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Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner. Graphical Abstract

Introduction Data on how the health belief model constructs might predict the self-care behavior of patients with cardiovascular disease (CVD) post-coronavirus disease (COVID-19) 2019 pandemic are scarce. Objective This study determines the predictors influencing patients’ intention to adhere to self-care for CVD in the United Arab Emirates after the COVID-19 pandemic. Methods A descriptive cross-sectional design was used. A total of 222 patients with CVD were purposively selected. Three scales were used: Health Beliefs Related to Cardiovascular Disease , Physical Activity Measurement , and Behavioral Intention Measurement . Various socio-demographic and clinical characteristics and the participants’ health belief components were considered potential factors in the multivariate analysis to identify the independent predictors of the intention of self-care behaviors. Results The participants had a high level of perceived CVD risk (M = 4.02, SD = 0.714) and high level of perceived benefits regarding adopting healthy behavior (M = 4.30, SD = 0.817). The multiple linear regression revealed that not performing regular sweating exercises (β = 0.230), not receiving smoking cessation instructions (β = 0.214, p = .005), being sufficiently active (β = 0.304), and having no history of heart surgery (β = 0.155) were the independent predictors of low intention scores. The perceived benefits and perceived cues to the action of the Health Belief Model (HBM) were significant independent predictors of behavior intention and were responsible for a 22% increase in the participants’ intention variances . Conclusions In a post-COVID CVD, this investigation delineated perceived benefits and cues to action derived from the HBM as the most robust prognosticators of behavioral intention (accounting for 47% of the variance), superseding sociodemographic and clinical parameters (explaining 25% of the variance). These results advocate for tailored interventions accentuating individual advantages and explicit prompts for behavioral modifications within this demographic.

Mahmoud Izraiq,1 Kais AlBalbissi,2 Raed Alawaisheh,1 Ahmad Toubasi,2 Yaman B Ahmed,3 Marah Mahmoud,1 Karam I Khraim,1 Mohammed AL-Ithawi,1 Obada Mohammad Mansour,1 Anoud Hamati,1 Farah A Khraisat,2 Hadi Abu-Hantash4 1Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan; 2Cardiology Section, Internal Medicine Department, Jordan University Hospital, Amman, Jordan; 3Cardiology Section, Internal Medicine Department, King Abdullah University Hospital, Irbid, Jordan; 4Department of Cardiology, Amman Surgical Hospital, Amman, JordanCorrespondence: Mahmoud Izraiq, Email izraiq@yahoo.comBackground: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction.Methodology: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF < 40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction.Results: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study.Conclusion: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.Keywords: Heart failure, mortality prediction, machine learning, patient outcomes, Jordan heart failure registry

Introduction: Acute accidental poisoning in children remains a significant public health issue and a predictable cause of morbidity around the world. To take preventive measures, it is necessary to identify the pattern of this problem. Objective: To determine the extent and characteristics of paediatric poisoning, an epidemiological investigation specific to each country is required. The goal of our research was to determine the current pattern of acute poisoning in children between (0−5) years old in Jordan. Methods: This retrospective study performs a descriptive analysis of the Jordan University Hospital’s National Poison Information Center (NPIC) database and describes the epidemiology of acute poisoning in children between (0−5) years old during a period of two years (2018–2019). Results: Paediatric poisoning (0−5) years old accounts for approximately 88% of poisoning cases in Jordan between 2018 and 2019.Out of 3531 paediatric poisoning cases, 44.9% of cases were in children between (2−3) years old, 63.4% of subjects were male. 40.9% of calls were from governmental hospitals. Most cases occurred at home (98.7%) and were unintentional (98.6%). Medication poisoning was the commonest among cases (71.0%). Besides, 89.4% were asymptomatic at the time of call, and Central Nervous System (CNS) symptoms being the most common (3.6%) among the symptomatic cases. Conclusions: Most cases of paediatric poisoning handled by the NPIC was due to medications. To prevent or minimize these cases, it is necessary to educate parents and other caregivers about proper medication storage and use, and in case of poisoning, urgent referral to health facilities is required.

