Factors Associated with Students’ Daily Fruit and Vegetable Recommendation at Umm Al-Qura University

Introduction: There is a lack of the awareness in the fruit and vegetable (F & V) recommendation among Saudi society. Although the known facts of the benefits of F & V on health, information on Saudi society following the advised recommendation whether by WHO or local are still unknown. Aim: This cross-sectional study aims to examine the perception of F & V intake among students at Umm Al-Qura University and to assess if they follow these recommendations of F & V locally and internationally. Method: Students from Umm Al-Qura University participated in this study (n = 98, age between 18 - 55). All data were collected using short online survey via Online survey—Survey Monkey in 2019. Result: 55% students were not aware of these recommendations whereas 45% were aware of WHO recommendation. The majority of students 53% have less than two portions a day of F & V. Only 14% have between two to four portions a day. 32% do not eat any portion of F & V daily. 59%, 14%, 5% of students believe that they should have five, seven, more than seven portions a day for being healthy respectively. Maintaining health, reducing the risk for chronic disease and providing essential vitamins and minerals to the body were all reasons why students believe that they should have F & V everyday by 71%. Busy lifestyle, F & V cost, lack of the awareness about the health benefits, dislike of the taste, some F & V spoiled easily were all factors affecting eating F & V daily. Conclusion: University students need educational and nutritional campaigns to spread the awareness about the health benefits of F & V. Most importantly the ministry of health should support those campaigns to increase students’ awareness and build a healthy society with a good habit.

Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Eco-toxicity and health risk assessment of polycyclic aromatic hydrocarbons in surface sediments of Burullus Lake in Egypt

Polycyclic aromatic hydrocarbons(PAHs)are ubiquitous environmental contaminants of growing concern due to their potential ecological and human health risks.This study presents a comprehensive assessment of PAHs contamination in the surface sediments of Burullus Lake,a vital and second largest delta lake in Egypt.The aim was to evaluate the eco-toxicity and potential health risks associated with the presence of these compounds.Surface seven sediment samples were collected from various drains in the southern part of Burullus Lake.Soxhlet extraction method was employed to extract PAHs(16PAHs)from the sediment sample.Analytically,target compounds were located using HPLC.The results showed that samples contained PAHs levels ranging from 0.038×10^(-6)to 0.459×10^(-6),which is considered heavily polluted by the European standard for PAHs pollution.Additionally,there was no apparent source of PAHs in the ElKhashah drain or the Brinbal Canal,as HPLC found none of the compounds.The most prevalent compound in sediment samples along the study area was fluoranthene.The diagnostic indices in the present study indicated that the hydrocarbons in the region originated from pyrolytic and man-made sources along the drains of Burullus Lake.The principal component analysis(PCA)and diagnostic ratios revealed that coal combustion and pyrolytic sources were responsible for the PAHs contamination in the surface sediments.The non-carcinogenic risk(HI),which is the product of the HQs for the adult and child populations,respectively,was calculated.HI values under 1,therefore,demonstrated that they had no carcinogenic effects on human health.TEQs and MEQs in the sediments of Burullus Lake do not have a cancer-causing impact on people.For the safety of nearby wildlife,aquatic life,and people,all activities that raise petroleum hydrocarbon levels in Burullus Lake must be adequately regulated and controlled.According to the ecological risk assessment,there is little chance that PAHs will be found in the sediments of Burullus Lake.This study underscores the urgent need for effective pollution control measures and regular monitoring of PAHs levels in Burullus Lake sediments to protect the aquatic ecosystem and public health.It also highlights the importance of considering eco-toxicity and human health risks in integrated risk assessments of PAHs-contaminated environments.

Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,etc.Recognizing Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems.Deep learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a DoS outbreak.The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and functionality.Subsequently,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT environment.The PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service(DDoS)attack detection in an IoT platform.Initially,the PIODL-ADC model utilizes Z-score normalization to scale input data into a uniformformat.For handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s accuracy.Also,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDoS attacks.Moreover,reptile search algorithm(RSA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC method.The experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.

