14 research outputs found
Depression among sickle cell anemia patients in the Eastern Province of Saudi Arabia
No full textObjectives: To determine the prevalence of, and factors associated with, depression among sickle cell anemia adult patients in the Eastern Province of Saudi Arabia.
Materials and Methods: A cross-sectional study was conducted between December 2014 and May 2015 among sickle cell anemia patients aged 16–70 years from the outpatient hematology clinics at Qatif Central Hospital. A total of 110 successive participants consented and answered an anonymous, self-administered, questionnaire and the Arabic version of the Beck Depression Inventory-II. Individuals were considered depressed if they scored ≥14 in Beck Depression Inventory-II. Simple logistic regression was used to compare differences between the depressed and nondepressed groups. Odds ratios (ORs) with 95% confidence intervals (95% CI) were reported.
Results: Depression was detected in 53 participants (48.2%). Bivariate analysis showed that lower educational qualification (OR = 2.5; 95% CI = 1.1–5.3; P = 0.021), higher frequency of vaso-occlusive crises (OR = 3.4; 95% CI = 1.3–8.7; P = 0.008) and frequent visits to the hematology clinic (OR = 5.3; 95% CI = 1.4–19.9; P = 0.008) were significantly associated with depression.
Conclusion: This study revealed that there is high prevalence of depression among sickle cell anemia patients in the Eastern Province of Saudi Arabia
Blockchain-enabled Zero Trust-based Secure and Energy Efficient scheme for 6G-enabled UASNs
No full textAbstract Marine networks such as the Underwater Acoustic Sensor Network (UASN) are the backbone for the exploration and monitoring of marine environments. These sensor nodes are deployed in extremely harsh underwater conditions, which are prone to various problems, including safety threats and energy drain. However, with their dramatic significance in multiple aspects, solutions for ensuring secured and efficient communications of these networks become essential. Herein, we put forth a new scheme termed Blockchain-enabled Zero Trust-based Secure and Energy Efficient (BZTSEE), where blockchain and a zero trust notion have been worked together into UASNs whose communications are enabled through 6G. BZTSEE works on identifying security and privacy issues while providing an energy-efficient solution. Using blockchain ensures secure and trustworthy communication, transparency in data sharing, maintenance of trust among nodes, and protection of user privacy. The scheme uses a PBFT protocol to defend itself against malicious actions and keep data secured. Several simulations show how BZTSEE works well under diverse conditions, including the number of nodes and attacks. The results render it quite effective for both small and large UASNs. In short, BZTSEE considerably augments the aspects of security, privacy, trust, and energy savings-a perfect fit for future UASNs
Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks
No full textAd hoc networks offer promising applications due to their ease of use, installation, and deployment, as they do not require a centralized control entity. In these networks, nodes function as senders, receivers, and routers. One such network is the Flying Ad hoc Network (FANET), where nodes operate in three dimensions (3D) using Unmanned Aerial Vehicles (UAVs) that are remotely controlled. With the integration of the Internet of Things (IoT), these nodes form an IoT-enabled network called the Internet of UAVs (IoU). However, the airborne nodes in FANET consume high energy due to their payloads and low-power batteries. An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET. This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm (IEE-DLSA), featuring a relay mechanism to provide optimal routing with energy efficiency in FANET. The energy efficiency of IEE-DLSA is enhanced using the Red-Black (R-B) tree to ensure the fairness of advanced energy-efficient nodes. Maintaining link stability, transmission loss avoidance, delay awareness with defined threshold metrics, and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA. The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols. The evaluation metrics considered in this study include network delay, packet delivery ratio, network throughput, transmission loss, network stability, and energy consumption
Unleashing the Potentials of IoT with Focus on Energy and Path Loss for Internet of Medical Things
