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Abstract Background Eplerenone and spironolactone, recognized as mineralocorticoid receptor antagonists (MRAs), have been reported to improve clinical prognosis among individuals diagnosed with heart failure (HF). However, the difference in the clinical effects between eplerenone and spironolactone in individuals with HF remains uncertain. We aimed to assess the impact of eplerenone compared to spironolactone on clinical outcomes within the HF population. Methods An extensive search was executed in several databases (PubMed, Web of Science, Scopus, Cochrane Library). All relevant studies evaluating eplerenone compared to spironolactone in patients with HF were included. Dichotomous data were pooled as Hazard ratio (HR) or Risk ratio (RR) with a 95% confidence interval (CI). Our main outcome was all-cause mortality. Secondary outcomes included death from cardiovascular causes, treatment withdrawal, and gynecomastia. Results Ten studies, comprising 21,930 HF individuals, were included in our investigation. Eplerenone showed a lower risk of all-cause mortality (HR = 0.78, 95%CI [0.64 to 0.94], P = 0.009) and cardiovascular mortality (HR = 0.54, 95%CI [0.39, 0.74], P = 0.0001) compared to spironolactone. Furthermore, eplerenone exhibited a reduced risk of treatment withdrawal (RR = 0.69, 95% CI [0.62, 0.78], P = 0.0001) and gynecomastia (RR = 0.07, 95% CI [0.02 to 0.31], P = 0.0001) than spironolactone. Conclusion Eplerenone revealed lower all-cause and cardiovascular mortality events in comparison to spironolactone. Moreover, eplerenone was associated with lower gynecomastia and treatment withdrawal events compared to spironolactone. Further well-designed randomized controlled trials are still warranted better to identify the clinical differences between eplerenone and spironolactone. Trial registration Protocol registration: https://doi.org/10.17605/OSF.IO/VNMGK

The demand for effective e-learning platforms requires prioritizing pedagogical excellence in online educational websites. Current approaches struggle with uncertainties, hindering optimal e-learning environments due to a lack of comprehensive evaluation in traditional methods. An integrated approach is crucial to avoid inefficiencies and incomplete understanding of learner needs. This research introduces a pioneering methodology integrating Inter-Valued Fuzzy (IVF) COPRAS-EDAS-PIV hybrid Multiple Criteria Decision-Making (MCDM) techniques, addressing existing limitations. Leveraging the IVF concept allows a holistic assessment of pedagogical parameters, ensuring a thorough understanding of the decision-making landscape. The study involves an extensive literature review, parameter identification, and data acquisition through group decision-making. The selection of a suitable e-learning website is based on seven conflicting parameters, and preference ranking orders are prescribed using EDAS, COPRAS, and PIV MCDM model. Rigorous analysis using these techniques facilitates precise ranking and informed decision-making. The findings underscore the efficacy of the proposed IVF-MCDM approach for the design of a pedagogical e-learning website. Final results reveal that alternative 5 as the most preferable, followed by alternative 2, while alternative 3 is the least favored option among the group. Comparative and sensitivity analyses validate the approach’s superiority, enabling stakeholders to make well-informed decisions for optimal e-learning websites that cater to diverse learner needs, thus enhancing the overall online learning experience.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

Abstract Background and aim Interventional cardiologists face challenges in managing chronic total occlusion (CTO) lesions, with conflicting results when comparing rotational atherectomy (RA) to conventional PCI. This meta-analysis aims to provide a critical evaluation of the safety and feasibility of RA in CTO lesions. Methods PubMed, Scopus, Web of Science, Ovid, and Cochrane central library until April 2023 were searched for relevant studies. MACE was our primary outcomes, other outcomes were all cause of death, cardiac death, MI, and TVR. Also, we reported angiographic outcomes as technical success, procedural success, and procedural complications in a random effect model. The pooled data was analyzed using odds ratio (OR) with its 95% CI using STATA 17 MP. Results Seven studies comprising 5494 patients with a mean follow-up of 43.1 months were included in this meta-analysis. Our pooled analysis showed that RA was comparable to PCI to decrease the incidence of MACE (OR = 0.98, 95% CI [0.74 to 1.3], p = 0.9). Moreover, there was no significant difference between RA and conventional PCI in terms of other clinical or angiographic outcomes. Conclusion Our study showed that RA had comparable clinical and angiographic outcomes as conventional PCI in CTO lesions, which offer interventional cardiologists an expanded perspective when addressing calcified lesions. PROSPERO registration CRD42023417362.

