Prognostic Factors for Diabetic Foot at CNHU-HKM

Introduction: Predictions on the prevalence of diabetes mellitus, according to the International Diabetes Federation, indicated 9.3% in 2019 and nearly 10.9% of the general population in 2045. In Benin, the increase in this prevalence, according to the World Health Organization (WHO), is constantly increasing. Diabetic foot is one of its most common complications. The aim of this work was to study the prognostic factors of diabetic foot in the Department of Endocrinology, Metabolism and Nutrition of the CNHU-HKM of Cotonou. Patients and method: This is a descriptive and analytical retrospective study of the prognostic factors of diabetic foot over a period of 3 years from January 2019 to December 2021 in patients who have been hospitalized or followed on an outpatient basis for diabetic foot in the Endocrinology, Metabolism and Nutrition Department of the CNHU-HKM of Cotonou. Results: A total of 112 patients were included in this study. The average age of the patients was 59.70 ± 2.10 years. A male predominance was noted with a sex ratio (M/F) of 1.7. Mixed gangrene and phlegmons were the most common lesions. According to the classification of diabetic feet according to the University of Texas, 59.1% of patients had a 100% risk of amputation. Ten patients died from sepsis (8.9%). The average blood glucose on admission was 2.74 ± 0.23 g/l, reflecting the glycemic imbalance in these patients. There is a statistically significant association between the duration of progression of diabetes, the type of lesion and amputation. Patients whose diabetes has lasted more than 30 years and patients who are not monitored have a greater risk of death. Conclusion: Diabetic patients most often consulted at a late stage, compromising conservative treatment. The duration of diabetes and the type of lesion on admission were the main factors leading to amputation, thus compromising the functional prognosis. As for death, it was mainly linked to irregular monitoring of diabetes and the duration of diabetes. Effective prevention and management of diabetic feet requires patient education about the diabetic foot and systematic screening of at-risk feet in consultation.

AI Fairness-From Machine Learning to Federated Learning

This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also discussed.For a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and classified.However,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness.The privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are unavoidable.The imbalance between algorithms’utility and humanization has further reinforced suchworries.Even for federated learning systems,these constraints on precision AI fairness still exist.Aperspective solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the dilemma of traditional particle identification methods.This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies.Multiple intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 simulation.Tree-based machine learning algorithms and deep learning algorithms e.g.TabNet show excellent performance and generalization ability.Adding additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is nonuniform.Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

Classification of aviation incident causes using LGBM with improved cross-validation

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

Ferroptosis mechanism and Alzheimer's disease

Regulated cell death is a genetically determined form of programmed cell death that commonly occurs during the development of living organisms.This process plays a crucial role in modulating homeostasis and is evolutionarily conserved across a diverse range of living organisms.Ferroptosis is a classic regulatory mode of cell death.Extensive studies of regulatory cell death in Alzheimer’s disease have yielded increasing evidence that fe rroptosis is closely related to the occurrence,development,and prognosis of Alzheimer’s disease.This review summarizes the molecular mechanisms of ferroptosis and recent research advances in the role of ferro ptosis in Alzheimer’s disease.Our findings are expected to serve as a theoretical and experimental foundation for clinical research and targeted therapy for Alzheimer’s disease.

Efficacy of acupoint injection in the treatment of chronic eczema and its influence on peripheral blood T cells

BACKGROUND Chronic eczema significantly impacts daily life,social interactions,and quality of life;however,no curative treatment has been identified.AIM To determine the clinical efficacy of acupoint injection for chronic eczema and its influence on peripheral blood T cells.METHODS Eighty patients with chronic eczema treated at our hospital between June 2022 and March 2023 were randomly assigned to a control group(n=40),which received conventional Western medicine treatment,or an observation group(n=40),which received routine Western medicine treatment plus acupoint injection of triamcinolone acetonide.Response and adverse reaction rates,as well as differences in the levels of serum cytokines IFN-γ,IL-2,IL-4,and IL-10 before and after treatment were investigated.RESULTS No difference in overall response rates were found between the observation and control groups(100%vs 90%,respectively;P>0.05);however,the observation group had a higher marked response rate than the control group(87.5%vs 52.5%;P<0.05).Both groups had decreased Eczema Area and Severity Index scores and increased pruritus after treatment(P<0.05),particularly in the observation group(P<0.05).The observation group had an adverse reaction rate of 2.5%(1/40),which did not differ significantly from that of the control group(P>0.05).The observation group exhibited higher post-treatment INF-γand IL-2 but lower IL-4 levels than the control group(P<0.05);however,no significant inter-group difference was observed in post-treatment IL-10 levels(P>0.05).CONCLUSION Acupoint injection of triamcinolone acetonide is safe and effective in treating chronic eczema.Its therapeutic mechanism is related to the regulation of peripheral blood T cell levels,inhibition of inflammatory reactions,and mitigation of immune imbalance.