No full textThe Internet of Things (IoT) encompasses a broad platform of sensor networks incorporating independent wireless networks. With advancements in sensor technology and IoT-enabled networks, their applications in the medical field have led to the development of the Internet of Medical Things (IoMT). In IoMT, sensor nodes monitor and evaluate patient conditions such as heartbeat, blood sugar levels, blood pressure and temperature, and can also remotely track patient activities through remote analysis. These IoMT systems utilize tiny sensors with limited communication ranges to gather essential patient information. Wireless devices are equipped with a short range and need a direct communication path. However, transmitting data from the source node to the destination node ultimately results in energy consumption and path loss. Path loss models and energy consumption models are essential to address these issues. In this paper, we propose a novel routing protocol named Energy Efficient and Path Loss Preserving (EEPLP) for IoMT. The EEPLP protocol focuses on energy efficiency and path loss preservation based on the relay approach. Two models are being proposed, one for path loss and the other for energy consumption. Finally, both models are merged since the major contribution is to avoid path loss and enhance the protocol’s energy efficiency. The EEPLP evaluates the state-of-the-art existing approaches of IoMT. The protocol is evaluated by simulating conditions and compared with other similar routing protocols already deployed in the IoMT; it has been observed that the EEPLP scheme has the potential to be maneuvered in IoMT structures with core targets of energy efficiency as well as path loss preservation techniques
Towards a comprehensive cancer control policy in Saudi Arabia
No full textCancer is emerging as a leading cause of morbidity and mortality in Saudi Arabia, with the incidence projected to double in the next 20 years. The health-care system in the country has witnessed considerable reforms over the past four decades, resulting in better control of communicable and non-communicable diseases and, subsequently, longer life expectancies. The Health Sector Transformation Program, a part of the Saudi Vision 2030, aims to strengthen the prevention and control of non-communicable diseases including cancer, improve access to care, deliver high-quality care services, improve patients' quality of life, and increase support for research and innovation. This Series paper highlights the considerable progress of the national cancer control programme, identifying remaining challenges and future opportunities. We envision that this paper will inform the development of the next National Cancer Control Plan to be sustainable, evidence-based, integrated, patient-centred, and value-driven for society.</p
Appropriateness of antibiotic prescribing for urinary tract infection in the emergency department of a tertiary care hospital
No full textBackground: Urinary tract infections (UTIs) are frequently diagnosed in the Emergency Departments (EDs) in Saudi Arabia. Despite their prevalence, there is a lack of localized data on antibiotic prescribing practices for UTIs in EDs, hampering optimal patient care and antibiotic resistance management. Objective: This study aimed to evaluate the appropriateness of antibiotic prescriptions for UTI patients by examining adherence to treatment guidelines at a tertiary care ED in Saudi Arabia. Methods: A retrospective cohort analysis was performed in UTI patients with ≥18 years of age who were presented to the ED at a tertiary care hospital between January 2022 and January 2023. 179 patients were randomly selected for enrollment, and data related to their demographics, medical history, microbial isolation, administered antibiotics, and incidence of recurrent infections were collected. Results: Ciprofloxacin (38 %), Cefuroxime (26.8 %), and Nitrofurantoin (16.8 %) were the most prescribed antibiotics. The female population accounted for 87.3 % of UTI cases. Notably, 40.7 % lacked microbial growth or data. Inappropriateness was found in 55.3 % of selection, 27.9 % of dosing, and 38.5 % of treatment duration confined to antibiotics in UTI management. Moreover, complicated UTIs rather than duration and indication for antibiotic therapy were independent risk factors for the re-occurrence of UTIs. Conclusions: The study depicted an overall 44.7 % of appropriate antibiotic prescriptions in UTI patients. However, it also highlighted the necessity for tailored interventions and promotional efforts aimed at promoting the rational use of antibiotics in patients, thereby preventing resistance and treatment failure
Drug Utilization Evaluation and Impact of Pharmacist Interventions on Optimization of Piperacillin/Tazobactam Use: A Retrospective Analysis and Prospective Audit