Enhancing IoT Security:Quantum-Level Resilience against Threats

The rapid growth of the Internet of Things(IoT)operations has necessitated the incorporation of quantum computing technologies tomeet its expanding needs.This integration ismotivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario.The combination of IoT and quantum computing creates new privacy and security problems.This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications.We investigate the incorporation of quantum computing approaches within IoT security frameworks,with a focus on developing effective security mechanisms.Our research,which uses quantum algorithms and cryptographic protocols,provides a unique solution to protecting sensitive information and assuring the integrity of IoT systems.We rigorously analyze critical quantum computing security properties,building a hierarchical framework for systematic examination.We offer concrete solutions flexible to diverse aswell as ambiguous opinions through using a unified computational model with analytical hierarchy process(AHP)multi-criteria decision-making(MCDM)as the technique for ordering preferences by similarity to ideal solutions(TOPSIS)in a fuzzy environment.This study adds practical benefit by supporting practitioners in recognizing,choosing,and prioritizing essential security factors from the standpoint of quantum computing.Our approach is a critical step towards improving quantum-level security in IoT systems,strengthening their resilience against future threats,and preserving the IoT ecosystem’s long-term prosperity.

Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

Improving Channel Estimation in a NOMA Modulation Environment Based on Ensemble Learning

This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Trends in upper gastrointestinal bleeding management

Upper gastrointestinal bleeding(UGIB)can be attributed to either non-variceal or variceal causes.The latter is more aggressive with hemodynamic instability secondary to decompensated cirrhosis and portal hypertension.Non-variceal UGIB(NVUGIB)occurs due to impaired gastroprotective mechanisms attributed to several drugs such as anticoagulants and nonsteroidal anti-inflammatory drugs.Helicobacter pylori infection contributes to the development of peptic ulcer bleeding as well.NVUGIB presentation can be either hemodynamically stable or unstable.During the initial assessment a scoring system including patient-related factors(current cardiac,renal,and liver diseases and hemodynamic and labo-ratory parameters)is used to determine the patient’s prognosis.The Glasgow Blatchford score has been shown to be the most useful and precise.Those with high-risk NVUGIB require urgent assessment and upper endoscopy to achieve better short-term and long-term outcomes such as less hospitalization,blood transfusion,and surgery.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features.The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their characteristics.Then,two distinct kinds of features are obtained from the segmented images to help identify the objects of interest.An Artificial Neural Network(ANN)is then used to recognize the objects based on their features.Experiments were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested approach.The findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

Neglected congenital bilateral knee dislocation treated by quadricepsplasty with semitendinosus and sartorius transfer:A case report

BACKGROUND Congenital knee dislocation(CKD)is a rare condition,which accounts for 1%of congenital hip dislocations.It can present as an isolated condition or coexist with other genetic disorders.Treatment options include serial casting,percutaneous quadriceps recession,and V-Y quadricepsplasty(VYQ).The pathogenesis and hereditary patterns of CKD are not fully understood,with most cases being familial.CKD is usually managed immediately after birth.However,in this report,the patient was neglected for 2 years.CASE SUMMARY A 2-year-old girl with bilateral CKD after birth presented to our hospital after failed serial casting;the patient had seizures and limited access to healthcare because of her family’s low socioeconomic status.Her birth was noted for a breech presentation accompanied by oligohydramnios.The delivery took a long time,requiring immediate medical interventions.As an infant,she had chronic diseases,including a small patent ductus arteriole,multicystic dysplastic kidney disease,and epilepsy.She was found to have a bilateral knee dislocation of approximately-90°on hyperextension.A multidisciplinary team was involved,and medical care was optimized.She underwent VYQ plus semitendinosus and sartorius transfer.After four postoperative follow-ups,her knees were regaining mobility,and she could walk for 2-3 steps without assistance.CONCLUSION This report highlights the importance of early intervention and recommends extensive studies of the management in similar cases.

