A Systematic Evaluation of the Quality of Health Information on Cleft Lip and Palate in the Arabic Language.

Background: The internet serves as a vital health information resource, yet the quality of data on specific health conditions, especially in Arabic, is often overlooked. This research assesses the quality of Arabic online information about cleft lip and palate (CLP) and proposes avenues for enhancement. Methods: From July to August 2022, a systematic evaluation of Arabic articles on CLP was performed using the DISCERN tool for quality assessment. Searches on Google and Bing resulted in 119 articles that met the study's criteria. Results: The quality of available Arabic information on CLP displayed substantial gaps. Commercial sources dominated (49.6%), followed by private (32.8%) and nonprofit entities (17.6%). The average DISCERN score was 2.26 of 5 (SD = 1.06), indicating the need for enhanced content, particularly concerning treatment risks. Conclusions: The study underscores the subpar quality of Arabic CLP information online, which might mislead patients and impede access to accurate advice. Nonprofit organizations should bolster their online footprint, offering refined health content. A deep dive into DISCERN scores reveals pinpointed improvement areas. Clinicians should direct patients and their families to reliable information sources. Addressing these gaps promises improved CLP knowledge in Arabic, fostering superior patient education and outcomes for those with this condition.

Understanding factors influencing the implementation of medicine risk communications by healthcare professionals in clinical practice: a systematic review using the Theoretical Domains Framework.

BACKGROUND: Adverse drug reactions (ADRs) are known to cause hospitalisation, longer hospital stays, as well as higher healthcare costs and mortality. Unrecognised ADRs are anticipated throughout the medicine lifecycle as, before the medicine reaches the market, clinical trials are conducted for a short period on a limited number of people, who might underrepresent the actual population. After the medicine reaches the market, emergent information that could affect its benefit-to-risk balance is usually shared by regulatory agencies and pharmaceutical companies through medicine risk communications. Medicines risk communications aim to prevent harm to patients by targeting their behaviour, knowledge, and attitudes, as well as those of health care professionals (HCPs). Despite their important role in translating these communications into their clinical practice, HCPs do not always adhere to the recommendations provided in risk communications. Measurement of medicine risk communications' effectiveness does not necessarily guarantee their implementation, cost-effectiveness, or transferability in real-world situations. To enhance the impact of drug regulatory interventions, implementation science has been encouraged. However, implementation science was not previously used to identify factors affecting HCPs' implementation of medicines risk communications. A recently widely used framework is the Theoretical Domain Framework (TDF). In this systematic review, the TDF was employed to categorise a range of different factors that could affect HCPs' implementation of medicine risk communications within their clinical contexts. METHODS: The search strategy involved a set of predefined search terms and fifteen databases, such as EMBASE, PubMed, Web of Science and CINAHL PLUS. Searches were conducted from April to May 2018 and updated in June 2021 using PubMed, Scopus, and CINAHL PLUS. A second reviewer independently conducted the screening process of the initial search. The total number of records screened was 10,475. A study was included if it reported any factors influencing HCPs' uptake of medicine risk communications. Only studies with English or Arabic abstracts were included. Those studies that did not include pharmacovigilance-related medicine risk communications were excluded. Additionally, studies only assessing HCPs' practice or evaluating the effectiveness of risk minimisation measures were excluded. Likewise, studies related to occupational hazards, case reports, interventional studies, and studies not involving HCPs were excluded. In case the published information was insufficient to decide whether to include or exclude a study, the authors were contacted. Furthermore, the authors of seven eligible abstracts were contacted for full-text articles. The mixed method appraisal tool (MMAT) was used to evaluate the quality of the included studies. All included studies were assessed by one reviewer, and a total of 16 studies were assessed by two reviewers independently. Disagreements were resolved through discussion. Using thematic analysis and concept mapping, a narrative synthesis was performed, followed by a critical reflection on the synthesis process. This review presents the results of the concept mapping, which involved matching the identified factors to the TDF. RESULTS: A total of 28 studies were included. Eleven domains influenced HCPs' implementation of medicine risk communications. A large number of studies included factors related to the "Knowledge" domain (n = 23), followed by "Beliefs about Consequences" (n = 13), "Memory, Attention and Decision Processes" (n = 12) and "Environmental Context and Resources" domains (n = 12). Seven studies reported "social influences" and six studies included factors relating to "Goals", followed by four studies involving factors related to "Social/Professional Role and Identity". Underrepresented domains included "Emotion" (n = 2), "Beliefs about Capabilities" (n = 2), "Behavioural Regulation" (n = 1), and "Reinforcement" (n = 1). On the other hand, none of the identified factors were related to the "Skills", "Optimism", or "Intentions" domains. Except for "Beliefs about Consequences", most studies contributing to the other three most commonly reported domains ("Knowledge"; "Environmental Context and Resources"; and "Memory, Attention and Decision Processes") scored low (1 or 2 out of 5) on the MMAT quality assessment. Moreover, the same number of studies (n = 5) contributing to the "Beliefs about Consequences" domain had low (1 or 2 out of 5), and intermediate (3 out of 5) scores on the MMAT. CONCLUSION: Medicines risk communications are important tools for disseminating information that may influence the benefit-to-risk balance of medicines. Even though HCPs are required to implement the recommendations of these communications, they do not always adhere to them. Using the TDF enabled the categorization of the range of factors that affect whether or not HCPs implement the recommendations provided in a medicine risk communication. However, most of these factors relate to four domains only ("Knowledge"; "Beliefs about Consequences"; "Memory, Attention and Decision Processes"; and "Environmental Context and Resources"). Additionally, most of the studies contributing to three of these four domains were of low quality. Future research should focus on using implementation science to identify target behaviours for actionable medicine risk communications. Regulators should use such science to develop cost-effective strategies for improving the implementation of medicines risk communication by HCPs.

