Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to signi...Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.展开更多
The objective of the study is to examine the moderating influence of gender on the relationship between cultural values and Islamic work ethics(IWE)among Palestinian Arab high school teachers in Israel who represent a...The objective of the study is to examine the moderating influence of gender on the relationship between cultural values and Islamic work ethics(IWE)among Palestinian Arab high school teachers in Israel who represent an ethnic and religious minority within a Western-oriented framework.The study sample comprised 1,245 Arab teachers(759 females and 476 males).Data analysis was conducted using structural equation modeling with AMOS,focusing on path analysis.The research findings highlight a substantial relationship between cultural values and Islamic work ethics,with gender as a moderating variable.Additionally,the results indicate a significant positive relationship between the cultural value dimension of uncertainty avoidance and both dimensions of Islamic work ethics-dedication and social responsibility in the workplace,along with independence,diligence,and achievement.In contrast,a pronounced and significant negative relationship was identified between the cultural dimension of femininity/masculinity and these two dimensions of Islamic work ethics.展开更多
Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of ...Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of the 10.4 million deaths of children under five that take place in developing countries. The aim of this study was to evaluate the influence of gum Arabic (GA) and texturized soy protein (TSP) and their interactive effect on proximate, functional, and textural properties of the protein-rich snack stick produced from ground green maize, GA powder, and ground TSP. GA varied at 0%, 4%, 8%, and 12%, while TSP varied at 0%, 12%, 24% and 36%. The 5 cm long protein-rich snack sticks were made using a sausage stuffer and baked in an oven at 110˚C for 1 hr 30 minutes. The snack sticks were subjected to proximate, functional and textural analysis using the standard methods. Increasing GA resulted in a significant (p p < 0.05) increased the protein content (32.46%), Ash content (3.6%), fat (11.96%), and moisture content (16.25%) of protein-rich snack sticks. The interactive effect between GA and TSP led to a decrease in fibre and carbohydrates. Results from this study show GA and TSP significantly enhanced the physico-chemical properties of protein-rich snack sticks. A sample with 4% GA and 36% TSP is recommended for the best physico-chemical attributes of the protein-rich snack stick.展开更多
Liver transplantation is a vital intervention for patients with end-stage liver disease;however,the Arab world faces significant barriers that hinder access to this life-saving procedure in terms of both practice and ...Liver transplantation is a vital intervention for patients with end-stage liver disease;however,the Arab world faces significant barriers that hinder access to this life-saving procedure in terms of both practice and research.This narrative review explores the multifaceted challenges,including financial constraints,limited healthcare infrastructure,cultural factors,and the prevalence of infectious diseases.In the Arab countries,both culture and religion were found to play major roles in the acceptability of liver transplantation.High rates of misconceptions and financial strain on patients and healthcare systems necessitate more transplantation programs and improved financial coverage and insurance policies.Enhancing healthcare facilities and improving access to innovative technologies through research is essential for optimizing transplantation outcomes,considering that common diseases in the region decrease the donor pool and increase complication risks.Public health initiatives to prevent and control prevalent liver diseases,particularly hepatitis,and to manage infection risk are also critical.Stricter regulations should be enforced in less developed countries in the region along with early screening practices to address inherited blood disorders and infectious diseases.Additionally,targeted research on liver diseases specific to the Arab context is crucial,along with fostering dialogue about cultural,religious,economic,and health-related factors affecting donor and recipient eligibility.By tackling these complex barriers through targeted comprehensive strategies,the Arab world can advance to a more equitable and effective liver transplantation system,ultimately improving patient outcomes and quality of life.展开更多
Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)...Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.展开更多
