Interplay of serum biomarkers bilirubin andγ-glutamyltranspeptidase in predicting cardiovascular complications in type-2 diabetes mellitus

This editorial synthesizes insights from a series of studies examining the interplay between metabolic and oxidative stress biomarkers in cardiovascular disease(CVD),focusing particularly on type-2 diabetes mellitus(T2DM)and acute coronary syndrome(ACS).The central piece of this synthesis is a study that investigates the balance between oxidative stress and antioxidant systems in the body through the analysis of serum bilirubin andγ-glutamyltranspeptidase(γ-GGT)levels in T2DM patients with ACS.This study highlights serum bilirubin as a protective antioxidant factor,while elevatedγ-GGT levels indicate increased oxidative stress and correlate with major adverse cardiovascular events.Complementary to this,other research contributions revealγ-GGT’s role as a risk factor in ACS,its association with cardiovascular mortality in broader populations,and its link to metabolic syndrome,further elucidating the metabolic dysregulation in CVDs.The collective findings from these studies underscore the critical roles ofγ-GGT and serum bilirubin in cardiovascular health,especially in the context of T2DM and ACS.By providing a balanced view of the body’s oxidative and antioxidative mechanisms,these insights suggest potential pathways for targeted interventions and improved prognostic assessments in patients with T2DM and ACS.This synthesis not only corroborates the pivotal role ofγ-GGT in cardiovascular pathology but also introduces the protective potential of antioxidants like bilirubin,illuminating the complex interplay between T2DM and heart disease.These studies collectively underscore the critical roles of serum bilirubin andγ-GGT as biomarkers in cardiovascular health,particularly in T2DM and ACS contexts,offering insights into the body’s oxidative and antioxidative mechanisms.This synthesis of research supports the potential of these biomarkers in guiding therapeutic strategies and improving prognostic assessments for patients with T2DM and some CVD.

Fabrication of Semiconductor Polymer Membranes Combined with a Colored Charge Transfer Complexes Used in the Manufacture of Solar Cells as a Source of Alternative Energy

The main task of this article was to prepared of new pigment model in situ solar cells accordance to charge-transfer complexes of rhodamine C(RhC) donor as dye laser gain media with iodine(σ-acceptor) and chloranilic acid, CLA(π-acceptor). The synthesis stoichiometry of these complexes were of 1∶2(donor∶acceptor) with general formulas [(RhC)]I·I3 and [(RhC)(CLA)2]. The discussed data of elemental analysis, conductivity measurements, FT-IR, UV-Vis spectroscopy and photometric titration data visualized the stoichiometry, formula and complexity of the complexes. The physicochemical and spectroscopic analyses obtained suggested that the electron transfer occurred through nitrogen atom in a tertiary amine —N(C2H5)2 of RhC donor with acceptor. The synthesized solid complexes were under go to thermogravimetric analyses to investigate their thermal stability and decomposition steps. The molar conductance measurements revealed that RhC complexes have an electrolytic statement. The thermal stability of rhodamine C complexes was enhanced in comparable with RhC itself. The polymer membranes of poly-methyl methacrylate)(PMMA) combined with the RhC charge(transfer complexes in chloroform solvent have been prepared and characterized by(infrared & electronic) spectroscopy and scanning electron microscopy(SEM) morphological examination. The photo-stability properties of the RhC complexes have been investigated.

Relative Time Quantum-based Enhancements in Round Robin Scheduling

Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system.Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things,Fog computing and Mobile based applications.The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm(RR)has been widely used for it.The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling.We have carried out an experimental study where we have developed four variations of RR,each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm,and results highlighted that these variations performed better than conventional RR algorithm.In the future,we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time.This way the task performance of modern-day systems can be improved to make them more efficient.

