Definability and Textural Rough Set Algebras

In this paper,a counterpart of definability is studied in texture spaces.The concept of textural complete field is defined and the relations with textural definable sets are investigated.If a texture is discrete,then textural definability coincides with definability.Using this fact,we obtain some basic results for definability in rough set algebras.Further,we discuss on definability for fuzzy rough sets considering textural fuzzy direlations.

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Multipath Selection Algorithm Based on Dynamic Flow Prediction

Traditional traffic management techniques appear to be incompetent in complex data center networks, so proposes a load balancing strategy based on Long Short-Term Memory (LSTM) and quantum annealing by Software Defined Network (SDN) to dynamically predict the traffic and comprehensively consider the current and predicted load of the network in order to select the optimal forwarding path and balance the network load. Experiments have demonstrated that the algorithm achieves significant improvement in both system throughput and average packet loss rate for the purpose of improving network quality of service.

An Integrated Load Balancing Scheme Based on SDN ASR Algorithm

At present,the flow table of the SDN switch is stored in the costly Ternary Content Addressable Memory(TCAM)cache.Due to the cost problem,the number of flow tables that the SDN switch can store is extremely limited,which is far less than the number of traffic,so it is prone to overflow problem,and leads to network paralysis.That has become a bottleneck in restricting the processing capacity of the data center,and will become a weak point focused by attackers.In this paper,we propose an algorithm for the Alarm Switch Remove(ASR)that fully loads the flow table space in SDN,and further put forward an integrated load balancing scheme in SDN.Finally,we use Mininet to verify that the scheme can ease the SDN switch flow table overflow problem and increase network throughput.

Software defined satellite networks:A survey

In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.

Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances,Challenges and Future Directions

Software-Defined Networking(SDN)enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions.Recently Machine Learning(ML)techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems(IDSs)considering logically centralized control and global view of the network provided by SDN.Many IDSs have developed using advances in machine learning and deep learning.This study presents a comprehensive review of recent work ofML-based IDS in context to SDN.It presents a comprehensive study of the existing review papers in the field.It is followed by introducing intrusion detection,ML techniques and their types.Specifically,we present a systematic study of recent works,discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN,and promising future works in this field.

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Task offloading is a key strategy in Fog Computing (FC). Thedefinition of resource-constrained devices no longer applies to sensors andInternet of Things (IoT) embedded system devices alone. Smart and mobileunits can also be viewed as resource-constrained devices if the power, cloudapplications, and data cloud are included in the set of required resources. Ina cloud-fog-based architecture, a task instance running on an end device mayneed to be offloaded to a fog node to complete its execution. However, ina busy network, a second offloading decision is required when the fog nodebecomes overloaded. The possibility of offloading a task, for the second time,to a fog or a cloud node depends to a great extent on task importance, latencyconstraints, and required resources. This paper presents a dynamic service thatdetermines which tasks can endure a second offloading. The task type, latencyconstraints, and amount of required resources are used to select the offloadingdestination node. This study proposes three heuristic offloading algorithms.Each algorithm targets a specific task type. An overloaded fog node can onlyissue one offloading request to execute one of these algorithms accordingto the task offloading priority. Offloading requests are sent to a SoftwareDefined Networking (SDN) controller. The fog node and controller determinethe number of offloaded tasks. Simulation results show that the average timerequired to select offloading nodes was improved by 33% when compared tothe dynamic fog-to-fog offloading algorithm. The distribution of workloadconverges to a uniform distribution when offloading latency-sensitive nonurgenttasks. The lowest offloading priority is assigned to latency-sensitivetasks with hard deadlines. At least 70% of these tasks are offloaded to fognodes that are one to three hops away from the overloaded node.

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

In light of the escalating demand and intricacy of services in contemporary terrestrial,maritime,and aerial combat operations,there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks.Software-Defined Networking(SDN)proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts,due to its intrinsic ability to flexibly allocate and centrally administer network resources.This study pivots around the optimization of SDN controller deployment within airborne data link clusters.A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed.Within this architectural framework,the controller deployment issue is reframed as a two-fold problem:subdomain partition-ing and central interaction node selection.We advocate a subdomain segmentation approach grounded in node value ranking(NDVR)and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm(AFSA).The advanced NDVR-AFSA(Node value ranking-Improved artificial fish swarm algorithm)algorithm makes use of a chaos algorithm for population initialization,boosting population diversity and circumventing premature algorithm convergence.By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations,the algorithm’s search range adaptability is enhanced,thereby increasing the possibility of obtaining globally optimal solutions,while concurrently augmenting cluster reliability.The simulation results verify the advantages of the NDVR-IAFSA algorithm,achieve a better load balancing effect,improve the reliability of aviation data link cluster,and significantly reduce the average propagation delay and disconnection rate,respectively,by 12.8%and 11.7%.This shows that the optimization scheme has important significance in practical application,and can meet the high requirements of modern sea,land,and air operations to aviation airborne communication networks.

