Impact of Healthcare Insurance on the Efficiency of Emergency Services in Private-Sector Hospitals in Al-Ahssa, KSA: An Analytical Comparative Study for Insured and Non-Insured Patients

Kingdom of Saudi Arabia is in a peculiar situation regarding healthcare; there is the free healthcare provided by the governmental hospitals in addition to that offered by private-sector hospitals for insured and non-insured patients. Aims of the study are to evaluate the level of efficiency of ED services in private-sector hospitals and to compare between insured and non-insured patient regarding their evaluation of efficiency of services provided in ED in private-sector hospitals in Al-Ahssa region, KSA. Research design: descriptive comparative research design. Setting: The study was conducted in three EDs (emergency departments) of three private hospitals in Al-Ahssa region, KSA which are: Al Mana General Hospital, AlMosa Specialist Hospital and AlAhssa Hospital Subjects: convenient sample of 150 patients (97 patients were insured however 53 patients) at the EDs of the three private hospitals were selected. Tools for data collection: Self-administered Patient Evaluation Questionnaire (PEQ). Results: Insured patients have higher means of efficiency of ED services in private hospitals in all axes of efficiency than non-insured. Recommendations: MOH should develop a program of cost share between citizens and MOH to promote parallel access with MOH hospitals for non-insured patients. Also, there should be a continuous monitoring of patients’ experiences of the received healthcare services in private-sector hospitals. Key words: Health Insurance, Efficiency, Emergency Services

Actual and standard crop coefficients for semi‑natural and planted grasslands and grasses: a review aimed at supporting water management to improve production and ecosystem services

Natural and planted grasslands play a very important role in agriculture as source of various ecosystem services, including carbon sequestration and biodiversity, and are responsible for a large fraction of agricultural water use in rainfed and irrigated fields. It is, therefore, relevant to precisely know their water use and vegetation requirements with consideration of relevant climate, from extremely cold, dry, with long winter seasons, to tropical humid and hot climates, thus with a large variability of vegetation. Semi-natural grasslands are basically used for grazing and mainly refer to highland pastures and meadows, steppes, savannas, pampas, and mixed forest systems. The FAO method to compute crop (vegetation) evapotranspiration (ETc) through the product of a crop coefficient (K c ) by the reference evapotranspiration (ETo ) is adopted. The selected papers were those where actual ETc (ETc act ) was derived from field observations and ETo was computed with the FAO56 definition, or with another method that could be referred to the former. Field derived ETc act methods included soil water balance, Bowen ratio and eddy covariance measurements, as well as remote sensing vegetation indices or surface energy balance models, thus reviewed Kc act (ETc act/ETo) values were obtained from field data. These Kc act refer to initial, mid-season and end season (K c act ini , K c act mid , K c act end ) when reported values were daily or monthly; otherwise, only average values (K c act avg ) were collected. For cases relative to cold or freezing winters, data refer to the warm season only. For grasses cut for hay, K c act ini , Kc act mid , and Kc act end refer to a cut cycle. Kc act values rarely exceeded 1.25, thus indicating that field measurements reported did respect the available energy for evaporation. Overall, K c act mid for semi-natural grasslands in cold climates were lower than those in hot climates except when available water was high, with K c act mid for meadows and mountain pastures gener- ally high. Steppes have K c act mid values lower than savannas. Grasses commonly planted for hay and for landscape generally showed high K c act mid values, while a larger variability was observed with grasses for grazing. The collected K c act values were used to define standard Kc values for all grassland and grasses. Nevertheless, the tabulated Kc act are indicative values of K c to be used for actual water management purposes and/or irrigation scheduling of planted grasslands. It is expected that a better knowledge of the standard and/or indicative K c values for a wide variety of grasslands and grasses will support better management aimed to improve grass productivity and ecosystem services, including biodiversity and carbon sequestrationinfo:eu-repo/semantics/publishedVersio

Adaptive Modulation for Orthogonal Frequency Division Multiplexing

Orthogonal frequency division multiplexing (OFDM) is one of the promising technologies to improve the spectral efficiency, enhance system capacity and mitigate inter-symbol interference in the wireless communication system. Adaptive modulation (AM) with OFDM has been suggested as a bandwidth efficient transmission technique in wireless fading environments. In this paper, adaptive modulation techniques is used with OFDM system to improve the throughput performance of the system using four modulation schemes BPSK, QPSK, 8QAM, and 16QAM. Two algorithms to estimate SNR are suggested. The first algorithm is to estimate the SNR for each OFDM symbol in the frame. The second algorithm is to estimate the channel SNR for each subcarrier in the OFDM symbol. The comparison between the results of the two algorithms on the performances of BER and throughput for OFDM system is shown. Furthermore, the effect of various types of channel equalization on performances of system will be determined. The obtained results show that a significant improvements in terms of bit error rate (BER) and throughput can be achieved demonstrating the superiority of the adaptive modulation schemes compared to fixed transmission schemes

Classification of Real and Fake Human Faces Using Deep Learning

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%

Improving Medical Emergency Team Response Time for Urgent Care Patients in Primary Health Care Centers, Riyadh, Saudi Arabia