Intention and practice on personal preventive measures against COVID-19 among older adults in the Kingdom of Saudi Arabia: an epidemiological study using the Theory of Planned Behaviour.

Introduction: Older adults aged 65 years and above are among the most vulnerable to adverse outcomes and death following a COVID-19 infection. The weekly epidemiological updates by the World Health Organisation show that the continued emergence of concerning subtypes of the virus indicates that the pandemic remains a public health concern and the public should continue to comply with personal preventive measures (PPMs). This study applies the Theory of Planned Behaviour (TPB) which is rooted in the field of Public Health, Epidemiology, and Preventive Medicine to Saudi older adults to predict their health behaviour. Methods: This behavioural epidemiological study recruited older adult participants aged 65 years of age and above. A tool which consisted of sociodemographic and health-related questions, as well as questions regarding the components of the TPB, namely, Attitude, Subjective Norm, Perceived Behavioural Control was used. Bivariate analyses, followed by unadjusted and adjusted multivariable logistic regression analyses were performed to derive odds ratios and 95% confidence intervals. Results: The total number of participants was 502. The mean age was 70.34 years, with similar distributions between males and females. In total, 52.2% intended to practice PPMs, whereas only 48% had a good practice. Also, 56% had a favourable Attitude towards PPMs, 61.4% had a positive Subjective Norm and 39.8% had perceived they had a high control over their behaviour. Females, and high educational status were predictors for high intention to practice PPMs (OR = 1.59, 95% CI = 1.01-2.52 and OR = 2.72, 95% CI = 1.44-5.16 respectively). Further predictors included Attitudes, Subjective Norm and Perceived Behavioural Control. Results also show that intention to practice was significantly associated with a lower odd of practicing PPMs (OR = 0.06, 95% CI = 0.04-0.10). Conclusion: Current findings highlight the need to continue with public health efforts targeting vulnerable older adults. Also, the fact that intention negatively predicted practice highlights the need for further behavioural epidemiological studies addressing the intention-behaviour gap.

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning.

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner's search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners' improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

Primary Thymic Hodgkin Lymphoma Coexisting with Thymoma and Myasthenia Gravis: A Case Report.

