Nursing Care Systematization with Case-Based Reasoning and Artificial Intelligence

Paediatric nurses' perspectives on artificial intelligence applications: A cross-sectional study of concerns, literacy levels and attitudes

AIMS

This study aimed to explore the correlation between artificial intelligence (AI) literacy, AI anxiety and AI attitudes among paediatric nurses, as well as identify the influencing factors on paediatric nurses' AI attitudes.

DESIGN

A descriptive, correlational and cross-sectional research.

METHODS

This study was conducted between January and February 2024 with 170 nurses actively working in paediatric clinics in Turkey. The data collection tools included the Nurse Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Artificial Intelligence Literacy Scale (AILS) and the Artificial Intelligence Anxiety Scale (AIAS). To determine the associations between the variables, the data was analysed using IBM SPSS 28, which included linear regression and Pearson correlation analysis.

RESULTS

The study indicated significant positive correlations between paediatric nurses' age and their AIAS scores (r = .226; p < .01) and significant negative correlations between paediatric nurses' age and their AILS (r = -.192; p < .05) and GAAIS scores (r = -.152; p < .05). The GAAIS was significantly predictive (p < .000) and accounted for 50% of the variation in AIAS and AILS scores.

CONCLUSION

Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety. The relationship between the age of the paediatric nurses and the anxiety, AI literacy and attitudes towards AI was demonstrated. Healthcare and educational institutions should create customized training programs and awareness-raising activities for older nurses, as there are noticeable variations in the attitudes of paediatric nurses towards AI based on their age.

IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE

Providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety. This training has the potential to impact their attitudes positively and reduce their anxiety.

REPORTING METHOD

The study results were critically reported using STROBE criteria.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

Retracted: Nursing Care Systematization with Case-Based Reasoning and Artificial Intelligence

[This retracts the article DOI: 10.1155/2022/1959371.].

Treatment Protocols in the Efficacy and Safety of Anti-EGFR Medicines in Combination with Standard Therapy for Patients with Nasopharyngeal Cancer: A Meta-Analysis

Objective

This study was conducted to compare the efficacy of standard therapy (radiotherapy/RT/CT) with that of antiepidermal growth factor receptor (anti-EGFR) monoclonal antibody (NPC) therapy in patients with advanced nasopharyngeal cancer.

Methods

A meta-analysis was performed to meet the objective of this study. The English databases PubMed, Cochrane Library, and Web of Science were searched. The literature review compared anti-EGFR-targeted therapy with conventional therapy practices. The main outcome measure was overall survival (OS). Secondary goals were progression-free survival (PFS), locoregional recurrence-free survival (LRRFS), distant metastasis-free survival (DMFS), and adverse events (grade 3).

Results

The database search resulted in 11 studies, with a total of 4219 participants. It was found that combining an anti-EGFR regimen with conventional therapy did not enhance OS (hazard ratio [HR] = 1.18; 95%confidence interval [CI] = 0.51-2.40; p = 0.70) or PFS appreciably (HR = 0.95; 95%CI = 0.51-1.48; p = 0.88) in patients with nasopharyngeal carcinoma. While LRRFS increased considerably (HR = 0.70; 95%CI = 0.67-1.00; p = 0.01), the combined regimen did not improve DMFS (HR = 0.86; 95%CI = 0.61-1.12; p = 0.36). Treatment-related adverse events included haematological toxicity (RR = 0.2; 95%CI = 0.08-0.45; p = 0.01), cutaneous reactions (RR = 7.05; 95%CI = 2.15-23.09; p = 0.01), and mucositis (RR = 1.96; 95%CI = 1.58-2.09; p = 0.01).

Conclusions

Individuals who have nasopharyngeal cancer do not have an increased chance of surviving until a local recurrence of their disease if they get normal therapy in addition to an anti-EGFR regimen. However, this combination does not enhance overall survival. On the other hand, this factor adds to an increase in the number of adverse effects.