The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the sel...The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the selective separation of molybdenite from chalcopyrite during flotation.Microflotation results indicated that the inhibitory capacity of GA was stronger toward molybdenite than chalcopyrite.At pH 8.0 with 20 mg/L GA addition,the recovery rate of chalcopyrite in the concentrate obtained from mixed mineral flota-tion was 67.49%higher than that of molybdenite.Furthermore,the mechanism of GA was systematically investigated by various surface characterization techniques.Contact angle tests indicated that after GA treatment,the hydrophobicity of the molybdenite surface signifi-cantly decreased,but that of the chalcopyrite surface showed no apparent change.Fourier transform-infrared spectroscopy and X-ray photoelectron spectroscopy revealed a weak interaction force between GA and chalcopyrite.By contrast,GA was primarily adsorbed onto the molybdenite surface through chemical chelation,with possible contributions from hydrogen bonding and hydrophobic interactions.Pre-adsorbed GA could prevent butyl xanthate from being adsorbed onto molybdenite.Scanning electron microscopy–energy-dispersive spectrometry further indicated that GA was primarily adsorbed onto the“face”of molybdenite rather than the“edge.”Therefore,GA could be a promising molybdenite depressant for the flotation separation of Cu–Mo.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a nee...BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a need for culturally and linguistically adapted instruments.The American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire(AAOS-FAOQ)is a widely used tool but has not been adapted for Arabic-speaking patients.AIM To translate,cross-culturally adapt,and validate the AAOS-FAOQ for Arabicspeaking patients with traumatic foot and ankle injuries.METHODS The cross-cultural adaptation followed established guidelines,involving forward and backward translations,expert review,and pre-testing.The final Arabic version was administered alongside the Arabic Short-Form 36(SF-36)to 100 patients for validity testing.Reliability was assessed through test-retest methods with 20 patients completing the questionnaire twice within 48 hours.Pearson correlation coefficients measured convergent and divergent validity with SF-36 subscales,while Cronbach's alpha and intraclass correlation coefficients(ICC)determined internal consistency and reliability.RESULTS Out of 100 patients,92 completed the first set of questionnaires.The Arabic AAOS-FAOQ showed strong correlations with the SF-36 subscales,particularly in physical function and bodily pain(r>0.6).Test-retest reliability was robust,with ICCs of 0.69 and 0.66 for the Global Foot and Ankle Scale and Shoe Comfort Scale,respectively.Cronbach's alpha for internal consistency ranged from 0.7 to 0.9.CONCLUSION The Arabic version of the AAOS-FAOQ demonstrated validity and reliability for use in Arabic-speaking patients with traumatic foot and ankle injuries.This adaptation will enhance the inclusion of this population in orthopedic clinical studies,improving the generalizability of research findings and patient care.展开更多
Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;howev...Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.展开更多
In the context of Arab cities,this study explores the intricate interplay between cultural,historical,and environmental elements that shape their unique soundscapes.The paper aims to shed light on this underrepresente...In the context of Arab cities,this study explores the intricate interplay between cultural,historical,and environmental elements that shape their unique soundscapes.The paper aims to shed light on this underrepresented field of study by employing a three-fold research approach:systematic review,a comprehensive literature review,and the formulation of a future research agenda.The first part of the investigation focuses on research productivity in the Arab world regarding soundscape studies.An analysis of publication trends reveals that soundscape research in Arab cities is still an emerging area of interest.Critical gaps in the existing body of literature are identified,highlighting the importance of addressing these gaps within the broader context of global soundscape research.The second part of the study delves into the distinctive features that inform the soundscapes of Arab cities.These features are categorized into three overarching groups:(i)cultural and religious life,(ii)daily life,and(iii)heritage and history,by exploring these factors,the study aims to elucidate the multifaceted nature of Arab urban soundscapes.From the resonating calls to prayer and the vibrant ambiance of traditional cafes to the bustling markets and architectural characteristics,each factor contributes to the auditory tapestry that defines Arab cities.The paper concludes with a forward-looking research agenda,proposing sixteen key questions organized into descriptive and comparative categories.These questions emphasize the need for a more profound understanding of sound perception,sources,and the impact of urban morphology on the soundscape.Additionally,they highlight the need for interdisciplinary research,involving fields such as urban planning,architecture,psychology,sociology,and cultural studies to unravel the complexity of Arab urban soundscapes.展开更多
The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to va...The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.展开更多
Objective:Bladder cancer(BC)is a significant public health concern in the Middle East and North Africa,but the epidemiology and clinicopathology of the disease and contributors to high mortality in this region remain ...Objective:Bladder cancer(BC)is a significant public health concern in the Middle East and North Africa,but the epidemiology and clinicopathology of the disease and contributors to high mortality in this region remain poorly understood.The aim of this systematic review was to investigate the epidemiological features of BC in the Arab world and compare them to those in Western countries in order to improve the management of this disease.Methods:An extensive electronic search of the PubMed/PMC and Cochrane Library databases was conducted to identify all articles published until May 2022,following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.A total of 95 articles were included in the final analysis after title,abstract,and full-text screening,with additional data obtained from the GLOBOCAN and WHO 2020 databases.展开更多
Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthr...Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.展开更多
Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The diffic...Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.展开更多
This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingl...This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.展开更多
With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,l...With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.展开更多
Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of ph...Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of phenolic compounds, such as flavonoids, terpenoids and eugenol. In turn, the most common uses of gum arabic are in the form of powder for addition to soft drink syrups, cuisine and baked goods, specifically to stabilize the texture of products, increase the viscosity of liquids and promote the leavening of baked products (e.g., cakes). Both eugenol, extracted from cloves, and gum arabic, extracted from the hardened sap of two species of the Acacia tree, are dietary constituents routinely consumed virtually throughout the world. Both of them are also widely used medicinally to inhibit oxidative stress and genotoxicity. The prevention arm of the study included groups: Ia, IIa, IIIa, Iva, V, VI, VII, VIII. Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the same period and for an additional 9 weeks, the animals received either water, 10% GA, EUG, or 10% GA + EUG by gavage. The treatment arm of the study included groups Ib, IIb, IIIb e IVb, IX, X, XI, XII). Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the subsequent 9 weeks, the animals received either water, 10% GA, EUG or 10% GA + EUG by gavage. The novelty of this study is the investigation of their use alone and together for the prevention and treatment of experimental colorectal carcinogenesis induced by dimethylhydrazine. Our results show that the combined use of 10% gum arabic and eugenol was effective, with antioxidant action in the colon, as well as reducing oxidative stress in all colon segments and preventing and treating genotoxicity in all colon segments. Furthermore, their joint administration reduced the number of aberrant crypts and the number of aberrant crypt foci (ACF) in the distal segment and entire colon, as well as the number of ACF with at least 5 crypts in the entire colon. Thus, our results also demonstrate the synergistic effects of 10% gum arabic together with eugenol (from cloves), with antioxidant, antigenotoxic and anticarcinogenic actions (prevention and treatment) at the doses and durations studied, in the colon of rats submitted to colorectal carcinogenesis induced by dimethylhydrazine.展开更多
基金financed by the European Union-NextGenerationEU,through the National Recowery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001-C01.
文摘Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.
文摘The objective of the study is to examine the moderating influence of gender on the relationship between cultural values and Islamic work ethics(IWE)among Palestinian Arab high school teachers in Israel who represent an ethnic and religious minority within a Western-oriented framework.The study sample comprised 1,245 Arab teachers(759 females and 476 males).Data analysis was conducted using structural equation modeling with AMOS,focusing on path analysis.The research findings highlight a substantial relationship between cultural values and Islamic work ethics,with gender as a moderating variable.Additionally,the results indicate a significant positive relationship between the cultural value dimension of uncertainty avoidance and both dimensions of Islamic work ethics-dedication and social responsibility in the workplace,along with independence,diligence,and achievement.In contrast,a pronounced and significant negative relationship was identified between the cultural dimension of femininity/masculinity and these two dimensions of Islamic work ethics.
文摘Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of the 10.4 million deaths of children under five that take place in developing countries. The aim of this study was to evaluate the influence of gum Arabic (GA) and texturized soy protein (TSP) and their interactive effect on proximate, functional, and textural properties of the protein-rich snack stick produced from ground green maize, GA powder, and ground TSP. GA varied at 0%, 4%, 8%, and 12%, while TSP varied at 0%, 12%, 24% and 36%. The 5 cm long protein-rich snack sticks were made using a sausage stuffer and baked in an oven at 110˚C for 1 hr 30 minutes. The snack sticks were subjected to proximate, functional and textural analysis using the standard methods. Increasing GA resulted in a significant (p p < 0.05) increased the protein content (32.46%), Ash content (3.6%), fat (11.96%), and moisture content (16.25%) of protein-rich snack sticks. The interactive effect between GA and TSP led to a decrease in fibre and carbohydrates. Results from this study show GA and TSP significantly enhanced the physico-chemical properties of protein-rich snack sticks. A sample with 4% GA and 36% TSP is recommended for the best physico-chemical attributes of the protein-rich snack stick.