BACKGROUND Myasthenia gravis is a neuromuscular disorder that is strongly associated with thymoma. Although the presence of myasthenia gravis with other tumors is uncommon, approximately 50% of patients with thymoma have myasthenia gravis. Thymic Hodgkin lymphoma should be considered due to the multiple reported cases of patients with myasthenia gravis and Hodgkin lymphoma. In this report, we present the case of 24-year-old woman with myasthenia gravis who was incidentally found to have coexisting thymoma with thymic Hodgkin lymphoma. CASE REPORT A 24-year-old woman with a known case of vitiligo presented with a 2-year history of diplopia and incidental anterior mediastinal mass. Following investigations, myasthenia gravis was diagnosed and managed by pyridostigmine, prednisolone, and azathioprine. Regarding the anterior mediastinal mass, thymoma was suspected based on the presence of myasthenia gravis and radiological findings. She underwent extended transsternal thymectomy. The final histopathological report of the dissected thymus disclosed Hodgkin lymphoma pathology coexisting with thymoma. After the diagnosis of Hodgkin lymphoma nodular sclerosis type IIA was confirmed, 6 cycles of chemotherapy were administered. Four years of follow-up revealed no evidence of Hodgkin lymphoma. However, her symptoms of myasthenia gravis persisted despite Hodgkin lymphoma remission. CONCLUSIONS There is an unclear association between myasthenia gravies and Hodgkin lymphoma. Prior reports revealed regression of myasthenia gravies following Hodgkin lymphoma management, which suggests that myasthenia could be a complication of Hodgkin lymphoma. However, in our case, myasthenia gravis persisted after Hodgkin lymphoma management; therefore, further studies are needed to explore this association.

The Effect of Lower Limb Combined Neuromuscular Electrical Stimulation on Skeletal Muscle Signaling for Glucose Utilization, Myofiber Distribution, and Metabolic Function after Spinal Cord Injury.

Maintaining healthy myofiber type and metabolic function early after spinal cord injury (SCI) may prevent chronic metabolic disorders. This study compares the effects of a 2-5 week combined (aerobic + resistance) neuromuscular electrical stimulation (Comb-NMES) regimen versus a sham control treatment on muscle protein signaling for glucose uptake, myofiber type distribution, and metabolic function. Twenty participants (31 ± 9 years of age) with an SCI (C4-L1, AIS level A-C) within 14 days of the SCI were randomly assigned to control (N = 8) or Comb-NMES (N = 12). Sessions were given three times per week. Fasting blood samples and vastus lateralis muscle biopsies were collected 24-48 h before or after the last session. Western blots were performed to quantify proteins, immunohistochemical analyses determined muscle myofiber distribution, and enzymatic assays were performed to measure serum glucose, insulin, and lipids. Our main findings include a decrease in fasting glucose (p < 0.05) and LDL-C (p < 0.05) levels, an upregulation of CamKII and Hexokinase (p < 0.05), and an increase in type I (+9%) and a decrease in type IIx (-36%) myofiber distribution in response to Comb-NMES. Our findings suggest that maintaining healthy myofiber type and metabolic function may be achieved via early utilization of Comb-NMES.

An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence.

The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms.

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications.

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials' exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm's regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon's rank-sum and ANOVA tests.

Hybrid Nanoparticles of Manganese Oxide and Highly Reduced Graphene Oxide for Photodynamic Therapy.

BACKGROUND: Graphene-based nanomaterials possess unique optical, physicochemical and biomedical properties which make them potential tools for imaging and therapy. Manganese oxide nanoparticles are attractive candidates for contrast agents in magnetic resonance imagint (MRI). We used manganese oxide (Mn3O4) and highly reduced graphene oxide (HRG) to synthesize hybrid nanoparticles (HRG-Mn3O4) and tested their efficacy for photodynamic therapy (PDT) in breast cancer cells. METHODS: The newly synthesized nanoparticles were characterized by transmission electron microscopy (TEM), energy-dispersive X-ray (EDX) spectroscopy, UV-visible spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, thermogravimetry, and X-ray diffraction (XRD) analyses. We used standard protocols of cytotoxicity and PDT after exposing A549 cells to various concentrations of hybrid nanoparticles (HRG-Mn3O4). We also performed fluorescence microscopy for live/dead cellular analysis. A549 cells were incubated with nanoparticles for 24 h and stained with fluorescein diacetate (green emission for live cells) and propidium iodide (red emission for dead cells) to visualize live and dead cells, respectively. RESULTS: The cell viability analysis showed that more than 98% of A549 cells survived even after the exposure of a high concentration (100 µg/mL) of nanomaterials. These results confirmed that the HRG-Mn3O4 nanoparticles are nontoxic and biocompatible at physiological conditions. When the cell viability analysis was performed after laser irradiation, we observed significant and concentration-dependent cytotoxicity of HRG-Mn3O4 as compared to Mn3O4 nanoparticles. Fluorescence microscopy showed that almost 100% cells were viable when treated with phosphate buffered saline or Mn3O4 while only few dead cells were detected after exposure of HRG-Mn3O4 nanoparticles. However, laser irradiation resulted in massive cellular damage by HRG-Mn3O4 nanoparticles which was directly related to the generation of reactive oxygen species (ROS). CONCLUSIONS: HRG-Mn3O4 hybrid nanoparticles are stable, biocompatible, nontoxic, and possess therapeutic potential for photodynamic therapy of cancer. Further studies are warranted to explore the MRI imaging ability of these nanomaterials using animal models of cancer.