Abstract Background Drug-coated balloons (DCBs) are an established strategy for coronary artery disease. However, the new generation drug-eluting stent (DES) is recommended for patients with Acute myocardial infarction (AMI) for coronary artery revascularization. Our aim is to provide a comprehensive appraisal of the efficacy of DCBs in patients with AMI undergoing PCI. Methods We searched the WOS, PubMed, Scopus, and Cochrane CENTRAL till March 2023, for studies that compared DCBs versus DES in patients with AMI undergoing PCI. We used a random-effects model to compare major adverse cardiac events (MACE), cardiac death, all-cause death, myocardial infarction, target lesion revascularization (TLR), stent thrombosis, Late lumen Loss (LLL), and minimum lumen diameter (MLD) between the two groups. Results Thirteen studies comprising 2644 patients were included. The pooled OR showed non-inferiority of DCB over DES in terms of MACE (OR = 0.89, 95% CI [0.57 to 1.40], p = 0.63). When we defined MACE as a composite of cardiac death, MI, and TLR; the pooled OR favored DCB over DES (OR = 0.50, 95% CI [0.28 to 0.9], p = 0.02). Moreover, DCB was not inferior to DES in terms of all-cause mortality (OR = 0.88, 95% CI: 0.43 to 1.8, p = 0.73), cardiac mortality, (OR = 0.59, 95% CI: 0.22 to 1.56, p = 0.29), MI (OR = 0.88, 95% CI: 0.34 to 2.29, p = 0.79), stent thrombosis (OR = 1.21, 95% CI: 0.35 to 4.23, p = 0.76), TLR (OR = 0.9, 95% CI: 0.43 to 1.93, p = 0.8), LLL (MD = -0.6, 95% CI: -0.3 to 0.19, p = 0.64), or MLD (MD = -0.4, 95% CI: -0.33 to 0.25, p = 0.76). Conclusion Our meta-analysis indicated that DCB intervention was not inferior to DES in the PCI setting in patients with AMI, and can be recommended as a feasible strategy in AMI. PROSPERO registration CRD42023412757.

IntroductionSaffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron.MethodsIn this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose.ResultsThe observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency.DiscussionA discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.

Abstract Medical imaging is considered one of the most important areas within scientific imaging due to the rapid and ongoing development in computer‐aided medical image visualisation, advances in analysis approaches, and computer‐aided diagnosis. Here, the principles of quantum computation and information to develop the field of medical image processing will be reviewed. The advancement of quantum computation in image processing has proved its outstanding properties for processing and storage capacity compared to the classical methods. This review provides a comprehensive summary of the common advanced approaches, methodologies and advanced applications in medical images based on quantum computation.

Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.

Novel mono, di and tetra cationic surfactant (CS) molecules were synthesized by quaternization reaction of diamide with cetyl-2-chloroacetate with different ratio 1:1, 1:2 and 1:4. The chemical structures of the synthetic compounds were proven by FTIR and 1HNMR. The synthetic cationic surfactants were examined as corrosion inhibitors for carbon steel (CSt) in 1.0 M HCl solutions using chemical and electrochemical measurements. Some surface-active properties were determined and linked to the inhibition process. The inhibition efficiency increases with increasing concentration of CS molecules and decreasing temperature. Potentiodynamic polarization technique showed that the three CS molecules are classified as mixed inhibitors. The inhibition process was explained by its spontaneous adsorption on the surface of CSt, according to Langmuir isotherm. The results confirmed that the cationic surfactants successfully inhibited the corrosion of CSt in the investigated corrosion medium and the inhibition efficiency reached 96.08%.