No full text(1) Background: Piperacillin/tazobactam is a broad-spectrum antimicrobial encompassing most Gram-positive and Gram-negative aerobic and anaerobic bacteria. The inappropriate use of such broad-spectrum antibiotics is an important contributor to the rising rates of antimicrobial drug resistance worldwide. Drug utilization evaluation studies and pharmacists’ interventions are vital to assess, develop, and promote the rational use of antibiotics. This drug utilization study aimed to evaluate the current utilization practice of piperacillin/tazobactam in a hospital setting and assess the impact of pharmacist intervention in improving its appropriate use. (2) Methodology: In this study, we used a retrospective cohort and a prospective cohort, a cross-sectional, observational method. It included a retrospective (Cycle A/pre-intervention-CycA) phase followed by an educational interventional phase conducted by the pharmacists. During the 2 months of educational intervention, pharmacists used several methods, including workshops, lectures, oral presentations, and the development and reinforcement of clinical pathways to promote the judicious use of piperacillin/tazobactam. This was followed by a prospective (Cycle B/post-intervention-CycB) phase to improve piperacillin/tazobactam usage appropriateness. The appropriateness criteria for this drug utilization evaluation were established based on antimicrobial guidelines, the published literature, the institutional antibiogram, consultation from the antimicrobial stewardship committee, and the product monograph (Tazocin). The appropriateness of CycA and CycB patients was compared using the measurable elements, including indication and dose based on renal function, timely order for cultures, de-escalation, and use of extended infusion protocol. (3) Results: The study population comprised 100 patients in both CycA and CycB. The mean age of the patients was 66.28 ± 16.15 and 67.35 ± 17.98, and the ratios of men to women were found to be 49:51 and 61:39 in CycA and CycB, respectively. It was observed that inappropriate usage was high in CycA patients, and the appropriateness was improved in CycB patients. A total of 31% of inappropriate empirical broad-spectrum use was found in CycA, and it was reduced to 12% in CycB patients. The transition of appropriateness was observed in all measurable criteria, which includes the optimized dose according to the renal function (CycA = 49% to CycB = 94%), timely bacterial culture orders (CycA = 47% to CycB = 74%), prompt de-escalation (CycA = 31% to CycB = 53%), and adherence to extended infusion institutional guidelines (CycA = 34% to CycB = 86%). (4) Conclusions: The study highlighted important aspects of inappropriate piperacillin/tazobactam use. This can be considerably improved by proper education and timely interventions based on the pharmacists’ vigilant approach. The study results emphasized the need for surveillance of piperacillin/tazobactam usage by conducting similar drug utilization evaluations and practice to improve quality and safety in healthcare organizations globally
Pneumonia detection: A comprehensive study of diverse neural network architectures using chest X-rays
No full textPneumonia is of deep concern in healthcare worldwide, being the most deadly infectious disease, especially among children. Chest radiographs are crucial for detecting it. However, certain vulnerable groups exhibit heightened susceptibility, emphasizing the critical nature of accurate diagnosis and timely intervention. This paper presents convolutional neural network (CNN) models for the detection of pneumonia from chest X-rays images. Among 20 different CNN models, we identified EfficientNet-B0 as the most accurate and efficient, boasting an impressive accuracy rate of 94.13%. Furthermore, the precision, recall, and F-score metrics for this model stand at 93.50%, 92.99%, and 93.14%, respectively. This research underscores the potential of CNNs to revolutionize pneumonia diagnosis
Pneumonia Detection: A Comprehensive Study of Diverse Neural Network Architectures using Chest X-Rays
No full textPneumonia is of deep concern in healthcare worldwide, being the most deadly infectious disease, especially among children. Chest radiographs are crucial for detecting it. However, certain vulnerable groups exhibit heightened susceptibility, emphasizing the critical nature of accurate diagnosis and timely intervention. This paper presents convolutional neural network (CNN) models for the detection of pneumonia from chest X-rays images. Among 20 different CNN models, we identified EfficientNet-B0 as the most accurate and efficient, boasting an impressive accuracy rate of 94.13%. Furthermore, the precision, recall, and F-score metrics for this model stand at 93.50%, 92.99%, and 93.14%, respectively. This research underscores the potential of CNNs to revolutionize pneumonia diagnosis