Mahmoud Hamdan,1,2 Amar Hisham Jaaffar,3,4 Omar Khraisat,5 Marwan Rasmi Issa,6 Mu’taman Jarrar7,8 1Quality Management and Patient Safety Administration, King Saud Medical City, Riyadh, Saudi Arabia; 2College of Graduate Studies (COGS), Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Kajang, Selangor, 4300, Malaysia; 3Institute of Energy Policy and Research (Iepre), Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Kajang, Selangor, 4300, Malaysia; 4College of Business Management and Accounting (COBA), Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Kajang, Selangor, 43000, Malaysia; 5Faculty of Nursing, Al-Ahliyya Amman University, Amman, Jordan; 6Skills Development Training Center, King Saud Medical City, Riyadh, 12746, Saudi Arabia; 7Medical Education Department, King Fahd Hospital of the University, Al-Khobar, Saudi Arabia; 8Vice Deanship for Development and Community Partnership, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaCorrespondence: Mahmoud Hamdan, College of Graduate Studies (COGS), Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, Kajang, Selangor, 4300, Malaysia, Tel +966580405515, Email Mahmoud_hamdan84@yahoo.comBackground: To ensure best possible patient outcomes, patient safety is a major component of healthcare delivery system that needs to be prioritized. Safety practices among nurses are essential to maintain patient safety, especially the practices of medication administration, handover, patient falls and unplanned extubations prevention.Purpose: To investigate the mediating effect of patient safety culture between the relationship of transformational leadership and safety practices among nurses.Methods: The data in this cross-sectional study were gathered from a survey targeted clinical nurses using a random sampling technique. The study was conducted in a medical city in Saudi Arabia, and two hundred nurses were surveyed. The Multifactor Leadership, Hospital Survey on Patient Safety Culture, and Nursing Safety Practice questionnaires were used in the study.Results: The results revealed significant positive associations between transformational leadership, patient safety culture, and nursing safety practices. Moreover, patient safety culture mediates the association between transformational leadership and safety practices among nurses.Conclusion: Enhancing transformational leadership capabilities among nurse managers should be considered in order to improve nursing safety practices. Additionally, patient safety culture should be measured and improved periodically to ensure better nursing safety practices.Keywords: nurses, safety practices, transformational leadership, patient safety, culture

Shared governance is a concept that has been gaining popularity in the nursing field. It is a framework that allows nurses to have a greater role in clinical decision-making. This approach recognizes the expertise and knowledge that nurses possess and allows them to be active participants in the decision-making process. It is a way to empower nurses and to ensure that the best possible care is being provided to patients. By promoting shared governance, nurses are able to work collaboratively with other healthcare professionals and provide high-quality care that is evidence-based and patient-centered. This article presents data that was collected in an empirical study to investigate the impact of implementing a shared governance model on the perceptions of professional governance among nurses working in a tertiary hospital in Saudi Arabia by measuring the level of shared governance from the lowest level, the traditional governance level (management and administration only), to the highest level, the self-governance level (staff only), through six dimensions of nursing professional governance, including personnel, information, resources, participation, practice, and goals. The study was conducted over 8 months between July 2022 to February 2023 with the involvement of a random sample of 200 clinical nurses who completed a structured questionnaire before and after the study interventions as part of quasi-research. The interventions included designing and implementing a shared governance model, and providing a shared governance training to clinical to nurse participants. The pretest-posttest experimental group showed that there were improvements in the level of shared governance (shared governance level - primarily management/administration with some staff input), which denotes the effectiveness of nursing professionals governance training among nurses working in a tertiary hospital in Saudi Arabia. The data used in this study can be utilized by future studies for benchmarking purposes.

The microstructure of liquid phase sintered M3:2 high speed steel and the effect of adding carbon and silicon on the microstructure was characterized by scanning electron microscopy, dispersive spectrometry, and X-ray diffraction. Various types of carbides were formed depending on the added carbon and/or silicon, the sintering atmosphere and the cooling rate. The microstructure of sintered M3:2 high speed steel samples in vacuum conditions without the addition of Si and graphite is composed mainly of MC and M6C carbides inside cells and primarily at cell boundaries. The M2C eutectic carbides in these samples were formed at cell boundaries and their amounts and morphology depends on the cooling rate. Sintered samples in N2 atmosphere with added carbon and silicon, M6C eutectic and M7C3 eutectic carbides were dominantly formed while carbonitrides were formed in smaller amounts.