The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings

Research and development of artificial intelligence (AI)-based technologies systems in healthcare has increased over the past decade, highlighting the strong potential of AI to improve the quality of nursing care. To meet the new demands for nursing care, it is necessary that AI be integrated into nursing science and healthcare setting, especially in nursing care. The current challenge is to transform this expanded set of technology into clinical benefits for patients, through more advanced, accurate, practical, effective, efficient, and economical and personalized care. Along with the potential positive outcomes, AI technology also has unintended consequences that have the potential to negatively impact and adversely affect the nursing profession and the primary purpose of nursing practice in healthcare system. This aimed to explore and discuss the impact of applying AI in nursing science and healthcare system to provide approximate nursing care. Some of the impacts that can be evaluated and seen today in the context of using AI technology systems in the scope of nursing and healthcare are expanding access to quality medical care, improving medical records, and improving the quality of services. The use of AI technology systems also has some issues, such as bias and algorithms, which are drawbacks that need to be considered when evaluating the accuracy of the displayed results.

Beyond the Psychopathological Approach to Study the Relationship between Body Dissatisfaction and Suicide: A Moderate and Mediated Analysis

This paper provides a comprehensive review of the literature to date on body dissatisfaction and suicide. A moderate and mediated regression analysis suggests that the relationship between these two variables is mediated by depression, anxiety, impulse control, self-esteem, and hopelessness. The relationship is not moderated by gender, ethnicity, or socioeconomic status. The implications for mental health practitioners are discussed in relation to management strategies for body dissatisfaction and suicide risk. Another finding showed that psychological distress had a significant effect on performance achievement and served as a mediating variable for the effect of social justice on performance achievement. This study only focused on the effect of psychological distress on the performance achievement of human resources in the context of Islamic microfinance institutions. Besides, in explaining the model, this study only focused on one point of view of the grand theory, i.e., social exchange theory. This study provided information on alternative strategies in managing and minimizing the risks of psychological distress to improve performance achievement in Islamic microfinance institutions. Apart from that, this study also provided an overview of how to manage social exchange relationships in the context of an organization, so they could be well-maintained. This study examined the effect of psychological distress on the performance achievement of human resources in the context of Islamic microfinance institutions. This study also specifically examined the role of formal justice and social justice as an antecedent of psychological distress. Eventually, this study used the perspective of social exchange theory for the first time in explaining the psychological distress model on the performance achievement of human resources.

Awareness of Medical Students toward Circadian Rhythm and Sleep Disorder Based on Biomedical Diagnosis

Background

Sleep disorders affect an individual's mental and physical health and vice versa. Sleep medicine is underrecognized as a specialty; therefore, many sleep disorders go undiagnosed. This study is aimed at assessing the knowledge of medical students toward circadian neuroscience and sleep disorder based on biomedical diagnosis.

Methods

This cross-sectional study was conducted in both male and female medical colleges from the third to the sixth year. A self-administered structured questionnaire consisting of sociodemographic data and the Assessment of Sleep Knowledge in Medical Education (ASKME) survey assessed the students' general knowledge and attitude towards sleep disorder and sleep medicine. Chi-square/Fisher exact tests were used to analyse the participants' knowledge level toward specific sociodemographic data. Also, for two-level continuous variables, the Wilcoxon two-sample test was used.

Results

The total number of participants was 296, with 154 female and 142 male participants. The prevalence of inadequate knowledge was considerable with 96.62% of students, compared to adequate knowledge with only 3.38%. The students' attitude to sleep medicine was negative 14.53% and positive among 85.47%. We found that gender was significantly associated with attitude with a p value = 0.0057. The specific interest in sleep medicine had a significant association with knowledge and attitude, p value of 0.0522 and 0.0059, respectively.

Conclusion

This study concluded that medical students possess inadequate knowledge regarding sleep medicine, yet they have a positive attitude towards it.

