value < 0.005), Cytomegalovirus was associated with anemia at 6 months and 2 years post-transplant and significantly associated with graft failure (p-value < 0.037). Conclusion: The results of this study indicate that post-transplant anemia is associated with graft failure and patient mortality. Therefore, managing anemia post-transplant should be addressed more carefully.

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City.

The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process.

Humoral and Cellular Immunity following Five Doses of COVID-19 Vaccines in Solid Organ Transplant Recipients: A Systematic Review and Meta-Analysis.

Solid organ transplant (SOT) recipients are at increased risk of COVID-19 infection because of their suppressed immunity. The available data show that COVID-19 vaccines are less effective in SOT recipients. We aimed to assess the cellular and humoral immunogenicity with an increasing the number of doses of COVID-19 vaccines in SOT recipients and to identify factors affecting vaccine response in this population. A systematic review and meta-analysis were conducted to identify ongoing and completed studies of humoral and cellular immunity following COVID-19 vaccines in SOT recipients. The search retrieved 278 results with 45 duplicates, and 43 records did not match the inclusion criteria. After title and abstract screening, we retained 189 records, and 135 records were excluded. The reasons for exclusion involved studies with immunocompromised patients (non-transplant recipients), dialysis patients, and individuals who had already recovered from SARS-CoV-2 infection. After full-text reading, 55 observational studies and randomized clinical trials (RCTs) were included. The proportion of responders appeared higher after the third, fourth, and fifth doses. The risk factors for non-response included older age and the use of mycophenolate mofetil, corticosteroids, and other immunosuppressants. This systematic review and meta-analysis demonstrates the immunogenicity following different doses of COVID-19 vaccines among SOT patients. Due to the low immunogenicity of vaccines, additional strategies to improve vaccine response may be necessary.

Highlights on the Development, Related Patents, and Prospects of Lenacapavir: The First-in-Class HIV-1 Capsid Inhibitor for the Treatment of Multi-Drug-Resistant HIV-1 Infection.

The multidrug-resistant (MDR) human immunodeficiency virus 1 (HIV-1) infection is an unmet medical need. HIV-1 capsid plays an important role at different stages of the HIV-1 replication cycle and is an attractive drug target for developing therapies against MDR HIV-1 infection. Lenacapavir (LEN) is the first-in-class HIV-1 capsid inhibitor approved by the USFDA, EMA, and Health Canada for treating MDR HIV-1 infection. This article highlights the development, pharmaceutical aspects, clinical studies, patent literature, and future directions on LEN-based therapies. The literature for this review was collected from PubMed, authentic websites (USFDA, EMA, Health Canada, Gilead, and NIH), and the free patent database (Espacenet, USPTO, and Patent scope). LEN has been developed by Gilead and is marketed as Sunlenca (tablet and subcutaneous injection). The long-acting and patient-compliant LEN demonstrated a low level of drug-related mutations, is active against MDR HIV-1 infection, and does not reveal cross-resistance to other anti-HIV drugs. LEN is also an excellent drug for patients having difficult or limited access to healthcare facilities. The literature has established additive/synergistic effects of combining LEN with rilpivirine, cabotegravir, islatravir, bictegravir, and tenofovir. HIV-1 infection may be accompanied by opportunistic infections such as tuberculosis (TB). The associated diseases make HIV treatment complex and warrant drug interaction studies (drug-drug, drug-food, and drug-disease interaction). Many inventions on different aspects of LEN have been claimed in patent literature. However, there is a great scope for developing more inventions related to the drug combination of LEN with anti-HIV/anti-TB drugs in a single dosage form, new formulations, and methods of treating HIV and TB co-infection. Additional research may provide more LEN-based treatments with favorable pharmacokinetic parameters for MDR HIV-1 infections and associated opportunistic infections such as TB.

Effect of zinc supplementation on symptom reduction and length of hospital stay among pediatric patients with Coronavirus disease 2019 (COVID-19).