Modeling the Dynamics of Tuberculosis with Vaccination,Treatment,and Environmental Impact:Fractional Order Modeling

A mathematical model is designed to investigate Tuberculosis(TB)disease under the vaccination,treatment,andenvironmental impact with real cases.First,we introduce the model formulation in non-integer order derivativeand then,extend the model into fractional order derivative.The fractional system’s existence,uniqueness,andother relevant properties are shown.Then,we study the stability analysis of the equilibrium points.The diseasefree equilibrium(DFE)D_(0)is locally asymptotically stable(LAS)when R_(v)<1.Further,we show the globalasymptotical stability(GAS)of the endemic equilibrium(EE)D*for R_(v)>1 and D_(0)for R_(v)≤1.The existenceof bifurcation analysis in the model is investigated,and it is shown the system possesses the forward bifurcationphenomenon.Sensitivity analysis has been performed to determine the sensitive parameters that impact R_(v).Weconsider the real TB statistics from Khyber Pakhtunkhwa in Pakistan and parameterized the model.The computedbasic reproduction number obtained using the real cases is R0≈3.6615.Various numerical results regardingdisease elimination of the sensitive parameters are shown graphically.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

Understanding the Novel Approach of Nanoferroptosis for Cancer Therapy

As a new form of regulated cell death,ferroptosis has unraveled the unsolicited theory of intrinsic apoptosis resistance by cancer cells.The molecular mechanism of ferroptosis depends on the induction of oxidative stress through excessive reactive oxygen species accumulation and glutathione depletion to damage the structural integrity of cells.Due to their high loading and structural tunability,nanocarriers can escort the delivery of ferro-therapeutics to the desired site through enhanced permeation or retention effect or by active targeting.This review shed light on the necessity of iron in cancer cell growth and the fascinating features of ferroptosis in regulating the cell cycle and metastasis.Additionally,we discussed the effect of ferroptosis-mediated therapy using nanoplatforms and their chemical basis in overcoming the barriers to cancer therapy.

HB-Continuous Mappings in L-Topological Space

In this paper, we introduce and study the notion of HB-closed sets in L-topological space. Then, HB-convergence theory for L-molecular nets and L-ideals is established in terms of HB-closedness. Finally, we give a new definition of fuzzy H-continuous [1] which is called HB-continuity on the basis of the notion of H-bounded L-subsets in L-topological space. Then we give characterizations and properties by making use of HB-converges theory of L-molecular nets and L-ideals.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

The interplay mechanism between IDH mutation, MGMT-promoter methylation, and PRMT5 activity in the progression of grade 4 astrocytoma: unraveling the complex triad theory

Background:The dysregulation of Isocitrate dehydrogenase(IDH)and the subsequent production of 2-Hydroxyglutrate(2HG)may alter the expression of epigenetic proteins in Grade 4 astrocytoma.The interplay mechanism between IDH,O-6-methylguanine-DNA methyltransferase(MGMT)-promoter methylation,and protein methyltransferase proteins-5(PRMT5)activity,with tumor progression has never been described.Methods:A retrospective cohort of 34 patients with G4 astrocytoma is classified into IDH-mutant and IDH-wildtype tumors.Both groups were tested for MGMT-promoter methylation and PRMT5 through methylation-specific and gene expression PCR analysis.Inter-cohort statistical significance was evaluated.Results:Both IDH-mutant WHO grade 4 astrocytomas(n=22,64.7%)and IDH-wildtype glioblastomas(n=12,35.3%)had upregulated PRMT5 gene expression except in one case.Out of the 22 IDH-mutant tumors,10(45.5%)tumors showed MGMT-promoter methylation and 12(54.5%)tumors had unmethylated MGMT.All IDH-wildtype tumors had unmethylated MGMT.There was a statistically significant relationship between MGMT-promoter methylation and IDH in G4 astrocytoma(p-value=0.006).Statistically significant differences in progression-free survival(PFS)were also observed among all G4 astrocytomas that expressed PRMT5 and received either temozolomide(TMZ)or TMZ plus other chemotherapies,regardless of their IDH or MGMT-methylation status(p-value=0.0014).Specifically,IDH-mutant tumors that had upregulated PRMT5 activity and MGMT-promoter methylation,who received only TMZ,have exhibited longer PFS.Conclusions:The relationship between PRMT5,MGMT-promoter,and IDH is not tridirectional.However,accumulation of D2-hydroxyglutarate(2-HG),which partially activates 2-OG-dependent deoxygenase,may not affect their activities.In IDH-wildtype glioblastomas,the 2HG-2OG pathway is typically inactive,leading to PRMT5 upregulation.TMZ alone,compared to TMZ-plus,can increase PFS in upregulated PRMT5 tumors.Thus,using a PRMT5 inhibitor in G4 astrocytomas may help in tumor regression.