Data Analysis of Network Parameters for Secure Implementations of SDN-Based Firewall

Software-Defined Networking(SDN)is a new network technology that uses programming to complement the data plane with a control plane.To enable safe connection,however,numerous security challenges must be addressed.Flooding attacks have been one of the most prominent risks on the internet for decades,and they are now becoming challenging difficulties in SDN networks.To solve these challenges,we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system.This study offers a systematic strategy for wrapping up the examination of SDN operations.The Mininet simulator examines the effectiveness of SDN-based firewalls at various network tiers.The fundamental network characteristics that specify how SDN should operate.The three main analytical measures of the network are jitter,response time,and throughput.During regular operations,their behavior evaluates in the standard SDN conditions of Transmission Control Protocol(TCP)flooding and User Datagram Protocol(UDP)flooding with no SDN occurrences.Low Orbit Ion Cannon(LOIC)is applied to launch attacks on the transmission by the allocated server.Wireshark and MATLAB are used for the behavioral study to determine how sensitive the parameters are used in the SDN network and monitor the fluctuations of those parameters for different simulated scenarios.

Multivariate Broadcast Encryption with Group Key Algorithm for Secured IoT

The expanding and ubiquitous availability of the Internet of Things(IoT)have changed everyone’s life easier and more convenient.Same time it also offers a number of issues,such as effectiveness,security,and excessive power consumption,which constitute a danger to intelligent IoT-based apps.Group managing is primarily used for transmitting and multi-pathing communications that are secured with a general group key and it can only be decrypted by an authorized group member.A centralized trustworthy system,which is in charge of key distribution and upgrades,is used to maintain group keys.To provide longitudinal access controls,Software Defined Network(SDN)based security controllers are employed for group administration services.Cloud service providers provide a variety of security features.There are just a few software security answers available.In the proposed system,a hybrid protocols were used in SDN and it embeds edge system to improve the security in the group communication.Tree-based algorithms compared with Group Key Establishment(GKE)and Multivariate public key cryptosystem with Broadcast Encryption in the proposed system.When all factors are considered,Broadcast Encryption(BE)appears to become the most logical solution to the issue.BE enables an initiator to send encrypted messages to a large set of recipients in a efficient and productive way,meanwhile assuring that the data can only be decrypted by defining characteristic.The proposed method improves the security,efficiency of the system and reduces the power consumption and minimizes the cost.

Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation services.However,it also provides a sequence of novel cyber-security issues.Although IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers.Cyberattacks result in financial loss and data breaches for organizations and individuals.So,it becomes crucial to secure the IoT environment from such cyberattacks.With this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT environment.The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT envi-ronment.To attain this,the ECSADL-CAD model initially pre-processes the data.In the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack detection.At last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model.The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

An Intrusion Detection System for SDN Using Machine Learning

Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.

Clinical pharmacists’ involvement in carbapenem antibiotics management at Wenzhou Integrated Hospital

BACKGROUND Carbapenem antibiotics are a pivotal solution for severe infections,particularly in hospital settings.The emergence of carbapenem-resistant bacteria owing to the irrational and extensive use of carbapenems underscores the need for meticulous management and rational use.Clinical pharmacists,with their specialized training and extensive knowledge,play a substantial role in ensuring the judicious use of carbapenem.This study aimed to elucidate the patterns of carbapenem use and shed light on the integral role played by clinical pharmacists in managing and promoting the rational use of carbapenem antibiotics at Wenzhou Integrated Traditional Chinese and Western Medicine Hospital.AIM To analyze carbapenem use patterns in our hospital and role of clinical pharmacists in managing and promoting their rational use.METHODS We performed a retrospective analysis of carbapenem use at our hospital between January 2019 and December 2021.Several key indicators,including the drug utilization index,defined daily doses(DDDs),proportion of antimicrobial drug costs to total hospitalization expenses,antibiotic utilization density,and utilization rates in different clinical departments were comprehensively analyzed.RESULTS Between 2019 and 2021,there was a consistent decline in the consumption and sales of imipenem-cilastatin sodium,meropenem(0.3 g),and meropenem(0.5 g).Conversely,the DDDs of imipenem-cilastatin sodium for injection increased in 2020 and 2021 vs 2019,with a B/A value of 0.67,indicating a relatively higher drug cost.The DDDs of meropenem for injection(0.3 g)exhibited an overall upward trend,indicating an increasing clinical preference.However,the B/A values for 2020 and 2021 were both>1,suggesting a relatively lower drug cost.The DDDs of meropenem for injection(0.5 g)demonstrated a progressive increase annually and consistently ranked first,indicating a high clinical preference with a B/A value of 1,signifying good alignment between economic and social benefits.CONCLUSION Carbapenem use in our hospital was generally reasonable with a downward trend in consumption and sales over time.Clinical pharmacists play a pivotal role in promoting appropriate use of carbapenems.