Objectives: This study aimed to do a comprehensive systematic review on the comparison of digital and conventional workflows regarding prosthetic outcomes, accuracy of implant impressions, framework passivity and fit, and clinical fabrication of multi-unit implant-supported fixed restorations. Materials and Methods: The EMBASE, PubMed, Scopus, and Cochrane Library databases were searched for relevant articles published up until April 2020. Results: No in-vivo article was found to compare full digital and conventional workflows regarding the accuracy of implant impressions, passivity and fit of frameworks, and prosthetic outcomes. There was no study to investigate full digital and conventional workflows for clinical fabrication of multi-unit implant-supported fixed restorations. Conclusion: This empty review highlights the need for further research to compare full digital and conventional workflows for implant-supported restorations.

Background: With an increase in elderly people, it is essential to address the issue of cognitive impairment and support healthy aging. This study aimed to assess cognitive impairment and factors associated with it among older adults. Materials and Methods: A cross-sectional study was carried out in different catchment areas within the Jerash governorate in the north of Jordan. The Elderly Cognitive Assessment Questionnaire (ECAQ) and a household face-to-face interview were used to collect data from 220 older adult participants aged 60 years and more. Descriptive statistics were conducted to describe the study variables. Correlation tests were applied to find associations between them. Logistic regression analysis was applied, with a minimum significance level (p < 0.05). Results: About 9.10% of the older adults had cognitive impairment. Cognitive impairment was correlated with age, self-perceived health, hypertension, stroke, and mental illness. The primary predictors of cognitive impairment were age [odds ratio (OR) =1.07 (1.01–1.14), p = 0.001] and stroke [OR = 10.92 (1.44–82.85), p = 0.001]. Conclusions: While many factors were correlated with cognitive impairment, the strongest predictors of cognitive impairment were age and stroke.

Abstract Purpose This study aimed to assess the impact of the Nursing Spiritual Care Module on the competence of nurses in providing spiritual care in the context of Malaysia. Method This study employed an experimental design and involved a total of 122 nurses, with 59 in the experimental group and 63 in the control group. Participants were selected from palliative care wards associated with Hospital Universiti Sains Malaysia. Nurses in the experimental group underwent a two-week educational module on nursing spiritual care, while nurses in the control group attended a single lecture on spiritual care provided by the hospital. Results The results indicated no significant differences in sociodemographic characteristics between the two groups. A significant difference in spiritual care competence within the intervention group and the control group over time (p-value = 0.001), between the two groups (p-value = 0.038), and in the interaction between time and group (p-value = 0.001). Conclusion The Nursing Spiritual Care Module is crucial in aiding nurses and healthcare professionals in cultivating the appropriate and wholesome attitudes and practices necessary to address the spiritual needs of patients.

Background: Self-medication is a global concern among professionals and non-professionals, with a rapid increase in prevalence. The study aims to assess the prevalence of self-medication and its associated factors among university students. Materials and Methods: A cross-sectional, descriptive study was conducted in three universities, and a total of 817 college students participated in this study. Results: About 75.40% of the participants reported using medications without a professional prescription. The category of analgesics was the most commonly used in self-medication (82.80%), while the most common symptom was a headache (81.50%). Almost 74.10% percent of participants who have practiced self-medication stated that the reason was the “lack of time to consult a physician.” Most participants who have used self-medication (90.30%) stated that the source of knowledge was “previous prescription.” Conclusions: Health education programs concerning self-mediation should be held in university settings to improve attitudes and practices toward self-mediation.

The modern digital environment is becoming increasingly interconnected, underscoring the critical need to safeguard network infrastructures. Detecting anomalies in network traffic remains essential as cyber threats continue to evolve. Analyzing trends, patterns, and relationships in network traffic data over time poses challenges. On the other hand, traditional generative neural networks emphasize detecting network attacks but encounter difficulties due to limitations in capturing the temporal and dynamic aspects of network traffic. This paper introduces a new methodology aimed at enhancing the identification of irregularities in network traffic using a Temporal Metric-Driven GRU Embedded Generative Neural Network (TMG-GRU-VAE). This method incorporates Gated Recurrent Units (GRU) into variational autoencoders to effectively train on the temporal characteristics of network traffic in temporal sequential networks. Moreover, we present a Temporal Correlation Index (TCI) score designed for anomaly detection in Network Intrusion Detection Systems (NIDS). This innovative metric offers a sophisticated and dynamic assessment of temporal behavior within network traffic. TCI’s ability to distinguish between normal and anomalous temporal patterns plays a pivotal role in mitigating false positives. Our proposed method greatly improves the detection of small changes in abnormal sequences over time, enhancing accuracy by making anomalies stand out more clearly and reducing false alarms, thereby making the system more reliable. The proposed work, validated using the CIC-IDS-2017 and CIC-IDS-2018 datasets, demonstrates a significant decrease in False Positives (FP) across all models. Notable improvements range from 7.2% to 12.9% for the CIC-IDS-2017 dataset and from 7.1% to 14.1% for the CIC-IDS-2018 dataset. This highlights its significant impact on decreasing false positive rates.