Objectives: Zinc is considered an essential multipurpose trace element because of its ability to act as a cofactor and signaling molecule. As reported in earlier studies of pediatric respiratory infection management, zinc exhibits potent immunoregulatory and antiviral properties, but its effects on pediatric patients with COVID-19 remain unknown. The aim of this study was to determine the extent to which zinc supplementation improves COVID-19 symptoms, length of hospitalization and, to determine how zinc supplementation impacts ICU admission, in-hospital mortality, need for ventilation, duration of ventilation, need for vasopressors, development of liver injury, or respiratory failure. Methods: Pediatric patients younger than 18 years with confirmed COVID-19 infection during the study period (March 1, 2020, to December 31, 2021) were recruited for this retrospective cohort study. The study population was divided into two arms (zinc/no zinc supplementation as an adjunct to standard therapy). Results: Of 169 hospitalized patients who were screened, 101 met the inclusion criteria. No statistically significant association was found between the administration of zinc as adjunctive therapy and symptom reduction, intensive care unit (ICU) admission, or mortality (p = 0.105; p = 0.941, and p = 0.073, respectively). However, zinc supplementation was associated with a statistically significant reduction in respiratory failure and length of hospitalization (p = 0.004 and p = 0.017, respectively), also, zinc administration was associated with elevated serum creatinine (p = 0.01*). Conclusions: Among pediatric patients with COVID-19, zinc supplementation was associated with shorter hospital stay. However, there was no significant difference between the two groups in terms of symptom improvement, in-hospital mortality, or ICU admission. In addition, the study raises question about the possibility of kidney injury as indicated by high levels of serum creatinine.

Variants of SARS-CoV-2: Influences on the Vaccines' Effectiveness and Possible Strategies to Overcome Their Consequences.

The immune response elicited by the current COVID-19 vaccinations declines with time, especially among the immunocompromised population. Furthermore, the emergence of novel SARS-CoV-2 variants, particularly the Omicron variant, has raised serious concerns about the efficacy of currently available vaccines in protecting the most vulnerable people. Several studies have reported that vaccinated people get breakthrough infections amid COVID-19 cases. So far, five variants of concern (VOCs) have been reported, resulting in successive waves of infection. These variants have shown a variable amount of resistance towards the neutralising antibodies (nAbs) elicited either through natural infection or the vaccination. The spike (S) protein, membrane (M) protein, and envelope (E) protein on the viral surface envelope and the N-nucleocapsid protein in the core of the ribonucleoprotein are the major structural vaccine target proteins against COVID-19. Among these targets, S Protein has been extensively exploited to generate effective vaccines against COVID-19. Hence, amid the emergence of novel variants of SARS-CoV-2, we have discussed their impact on currently available vaccines. We have also discussed the potential roles of S Protein in the development of novel vaccination approaches to contain the negative consequences of the variants' emergence and acquisition of mutations in the S Protein of SARS-CoV-2. Moreover, the implications of SARS-CoV-2's structural proteins were also discussed in terms of their variable potential to elicit an effective amount of immune response.

CYP3A5 Polymorphisms Leading to Tacrolimus Toxicity Following an Adult Renal Transplant.

Tacrolimus is one of the calcineurin inhibitors used for maintaining immuno-suppression in thoracic and abdominal transplantations including heart, lung, liver, intestine, pancreas, and renal transplants. It has a narrow therapeutic window requiring therapeutic drug monitoring (TDM). Genetic polymorphism in the expression of cytochrome P3A5 enzyme plays a significant role in the bioavailability of tacrolimus in patients, leading to toxicity or rejection. In this case, we studied a renal transplant patient who received a standard dose of tacrolimus and experienced toxicity related to the poor expression of cytochrome P450 3A5 (CYP3A5), which required the withholding of tacrolimus and cutting the dose for several days with more frequent TDM. Similar cases have been reported before, yet there is no consensus on the appropriate dosage. The projected cost of additional TDM and hospitalization significantly exceeds the one-time cost of genetic CYP3A5 testing. In high-risk renal transplant recipients, pharmacogenetic testing must be considered to cut the time to limit TDM, prevent extended hospitalization, and reduce the total cost of transplantation.

Burnout Among Respiratory Therapists in a tertiary hospital in Saudi Arabia.