Cognitive frailty in the elderly:a concept analysis

Frailty is a recession of age-related reserves caused by a variety of causes and is becoming the most important clinical syndrome that affects the health of the elderly.In the elderly,frailty and cognitive dysfunction often exist,and some people have proposed cognitive frailty.Cognitive frailty is an elderly syndrome that increases the risk of dementia,in the same time,and can independently predict the adverse health outcomes of the patient and affect the quality of the patient's survival.This paper,under the guidance of Walker and Avant method,provides theoretical basis for early recognition and intervention of cognitive weakness in the elderly.

Ground level utility of Access, Watch, Reserve classification: Insights from a tertiary care center in North India

BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effective treatment.The World Health Or-ganization(WHO)Access,Watch,Reserve(AWaRe)classification system was introduced to address this issue and guide appropriate antibiotic prescribing.However,there is a lack of studies examining the prescribing patterns of antimi-crobials using the AWaRe classification,especially in North India.Therefore,this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India.Ophthalmology,Obstetrics and Gynecology).Metronidazole and ceftriaxone were the most prescribed antibiotics.According to the AWaRe classification,57.61%of antibiotics fell under the Access category,38.27%in Watch,and 4.11%in Reserve.Most Access antibiotics were prescribed within the Medicine department,and the same department also exhibited a higher frequency of Watch antibiotics prescriptions.The questionnaire survey showed that only a third of participants were aware of the AWaRe classification,and there was a lack of knowledge regarding AMR and the potential impact of AWaRe usage.RESULTS The research was carried out in accordance with the methodology presented in Figure 1.A total of n=123 patients were enrolled in this study,with each of them receiving antibiotic prescriptions.The majority of these prescriptions were issued to inpatients(75.4%),and both the Medicine and Surgical departments were equally represented,accounting for 49.6%and 50.4%,respectively.Among the healthcare providers responsible for prescribing antibiotics,72%were Junior Residents,18.7%were Senior Residents,and 9.3%were Consultants.These findings have been summarized in Table 1.The prescriptions included 27 different antibiotics,with metronidazole being the most prescribed(19%)followed by ceftriaxone(17%).The mean number of antibiotics used per patient was 1.84±0.83.The mean duration of antibiotics prescribed was 6.63±3.83 days.The maximum number of antibiotics prescribed per patient was five.According to the AWaRe classification,57.61%of antibiotics fell under the Access,38.27%in Watch,and 4.11%in Reserve categories,suggesting appropriate antibiotic selection according to these criteria.The distribution of antibiotics prescribed according to the WHO AWaRe categories is presented in Figure 2.The difference in prescribing frequencies amongst departments can be noted.Most of the antibiotics prescribed in the Access category were from the Medicine department(75.4%),followed by Surgery(24.6%).For Watch antibiotics,Medicine had a higher proportion(63.4%)compared to Surgery(36.6%).In terms of seniority,Junior Residents prescribed the highest number of antibiotics for both Access and Watch categories in Medicine and Surgery departments.Senior residents and Consultants prescribed a lower number of antibiotics in all categories and departments.Only a few antibiotics were prescribed in the Reserve category,with most prescriptions being from the Medicine department.The study also evaluated the Knowledge and Awareness of Healthcare professionals towards the WHO AWaRe classi-fication through a questionnaire survey.A total of 93 participants responded to the survey.Among them,most parti-cipants were Junior Residents(69.9%),followed by Senior Residents(25.8%)and Faculty(4.3%).When enquired if they knew about the WHO AWaRe classification only 33.3%of the participants responded positively.Of those who were aware of the AWaRe classification,the most common source of information was the internet(31.2%),followed by the antimicrobial policy of their institution(15.1%)as seen in Table 2.The survey results on the knowledge and awareness of AMR among healthcare professionals are also presented in Tables 3 and 4.Out of the 93 participants,68(73.1%)agreed that the emergence of AMR is inevitable,while only 13(14.0%)disagreed that AWaRe usage will result in the inability to treat serious infections.Additionally,58(62.4%)agreed that it will lead to lengthier hospital stays,43(46.2%)agreed that the success of chemotherapy and major surgery will be hampered,and the majority also agreed that its use will lead to increased cost of treatment and increased mortality rates.Regarding the utilization of AWaRe in the hospital summarized in Tables 4 and 5,35.5%of the participants agreed that it should be used,while only 2.2%disagreed.Additionally,34.4%agreed that AWaRe reduces adverse effects of inappro-priate prescription.However,37.6%of the participants considered that AWaRe threatens a clinician's autonomy and 30.1%thought that its use can delay treatment.Additionally,the DDD of each drug was also evaluated.The usage of various antimicrobial drugs in a hospital setting,along with their daily doses and DDD according to the WHO's Anatomical Therapeutic Chemical classification system was calculated.Some of the important findings include high usage rates of ceftriaxone and metronidazole,and relatively low usage rates of drugs like colistin and clindamycin.Additionally,some drugs had wider ranges than others.Comparison of WHO defined DDD with Daily Drug dose(Mean)in the studied prescriptions is represented in the Clustered Bar chart in Figure 3.Finally,the Mean Daily Drug Dose for prescribed drugs was compared with WHO defined DDD for each drug using a Student’s T test.The mean daily drug dose of amoxy/clav was significantly higher than the WHO DDD(1.8 vs 1.50,P=0.014),while the mean daily drug dose of metronidazole and doxycycline were significantly lower than the WHO DDD(P<0.001 and P=0.008,respectively).The mean daily drug dose of piperacillin/tazobactam,amikacin,clindamycin,and levofloxacin did not show significant differences compared to the WHO DDD(P>0.05).CONCLUSION This research indicates an appropriate proportion of prescriptions falling under the Access category(57.61%),suggesting appropriate antibiotic selection,a significant proportion also belongs to the Watch category(38.27%),emphasizing the need for greater caution to prevent the escalation of AMR.There is a moderate level of awareness among healthcare professionals about AMR and the steps being taken to tackle it,highlighting the gap in implementation of policies and need for more steps to be taken in spreading the knowledge about the subject.However,there is a significant difference between the WHO DDD and the prescribed daily dose in the analysed prescriptions suggesting overuse and underuse of antibiotics.