文摘Liver transplantation is a vital intervention for patients with end-stage liver disease;however,the Arab world faces significant barriers that hinder access to this life-saving procedure in terms of both practice and research.This narrative review explores the multifaceted challenges,including financial constraints,limited healthcare infrastructure,cultural factors,and the prevalence of infectious diseases.In the Arab countries,both culture and religion were found to play major roles in the acceptability of liver transplantation.High rates of misconceptions and financial strain on patients and healthcare systems necessitate more transplantation programs and improved financial coverage and insurance policies.Enhancing healthcare facilities and improving access to innovative technologies through research is essential for optimizing transplantation outcomes,considering that common diseases in the region decrease the donor pool and increase complication risks.Public health initiatives to prevent and control prevalent liver diseases,particularly hepatitis,and to manage infection risk are also critical.Stricter regulations should be enforced in less developed countries in the region along with early screening practices to address inherited blood disorders and infectious diseases.Additionally,targeted research on liver diseases specific to the Arab context is crucial,along with fostering dialogue about cultural,religious,economic,and health-related factors affecting donor and recipient eligibility.By tackling these complex barriers through targeted comprehensive strategies,the Arab world can advance to a more equitable and effective liver transplantation system,ultimately improving patient outcomes and quality of life.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number“NBU-FFR-2025-1197-01”.
文摘Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.
基金supported by the National Key Research and Development Program of China(Nos.2022YFC2904502 and 2022YFC2904501)the Major Science and Technology Projects in Yunnan Province,China(No.202202AB080012).
文摘The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the selective separation of molybdenite from chalcopyrite during flotation.Microflotation results indicated that the inhibitory capacity of GA was stronger toward molybdenite than chalcopyrite.At pH 8.0 with 20 mg/L GA addition,the recovery rate of chalcopyrite in the concentrate obtained from mixed mineral flota-tion was 67.49%higher than that of molybdenite.Furthermore,the mechanism of GA was systematically investigated by various surface characterization techniques.Contact angle tests indicated that after GA treatment,the hydrophobicity of the molybdenite surface signifi-cantly decreased,but that of the chalcopyrite surface showed no apparent change.Fourier transform-infrared spectroscopy and X-ray photoelectron spectroscopy revealed a weak interaction force between GA and chalcopyrite.By contrast,GA was primarily adsorbed onto the molybdenite surface through chemical chelation,with possible contributions from hydrogen bonding and hydrophobic interactions.Pre-adsorbed GA could prevent butyl xanthate from being adsorbed onto molybdenite.Scanning electron microscopy–energy-dispersive spectrometry further indicated that GA was primarily adsorbed onto the“face”of molybdenite rather than the“edge.”Therefore,GA could be a promising molybdenite depressant for the flotation separation of Cu–Mo.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
文摘BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a need for culturally and linguistically adapted instruments.The American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire(AAOS-FAOQ)is a widely used tool but has not been adapted for Arabic-speaking patients.AIM To translate,cross-culturally adapt,and validate the AAOS-FAOQ for Arabicspeaking patients with traumatic foot and ankle injuries.METHODS The cross-cultural adaptation followed established guidelines,involving forward and backward translations,expert review,and pre-testing.The final Arabic version was administered alongside the Arabic Short-Form 36(SF-36)to 100 patients for validity testing.Reliability was assessed through test-retest methods with 20 patients completing the questionnaire twice within 48 hours.Pearson correlation coefficients measured convergent and divergent validity with SF-36 subscales,while Cronbach's alpha and intraclass correlation coefficients(ICC)determined internal consistency and reliability.RESULTS Out of 100 patients,92 completed the first set of questionnaires.The Arabic AAOS-FAOQ showed strong correlations with the SF-36 subscales,particularly in physical function and bodily pain(r>0.6).Test-retest reliability was robust,with ICCs of 0.69 and 0.66 for the Global Foot and Ankle Scale and Shoe Comfort Scale,respectively.Cronbach's alpha for internal consistency ranged from 0.7 to 0.9.CONCLUSION The Arabic version of the AAOS-FAOQ demonstrated validity and reliability for use in Arabic-speaking patients with traumatic foot and ankle injuries.This adaptation will enhance the inclusion of this population in orthopedic clinical studies,improving the generalizability of research findings and patient care.