The Predictive Value of Neutrophil-Lymphocyte Ratio in Patients with Polycythemia Vera at the Time of Initial Diagnosis for Thrombotic Events

Objective

To investigate and discuss the predictive value of the neutrophil-to-lymphocyte ratio (NLR) in patients with polycythemia vera (PV) at the time of initial diagnosis, as well as its clinical significance in predicting the occurrence of thrombotic events and the progression of future thrombotic events during follow-ups, with the goal of providing a reference for the early identification of high-risk PV patients and the early intervention necessary to improve the prognosis of PV patients.

Method

A total of 170 patients diagnosed with PV for the first time were enrolled in this study. The risk factors affecting the occurrence and development of thrombotic events in these patients were statistically analyzed.

Results

NLR (P = 0.030), WBC count (P = 0.045), and history of previous thrombosis (P < 0.001) were independent risk factors for thrombotic events at the time of initial diagnosis. Age ≥ 60 years (P = 0.004), NLR (P = 0.025), history of previous thrombosis (P < 0.001), and fibrinogen (P = 0.042) were independent risk factors for the progression of future thrombotic events during follow-ups. The receiver operating characteristic curve (ROC curves) showed that NLR was more effective in predicting the progression of future thrombotic events than age ≥ 60 years, history of previous thrombosis, and fibrinogen. Kaplan-Meier survival analysis showed progression-free survival time of thrombotic events in the high NLR value group (NLR ≥ 4.713) (median survival time 22.033 months, 95% CI: 4.226-35.840), which was significantly lower compared to the low NLR value group (NLR < 4.713) (median overall survival time 66.000 months, 95% CI: 50.670-81.330); the observed difference was statistically significant (P < 0.001). The 60-month progression-free survival in the low NLR value group was 58.8%, while it was 32.8% in the high NLR value group.

Conclusion

Peripheral blood NLR levels in patients with PV resulted as an independent risk factor for the occurrence of thrombotic events at the time of initial diagnosis and for the progression of future thrombotic events during follow-ups. Peripheral blood NLR levels at the time of initial diagnosis and treatment had better diagnostic and predictive value for the progression of future thrombotic events in patients with PV than age ≥ 60 years, history of previous thrombosis, and fibrinogen.

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.

A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

It makes no difference whether a person is male or female when it comes to neurodegenerative disorders; both sexes are equally susceptible to their devastating effects. Sometimes, it is unclear why a person in their life got a condition that is well-known in the world, such as Parkinson’s disease. Other times, it is evident why the individual obtained the ailment (PD). In modern times, a variety of cutting-edge algorithms that are based on treatment protocols have been developed for the purpose of diagnosing Parkinson’s disease. The approach that is presented in this article is the most current one; it was created using deep learning, and it can predict how severely Parkinson’s disease would affect a patient. In order to diagnose this condition, it is necessary to conduct a comprehensive medical history, a history of any past treatments, physical exams, and certain blood tests and brain films. Because they are less time-consuming and costly, diagnoses are becoming an increasingly important part of medical practice. The diagnosis of Parkinson’s disease by the physician is supported by the findings of the present research, which analyzed the voices of 253 participants. Preprocessing is done in order to get the most accurate results possible from the data. In order to carry out the technique of balancing, a methodical sampling approach was used to choose the data that would afterwards be evaluated. Using a feature selection approach that was determined by the magnitude of the label’s influence, many data groups were created and organized. DT, SVM, and kNN are three methods that are used in classification algorithms and performance assessment criteria. The model was developed as a result of selecting the classification method and data group that had the greatest performance value. This decision led to the creation of the model. During the process of building the model, the SVM technique was used, and data comprising 45% of the original data set were utilized. The information was arranged in descending order of significance, beginning with the most pertinent. In addition to achieving exceptional outcomes in every other aspect of the project, the performance accuracy target was successfully met at 86 percent. As a consequence of this, it has been decided that the physician will be provided with medical decision support with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson’s disease and the model that has been developed. This has led to the conclusion that medical decision support will be offered to the physician.