This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person’s growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

Edge detection is the diverse way used to detect boundaries in digital images. Many methods exist to achieve this purpose, yet not all of them can produce results with high detection ratios. Some may have high complexity, and others may require numerous inputs. Therefore, a new multi-phase algorithm that depends on information theory is introduced in this article to detect the edges of aerial images adequately in a fully automatic manner. The proposed algorithm operated by utilizing Shannon and Hill entropies with specific rules along with a non-complex edge detector to record the vital edge information. The proposed algorithm was examined with different aerial images, its performances appraised against six existing approaches, and the outcomes were assessed using three image evaluation methods. From the results, promising performances were recorded as the proposed algorithm performed the best in many aspects and provided satisfactory results. The results of the proposed algorithm had high edge detection ratios as it was able to capture most of the significant edges of the given images. Such findings make the proposed algorithm desirable to be used as a key image detection method with other image-related applications.

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for medical diagnosis and a wide range of uses is computed tomography (CT). This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework.

The proposed work describes an approach for segmentation of activated sludge flocs from the microscopic images for monitoring wastewater treatment. The morphological features of flocs (microbial aggregates) and filaments are related to the state of an activated sludge wastewater treatment plant and must be monitored for proper functioning. Hence, image processing and analysis could be a time-saving monitoring tool. To address this challenge, we propose a novel framework involving a multiphase edge detection algorithm based on information theory. The proposed framework is evaluated and scrutinized critically considering the artifacts found in the photographs tested. To evaluate the segmentations, gold approximation of estimated truth images is created. In addition, the performance was subjectively evaluated for its potential for segmenting activated sludge images. Experimental results show that the proposed framework exhibits the good results and demonstrates its effectiveness.

Pot experiments were carried out using sand (collected from Ismailia ARS), in the greenhouse to study the effect of biochar application, on K and P release from their natural bearing minerals i.e.,K- feldspar and rock phosphate and\or bentonite on some growth of maize plants parameters, their P and k uptake and the soil available P and K. sixty three pots ever filled with sand(10 kg each biochar was added to all pots expect control at rates 0,1.5 , 3 ton per fed. The studied mineral are K feldspar and rock phosphate as source of K and P respectively. Bentonite was also studied because it is the most common mineral used for reclamation of sandy soil .Each mineral was added at rate of 0,1.5, 3 ton / fed the pots are as follow : biochar alone , biochar + K feldspar and biochar +bentonite and biochar and rock phosphate . maize was planted as the common way in which maize seed 6 grains for each pot inoculated with P and K solubilizing bacteria were planted . the usual N fertilizer (NH4 )2SO4 200kg /fed and irrigated as needed . Maize grow parameter i.e plant height and dry weight of maize plant were measured after 2 months of plantation . K and P as well as their uptake were measure. Results revealed that, the use of biochar at 3 tons fed-1 in combination with 3 tons of any of feldspar, bentonite or rock phosphate enhanced significantly growth parameters of maize plants compared to the control treatments .The highest values plant length and dry matter were were 89.00 cm, 11.4 g pot-1 against 36.6 cm and 2.8 g pot-1 for the control treatments., same treatments increased significantly P and K uptake of maize plants and the soil available p and K compared to the control treatments. The highest significant values in P uptake were 22.5, 18.3 & 16.7 mg plant-1 for the treatments (3 tons fed-1)of biochar with either 3ton rock phosphate ,3ton bentonite or 3 ton K-feldspar .while K uptake were 63.9, 61.8 & 60.1 mg plant -1 for biochar ( 3 tons fed-1 )with bentonite (3 tons fed-1 ), feldspar (3 tons fed-1 ) and bentonite (1.5 tons fed-1 ). Regarding available P the highest obtained values were 49.3 mg Kg -1 for 3 ton biochar + 3ton rock phosphate , 24.1 mg Kg -1 for 3 ton biochar + 1.5 ton bentonite and 24.0 mg/ kg-1 for 3 ton biochar + 1.5 ton K- feldspar. Increase either K- feldspar or bentonite to 3 ton had negative effect on available p .