Human activity recognition (HAR) plays a crucial role in assisting the elderly and individuals with vascular dementia by providing support and monitoring for their daily activities. This paper presents a deep learning (DL)-based approach to HAR, leveraging convolutional neural network (CNN), convolutional long short-term memory (ConvLSTM), and long-term recurrent convolutional network (LRCN) architectures. These models are designed to extract spatial features and capture temporal dependencies in video data, enhancing the accuracy of activity classification. We conducted experiments on the UCF50 and HMDB51 video datasets, encompassing diverse human activities. Our evaluation demonstrates that the ConvLSTM model achieves an accuracy of 82% on UCF50 and 68% on HMDB51, while the LRCN model gives accuracies of 93.44% and 71.55%, respectively. Finally, the CNN model outperforms with an accuracy rate of 99.58% for the UCF50 and 92.70% for the HMDB51 datasets. These significant improvements showcase the effectiveness of integrating convolutional and recurrent neural networks for HAR tasks. Our research contributes to advancing HAR systems with potential applications in healthcare, assisted living, and surveillance. By accurately recognizing human activities, our models can assist in remote patient monitoring, fall detection, and public safety initiatives. These findings underscore the importance of DL in enhancing the quality of life and safety for individuals in various contexts.

The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market.

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats.

Abstract Background and objectives Narghile is becoming the favorite form of tobacco use by youth globally. This problem has received more attention in recent years. The aim of this study was to investigate the prevalence and pattern of narghile use among students in three public Jordanian universities; to assess their beliefs about narghile's adverse health consequences; and to evaluate their awareness of oral health and oral hygiene. Methods The study was a cross-sectional survey of university students. A self-administered, anonymous questionnaire was distributed randomly to university students in three public Jordanian universities during December, 2008. The questionnaire was designed to ask specific questions that are related to smoking in general, and to narghile smoking in specific. There were also questions about oral health awareness and oral hygiene practices. Results 36.8% of the surveyed sample indicated they were smokers comprising 61.9% of the male students and 10.7% of the female students in the study sample. Cigarettes and narghile were the preferred smoking methods among male students (42%). On the other hand, female students preferred narghile only (53%). Parental smoking status but not their educational level was associated with the students smoking status. Smokers had also significantly poor dental attendance and poor oral hygiene habits. Conclusion This study confirmed the spreading narghile epidemic among young people in Jordan like the neighboring countries of the Eastern Mediterranean region. Alarming signs were the poor oral health awareness among students particularly smokers.

BackgroundReducing the risk of infection transmission by getting emergency care for pediatric patients is a challenging task.AimThe study aim was to assess emergency nurses' readiness to provide care for pediatric patients with infectious diseases.MethodTwo hundred Jordanian emergency department nurses were surveyed using a descriptive design.ResultsThe study revealed that insufficient safety and infection control procedures were put into place, starting with family support to allow nurses to work 145 (78%), family care plans intended to assist caregivers 139 (74.7%), the availability of respiratory protection and a backup plan for standard precautions, training requirements, and equipment 131 (70.4%), create a unit pandemic safety strategy 124 (66.7%), have a plan for emergencies for at-risk staff 116 (62.4%), have a hospital pandemic safety plan 113 (60.8%), manage inventory 102 (54.8%), use reuse guidelines if there will be severe shortages 99 (53.2%), create a strategy for nurses' access to healthcare for themselves and their families 96 (51.6%), and end with any required system updates for new policies 88 (47.3%). Staff nurses made up a large proportion of participants (145; 78%; 115; 62.8%) who said they lacked experience with care for pediatric patients with infectious illnesses who were critically sick. A 62.8% of nurses reported they did not have training in infectious disease emergency prevention and control for pediatric patients. What nurses prioritize it was determined that the concept of crisis standards of care (34.9%) was the most important educational topic for training emergency room nurses to care for pediatric patients who are critically ill with infectious infections, while the clarity of communication pathways was ranked lowest.ConclusionMore training and support are needed for emergency room nurses to properly care for children's patients with infectious illnesses.