Background: Respiratory therapists (RTs) provide various types of patient care in different clinical settings. Burnout can have an undesirable effect on RTs and their patients. Managing the COVID-19 pandemic, specifically in hospitals, could induce stress in RTs and increase the likelihood of burnout. Therefore, this study aimed to measure the level of burnout among RTs during the COVID-19 pandemic.Method: A validated cross-sectional questionnaire was used to identify the prevalence and characteristics of burnout among RTs. The questionnaire was administered by the respiratory care department of a tertiary healthcare center in Riyadh, Saudi Arabia. The data analysis included descriptive, inferential, and correlational tests.Results: The survey was distributed among 100 RTs with a response rate of 66%. Most respondents reported high burnout levels in the three domains of burnout: 77% reported emotional exhaustion, 98% reported depersonalization, and 73% reported low personal achievement. Univariate analysis revealed that emotional exhaustion was significantly higher among RTs who were male, married, or working in critical care settings. Female RTs and those working in general care settings reported feelings of reduced personal achievement. Furthermore, the analysis revealed a moderate positive correlation between the years of experience and emotional exhaustion (r=0.6, p<0.001).Conclusions: A high prevalence of burnout was observed among RTs in one Saudi tertiary hospital. Burnout is associated with several factors, and interventions should target all domains of burnout. RT management and staff are responsible for addressing their needs and collaboratively working together to overcome burnout. Further investigations focusing on techniques and strategies to alleviate burnout are required.

A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients' cough and breathing sounds.

The goal of this paper is to classify the various cough and breath sounds of COVID-19 artefacts in the signals from dynamic real-life environments. The main reason for choosing cough and breath sounds than other common symptoms to detect COVID-19 patients from the comfort of their homes, so that they do not overload the Medicare system and therefore do not unwittingly spread the disease by regularly monitoring themselves. The presented model includes two main phases. The first phase is the sound-to-image transformation, which is improved by the Mel-scale spectrogram approach. The second phase consists of extraction of features and classification using nine deep transfer models (ResNet18/34/50/100/101, GoogLeNet, SqueezeNet, MobileNetv2, and NasNetmobile). The dataset contains information data from almost 1600 people (1185 Male and 415 Female) from all over the world. Our classification model is the most accurate, its accuracy is 99.2% according to the SGDM optimizer. The accuracy is good enough that a large set of labelled cough and breath data may be used to check the possibility for generalization. The results demonstrate that ResNet18 is the best stable model for classifying cough and breath tones from a restricted dataset, with a sensitivity of 98.3% and a specificity of 97.8%. Finally, the presented model is shown to be more trustworthy and accurate than any other present model. Cough and breath study accuracy is promising enough to put extrapolation and generalization to the test.

Evaluation of Compliance Issues to Anti-glaucoma Medications Before and After a Structured Interventional Program.

Background Glaucoma is one of the most common eye diseases in the elderly and the major cause of irreversible vision loss worldwide. Adherence to life-long therapies is crucial to prevent glaucoma progression. The current study aims to assess the educational element and its impact on glaucoma medication compliance over short and long periods. Methods This was a survey-based, prospective, interventional study, conducted via interviews of all glaucoma patients presented to the Ophthalmology Center at King Abdullah Medical City (KAMC), Makkah, Saudi Arabia. To achieve the study's aim, a questionnaire with 31 items was utilized, followed by a structured program between September 2019 to June 2021. After that, a second questionnaire was used after a one month to one year to re-evaluate the intervention. Data was automatically collected in Microsoft Excel (Microsoft Corporation, Redmond, Washington, United States) and entered into IBM SPSS Statistics for Windows, Version 22.0 (Released 2013; IBM Corp., Armonk, New York, United States) for analysis. Results Non-compliance was detected in 15.7% of all recruited patients (n=134). However, the non-compliance percentage dropped to 10 (7.5%) after the structured program (P=0.028). Contributing factors were low educational level, bilateral eye disease, duration of treatment more than two years, and having more than two eye treatment bottles; however, the P-value was insignificant. Conclusions About one-sixth of our glaucoma patients were found to be non-compliant. However, the non-compliance reduced by more than half after the structured educational program. Treatment adherence can be improved by implementing awareness and correcting the beliefs about illness and medicines, thus potentially delaying disease development.

Foam Synthesis of Nickel/Nickel (II) Hydroxide Nanoflakes Using Double Templates of Surfactant Liquid Crystal and Hydrogen Bubbles: A High-Performance Catalyst for Methanol Electrooxidation in Alkaline Solution.