Human immunodeficiency virus patients with low CD4 counts are more likely to have precancerous polyps identified during index colonoscopy

BACKGROUND Antiretroviral treatment(ART)has improved the life expectancy of patients living with human immunodeficiency virus(HIV).As these patients age,they are at increased risk for developing non-acquired immunodeficiency syndrome defining malignancies(NADMs)such as colon cancers.AIM To determine which factors are associated with the development of precancerous polyps on screening colonoscopy in patients with HIV and to investigate whether HIV disease status,measured by viral load and CD4 count,might influence precancerous polyp development.METHODS A retrospective review of records at two urban academic medical centers was performed for HIV patients who had a screening colonoscopy between 2005-2015.Patients with a history of colorectal cancer or polyps,poor bowel preparation,or inflammatory bowel disease were excluded.Demographic data such as sex,age,race,and body mass index(BMI)as well as information regarding the HIV disease status such as CD4 count,viral load,and medication regimen were collected.Well-controlled patients were defined as those that had viral load<50 copies,and poorly-controlled patients were those with viral load≥50.Patients were also stratified based on their CD4 count,comparing those with a low CD4 count to those with a high CD4 count.Using colonoscopy reports in the medical record,the size,histology,and number of polyps were recorded for each patient.Precancerous polyps included adenomas and proximal serrated polyps.Data was analyzed using Fisher’s exact tests and logistic regression through SAS 3.8 software.RESULTS Two hundred and seven patients met our inclusion criteria.The mean age was 56.13 years,and 58%were males.There were no significant differences in terms of age,race or ethnicity,insurance,and smoking status between patients with CD4 counts above or below 500.BMI was lower in patients with CD4 count<500 as compared to those with count>500(P=0.0276).In patients with CD4>500,53.85%of patients were female,and 70.87%of patients with CD4<500 were male(P=0.0004).Only 1.92%of patients with CD4≥500 had precancerous polyps vs 10.68%of patients with CD4<500(P=0.0102).When controlled for sex,BMI,and ART use,patients with CD4<500 were 9.01 times more likely to have precancerous polyps[95%confidence interval(CI):1.69-47.97;P=0.0100].Patients taking non-nucleoside reverse transcriptase inhibitors were also found to be 10.23 times more likely to have precancerous polyps(95%CI:1.08-97.15;P=0.0428).There was not a significant difference noted in precancerous polyps between those that had viral loads greater or less than 50 copies.CONCLUSION Patients with low CD4 counts were more likely to have precancerous polyps on their screening colonoscopy although the etiology for this association is unclear.We also found an increased risk of precancerous polyps in patients taking non-nucleoside reverse transcriptase inhibitors,which is contradictory to prior literature showing ART has decreased the risk of development of NADMs.However,there have not been studies looking at colorectal cancer and ART by drug class,to our knowledge.Further prospective studies are needed to determine the effect of HIV control and therapies on polyp development.