Antimicrobial stewardship implementation before and during the COVID-19 pandemic in the acute care settings: a systematic review.

INTRODUCTION: Antibiotics are widely administered for various indications, leading to increased antimicrobial resistance (AMR) in acute care hospitals. Since the onset of the COVID-19 pandemic, Antimicrobial Stewardship (AMS) effective strategies should be used to maintain the rational use of antibiotics and decrease the threat of Antimicrobial Resistance (AMR). AIM: This systematic literature review aims to investigate the AMS intervention Before-the-pandemic (BP) and During-the-pandemic (DP) from the literature. DESIGN AND SETTING: Systematic literature review of primary studies on AMS implementation in acute care settings. METHODS: Relevant studies published between 2000 and March 2021 were obtained from Medline (via PubMed), OVID, CINAHL, International Pharmaceutical Abstracts, Psych Info, Scopus, Web of Science, Cochrane Library, OpenGrey, and Google Scholar, using a comprehensive list of search terms. Public Health England (PHE) toolkit was agreed upon as a gold standard for the AMS implementation. RESULTS: There were 8763 articles retrieved from the databases. Out of these, 13 full-text articles met the inclusion criteria for the review. The AMS implementation was identified in the included studies into AMS strategies (Core strategies & Supplemental strategies), and AMS measures BP and DP. CONCLUSION: This Systematic literature review summarises AMS implementation strategies and measures all over the previous 20 years of research. There are many lessons learnt from COVID-19 pandemic. The proper selection of the AMS implementation strategies and measures appeared to be effective in maintaining the appropriate use of antibiotics and decreasing the AMR threat, especially during the COVID-19 pandemic. Further studies are required to provide empirical data to evaluate the AMS implementation and identify which of these strategies and measures were effective BP and DP. In order to be prepared for any emergency/crisis or future pandemics.

Quality of Life Among Down Syndrome Patients With and Without Congenital Heart Disease at King Abdulaziz University Hospital, Jeddah, Saudi Arabia.

Background Congenital heart diseases (CHD) are common in Down syndrome patients who will often have additional anomalies, in which the presence of them and their management are expected to impact their quality of life (QoL). There are limited studies trying to evaluate the impact of CHD on the QoL in children with Down syndrome. Methods The present study comprised 97 Down syndrome children. The children's parents responded to phone interviews filling out TNO-AZL (Netherlands Organisation for Applied Scientific Research Academic Medical Centre) Preschool Quality of Life (TAPQOL) and TNO-AZL Child Quality of Life Parent Form (TACQOL-PF) questionnaires. Children were divided into two groups according to their age: group A (one to five years) and group B (six to 15 years). The results were analyzed using Statistical Package for Social Sciences (SPSS) software, version 21 (IBM Corp., Armonk, NY). Results CHD negatively affected motor skills in younger but not older children. All other QoL-related parameters were unaffected by CHD. Conclusion Down syndrome children with CHD demonstrated similar QoL to Down syndrome children without CHD, with the exception of having a lower motor outcome as infants/toddlers. This difference improved with time and did not exist in older children.

Translation and Validation of the Arabic Version of the Eating Behavior After Bariatric Surgery (EBBS) Questionnaire.