This work demonstrates the chemical synthesis of two-dimensional nanoflakes of mesoporous nickel/nickel (II) hydroxide (Ni/Ni(OH)2-NFs) using double templates of surfactant self-assembled thin-film and foam of hydrogen bubbles produced by sodium borohydride reducing agent. Physicochemical characterizations show the formation of amorphous mesoporous 2D nanoflakes with a Ni/Ni(OH)2 structure and a high specific surface area (165 m2/g). Electrochemical studies show that the electrocatalytic activity of Ni/Ni(OH)2 nanoflakes towards methanol oxidation in alkaline solution is significantly enhanced in comparison with that of parent bare-Ni(OH)2 deposited from surfactant-free solution. Cyclic voltammetry shows that the methanol oxidation mass activity of Ni/Ni(OH)2-NFs reaches 545 A/cm2 gcat at 0.6 V vs. Ag/AgCl, which is more than five times higher than that of bare-Ni(OH)2. Moreover, Ni/Ni(OH)2-NFs reveal less charge transfer resistance (10.4 Ω), stable oxidation current density (625 A/cm2 gcat at 0.7 V vs. Ag/AgCl), and resistance to the adsorption of reaction intermediates and products during three hours of constant-potential methanol oxidation electrolysis in alkaline solution. The high-performance electrocatalytic activity of Ni/Ni(OH)2 nanoflakes is mainly derived from efficient charge transfer due to the high specific surface area of the 2D mesoporous architecture of the nanoflakes, as well as the mass transport of methanol to Ni2+/Ni3+ active sites throughout the catalyst layer.

The Impact of Snapchat Use on Self-Image and Inclination Toward Cosmetic Procedures in Saudi Arabia.

Background Exposure to social media gives individuals more significant body image concerns, and many studies have emphasized that people with a negative body image are more interested in cosmetic surgery. So, increased exposure to celebrities with aesthetic images and exposure to cosmetic Surgery information and advertisements lead to negative body image in the exposed populations. Aim This study aimed to determine the impact of social media on self-image inclination toward cosmetic procedures. Subjects and methods This is a cross-sectional study conducted among an adult population in the Kingdom of Saudi Arabia. A self-administered questionnaire was distributed among participants using social media platforms. the questionnaire was sought information on socio-demographic characteristics (age, gender, education, etc.), the influence of social media on the decision to undergo cosmetic surgery, and the Acceptance of Cosmetic Surgery Scale (ACSS). Results Among the 1064 participants recruited, 41.4% were aged less than 25 with females outnumbering men substantially (82.1). Twenty-seven of the respondents increased their desire to undergo a cosmetic procedure due to advertisements or publications posted on social media. The total mean ACSS score was 3.36 (SD 1.69) out of 10 points, indicating lower acceptance. The socio-demographic variables associated with increased ACSS scores were being older, female, educated, having been married, and previous history of cosmetic surgery. Conclusion Despite the influence of Snapchat on body image, there was a low inclination to undergo cosmetic surgery among the general population in Saudi Arabia. The tendency to undergo cosmetic surgery tends to increase due to the influence of increased social media following, social media influencers, and advertisements and publications posted on social media platforms. More research is warranted to determine the perspectives of the general public regarding the influence of social media platforms on the inclination to undergo cosmetic surgery procedures for the enhancement of body appearance.

Future Anxiety Among Young People Affected by War and Armed Conflict: Indicators for Social Work Practice.

Strengthening the evidence base for professional social work intervention that contributes to providing psychosocial support to international students affected by war and conflict is a major priority as this vulnerable group of youth increases. Therefore, this study aimed to determine the level of future anxiety among international students coming from areas experiencing war and conflict. This study used the descriptive correlative approach, where the future anxiety scale was applied to a sample of 287 international students affected by war and conflicts. Findings showed that there are statistically significant differences between males and females (in favor of females) in the level of the social dimension of future anxiety. The current study results showed a statistically significant relationship between future anxiety and some variables related to war and conflict (living in a war environment - direct and indirect exposure to damage). There are statistically significant differences between those who lived in Yemen at the time of wars and those who did not live (in favor of those who lived in Yemen at the time of wars) in the level of future anxiety. There are also statistically significant differences between those exposed to harm or their family because of the war and those who were not exposed (in favor of those who were exposed) in the level of future anxiety as a whole. The study recommends developing psychosocial support services for this vulnerable group, considering the cultural context to promote women and protect them from discrimination in the services they deserve on an equal basis with men.