Explainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context.

Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied. The analysis identified the Random Forest model as the top performer, achieving an accuracy of 0.72, along with robust precision, recall, F1 score, and AUC-ROC score values. Conversely, the Support Vector Machine model exhibited the poorest performance metrics, indicating its limited predictive capability. Notably, the XAI approaches unveiled variations in the feature importance rankings across models, underscoring the need for further investigation. These findings offer valuable insights into breast cancer diagnosis and machine learning interpretation, aiding healthcare providers in understanding and potentially integrating such technologies into clinical practices.

Theoretical Insights into Different Complexation Modes of Dioxovanadium(V) Compounds with Pyridoxal Semicarbazone/Thiosemicarbazone/S-Methyl-iso-thiosemicarbazone Ligands.

Vanadium complexes have gained considerable attention as biologically active compounds. In this contribution, three previously reported dioxovanadium(V) complexes with pyridoxal semicarbazone, thiosemicarbazone, and S-methyl-iso-thiosemicarbazone ligands are theoretically examined. The intermolecular stabilization interactions within crystallographic structures were investigated by Hirshfeld surface analysis. These experimental structures were optimized at the B3LYP-D3BJ/6-311++G(d,p)(H,C,N,O,S)/def2-TZVP(V) level of theory, and crystallographic and optimized bond lengths and angles were compared. High correlation coefficients and low mean absolute errors between these two data sets proved that the selected level of theory was appropriate for the description of the system. The changes in structures and stability were examined by adding explicit solvent molecules. The Quantum Theory of Atoms in Molecules (QTAIM) was employed to analyze the intramolecular interactions with special emphasis on the effect of substituents. A good correlation between electron density/Laplacian and interatomic distance was found. Through molecular docking simulations towards Bovine Serum Albumin (BSA), the binding affinity of complexes was further investigated. The spontaneity of binding in the active position of BSA was shown. Further experimental studies on this class of compounds are advised.

Nursing Professional Commitment as a Mediator of the Relationship Between Work Environment and Missed Nursing Care Among Nurses: A Cross-Sectional Analysis.

Introduction: Nursing care plays a pivotal role in promoting patient well-being and optimizing health outcomes. The nursing profession is characterized by its commitment to delivering high-quality care to patients. Objective: The purpose of the study was to explore the role of nursing professional commitment as a mediator between the work environment and missed nursing care. Methods: A cross-sectional analysis study "STROBE guideline" used an online structured questionnaire to collect data. It was conducted on a sample of 813 nurses who worked at seven governmental hospitals in Egypt, in the hospital wards, operating rooms, intensive care unit (ICU) or outpatient clinics, during a 4-month period from April to August 2022. Researchers used Characteristics of nurses, Nursing Work Index-Revised, Nursing Professional Commitment Scale, and MISSCARE Questionnaire to collect the data. Structural equation modeling by AMOS was used for testing nursing professional commitment as a mediator between the work environment and missed nursing care. Nurses' professional commitment was used as a mediator between work environment and missing nursing care. Results: The working environment has a direct impact of -0.175, an indirect impact of -0.139, and a total impact of -0.314. Furthermore, professional commitment has a direct impact of -0.421. Additionally, when the working environment increases by 1, professional commitment increases by 0.33. Similarly, when the working environment increases by 1, missed care decreases by 0.175. Moreover, when professional commitment, as a mediating factor, increases by 1, missed care decreases by 0.421. Conclusion: In conclusion, the findings of this study highlight the significant role of professional commitment as an intermediary factor between the working environment and missed nursing care. According to these results, it is necessary to formulate and implement intervention strategies to improve nurses' professional commitment and working environment, which is the key to reducing their missed nursing care.

Impact of Nurse Leaders Behaviors on Nursing Staff Performance: A Systematic Review of Literature.

Nursing leadership is critical in facilitating and improving nurse performance, which is essential for providing quality care and ensuring patient safety. The aim of this study is to explore the relationship between nursing leadership and nurse performance by understanding the leadership behaviors and factors that motivate nurses to perform well. To study the factors that nurses believe motivate them to perform better, a systematic review was undertaken, correlating these factors to leadership behaviors/styles. The PRISMA guidelines were followed to identify relevant articles. After applying the selection criteria, 11 articles were included in the final analysis. Overall, 51 elements that influence nurses' motivation to perform better were found and categorized into 6 categories, including autonomy, competencies, relatedness, individual nursing characteristics, relationships and support, and leadership styles/practices. It has been discovered that both direct and indirect nursing leadership behaviors affect nurses' performance. A better understanding of the factors that motivate nurses to perform well and facilitating them in the work environment through leadership behaviors/styles can improve nurses' performance. There is a need to increase research on nurse leadership and nurses' performance in the current innovative and technologically integrated work environment to identify new factors of influence.