Prevalence and associated factors of clubfoot in the eastern province of Saudi Arabia: A hospital-based study

BACKGROUND Clubfoot,or congenital talipes equinovarus,is a widely recognized cause of disability and congenital deformity worldwide,which significantly impacts the quality of life.Effective management of clubfoot requires long-term,multidiscip-linary intervention.It is important to understand how common this condition is in order to assess its impact on the population.Unfortunately,few studies have investigated the prevalence of clubfoot in Saudi Arabia.AIM To determine the prevalence of clubfoot in Saudi Arabia via the patient population at King Fahad University Hospital(KFUH).METHODS This was a retrospective study conducted at one of the largest hospitals in the country and located in one of the most densely populated of the administrative regions.RESULTS Of the 7792 births between 2015 to 2023 that were included in the analysis,42 patients were diagnosed with clubfoot,resulting in a prevalence of 5.3 per 1000 live births at KFUH.CONCLUSION The observed prevalence of clubfoot was significantly higher than both global and local estimates,indicating a substantial burden in the study population.

A Deep Learning Approach to Industrial Corrosion Detection

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection methods.While recent studies have made progress,a common challenge is the low accuracy of existing detection models.These models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource use.The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and EfficientNetB0.By leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial settings.This advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the field.The results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these challenges.Both CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

A concept analysis of vicarious resilience in mental health nursing

Objective:The introduction of the vicarious resilience concept in psychology and mental health nursing literature is a highly promising advancement.By utilizing this novel concept,experts in various domains can enhance their comprehension of how to foster resilience in individuals by observing and learning from the resilience of others.This concept analysis aims to elucidate the concept of vicarious resilience in mental health nursing by defining its related attributes,antecedents,and consequences.Methods:Walker and Avant’s strategy for concept analysis method was used.This review specifically examined mental health nurse providers.A comprehensive literature search was performed in the PubMed,Medline,the Cochrane Library,and CINAHL databases.The inclusion criterion was Englishlanguage documents on vicarious resilience within mental health nursing.Results:A total of 24 articles were included.The concept’s attributes were empathy,hope,resourcefulness,awareness,and spirituality.Antecedents were associated with listening to patients’trauma narratives,self-care,self-awareness,and support from colleagues.Consequences were enhanced well-being,changes in life goals,adaptation,personal growth,and increased personal resilience.Currently,there is only one tool in the empirical reference.Conclusion:Interpreting the concept of vicarious resilience in mental health nursing and determining its characteristics can be utilized to design nursing interventions to develop vicarious resilience and enhance the quality of care in mental health facilities.

Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision loss.Traditional detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and time-consuming.Therefore,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of DR.In this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB models.This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

Transfer Learning Empowered Skin Diseases Detection in Children

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)rays.If left untreated,these diseases can have severe consequences and spread,especially among children.Early detection is crucial to prevent their spread and improve a patient’s chances of recovery.Dermatology,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and others.This study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the summer.The method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and ringworm.The proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,respectively.This illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

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.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

Artificial intelligence awareness and perceptions among pediatric orthopedic surgeons:A cross-sectional observational study

BACKGROUND Artificial intelligence(AI)is a branch of computer science that allows machines to analyze large datasets,learn from patterns,and perform tasks that would otherwise require human intelligence and supervision.It is an emerging tool in pediatric orthopedic surgery,with various promising applications.An evaluation of the current awareness and perceptions among pediatric orthopedic surgeons is necessary to facilitate AI utilization and highlight possible areas of concern.AIM To assess the awareness and perceptions of AI among pediatric orthopedic surgeons.METHODS This cross-sectional observational study was conducted using a structured questionnaire designed using QuestionPro online survey software to collect quantitative and qualitative data.One hundred and twenty-eight pediatric orthopedic surgeons affiliated with two groups:Pediatric Orthopedic Chapter of Saudi Orthopedics Association and Middle East Pediatric Orthopedic Society in Gulf Cooperation Council Countries were surveyed.RESULTS The pediatric orthopedic surgeons surveyed had a low level of familiarity with AI,with more than 60%of respondents rating themselves as being slightly familiar or not at all familiar.The most positively rated aspect of AI applications for pediatric orthopedic surgery was their ability to save time and enhance productivity,with 61.97%agreeing or strongly agreeing,and only 4.23%disagreeing or strongly disagreeing.Our participants also placed a high priority on patient privacy and data security,with over 90%rating them as quite important or highly important.Additional bivariate analyses suggested that physicians with a higher awareness of AI also have a more positive perception.CONCLUSION Our study highlights a lack of familiarity among pediatric orthopedic surgeons towards AI,and suggests a need for enhanced education and regulatory frameworks to ensure the safe adoption of AI.

Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.

Fake News Detection on Social Media Using Ensemble Methods

In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive information.Fake news,a prevalent issue,particularly on social media,complicates the assessment of news credibility.The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing opinions.As the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful consequences.Despite numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or inconsistencies.Our study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news detection.We employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document Frequency.This comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news detection.Our findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and reliability.Moreover,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world scenarios.We introduce a pioneering ensemble technique that leverages both machine-learning and deep-learning classifiers.To identify the optimal ensemble configuration,we systematically tested various combinations.Experimental evaluations conducted on three diverse datasets related to fake news demonstrate the exceptional performance of our proposed ensemble model.Achieving remarkable accuracy levels of 97%,99%,and 98%on Dataset 1,Dataset 2,and Dataset 3,respectively,our approach showcases robustness and effectiveness in discerning fake news amidst the complexities of contemporary information landscapes.This research contributes to the advancement of fake news detection methodologies and underscores the significance of integrating feature extraction and feature selection strategies for enhanced performance,especially in the context of intricate,high-dimensional datasets.

Burden of routine orthopedic implant removal a single center retrospective study

BACKGROUND Open reduction and internal fixation represent prevalent orthopedic procedures,sparking ongoing discourse over whether to retain or remove asymptomatic implants.Achieving consensus on this matter is paramount for orthopedic surgeons.This study aims to quantify the impact of routine implant removal on patients and healthcare facilities.A retrospective analysis of implant removal cases from 2016 to 2022 at King Fahad Hospital of the University(KFHU)was conducted and subjected to statistical scrutiny.Among these cases,44%necessitated hospitalization exceeding one day,while 56%required only a single day.Adults exhibited a 55%need for extended hospital stays,contrasting with 22.8%among the pediatric cohort.The complication rate was 6%,with all patients experiencing at least one complication.Notably,34.1%required sick leave and 4.8%exceeded 14 d.General anesthesia was predominant(88%).Routine implant removal introduces unwarranted complications,particularly in adults,potentially prolonging hospitalization.This procedure strains hospital resources,tying up the operating room that could otherwise accommodate critical surgeries.Clearly defined institutional guidelines are imperative to regulate this practice.AIM To measure the burden of routine implant removal on the patients and hospital.METHODS This is a retrospective analysis study of 167 routine implant removal cases treated at KFHU,a tertiary hospital in Saudi Arabia.Data were collected in the orthopedic department at KFHU from February 2016 to August 2022,which includes routine asymptomatic implant removal cases across all age categories.Nonroutine indications such as infection,pain,implant failure,malunion,nonunion,restricted range of motion,and prominent hardware were excluded.Patients who had external fixators removed or joints replaced were also excluded.RESULTS Between February 2016 and August 2022,360 implants were retrieved;however,only 167 of those who met the inclusion criteria were included in this study.The remaining implants were rejected due to exclusion criteria.Among the cases,44%required more than one day in the hospital,whereas 56%required only one day.55%of adults required more than one day of hospitalization,while 22.8%of pediatric patients required more than one day of inpatient care.The complication rate was 6%,with each patient experiencing at least one complication.Sick leave was required in 34.1%of cases,with 4.8%requiring more than 14 d.The most common type of anesthesia used in the surgeries was general anesthesia(88%),and the mean(SD)surgery duration was 77.1(54.7)min.CONCLUSION Routine implant removal causes unnecessary complications,prolongs hospital stays,depletes resources and monopolizing operating rooms that could serve more critical procedures.