Hypoglycaemiac Medicinal Plants Used by Diabetics at CNHU-HKM

Introduction: Diabetes is a major public health problem. Medicinal plants are frequently used either combine with industrial treatment or exclusively, in Africa and particularly in Benin. Our study aims to identify the different medicinal plants used by diabetic patients at the CNHU-HKM. Method: we carried out a descriptive and analytical cross-sectional study. It took place at the University Clinic of Endocrinology, Metabolism and Nutrition of CNHU-HKM over a of 3 months period from 20th of June to 16th of September, 2022, over diabetic followed-up. Results: One hundred and seventy-three (173) patients were gathered using an anonymous inquiry form. In the study population, the age of the patients varied between 31 and 75 years with an average age of 59 +/− 1.43 years, women represented 59% with a sex ratio (male/female) of 0.69. Sixty-five (65) or 37.6% of the population had used medicinal plants. Among given reasons for using medicinal plants were, mainly the positiveness on a third party. Data analysis outcome twenty-nine species of plants belonging to twenty (20) botanical families, the most represented being the Annonaceae and Fabaceae. The most used species are Phyllanthus amarus (hlenwé in fon), Mangifera indica (mangatin in fon), Momordica charantia (gninsikin in fon), Combretum micranthum (kinkéliba in fon), and Picralima nitida (ayorkpè in fon). Most used parts of the plants are the leaves. The recipes are prepared mainly by infusion and are administered exclusively by mouth. Most of the patients who used the hypoglycaemic medicinal plants were satisfied and no adverse effects were reported by them. Conclusion: Hypoglycaemic medicinal plants could be subjected to pharmacognosy and marketed due to their richness in active components, after further toxicological studies.

Macroeconomic Asymmetries in the Eurozone Countries in the Time of Financial Crisis

This paper seeks to highlight the macroeconomic asymmetries and social impacts among eurozone countries which occurred during the time of the financial crisis,emphasising the urgent need to revise the framework of economic governance.The analysis focuses on the growing macroeconomic and social imbalances on a representative sample of selected eurozone member-states(Euro(€)North and Euro(€)South)which had posed a threat to economic sustainability and social coherence.

Is hamstrings-to-quadriceps torque ratio useful for predicting anterior cruciate ligament and hamstring injuries?A systematic and critical review

Background:For the past 30 years,the hamstring(H)-to-quadriceps(Q)(H:Q)torque ratio has been considered an important index of muscle strength imbalance around the knee joint.The purpose of this systematic review was to examine the value of H:Q torque ratio as an independent risk factor for hamstring and anterior cruciate ligament(ACL)injuries.Methods:Database searches were performed to identify all relevant articles in PubMed,MEDLINE,Cochrane Library,and Scopus.Prospective studies evaluating the conventional(concentric H:Q),functional(eccentric H:concentric Q),and mixed(eccentric H at 300/s:concentric Q at2400/s)H:Q ratios as risk factors for occurrence of hamstring muscle strain or ACL injury were considered.Risk of bias was assessed using the Quality In Prognosis Studies tool.Results:Eighteen included studies reported 585 hamstrings injuries in 2945 participants,and 5 studies documented 128 ACL injuries in 2772participants.Best evidence synthesis analysis indicated that there is very limited evidence that H:Q strength ratio is an independent risk factor for hamstring and ACL injury,and this was not different between various ratio types.Methodological limitations and limited evidence for ACL injuries and some ratio types might have influenced these results.Conclusion:The H:Q ratio has limited value for the prediction of ACL and hamstring injuries.Monitoring strength imbalances along with other modifiable factors during the entire competitive season may provide a better understanding of the association between H:Q ratio and injury.

MEM-TET: Improved Triplet Network for Intrusion Detection System

With the advancement of network communication technology,network traffic shows explosive growth.Consequently,network attacks occur frequently.Network intrusion detection systems are still the primary means of detecting attacks.However,two challenges continue to stymie the development of a viable network intrusion detection system:imbalanced training data and new undiscovered attacks.Therefore,this study proposes a unique deep learning-based intrusion detection method.We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data.Then the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to train.Finally,the distance relationship between the triples determines whether the traffic is an attack.In addition,to improve the accuracy of detecting unknown attacks,this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature space.The proposed approach’s effectiveness,stability,and significance are evaluated against advanced models on the Android Adware and General Malware Dataset(AAGM17),Knowledge Discovery and Data Mining Cup 1999(KDDCUP99),Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation Dataset(CICIDS2017),UNSW-NB15,Network Security Lab-Knowledge Discovery and Data Mining(NSL-KDD)datasets.The achieved results confirmed the superiority of the proposed method for the task of network intrusion detection.

Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders.Intrusion Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on cloud.IDS proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere identified.The current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature engineering.This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)model.The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)technique.For detecting and classification of intrusions,long short term memory(LSTM)model is exploited.In addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment.Once the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

Photobiomodulation provides neuroprotection through regulating mitochondrial fission imbalance in the subacute phase of spinal cord injury

Increasing evidence indicates that mitochonarial lission imbalance plays an important role in derayed neuronal cell death. Our previous study round that photo biomodulation improved the motor function of rats with spinal cord injury.However,the precise mechanism remains unclear.To investigate the effect of photo biomodulation on mitochondrial fission imbalance after spinal cord injury,in this study,we treated rat models of spinal co rd injury with 60-minute photo biomodulation(810 nm,150 mW) every day for 14 consecutive days.Transmission electron microscopy results confirmed the swollen and fragmented alte rations of mitochondrial morphology in neurons in acute(1 day) and subacute(7 and 14 days) phases.Photo biomodulation alleviated mitochondrial fission imbalance in spinal cord tissue in the subacute phase,reduced neuronal cell death,and improved rat posterior limb motor function in a time-dependent manner.These findings suggest that photobiomodulation targets neuronal mitochondria,alleviates mitochondrial fission imbalance-induced neuronal apoptosis,and thereby promotes the motor function recovery of rats with spinal cord injury.

Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous repercussions.An Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks.Although extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still challenging.Therefore,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm rate.Initially,to improve training data quality,various preprocessing techniques are utilized.The Adaptive Synthetic oversampling technique generates synthetic samples for minority classes.In the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its rank.Then by simple plurality voting,the most optimal features are retrieved.Then the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the performance.The experiments were carried out on the original imbalanced data and balanced data.The outcomes demonstrate that the balanced data scenario knocked out the imbalanced data.Finally,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD),Canadian Institute for Cybersecurity(CIC-IDS 2017),and UNSW-NB15,datasets respectively.In addition,the suggested technique has been examined and contrasted with the state of art methods on three datasets.

Role of mitophagy in the hallmarks of aging

Aging, subjected to scientific scrutiny, is extensively defined as a time-dependent decline in functions that involves the majority of organisms. The time-dependent accretion of cellular lesions is generally a universal trigger of aging, while mitochondrial dysfunction is a sign of aging. Dysfunctional mitochondria are identified and removed by mitophagy, a selective form of macroautophagy. Increased mitochondrial damage resulting from reduced biogenesis and clearance may promote the aging process. The primary purpose of this paper is to illustrate in detail the effects of mitophagy on aging and emphasize the associations between mitophagy and other signs of aging, including dietary restriction, telomere shortening, epigenetic alterations, and protein imbalance.The evidence regarding the effects of these elements on aging is still limited. And although the understanding of relationship between mitophagy and aging has been long-awaited, to analyze details of such a relationship remains the main challenge in aging studies.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause ofdeath and among over a hundred types of cancer;lung cancer is the secondmost common type of cancer as well as the leading cause of cancer-relateddeaths. Anyhow, an accurate lung cancer diagnosis in a timely manner canelevate the likelihood of survival by a noticeable margin and medical imagingis a prevalent manner of cancer diagnosis since it is easily accessible to peoplearound the globe. Nonetheless, this is not eminently efficacious consideringhuman inspection of medical images can yield a high false positive rate. Ineffectiveand inefficient diagnosis is a crucial reason for such a high mortalityrate for this malady. However, the conspicuous advancements in deep learningand artificial intelligence have stimulated the development of exceedinglyprecise diagnosis systems. The development and performance of these systemsrely prominently on the data that is used to train these systems. A standardproblem witnessed in publicly available medical image datasets is the severeimbalance of data between different classes. This grave imbalance of data canmake a deep learning model biased towards the dominant class and unableto generalize. This study aims to present an end-to-end convolutional neuralnetwork that can accurately differentiate lung nodules from non-nodules andreduce the false positive rate to a bare minimum. To tackle the problem ofdata imbalance, we oversampled the data by transforming available images inthe minority class. The average false positive rate in the proposed method isa mere 1.5 percent. However, the average false negative rate is 31.76 percent.The proposed neural network has 68.66 percent sensitivity and 98.42 percentspecificity.

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

Pharmacological effects of denervated muscle atrophy due to metabolic imbalance in different periods

Denervation-induced skeletal muscle atrophy can potentially cause the decline in the quality of life of patients and an increased risk of mortality.Complex pathophysiological mechanisms with dynamic alterations have been documented in skeletal muscle atrophy resulting from innervation loss.Hence,an in-depth comprehension of the key mechanisms and molecules governing skeletal muscle atrophy at varying stages,along with targeted treatment and protection,becomes essential for effective atrophy management.Our preliminary research categorizes the skeletal muscle atrophy process into four stages using microarray analysis.This review extensively discusses the pathways and molecules potentially implicated in regulating the four stages of denervation and muscle atrophy.Notably,drugs targeting the reactivare oxygen species stage and the inflammation stage assume critical roles.Timely intervention during the initial atrophy stages can expedite protection against skeletal muscle atrophy.Additionally,pharmaceutical intervention in the ubiquitin-proteasome pathway associated with atrophy and autophagy lysosomes can effectively slow down skeletal muscle atrophy.Key molecules within this stage encompass MuRF1,MAFbx,LC3II,p62/SQSTM1,etc.This review also compiles a profile of drugs with protective effects against skeletal muscle atrophy at distinct postdenervation stages,thereby augmenting the evidence base for denervation-induced skeletal muscle atrophy treatment.