Elderly and very elderly individuals might be subject to different approaches for the treatment of hypertension. We aimed to compare drug utilization in hypertensive old patients and very old patients in primary care, along with the evaluation of potentially inappropriate drug prescribing.In this cross-sectional study, we compared prescriptions of 65–79-year-old  vs. ≥80-year-old  with “essential hypertension” diagnosis, issued by 3:1 systematically-sampled primary care physicians (n = 1431) in İstanbul throughout 2016. Drug utilization patterns and distribution of antihypertensives based on drug class and combination status were evaluated. Frequency of potentially inappropriate drugs per Beers Criteria were identified and compared.Antihypertensive monotherapy practice was less common in OPP than VOPP . In both groups, the most commonly prescribed drugs were beta-blockers for monotherapy  and thiazide diuretics for combined therapy . Metoprolol was the most commonly prescribed antihypertensive both in OPP and VOPP (15.3% vs. 14.8%). Furosemide was ranked 10th in OPP and 3rd in VOPP (2.7% vs. 5.5%). Cardiovascular system drugs were the most commonly encountered potentially inappropriate medications in both groups . Regarding antihypertensive drugs, 2.2% of those in OPP and 2.4% of those in VOPP were identified as potentially inappropriate (p = 0.002).Prescribing preferences to old and very old patients mostly showed slight differences. Almost half of prescriptions comprising antihypertensive monotherapy might imply hesitancy to prescribe combinations. Overuse of risky drugs such as furosemide in both groups, especially in the very elderly, requires more attention. Hypertension is the most common chronic condition in the elderly with well-recognized outcomes such as cardiovascular morbidity and mortality ,2. AlthoOlder individuals might be at higher risk of polypharmacy due to higher number of comorbidities and related drug treatment burden. Aside from potential issues such as drug interactions, altered drug elimination and intolerance to adverse events, several drugs may interfere with the treatment of hypertension, interrupting control, and provoking any complications ,5. Also,Primary care plays a key role in the diagnosis, treatment, and follow-up of hypertension. High number of comorbidities and related burden of treatment in geriatric population might complicate the management of such chronic diseases in these centers . In thisIn this cross-sectional descriptive study, prescriptions issued in primary care centers of İstanbul in 2016 were examined retrospectively. Prior to data collection, ethical approval was obtained from İstanbul Medipol University, Non-Interventional Clinical Studies Ethics Committee .İstanbul was home to 14.8 million inhabitants in 2016, which comprised 18.5% of the population of Turkey.Prescriptions were split into two based on age definitions in European Society of Cardiology/European Society of Hypertension (ESC/ESH) guidelines, as those generated for individuals aged 65–79 years as “old patient prescriptions”  and ≥80 years as “very old patient prescriptions”  [t-test, and for else, the Mann–Whitney U test was used. In order to infer statistical significance, an overall 5% type-I error level was assumed as acceptable.Data were analyzed using IBM SPSS Statistics 22.0  and GraphPad Prism 5.0  software. Analyzed data were expressed as numbers, percentages, and/or mean ± standard deviation values, where appropriate. Frequency analysis was used for statistical evaluation, while categorical variables were compared by chi-square test. Normality of distribution for continuous variables was evaluated using the Kolmogorov–Smirnov analysis. Normally distributed data were compared using Student’s We identified that 61.8% of all prescriptions evaluated were issued to women, and the mean age of recipients was 74.4 ± 7.1 years. In total, 89.7% of the prescriptions included one or more antihypertensive drugs. The number of antihypertensives per prescription was 1.2 ± 0.7 .About 76.4% of the prescriptions were OPP, while the remaining 23.6% were issued as VOPP. Female predominance was higher in VOPP (p < 0.001). Less people in OPP than VOPP used monotherapy as antihypertensive regimen . Preference for sole SPC was more frequent in OPP , .