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

Impacts of mesenchymal stem cells and hyaluronic acid on inflammatory indicators and antioxidant defense in experimental ankle osteoarthritis

BACKGROUND No effective treatment guarantees full recovery from osteoarthritis(OA),and few therapies have disadvantages.AIM To determine if bone marrow mesenchymal stem cells(BMMSCs)and hyaluronic acid(HA)treat ankle OA in Wistar rats.METHODS BMMSCs were characterized using flow cytometry with detection of surface markers[cluster of differentiation 90(CD90),CD105,CD34,and CD45].Fifty male Wistar rats were divided into five groups of 10 rats each:Group I,saline into the right tibiotarsal joint for 2 days;Group II,monosodium iodate(MIA)into the same joint;Groups III,MIA+BMMSCs;Group IV,MIA+HA;and Group V,MIA+BMMSCs+HA.BMMSCs(1×106 cells/rat),HA(75μg/rat),and BMMSCs(1×106 cells/rat)alongside HA(75μg/rat)were injected intra-articularly into the tibiotarsal joint of the right hind leg at the end of weeks 2,3,and 4 after the MIA injection.RESULTS The elevated right hind leg circumference values in the paw and arthritis clinical score of osteoarthritic rats were significantly ameliorated at weeks 4,5,and 6.Lipid peroxide significantly increased in the serum of osteoarthritic rats,whereas reduced serum glutathione and glutathione transferase levels were decreased.BMMSCs and HA significantly improved OA.The significantly elevated ankle matrix metalloproteinase 13(MMP-13)mRNA and transforming growth factor beta 1(TGF-β1)protein expression,and tumor necrosis factor alpha(TNF-α)and interleukin-17(IL-17)serum levels in osteoarthritic rats were significantly downregulated by BMMSCs and HA.The effects of BMMSCs and HA on serum TNF-αand IL-17 were more potent than their combination.The lowered serum IL-4 levels in osteoarthritic rats were significantly upregulated by BMMSCs and HA.Additionally,BMMSCs and HA caused a steady decrease in joint injury and cartilage degradation.CONCLUSION BMMSCs and/or HA have anti-arthritic effects mediated by antioxidant and anti-inflammatory effects on MIAinduced OA.MMP-13 and TGF-β1 expression improves BMMSCs and/or HA effects on OA in Wistar rats.

Quality of life and psychological distress in end-stage renal disease patients undergoing hemodialysis and transplantation

BACKGROUND Among diverse profound impacts on patients’quality of life(QoL),end-stage renal disease(ESRD)frequently results in increased levels of depression,anxiety,and stress.Renal replacement therapies such as hemodialysis(HD)and transplantation(TX)are intended to enhance QoL,although their ability to alleviate psychological distress remains uncertain.This research posits the existence of a significant correlation between negative emotional states and QoL among ESRD patients,with varying effects observed in HD and TX patients.AIM To examine the relationship between QoL and negative emotional states(depression,anxiety,and stress)and predicted QoL in various end-stage renal replacement therapy patients with ESRD.METHODS This cross-sectional study included HD or TX patients in the Eastern Region of Saudi Arabia.The 36-item Short Form Survey and Depression Anxiety Stress Scale(DASS)was used for data collection,and correlation and regression analyses were performed.RESULTS The HD and TX transplantation groups showed statistically significant inverse relationships between QoL and DASS scores.HD patients with high anxiety levels and less education scored low on the physical component summary(PCS).In addition,the results of the mental component summary(MCS)were associated with reduced depression.Compared with older transplant patients,TX patients’PCS scores were lower,and depression,stress,and negative working conditions were highly correlated with MCS scores.CONCLUSION The findings of this study revealed notable connections between well-being and mental turmoil experienced by individuals undergoing HD and TX.The PCS of HD patients is affected by heightened levels of anxiety and lower educational attainment,while the MCS of transplant patients is influenced by advancing age and elevated stress levels.These insights will contribute to a more comprehensive understanding of patient support.