A preliminary exploration of attitudes, beliefs, and barriers to physical therapists' practices in ordering diagnostic imaging.

BACKGROUND: Diagnostic imaging (DI) studies are useful resources for examining musculoskeletal (MSK) conditions. When DI is used appropriately, it is an important tool for physical therapists (PTs). OBJECTIVE: The purpose of this study was to explore the attitudes, beliefs, and barriers to PTs' practices in referring to DI in Saudi Arabia (SA). METHODS: For this quantitative cross-sectional study, an online self-reported questionnaire was designed and distributed. A total of 138 PTs participated in the study. RESULTS: Although most PTs had access to DI, few reported being able to order DI. The PTs demonstrated positive attitudes regarding their ability to refer patients for DI. Nevertheless, some PTs have adopted several beliefs and awareness of PTs regarding the clinical appropriateness of referring to DI, which may increase the unnecessary utilisation of DI requests. More than half of the PTs in this study reported that they were unfamiliar with the clinical rules used for prescribing imaging. CONCLUSION: This study highlights recommendations to improve PTs' practices towards DI in SA, such as providing more DI courses targeting PTs and including clinical rules within DI education in undergraduate studies. Furthermore, a clear governmental policy is needed to permit PTs to refer to DI when specific criteria are met.

Long-term effect of non-severe COVID-19 on pulmonary function, exercise capacities and physical activities: a cross-section study in Sakaka Aljouf.

Background: COVID-19 has serious consequences on different body systems particularly the respiratory system with its impact on pulmonary function, exercise capacities, and physical activities. This study aimed to investigate the long-term effect of COVID-19 on pulmonary function, exercise capacities, and physical activities in patients with non-severe COVID-19. Methods: 160 individuals were selected to participate in a cross-section study. Group-I: 80 male and female patients with non-severe COVID-19 at least 3 months after the recovery time. Group-II: 80 male and female matched (non-infected with COVID-19) participants. The spirometer, six-minute walk test (6MWT), and International Physical Activity Questionnaire (IPAQ) were used to assess pulmonary function, exercise capacities, and physical activities respectively. The Kolmogorov-Smirnov test was used to test normality of data. The Mann-Whitney and independent t-tests were used to compare the significant differences between both groups. Results: The results show significant differences in FVC & FEV 1 of the pulmonary function, exercise capacities, and physical activities of the work & transportations between both COVID-19 and matched groups p-value = (0.001 & 0.001, 0.001 and 0.005 & 0.012) respectively. Conclusion: Pulmonary function, exercise capacities, and physical activities are negatively influenced by COVID-19 as long-term consequences indicating the need for extended health care, and prescription of proper rehabilitative training programs for non- severe COVID-19 patients whatever their severity degree of infection or history of hospitalization. Outcome reflections of the current results raise awareness of physical therapists to the importance of the proper rehabilitative training programs for non-severe COVID-19 patients.

Evaluation of the Mawid mobile healthcare application in delivering services during the COVID-19 pandemic in Saudi Arabia.

PURPOSE: The purpose of this study is to evaluate MAWID mobile application developed by the Ministry of Health, Saudi Arabia, which is used for primary care hospitals appointments management and for tracking and tracing COVID-19. PARTICIPANTS AND METHODS: An online questionnaire-based survey was used for collecting data related to three major factors including Ease of Use, Satisfaction, and Benefits of MAWID application among its users. Out of total 2542 participants, 345 participants completed only a part of the survey, and 204 participants did not use the application. After removing, 549 invalid responses, a final sample of 1993 was included for the data analysis. RESULTS: 82.1% of the participants referred MAWID as easy to use application, 79.8% were highly satisfied with the application, and majority of the participants reflected potential benefits of using the application. T-test results have revealed that significant differences existed between males and females, and young and older participants in relation to the Ease of Use and Satisfaction levels associated with MAWID application. CONCLUSION: Mobile applications can be very effective in delivering the healthcare services during pandemics. However, there is a need for regular evaluation and assessment to trach the change in users' needs and update the app according to the changing requirements.

An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray.

