Impact of a Designed Nursing Intervention protocol on Myocardial Infarction Patient's Outcome at a selected University Hospital in Egypt

Background: Myocardial infarction is a life threatening disease that influences the physical, psychological and social dimensions of the individual. Improper lifestyle is one of the causes of this disease.  The  designing and implementing  of  nursing  intervention protocol for MI patients could  be  one  of  the  important  and fundamental steps in improving MI patients outcomes. Aim: The aim of this study was to examine the impact of a designed nursing intervention protocol on myocardial infarction patient’s outcomes as indicated by higher post total mean knowledge scores, higher post total mean practices scores and high level of compliance to lifelong instruction. Research hypotheses: H1. Patients who will be exposed to a designed nursing intervention protocol will have a higher post total mean knowledge scores; H2. Patients who will be exposed to a designed nursing intervention protocol will have a higher post total mean practices scores; H3. Patients who will be exposed to a designed nursing intervention protocol will have a high level of compliance to lifelong instruction. Design: A quasi-experimental research design was utilized in this study Sample: A convenience sample of 40 adult male and female MI patients. Setting: The cardiac care units at a selected Cairo University Hospital were recruited to fulfill the aim of this study. Tools: Four tools were formulated& tested to collect data pertinent to the study; Socio-demographic and medical data sheet, Pre/Post knowledge questionnaire sheet, an Observational checklist and Compliance assessment sheet. Structured interview, reviewing medical records and direct observation were utilized for data collection. Results: The study results revealed that the post total mean knowledge scores of the studied subjects is increased significantly with value of t= 20.6 at p=0.000, higher post total practice scores among the studied subjects with t& p values (t=5.6 at p= 0.000 ) also, studied subjects had mild to high compliance level regarding the lifelong instructions. Conclusion: It can be concluded that, enrichment of patients' knowledge and practices in relation to their condition and utilization of the effective nursing intervention protocol as an approach of care could have a positive impact upon improvement of patients' outcome. Recommendations: The study recommended Conduction of further studies in order to assess the effectiveness of the designed protocol on patients' outcome regarding different cardiac disorders with replication of this study on a larger probability sample from different geographical locations at the Arab Republic of Egypt, in addition to establishment of cardiac rehabilitation center in the different heath care organizations. Keywards: Nursing intervention protocol, Myocardial Infarction, Outcomes, Cardiac care units

Full-thickness versus sliced cartilage in type I tympanoplasty, comparative study

Abstract Introduction The use of cartilage in type I tympanoplasty is associated with concern about a poor audiological outcome. Slicing the cartilage could be a tool to overcome such a feared problem. Objective To compare the healing and hearing outcomes of using sliced cartilage to full-thickness cartilage in type I tympanoplasty. Methods Seventy patients with small to medium-sized central dry tympanic membrane perforation were included in this prospective study. The patients were randomly assigned to one of these two groups: group A: full-thickness cartilage tympanoplasty was done, and group B: partial thickness cartilage tympanoplasty was done. The assessment of healing and hearing was done at 3 and 12 months postoperatively. Results The healing was achieved in 88.2% and 90.9% in group A and group B, respectively. In group A, the mean ABG was 23.44 dB preoperatively and 14.2 dB, and 12.6 dB in the first and second follow-ups, respectively. In group B, preoperative ABG was 23.58 dB compared to 7.9 dB and 6.93 dB in the two follow-ups, respectively. The results were significantly better in group B rather than group A at both follow-ups. Conclusion Hearing results are better when sliced cartilage is used in tympanoplasty type I than full-thickness cartilage

Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption

Background and Aim: due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. Methodology: An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using six S-Boxes, as in the traditional method. Moreover, the CAST encryption algorithm was modified to be used on the private keys and substitution stage (S-Boxes), with the keys and S-Boxes of the encryption algorithm being generated according to the 2D and 3D chaotic map functions. The proposed system passed all evaluation criteria, including (MSE, PSNR, EQ, MD, SC, NC, AD, SNR, SIM, MAE, Time, CC, Entropy, and histograms). Results: Moreover, the results also illustrate that the created S-Boxes passed all evaluation criteria; compared with the results of the traditional method that was used in creating S-Box, the proposed method achieved better results than other methods used in the other works. The proposed solution improves the entropy which is between (7.991–7.999), reduces the processing time which is between (0.5–11 s/Images), and improves NCPR, which is between (0.991–1). Conclusions: The proposed solution focuses on reducing the total processing time for encryption and decryption and improving transmission security. Finally, this solution provides a fast security system for surgical telepresence with secure real-time communication. The complexity of this work needs to know the S-Box creation method used, the chaotic method, the values of the chaotic parameters, and which of these methods was used in the encryption process

Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm

The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%

Antibacterial, Antifungal, and Anticancer Effects of Camel Milk Exosomes: An In Vitro Study

Camel milk (CM) has potent antibacterial and antifungal effects and camel milk exosomes (CM-EXO) have been shown to inhibit the proliferation of a large variety of cancer cells including HepaRG, MCF7, Hl60, and PANC1. However, little is known regarding the effects of CM-EXO on bacteria, fungi, HepG2, CaCo2, and Vero cells. Therefore, this study aimed to evaluate the antibacterial, antifungal, and anticancer effects of CM-EXO. EXOs were isolated from CM by ultracentrifugation and characterized by transmission electron microscope and flow cytometry. Unlike CM, CM-EXO (6 mg/mL) had no bactericidal effects on Gram-positive bacteria (Staphylococcus aureus, Micrococcus luteus, and Enterococcus feacalis) but they had bacteriostatic effects, especially against Gram-negative strains (Escherichia coli, Pseudomonas aeruginosa, and Proteus mirabilis), and fungistatic effects on Candida albicans. HepG2, CaCo2, and Vero cells were respectively treated with CM-EXOs at low (6.17, 3.60, 75.35 μg/mL), moderate (12.34, 7.20, 150.70 μg/mL), and high (24.68, 14.40, 301.40 μg/mL) doses and the results revealed that CM-EXOs triggered apoptosis in HepG2 and CaCo2 cells, but not in normal Vero cells, as revealed by high Bax expression and caspase 3 activities and lower expression of Bcl2. Interestingly, CM-EXOs also induced the elevation of intracellular reactive oxygen species and downregulated the expression of antioxidant-related genes (NrF2 and HO-1) in cancer cells but not in normal cells. CM-EXOs have antibacterial and antifungal effects as well as a selective anticancer effect against HepG2 and CaCo2 cells with a higher safety margin on normal cells

Ameliorative Effects of Camel Milk and Its Exosomes on Diabetic Nephropathy in Rats

Contradictory results were obtained regarding the effects of extracellular vesicles such as exosomes (EXOs) on diabetes and diabetic nephropathy (DN). Some studies showed that EXOs, including milk EXOs, were involved in the pathogenesis of DN, whereas other studies revealed ameliorative effects. Compared to other animals, camel milk had unique components that lower blood glucose levels. However, little is known regarding the effect of camel milk and its EXOs on DN. Thus, the present study was conducted to evaluate this effect on a rat model of DN induced by streptozotocin. Treatment with camel milk and/or its EXOs ameliorated DN as evidenced by (1) reduced levels of kidney function parameters (urea, creatinine, retinol-binding protein (RBP), and urinary proteins), (2) restored redox balance (decreased lipid peroxide malondialdehyde (MDA) and increased the activity of antioxidants enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx)), (3) downregulated expression of DN-related genes (transforming growth factor-beta 1 (TGFβ1), intercellular adhesion molecules 1 (ICAM1), and transformation specific 1 (ETS1), integrin subunit beta 2 (ITGβ2), tissue inhibitors of matrix metalloproteinase 2 (TIMP2), and kidney injury molecule-1 (KIM1)), and (4) decreased renal damage histological score. These results concluded that the treatment with camel milk and/or its EXOs could ameliorate DN with a better effect for the combined therapy

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models