Building Information Modeling (BIM) technologies have attracted much research attention, especially in the last fifteen years. The prospects and benefits that BIM promises for all participants in the design, construction and operation of buildings are hopeful and enthusiastic. Nevertheless, the global adoption of BIM has been slow and uneven. A bibliometric analysis shows that BIM research is concentrated in a few centers and performed by a narrow group of specialists. As a result, there are research areas with few or no publications (“blind spots”). One such area is the economics of BIM implementation. Considering the transition to the use of BIM as an investment project, the authors of the article formulate the necessary conditions for BIM implementation to become investment attractive. It is shown that the participation of governmental structures is necessary for a wide transition to digital technologies in the architectural and construction sphere.

AbstractThe effects of quasi-static strain rates on the tensile properties of two commercial ferrite-martensite dual-phase DP 600 and DP 800 steels were investigated using strip-shaped samples. The investigation was done by uniaxial tensile tests, covering applicable quasi-static strain rates. The two dual-phase steels show positive strain rate sensitivity. It is found that, as the flow stress increases, the strain-rate sensitivity exponent m decreases. The drop in the strain-rate sensitivity exponent m with strain is largely attributed to the decreased true strain rate caused by the increased instantaneous length of the specimen as the deformation progresses. To better describe the flow behavior of DP steels, a relationship combining the effect of both strain hardening exponent n and strain-rate sensitivity exponent m on the slope of the stress-strain is correlated. A good agreement between the extended Hollomon model and experimental tensile test data from stress-strain measurements is found.

Introduction The training of nursing staff to deliver extracorporeal membrane oxygenation in Lebanon has recently attracted attention. It is important to comprehend the background of nurses who take on this new duty. Objectives The main objectives of this study were to (1) explore the experiences of intensive care unit nursing staff who work with extracorporeal membrane oxygenation, (2) identify the psychological and physiological challenges experienced by intensive care unit nurses while managing patients with extracorporeal membrane oxygenation, and (3) determine the roles assumed by intensive care unit nurses while managing patients utilizing extracorporeal membrane oxygenation. Methods A qualitative phenomenological design utilizing semistructured interviews utilizing a purposive sampling of 15 intensive care unit nurses using extracorporeal membrane oxygenation devices. Interviews were audio recorded, verbatim transcribed, and thematically analyzed. Results Three themes emerged, namely (1) nurses’ role in extracorporeal membrane oxygenation, (2) skills and training, and (3) challenges faced by extracorporeal membrane oxygenation nurses. Conclusion This study demonstrates that nurses play a crucial role in providing extracorporeal membrane oxygenation patient care, placing them under significant occupational stress due to the intensive care unit's routine workload and the demands of extracorporeal membrane oxygenation. Additionally, role confusion brought on by a lack of professional practice norms and emotional exhaustion made occupational pressure worse.

Purpose: Dual antiplatelet therapy is standard for patients undergoing percutaneous coronary intervention (PCI) with stents. Traditionally, patients swallow the loading dose of a P2Y12 inhibitor before or during PCI. Time to achieve adequate platelet inhibition after swallowing the loading dose varies significantly. Chewed tablets may allow more rapid inhibition of platelet aggregation. However, data for this strategy in patients with stable ischemic heart disease or non-ST-elevation acute coronary syndrome (NSTE-ACS) are less robust. Methods: In this single-center prospective trial, 112 P2Y12-naïve patients with stable ischemic heart disease or NSTE-ACS on aspirin therapy and who received ticagrelor after coronary angiography but before PCI were randomized to chewing (n = 55) or swallowing (n = 57) the ticagrelor loading dose (180 mg). Baseline variables were compared using 2-sample t-test and chi-squared/Fisher’s exact tests as appropriate, with alpha set at 0.05. P2Y12 reaction units (PRU) were compared at baseline, 1 hour, and 4 hours using Wilcoxon rank-sum test. Patients then received standard ticagrelor dosing. Results: After exclusions, P2Y12 PRU in the chewed and swallowed groups at baseline, 1 hour, and 4 hours after ticagrelor loading dose were 243 vs 256 (P = 0.75), 143 vs 210 (P = 0.09), and 28 vs 25 (P = 0.89), respectively. No differences were found in major adverse cardiac events (MACE) or major bleeding at 30 days and 1 year. Conclusions: In patients with stable ischemic heart disease or NSTE-ACS, chewing rather than swallowing ticagrelor may lead to slightly faster inhibition of platelet aggregation at 1 hour with no increase in MACE or major bleeding.