PURPOSE: Complications after metabolic and bariatric surgery are common due to the patient's poor commitment to postoperative lifestyle changes. Therefore, intensive follow-up from a multidisciplinary team might improve outcomes. The present study aimed to translate and validate the Eating Behavior after Bariatric Surgery (EBBS) questionnaire into Arabic for use in clinical and research settings. MATERIALS AND METHODS: The study followed World Health Organization guidelines for translation and questionnaire adaptation, including forward translation, back translation, pilot testing, and the creation of the final version of the tool. A total of 390 patients who had undergone metabolic and bariatric surgery 3 years ago or more were involved in testing the questionnaire's validity and reliability. RESULTS: The mean age of participants was 36 years (range: 20 to 70 years), 56% were females, 94.1% were Saudis, and 56% had bachelor's degrees. The internal consistency of the questionnaire was tested using Cronbach's alpha. One item (alcohol consumption) was excluded during the reliability analysis due to low variance. The reliability analysis results showed that the 10 items were internally consistent, with a Cronbach's α of 0.851. CONCLUSION: The validation and reliability of the Arabic-language version of the EBBS questionnaire were found to be satisfactory. The presence of a validated Arabic version of this instrument may help practitioners estimate patients' adherence to dietary and lifestyle recommendations after metabolic and bariatric surgery. Furthermore, the questionnaire may aid in identifying factors that influence the efficacy of these procedures.

Factors influencing the implementation of medicine risk communications by healthcare professionals in clinical practice: A systematic review.

BACKGROUND: Regulatory medicines risk communications aim to prevent patient harm through the dissemination of safety information to healthcare professionals (HCPs), patients, and the public. Evidence suggests that in addition to implementing the required changes, HCPs also respond to these communications through unintended and unwarranted actions and behaviours such as stopping medicine courses unnecessarily, and blanket actions spilling over to unintended patients' populations. Misunderstanding and mis-implementation of medicines risk communications could jeopardise patients' safety and clinical outcomes. Therefore, it is important to understand the determinants that affect HCPs responses to medicines risk communications. This systematic review aims to identify the factors that affect the implementation of risk communications by healthcare professionals. METHODS: Fifteen databases, including EMBASE, PubMed, Scopus, Web of science, CINAHL PLUS were searched in April-May 2018, and the search was updated again in June 2021 to identify studies reporting on factors influencing HCPs' uptake of medicine risk alerts. We used keywords such as risk communication, safety update, and safety regulation. Studies were excluded if they did not involve pharmacovigilance or patient safety alerts; or if they only focused on measuring HCPs' practice after alerts; or evaluating the effectiveness of risk minimisation measures without reporting on factors affecting HCPs' actions. Studies relating to occupational hazards, case reports, interventional studies, and studies not involving HCPs were also excluded. The Mixed Method Appraisal Tool (MMAT) was used to assess the quality of the included studies. A Narrative synthesis approach was undertaken using thematic analysis and concept mapping, followed by a critical reflection of the synthesis. RESULTS: Twenty-eight studies met our criteria and were included in the synthesis. We identified four themes summarising the factors influencing HCPs' implementation of risk communications. These include HCPs: knowledge of medicine alerts; perceptions of alerts; attitudes, and concerns regarding medicine alerts; and the self-reported impact of these alerts. Our concept mapping exercise identified key interactions between different stakeholders, and these interactions determine HCPs' implementation of medicine risk communications. These stakeholders comprise of alert developers, including the sources and senders of safety information, and the receivers of safety information including health care institutions, HCPs, patients and their carers. CONCLUSIONS: Healthcare professionals are crucial to translating risk communication messages into clinical practice. However, if they have inadequate information about the content of the alert, and have inaccurate perceptions about the alert, they may not implement the required clinical changes as intended. Communication of medicine risk alerts does not always translate into improved patient care, due to a complex interaction between stakeholders involved in the creation and implementation of these alerts. These complex interactions should be the subject of future research efforts to understand the alert-implementation trajectory and identify the mediators for change and interventions to improve implementation.

The Incidence, Prevalence and Clinical Characteristics of Failure to Thrive in Children at King Abdullah Specialized Children Hospital, Riyadh, Saudi Arabia.