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
基金funding this work through Research Group No.KS-2024-376.
文摘Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.
文摘In the context of Arab cities,this study explores the intricate interplay between cultural,historical,and environmental elements that shape their unique soundscapes.The paper aims to shed light on this underrepresented field of study by employing a three-fold research approach:systematic review,a comprehensive literature review,and the formulation of a future research agenda.The first part of the investigation focuses on research productivity in the Arab world regarding soundscape studies.An analysis of publication trends reveals that soundscape research in Arab cities is still an emerging area of interest.Critical gaps in the existing body of literature are identified,highlighting the importance of addressing these gaps within the broader context of global soundscape research.The second part of the study delves into the distinctive features that inform the soundscapes of Arab cities.These features are categorized into three overarching groups:(i)cultural and religious life,(ii)daily life,and(iii)heritage and history,by exploring these factors,the study aims to elucidate the multifaceted nature of Arab urban soundscapes.From the resonating calls to prayer and the vibrant ambiance of traditional cafes to the bustling markets and architectural characteristics,each factor contributes to the auditory tapestry that defines Arab cities.The paper concludes with a forward-looking research agenda,proposing sixteen key questions organized into descriptive and comparative categories.These questions emphasize the need for a more profound understanding of sound perception,sources,and the impact of urban morphology on the soundscape.Additionally,they highlight the need for interdisciplinary research,involving fields such as urban planning,architecture,psychology,sociology,and cultural studies to unravel the complexity of Arab urban soundscapes.
文摘The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.
文摘Objective:Bladder cancer(BC)is a significant public health concern in the Middle East and North Africa,but the epidemiology and clinicopathology of the disease and contributors to high mortality in this region remain poorly understood.The aim of this systematic review was to investigate the epidemiological features of BC in the Arab world and compare them to those in Western countries in order to improve the management of this disease.Methods:An extensive electronic search of the PubMed/PMC and Cochrane Library databases was conducted to identify all articles published until May 2022,following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.A total of 95 articles were included in the final analysis after title,abstract,and full-text screening,with additional data obtained from the GLOBOCAN and WHO 2020 databases.
文摘Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.
文摘This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.
文摘With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.
文摘Spices are defined as any aromatic condiment of plant origin used to alter the flavor and aroma of foods. Besides flavor and aroma, many spices have antioxidant activity, mainly related to the presence in cloves of phenolic compounds, such as flavonoids, terpenoids and eugenol. In turn, the most common uses of gum arabic are in the form of powder for addition to soft drink syrups, cuisine and baked goods, specifically to stabilize the texture of products, increase the viscosity of liquids and promote the leavening of baked products (e.g., cakes). Both eugenol, extracted from cloves, and gum arabic, extracted from the hardened sap of two species of the Acacia tree, are dietary constituents routinely consumed virtually throughout the world. Both of them are also widely used medicinally to inhibit oxidative stress and genotoxicity. The prevention arm of the study included groups: Ia, IIa, IIIa, Iva, V, VI, VII, VIII. Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the same period and for an additional 9 weeks, the animals received either water, 10% GA, EUG, or 10% GA + EUG by gavage. The treatment arm of the study included groups Ib, IIb, IIIb e IVb, IX, X, XI, XII). Once a week for 20 weeks, the controls received saline s.c. while the experimental groups received DMH at 20 mg/kg s.c. During the subsequent 9 weeks, the animals received either water, 10% GA, EUG or 10% GA + EUG by gavage. The novelty of this study is the investigation of their use alone and together for the prevention and treatment of experimental colorectal carcinogenesis induced by dimethylhydrazine. Our results show that the combined use of 10% gum arabic and eugenol was effective, with antioxidant action in the colon, as well as reducing oxidative stress in all colon segments and preventing and treating genotoxicity in all colon segments. Furthermore, their joint administration reduced the number of aberrant crypts and the number of aberrant crypt foci (ACF) in the distal segment and entire colon, as well as the number of ACF with at least 5 crypts in the entire colon. Thus, our results also demonstrate the synergistic effects of 10% gum arabic together with eugenol (from cloves), with antioxidant, antigenotoxic and anticarcinogenic actions (prevention and treatment) at the doses and durations studied, in the colon of rats submitted to colorectal carcinogenesis induced by dimethylhydrazine.