Abstract Background Apitherapy is an emerging field in cancer research, particularly in developing communities. The potency of Melittin (MEL), a major constituent in bee venom is accounted for the cytotoxic capacity against cancer cells. It is postulated that the genotype of bees and the time of venom collection influences its specific activity against certain types of cancer. Method Hereby, Jordanian crude bee venom (JCBV) was collected during different seasons of the year, specifically spring, summer and autumn and investigated for in vitro antitumour effects. Venom collected during springtime comprised the highest quantity of MEL in comparison to venom collected some other time. Springtime-collected JCBV extract and MEL were tested on an immortal myelogenous leukaemia cell line, namely K562 leukemic cells. Treated cells were examined for cell modality via flow cytometry analysis and cell death mediating gene expressions. Results Springtime-collected JCBV extract and MEL showed an IC50 of 3.7 ± 0.37 μg/ml and 1.84 ± 0.75 μg/ml, respectively. In comparison to JCBV and positive control, MEL-treated cells exhibited late apoptotic death with a moderate cellular arrest at G0/G1 and an increase of cell number at G2/M phase. Expression of NF-κB/MAPK14 axis was inhibited in MEL and JCBV-treated cells, as well as expression of c-MYC and CDK4. Moreover, marked upregulation in ABL1, JUN and TNF was observed. In conclusion, springtime-collected JCBV showed the highest content of MEL while both JCBV and pure MEL showed apoptotic, necrotic, and cell cycle arrest efficiency against K562 leukemic cells. Conclusion Integration of bee venom in chemotherapy needs more investigation and should be carefully translated into clinical use. During such translation, the correlation of bee genotype, collection time and concentration of MEL in CBV should be profiled.

Background: It is well known that caregivers are at risk of suffering from diseases, including chronic ones, which might increase the burden of the healthcare worker. Materials and Methods: A cross-sectional study was conducted in Jordan on 143 caregivers who were randomly selected to participate in self-administered Quality of Life in Life-Threatening Illness—Family Carer Version (QOLLTI-F) questionnaire. The variables were tested using simple linear regression analyses in the SPSS. Results: Male Jordanian caregivers who provide unpaid care, reported lower quality of life than their female counterparts (p = 0.028), (p = 0.077), respectively. A high statistically significant result was found between unshared and shared care (p = 0.009). Statistically significant results were determined between single caregivers compared with those who were married or divorced (p = 0.894 and P = 0.041, respectively). Conclusions: This study concludes that gender, care status, type of care, and marital status are direct predictors of quality of life among Jordanian caregivers.

This study aims to provide a comprehensive understanding of consumer purchase (CPI) intention in the fast-food industry by examining the roles of cultural intelligence (CQ), perceived brand globalness (PBG), and consumer ethnocentrism (CET). An empirical survey was conducted among 290 Jordanian fast-food consumers. The findings underscore that CQ significantly impacts CPI by influencing consumers' attitudes and perceptions toward fast-food brands. Additionally, the results indicate a positive mediating role of PBG and a negative moderating role of CET in this relationship. These results have significant implications for fast-food companies looking to better customize tactics to cater to a diverse customer base. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control is required to ensure user privacy in the Internet of Things. Traditional access control methods are inappropriate for IoT systems because most conventional access control approaches are designed for centralized systems. This paper proposes a decentralized access control framework based on smart contracts with three parts: initialization, an access control protocol, and an inspection. Smart contracts are used in the proposed framework to store access control policies safely on the blockchain. The framework also penalizes users for attempting unauthorized access to the IoT resources. The smart contract was developed using Remix and deployed on the Ropsten Ethereum testnet. We analyze the performance of the smart contract-based access policies based on the gas consumption of blockchain transactions. Further, we analyze the system’s security, usability, scalability, and interoperability performance.