“Diseases of esophagus, stomach, and duodenum”, “diabetes mellitus”, and “soft tissue disorders” were among the top three comorbidities in both groups. Cardiovascular diseases constituted 8.1% of concomitant diagnoses in OPP, including “ischemic heart diseases”, which was ranked fifth and accounted for 4.5%. In VOPP, cardiovascular disorders comprised 9.7% of concurrent diagnoses, with “ischemic heart diseases” ranking fourth and making up 4.7% of all diagnoses . The mosAntihypertensive drugs accounted for 31.6%  and 30.4%  of all drugs in OPP and VOPP, respectively. The top two antihypertensives in both groups were metoprolol (15.3% vs. 14.8%) and amlodipine (9.5% vs. 10.0%), whereas the third drug was valsartan/hydrochlorothiazide in OPP (5.4%) and furosemide in VOPP (5.5%); with the latter being 10th in OPP 2.7%, .The top antihypertensive class preferred for monotherapy was beta-blockers in both OPP and VOPP . Calcium channel blockers (CCBs) and angiotensin-converting enzyme inhibitors (ACEIs) were second and third (p < 0.001 for each). The most commonly prescribed antihypertensive class in combination therapy was thiazide diuretics . These were followed by angiotensin receptor blockers (ARBs) and ACEIs (p < 0.001 for each). ARBs were present in 2.2% of ACEI prescriptions in OPP and 2.5% of those in VOPP .PIMs constituted 2.0% of all drugs prescribed, as 1.8% of drugs in OPP and 2.4% of drugs in VOPP were potentially inappropriate (p < 0.001). We identified at least one PIM in 6.6% of OPP and 8.8% of VOPP (p < 0.001), with multiple PIMs encountered in 0.3% of OPP and 0.5% of VOPP (p < 0.001). Cardiovascular system drugs were the top PIM class per 10,000 prescriptions in both OPP and VOPP (263.9 vs. 283.4). Share of cardiovascular PIMs were higher in VOPP (p = 0.004), . AntihypThis study assessed more than 500,000 prescriptions with hypertension diagnosis issued for elderly from different age groups, and established substantial findings about the distribution of antihypertensives, drug class preferences of physicians in monotherapy/combined therapy, comorbidities of the recipients, and other prescribing-related details. We identified that more than half of antihypertensive prescriptions contained antihypertensive drug combinations. Preferences of physicians regarding antihypertensive classes varied in monotherapy and combined therapy regimens, yet deviations in frequencies of each in OPP and VOPP were limited. Additionally, it was noteworthy that various drugs such as furosemide or those considered potentially inappropriate were prescribed considerably frequently to geriatric hypertensive patients, especially to the potentially frailer very old.Consistent with a nationwide study reporting higher prevalence of hypertension in women over 65 (approximately three-fourths vs. three-fifths in men), more prescriptions were written to women in both groups . RemarkaPrescriptions with antihypertensive monotherapy regimen were slightly more common in VOPP (45.3% vs. 43.3% in OPP). Due to the limited success of monotherapy in majority of patients, combining antihypertensives were recommended for most groups to achieve desired blood pressure levels . In thisThiazide diuretics were the top antihypertensive class in prescriptions with combination regimen, and slightly more common in OPP than VOPP (69.8% vs. 67.4%). Thiazides are deemed compatible to combine with many of the agents from other antihypertensive classes, which might explain thiazide-containing combination prescriptions being about 10-fold more than monotherapy counterparts in both groups . Among oARBs predominated over ACEIs in both OPP and VOPP. Although previous recommendations did not prioritize ARBs over ACEIs except select individuals mainly due to cost, ARBs were recently reported to have better safety profile , 21. PreHypertension is often accompanied by other diseases, mainly cardiovascular and metabolic disorders, and prevalence of comorbidities were reported to rise with age . Aside fPIMs were more commonly encountered in VOPP, which were prescribed to a potentially frailer population. Similarly, a Chinese study reported higher rates of inpatient PIM prescribing to those over 80 compared to those aged 65–79 (58.2% vs. 43.4%), . The topNSAIDs, which were frequently utilized in both groups, were among the drugs of which potential inappropriateness could not be confirmed due to lack of follow-up since those agents being listed in Beers Criteria only for chronic use. A primary care study from İstanbul, in which 44.4% of participants were ≥65 years, reported that NSAIDs were commonly included in prescriptions with hypertension diagnosis, and they were prescribed irrationally . Aside fThe results of this study should be interpreted with its limitations. Since the study data only included the prescription