Introduction Failure to thrive (FTT) in children involves insufficient weight or height gain, affecting general and hospitalized populations which leads to cognitive and behavioral changes. Causes include inadequate caloric intake and underlying diseases (organic - OFTT) or psychosocial factors (non-organic - NOFTT). Our study in King Abdullah Specialized Children Hospital (KASCH) aims to assess FTT incidence, prevalence, and clinical characteristics, and also, to distinguish between different causes. Methodology It is a retrospective cohort study, conducted at KASCH, Riyadh, Saudi Arabia. This study includes children under three years old with documented FTT from 2017 to 2019. Data was collected from the hospital's electronic system and it was analyzed by the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 29.0, Armonk, NY). Results Our study, including 214 FTT patients, revealed a balanced gender distribution of 109 males (50.9%), and 105 females (49.1%), with a majority of Saudi nationality 208 (97.2%). In most cases, 120 (56.1%) are in the 0-12 months age group. The prevalence of FTT was 26.75% (267 cases per 1000). Antenatal/post-natal features showed diverse delivery modes and NICU admissions. Chronic diseases like gastrointestinal diseases 62 (29.1%), cardiac 50 (23.4%), and pulmonary 50 (23.4%) conditions were prevalent. Associations were found between NICU admission and pre-term births, birth weight status, and congenital anomalies, highlighting significant clinical correlations. Conclusion Our study concluded the significant burden of FTT at KASCH. Chronic diseases were playing a major role as a cause of FTT. Thus, emphasizing the causes and knowing the importance of addressing the prevalence and incidence is effective for appropriate intervention.

Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients.

Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset.

An Integrated Classification and Association Rule Technique for Early-Stage Diabetes Risk Prediction.

The number of diabetic patients is increasing yearly worldwide, requiring the need for a quick intervention to help these people. Mortality rates are higher for diabetic patients with other serious health complications. Thus, early prediction for such diseases positively impacts healthcare quality and can prevent serious health complications later. This paper constructs an efficient prediction system for predicting diabetes in its early stage. The proposed system starts with a Local Outlier Factor (LOF)-based outlier detection technique to detect outlier data. A Balanced Bagging Classifier (BBC) technique is used to balance data distribution. Finally, integration between association rules and classification algorithms is used to develop a prediction model based on real data. Four classification algorithms were utilized in addition to an a priori algorithm that discovered relationships between various factors. The named algorithms are Artificial Neural Network (ANN), Decision Trees (DT), Support Vector Machines (SVM), and K Nearest Neighbor (KNN) for data classification. Results revealed that KNN provided the highest accuracy of 97.36% compared to the other applied algorithms. An a priori algorithm extracted association rules based on the Lift matrix. Four association rules from 12 attributes with the highest correlation and information gain scores relative to the class attribute were produced.

Numerical analysis of the chemically reactive EMHD flow of a nanofluid past a bi-directional Riga plate influenced by velocity slips and convective boundary conditions.

This report presents the three-dimensional electromagnetohydrodynamic flow of a zinc-oxide-water nanofluid past a bidirectional Riga plate with velocity slips and thermal and mass convection conditions. The Cattaneo-Christov heat and mas flux model, thermal radiation, chemical reaction and activation energy are considered to analyze the flow problem. The volume fraction of the ZnO nanoparticles is taken 6% in this analysis. An appropriate set of similarity variables is used to transform the partial differential equations into ordinary differential equations. During this process, some parameters are found and influences of these factors on the flow profiles are shown and discussed in detail. A numerical technique called NDSolve is considered for the solution of the nanofluid flow problem. The results showed that higher solid volume fraction and slip parameter have reduced velocities profiles and the increasing solid volume fraction and thermal Biot number have increased the temperature profile. Additionally, the concentration Biot number has increased the concentration profile. The modified Hartmann number has significantly increased the velocity profile. Dual impacts in velocity profiles along primary and secondary direction has been observed due to stretching ratio parameter. A comparison of current results has been carried with a fine agreement amongst current and established results.

Segmentation and Classification of White Blood Cells Using the UNet.

In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. These plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. This presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. This study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset. The overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.