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric

Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X-rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model's performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep-learning models can be created to assist health professionals in the early detection of pneumonia.

Knowledge and Behavior toward Venous Thromboembolism Event Prophylaxis and Treatment Protocols among Medical Interns in Riyadh

Objective

This study was aimed at evaluating the knowledge and behavior toward venous thromboembolism (VTE) prophylaxis among medical interns.

Methods

This is a questionnaire-based cross-sectional observational cohort study of medical interns that used a validated questionnaire. The questionnaire comprised of items that assessed behavior, knowledge, and self-assessment of VTE risk factors, diagnosis, and prophylaxis. The study was conducted in Riyadh, Saudi Arabia, from October 2020 till September 2021.

Results

The respondents were 246 medical interns. The overall rate of correct responses to behavior items was 41.82%. The overall rate of correct responses to knowledge items was 47.35%. A total of 61.8% responded negatively to the use of VTE risk assessment guidelines (p < 0.0001). For the self-assessment of knowledge of VTE, more than 70% believed they did not have appropriate knowledge, were not prepared to establish the risk of VTE, and were not prepared to provide adequate prophylaxis for VTE (p < 0.0001). A high proportion of medical interns (83.3%, p < 0.0001) believed they needed further training on this topic.

Conclusion

Participants in this study showed poor knowledge and negative behavior regarding the assessment of risk factors, diagnosis, and prophylaxis of VTE. The majority of participants reported they needed training on this topic. These findings underscore the need for educational programs during undergraduate training and orientation of medical interns for VTE risk assessment, diagnosis, and prophylaxis at the beginning of their internship.

Innovate a Standard for the Future Model of Nursing Care at Medical-Surgical Units in Najran University

Aim

Innovate a standard for the future model of nursing care at medical-surgical units in Najran University through a training program for the standard of the future model evaluation on studied nurses' knowledge, attitude about innovation standards, and innovative behavior among nurses.

Methods

A quasi-experimental research was used to achieve the study's goal; the research was carried out at Najran University Hospital at Najran, in the medical and surgical units, as well as outpatient clinics. The sample is a convenience type; 100 nurses were used. Tool. A structured questionnaire sheet was used for data collection that includes nurses' knowledge, attitude, and individual innovative scale.

Results

This reveals the studied nurses related to their individual innovative scale pre- and postintervention. Concerning resistance to change, the mean of them preintervention is x ® SD 9.08 ± 2.60. Concerning opinion leadership, the mean of them postintervention is x ® SD 14.32 ± 3.16. There is a highly significant difference (p < 0.01∗∗) preintervention as regards all domains listed.

Conclusion

The educational program significantly enhances nurses' knowledge and attitude, according to our present study. Nurses' innovative skills are also improved by enhancing their knowledge and attitude. Before and after the educational program was implemented, there was a highly positive linear association between the nurses' knowledge, attitude, and innovative skills at p < 0.01.

Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning

A blood count is one of the most important diagnostic tools in medicine and one of the most common procedures. It can reveal important changes in the body and is commonly used as the first stage in the process of evaluating patients' health. Even though this is a common practice, delivering examinations in laboratories can be difficult due to the availability of expensive technology that requires frequent maintenance. This study is developing an alternative deep learning computational model capable of automatically detecting cells in images of blood samples. Using object detection libraries, it was possible to train a model that was focused on this task and capable of detecting cells in images with adequate accuracy. When the identification of cells in images of blood samples was taken into account in the best results obtained, it was possible to count white cells with an accuracy of one hundred percent, red cells with an accuracy of 89%, and platelets with an accuracy of 96%, which generated subsidies to develop the primary components of a blood count. The components that were supposed to classify the various types of white cells were not carried out due to the limits of the dataset provided. On the other hand, the study can be broadened to include further works that deal with this issue because it produced satisfactory results.