The objective of the research article is to propose and validate a combination of machine learning and radiomics features to detect COVID-19 early and rapidly from chest X-ray (CXR) in presence of other viral/bacterial pneumonia and at different severity levels of diseases. It is vital to assess the performance of any diagnosis method on an independent data set and at very early stage of the disease when the disease severity of is very low. In such cases, most of the diagnosis methods fail. A total of 378 CXR images containing both normal lung and pneumonia (both COVID-19 and others lung conditions) were collected from publically available data set. 71 radiomics features for each lung segment were chosen from 100 extracted features based on Z-score heatmap and one way ANOVA test that can detect COVID-19. Three best performing classical machine learning algorithms during the training phase - 1) fine Gaussian support vector machine (SVM), 2) fine k-nearest neighbor (KNN) and 3) ensemble bagged model (EBM) trees were chosen for further evaluation on an independent test data set. The independent test data set consists of 115 COVID-19 CXR images collected from a local hospital and 100 CXR images collected from publically available data set containing normal lung and viral/bacterial pneumonia. Severity was scored between 0 to 4 by two experienced radiologists for each lung with pneumonia (both COVID-19 and non COVID-19) for the test data set. Ensemble Bagging Model Trees (EBM) with the selected radiomics features is the most suitable to distinguish between COVID-19 and other lung infections with an overall sensitivity of 87.8% and specificity of 97% (95.2% accuracy and 0.9228 area under curve) and is robust across severity levels. The method also can detect COVID-19 from CXR when two experienced radiologists were unable to detect any abnormality in the lung CXR (represented by severity score of 0). Once the CXR is acquired and lung is segmented, it takes less than two minutes for extracting radiomics features and providing diagnosis result. Since the proposed method does not require any manual intervention (e.g., sample collection etc.), it can be straightway integrated with standard X-ray reporting system to be used as an efficient, cost-effective and rapid early diagnosis device.

A comparative study of strategies for containing the COVID-19 pandemic in Gulf Cooperation Council countries and the European Union.

BACKGROUND: The COVID-19 pandemic has impacted every aspect of human lives including health, businesses, and lifestyles. In spite of governments implementing various strategies across the globe, the pandemic is still expanding with increasing numbers of positive cases. In addition, countries are reopening and easing lockdown restrictions in order to get their economies back on track, and this has led to an increase in the transmission of novel coronavirus. Therefore, it is essential to regularly review the containment strategies employed in different regions in order to understand the characteristics of COVID-19 transmission and to formulate a future course of actions. OBJECTIVE: The objective of this study is to review the COVID-19 transmission statistics in Gulf Cooperation Council (GCC) and European Union (EU) countries, and to compare these data with the various containment strategies implemented for containing the spread of the virus. METHODS: A review method was adopted along with different statistical methods for comparing and analyzing COVID-19 data and containment strategies. Transmission types and the Case Fatality Rate (CFR) in the countries in both regions are used to present the current state of the pandemic. In addition, changes in the number of COVID-19 cases are compared with the mitigation and suppression strategies implemented in both regions and their impact is analyzed. RESULTS: Countries in the EU were slow in reacting to the pandemic, as delays are observed in the implementation of mitigation strategies. However, suppression strategies were implemented soon after mitigation strategies. GCC countries, on the other hand, were quick to react, and they implemented both mitigation and suppression strategies simultaneously, as soon as the pandemic emerged. The CFR was found to be low among GCC countries compared to EU countries. In addition, a second wave of transmission was observed in the EU, whereas in GCC countries there has been no second wave, although a gradual increase in the number of cases is observed. Community transmission was observed among the majority of countries in both GCC and EU countries. CONCLUSIONS: With the reopening of markets, the focus of governments should be on developing integrated user-centric preventive strategies, with a blend of awareness creation, motivation, and support.

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study.

A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.

Knowledge and Opinions of Saudi Dentists Regarding Dental Treatment of Patients Undergoing Bisphosphonates.