Background: Emergency healthcare systems are of increasing concern in international healthcare developments and the global fight against the burdens of disease. Concerns about focusing on ambulance response time as a single indicator are addressed by showing a case-sensitive approach for developing emergency health care systems that in return can identify ambulance response as the main indicator for a particular system. Problem description: Data collected by monitoring the time of receiving the call at the ambulance division until transport time.Methods: The fishbone diagram is used as a tool for understanding the process variation, investigate and eliminating unusual occurrences by the following methodology: M.M.M.Main outcomes: Responding time in emergency cases not exceeding 5 minutes; To improve responding time; To improve types of communication; To improve compliance with the logbook of the emergency referred cases. The intervention plan was Continuous Training for the staff and Clear Guidelines for emergency calls. Review and assess the actual implementation of the project. Data will be collected by an Emergency Medical specialist. A copy of the data should be sent to CQI for a continuous monitoring process. Check the data for process improvement (incident report analysis). Document the results of the change. Modify the change, if necessary and possible. The Emergency Medical Technicians quality coordinator will check the Data. Continue improvement by close monitoring of the compliance.Results: the mean response time dropped from 15 minutes in January 2023 to less than 5 minutes in December 2023.Conclusion: this initiative positively impacts the emergency team's response time and improves patient satisfaction. Response time reduction was evident, and the quality of the health care service was improved. Reduction of response time also improved emergency preparednes

Tackling the Trickle: Ensuring Sustainable Water Management in the Arab Region

Abstract Water scarcity in the Arab region is intensifying due to population growth, economic development, and the impacts of climate change. It is manifested in groundwater depletion, freshwater ecosystem degradation, deteriorating water quality, low levels of water storage per capita, and added pressures on transboundary water resources. High‐income Arab countries have sought to circumvent the ever‐present challenges of water scarcity through agricultural imports (virtual water trade), desalination, and, increasingly, wastewater reuse. In this review article, we argue that the narrative of water scarcity and supply‐side technological fixes masks more systemic issues that threaten sustainable water management, including underperforming water utilities, protracted armed conflict and displacement, agricultural policies aimed at self‐sufficiency, evolving food consumption behaviors, the future of energy markets, and educational policy. Water management challenges, particularly on the demand side, and responses in the Arab region cannot be understood in isolation from these broader regional and international political and socioeconomic trends. Recognizing the complex and interdependent challenges of water management is the first step in reforming approaches and shifting to more sustainable development outcomes and stability in the Arab region and beyond

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA’s operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis

Treated municipal wastewater reuse for eggplant irrigation

In the Bekaa Valley of Lebanon, the availability of freshwater is limited and therefore farmers must start using alternative water sources such as treated wastewater for irrigating crops. The present study is of great importance, since it provides evidence of some on-farm options that farmers can adopt when irrigating with treated effluent, in order to minimize the health risks. Eggplant was grown under two water quality regimes (Freshwater (FW) and treated wastewater (TW)) and two agronomic practices (no mulch (N_Mu) and use of plastic mulch (Mu)). Treatments were arranged in a split plot design with four replicates. Water quality regime was the main plot factor, while agronomic practices were the subplot factors. Water quality, soil, the marketable yield and other parameters were measured. Fruit samples were evaluated for bacterial contamination. The drip line performance was monitored. The study results revealed that the treatment with treated effluent gave more fruits.m-2, yield and mean fruit weight than the treatment using fresh water irrigation, with an increase of 3.98%, 10.74% and 5.63%, respectively. In addition, the use of mulch (Mu) resulted in an increase in yield (24.23%) and number of fruits (14.11%). Concerning the emitters' performance and sensitivity to clogging, discharge reduction rate (Rd = 6.75%) for drippers delivering TW was lower than the admissible value of 20% discharge variation, indicating that the quality of water has little effect on emitter performance. Concerning bacterial contamination of fruits, irrigation with treated wastewater showed no contamination in terms of fecal streptococci, salmonella or E. coli. However, the fruits were contaminated with fecal coliforms that were present at a concentration less than 200 CFU.100 g-1. Following the World Health Organization Guidelines, pathogens could be reduced through post treatment health-protection control measures such as drip irrigation, product washing, disinfection and produce peelin

Evasion of anti-growth signaling: a key step in tumorigenesis and potential target for treatment and prophylaxis by natural compounds

The evasion of anti-growth signaling is an important characteristic of cancer cells. In order to continue to proliferate, cancer cells must somehow uncouple themselves from the many signals that exist to slow down cell growth. Here, we define the anti-growth signaling process, and review several important pathways involved in growth signaling: p53, phosphatase and tensin homolog (PTEN), retinoblastoma protein (Rb), Hippo, growth differentiation factor 15 (GDF15), AT-rich interactive domain 1A (ARID1A), Notch, insulin-like growth factor (IGF), and Krüppel-like factor 5 (KLF5) pathways. Aberrations in these processes in cancer cells involve mutations and thus the suppression of genes that prevent growth, as well as mutation and activation of genes involved in driving cell growth. Using these pathways as examples, we prioritize molecular targets that might be leveraged to promote anti-growth signaling in cancer cells. Interestingly, naturally-occurring phytochemicals found in human diets (either singly or as mixtures) may promote anti-growth signaling, and do so without the potentially adverse effects associated with synthetic chemicals. We review examples of naturally-occurring phytochemicals that may be applied to prevent cancer by antagonizing growth signaling, and propose one phytochemical for each pathway. These are: epigallocatechin-3-gallate (EGCG) for the Rb pathway, luteolin for p53, curcumin for PTEN, porphyrins for Hippo, genistein for GDF15, resveratrol for ARID1A, withaferin A for Notch and diguelin for the IGF1-receptor pathway. The coordination of anti-growth signaling and natural compound studies will provide insight into the future application of these compounds in the clinical setting

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London