A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data

By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and appropriate to use when delving into this data and arrive at an informed decision.

Measuring the Awareness of Chronic Kidney Disease (CKD) with Environmental Evaluation among Adult Diabetic Patients in Hail Region, Saudi Arabia

Introduction

Chronic kidney disease (CKD) is one of the main chronic complications of T2DM that happens among T2DM patients who have uncontrolled glucose. Because CKD is considered a silent disease, the diagnosis is usually made at late stages when there will be few chances to prevent the adverse outcome.

Aim

The goal of this study was to assess adult diabetic patients' awareness of developing chronic kidney disease at the community level in Hail region, Saudi Arabia, in 2022. Patients and Methods. This is a cross-sectional study conducted among diabetic patients in the Hail region, Saudi Arabia. A self-administered questionnaire translated into Arabic was distributed among patients with DM. The questionnaire covers social and demographic variables (such as age, gender, relationship status, and so on) as well as a 7-item questionnaire to assess the DM population's knowledge of CKD.

Results

400 DM patients responded to our survey (51% females vs 49% males). Patients who were diagnosed with type 2 diabetes were 23.8% and 40.5% had a diabetes duration of 5–15 years. Nearly half (46.8%) were considered as a poor level of awareness, 29.3% had a moderate, and 24% had a good awareness level. Factors associated with an increased level of awareness were being a bachelor's degree, being unmarried, being a student, and having a doctor as a source of CKD information.

Conclusion

There was a deficiency in the level of awareness among the diabetic patients in our region. Patients who were single with better education and who were well informed by the doctors about CKD information tend to be more aware of CKD as compared to other DM patients. Further research is warranted in order to establish the awareness level of DM patients regarding CKD and its complications.

The Effect of Chia Seed Extracts against Complete Freund’s Adjuvant-Induced Rheumatoid Arthritis in Rats

Background and Objectives. It is known that the oxidation of chia seeds is minimal or absent, due to the presence of bioactive compounds, having a great potential in the foods and pharmacological industry. This investigation was done to estimate the nutrition values and anti-oxidant activity of chia seed extracts. Materials and Methods. The protective effects of chia seed extract against complete Freund’s adjuvant (CFA) rheumatoid arthritis in rats were investigated with 100, 200, 300, and 400 ppm/kg BW rat/day chia seeds aqueous extract for 4 weeks. Results. The results obtained revealed that chia seed extract contained high proportions of protein and total dietary fiber values of 21.35% and 27.24%, respectively. Moreover, the results reported that the mineral contents were the greatest in phosphorous, potassium, magnesium, calcium, and sodium (450.09, 410.46, 245.22, 218.69, and 120.34 mg/100 g, respectively). Meanwhile, iron, zinc, and copper were the lowest in chia seeds (9.26, 4.67, and 3.66 mg/100 g, respectively). Besides, vitamins C and E reported 1.65 and 0.82 mg/100 g, respectively. Chia seed extracts were effective in vitro for the bioactive components such as phenolics, flavonoids, and anti-oxidant activities. The biomarkers of complete blood picture, lipid profile, anti-oxidant enzymes, TNF-α, and IL-10 had been improved. Histopathological examination of the rat knee confirmed health amelioration, revealing that chia seed extract consumption can lower pathological changes in injured rheumatoid arthritis rats. Conclusion. It could be seen that the chia seed extracts alleviated the harmful effect of rheumatoid arthritis CFA-induced rats.

Evaluation of Knowledge, Attitude, and Practice of Health Practitioners towards Fertility Preservation in Cancer Patients in an Environmental Region of Saudi Arabia

Background

Cancer patients face multiple challenges, such as infertility caused by exposure to gonadotoxic agents and gonadal irradiation during cancer treatment. Little is known about the health practitioners' knowledge and practice regarding fertility preservation and its available options in Saudi Arabia. Thus, this study is designed to evaluate the level of knowledge, attitude, and practice (KAP) towards fertility preservation in cancer patients among health practitioners in an environmental region in Saudi Arabia.