OBJECTIVES: Bisphosphonates (BPs) are used for treatment of various benign and malignant medical conditions. Medications-related osteonecrosis of the jaws (MRONJ) is a very serious complication of prolonged therapy with BPs. This study aimed to assess the level of knowledge among dental professionals in Saudi Arabia regarding MRONJ. MATERIALS AND METHODS: This was a questionnaire-based, cross-sectional study. A self-administered questionnaire comprising close-ended questions about BPs and MRONJ was sent to a convenience sample of dentists in Saudi Arabia.Statistical Analysis SPSS software was used for data analysis. Chi-square test was used to compare between the groups. The significance level was set at p < 0.05. RESULTS: A total of 607 dentists participated in this survey. Overall, the respondents showed insufficient level of knowledge regarding BPORNJ, with only 70% of the respondents had heard about BPORNJ, and less than 50% recognized the risk factors and clinical features of the disease. Additionally, while two-thirds of the respondents recognized the importance of taking BPs history, the majority did not know the correct treatment approach. Respondents with higher degrees (specialists) showed far better knowledge than general dentists. CONCLUSION: The knowledge regarding BPs therapy and its complications among Saudi dentists is unsatisfactory. Hence, interventions are highly required to improve dentists' knowledge regarding BPs and MRONJ.

Can the emergency department triage category and clinical presentation predict hospitalization of H1N1 patients?

BACKGROUND: Human H1N1 Influenza A virus was first reported in 2009 when seasonal outbreaks consistently occurred around the world. H1N1 patients present to the emergency departments (ED) with flu-like symptoms extending up to severe respiratory symptoms that require hospital admission. Developing a prediction model for patient outcomes is important to select patients for hospital admission. To date, there is no available data to guide the hospital admission of H1N1 patients based on their initial presentation. OBJECTIVE: The aim of this study was to investigate the predictors of hospital admission of H1N1 patients presenting in the ED. METHODS: We conducted a retrospective review of all laboratory-confirmed H1N1 cases presenting to the ED of a tertiary university hospital in the Eastern region of Saudi Arabia within the period from November 2015 to January 2016. We retrieved data of the initial triage category, vital signs, and presenting symptoms. Multivariate logistic regression analysis was performed to evaluate risk factors for hospital admission among H1N1patients presented to the ED. RESULTS: We identified 333 patients with laboratory-confirmed H1N1. Patients were classified into two groups: admitted group (n=80; 24%) and non-admitted group (n=253; 76%). Sixty patients (75%) were triaged under category IV. Triage category of level III and less were the most predictive for hospital admission. Multivariate regression analysis showed that of all vital signs, tachypnea was a significant risk factor for hospital admission (OR=1.1; 95% CI 1.02 to 1.13, p<0.01). The association between lower triage category and hospital stay was statistically significant (χ2 =6.068, p=0.037). Also, patients with dyspnea were 4.5 times more likely to have longer hospital stay (OR=4.5; 95% CI 1.2 to 17.1, p=0.025). CONCLUSION: Lower triage category and increased respiratory rate predict the need for hospital admission of H1N1 infected patients; while patients with dyspnea or bronchial asthma are likely to stay longer in the hospital. Further prospective studies are needed to evaluate the accuracy of using the CTAS and other clinical parameters in predicting hospitalization of H1N1 patients during outbreaks.

Perception and convenience of caring for children with autism spectrum disorder among family medicine residents in Riyadh 2018.

BACKGROUND: Autism spectrum disorder (ASD) is a group of childhood pervasive neurodevelopmental disorders characterized predominantly by persistent moderate to severe impairment in social skills, communication, and associated with restricted repetitive or stereotyped behaviors. Early diagnosis of this disorder is paramount, which then allows for a timely intervention to facilitate a positive prognosis. To the best of our knowledge, there is no study that has investigated the perception of ASD among family medicine residents in Saudi Arabia. OBJECTIVE: To determine the level of awareness of childhood autism among family medicine residents in Riyadh. SETTINGS AND DESIGN: A cross-sectional study was conducted in nine teaching primary healthcare centers in Riyadh. MATERIALS AND METHODS: The questionnaire was adopted from Unigwe et al. study and distributed to 344 available physicians, of which 277 were received back completed. RESULTS: Only 28 physicians (10.1%) answered positively if they had previously attended any ASD workshops or conferences. In addition, only 29 participants (10.5%) correctly answered 50% or more questions. The question that yielded the lowest scoring was "Recognizing the signs and symptoms of autism in individuals with good language and no apparent intellectual difficulties", while the question with the highest scoring was "Identifying stress in the parents and carers of my patients with autism." The regression analysis showed no association between the scores and the physicians' characteristics. CONCLUSIONS: The results show a low awareness level and moderate to low level of confidence in the physicians ability to recognize, identify, or communicate in regards to ASD. We recommend integrating lectures and clinical exposure of ASD to the residency training program curriculum.