Methods

The cross-sectional study was carried out between September 2020 and January 2021. A self-administered questionnaire was distributed among health practitioners from a variety of specialties who work closely with cancer patients.

Results

Out of 100 participants, 90% need more knowledge about fertility preservation. The lack of fertility preservation clinics in the patient's area and its unaffordable expenses significantly influenced the health practitioners' attitude towards fertility preservation discussion with cancer patients. The results revealed that 92% of the participants agreed that the Saudi Ministry of Health should establish practice guidelines and provide fertility preservation services for cancer patients.

Conclusions

The present study showed that clinical practitioners' knowledge remains insufficient. Education of health practitioners and the establishment of practice guidelines and fertility preservation clinics for cancer patients are required.

Development and Characterization of Multigrain Pan Bread Prepared Using Quinoa, Lupin, and Fenugreek Seeds with Yellow Maize as a Gluten-Free Diet

Celiac disease causes serious health problems for humans. Therefore, the consumption of gluten-free diets (GFDs) is the only therapy to prevent patients from developing the disease. The objective of the current study was to investigate the proximate analysis, mineral compositions, and antioxidant activities of the quinoa, germinated sweet lupin, fenugreek, and yellow maize, and they were used to develop gluten-free multigrain pan breads. A total of four different grain blend formulations were used to develop the pan bread. The textural properties, color, and sensory evaluation of the developed multigrain pan bread were also determined. The results of the present study showed a significantly higher fat content was found in germinated lupin (13.56%) and quinoa (12.76%), followed by germinated fenugreek and yellow maize (9.68% and 4.67%, respectively). The results indicated that the development of multigrain pan bread with fortification of quinoa, germinated lupin, germinated fenugreek, and yellow maize imparted significant improvement in the nutritional content. Therefore, it could be recommended that the addition of up to 15% of germinated lupin and fenugreek, 60% quinoa, and 10% yellow maize does not negatively affect the sensory characteristics and quality attributes of pan bread.

Medicinal Applications of Banana Peel Flour Used as a Substitute for Computing Dietary Fiber for Wheat Flour in the Biscuit Industry

The aim of this study, medicinal applications of banana peel flour, was used to estimate nutrition value and minerals content. The results indicated that banana peel flour was safe for food application, and it was lower in protein and fat and higher in dietary fiber and total carbohydrate which were 52.68 and 67.25%, respectively. Moreover, the minerals content of phosphorous, potassium, sodium, manganese, and calcium were 217.92, 110.24, 96.43, 85.25, and 80.27 mg/100 g, respectively. Furthermore, the problem of the study investigated the effect of banana peel flour incorporation with wheat flour with 72% extraction to produce biscuit blends at different ratios of 5.0, 10.0, 15.0, and 20.0%, respectively. The nutrition value and mineral content of banana peel flour in biscuits increased gradually in the blends, according to the results. The methods of study of the four biscuit blends were prepared with banana peel flour substituted and wheat flour extraction of 72 percent, and they were compared to a control biscuit. Sensory properties reported that the control biscuit was the highest score followed by blends which had contained 5, 10, and 15% banana peel flour. Meanwhile, the blend of biscuit fortified with 20% was not accepted by panelists. From the color biscuits, the blends containing 5, 10, and 15% banana peel flour had acceptable color, and the blends' color was not acceptable. This may be caused by dark color and high dietary fiber. The texture profile analyzer and physical characteristics were confirmed and gave the same results. The result is that it could be recommended that the banana peel flour, which has high fiber content, produce acceptable biscuits fortified with 5, 10, and 15% and become functional foods. Panelists did not find the 20% blend biscuits to be particularly appealing.

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.