information in the medical records, the diagnoses of the physicians were assumed as correct, and patient adherence to antihypertensives could not be evaluated. Also, blood pressure levels, physical examination data and laboratory values of the patients could not be obtained. In addition, due to the cross-sectional design of the study, clinical outcomes of the patients, such as effectiveness of the treatment, could not be assessed. All those issues complicated more profound evaluation of the association of individual treatment goals and prescribed antihypertensive regimen. Patient information was anonymized for ethical reasons, which prevented detecting previous prescriptions and prior medication history of the patients. Therefore, multiple prescriptions of several patients might have been possibly included. Moreover, this anonymized prescription record-based cross-sectional study did not involve follow-up of the patients, which made it unable to determine potential inappropriateness of some medications within the Beers list, e.g., NSAIDs and proton-pump inhibitors, clearly. Hence, we opted to present the drugs which are deemed potentially inappropriate only in long-term or first-line use separately in In conclusion, this study revealed that prescribing preferences of primary care physicians to hypertensive old and very old patients showed slight differences. Almost half of prescriptions comprising monotherapy as antihypertensive regimen might imply hesitancy to prescribe combinations; thus, this could result in inadequate blood pressure control for a considerable portion of patients. Along with combining RAAS blockers, overuse of drugs which might pose risks to especially very elderly, such as furosemide and NSAIDs, implies irrational prescribing. The results of the study might provide guidance for the issues that need attention in utilization of antihypertensives and other drugs in the elderly and the very elderly. Concordance with the up-to-date evidence and thorough evaluation before drug selection might provide the basis for appropriate prescribing and optimal treatment.


Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics “Molecular Modeling,” “Gene Expression,” “Biomarker,” “Coronavirus,” “Immunoinformatics,” “Cancer Bioinformatics,” and “Systems Biology.” Moreover, “Systems Biology” and “Coronavirus” had the largest and smallest clusters, respectively.  As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database.  The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. Bioinformatics emerged in the early 2000s in response to the exponentially increasing data generated by whole genome and protein sequencing techniques. In molecular biology, pioneering work on mapping and comparing protein and gene sequences led to an intensive phase that can be referred to as database mining and the development of computer algorithms that are efficient and reliable. Nowadays, bioinformatics has reached a high level of maturity and sophistication as a science. The field of bioinformatics is now being used in structural biology, computational chemistry, genetics, molecular biology, the pharmaceutical industry, and pharmacology.Development of software tools and algorithmsBiological data analysis and interpretation using various software tools and specific algorithms .Information technology, computer science, mathematics, statistics, and biotechnology are incorporated into bioinformatics to provide methods to extract information and discover biological processes . The terConsidering the importance of bioinformatics, research in this field has attracted the attention of researchers. Due to the importance of issues and topics related to the field of bioinformatics as an interdisciplinary science as well as to obtain the structure, development process, and changes made in this field, the use of an efficient method seems necessary to provide a potential to discover the latent topics in the heart of the studies through the latent relationships between the topics.Topic modeling is a statistical model to discover latent “topics,” which happens in a set of documents through machine learning. The core significance of topic modeling is to discover the patterns of words and the quality of documents connected with similar patterns . Topic mThis research was a descriptive-exploratory study. The studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The following search strategy was used with limiting organizational affiliation to Iran in the keywords, abstract, and title fields. It should be noted that the same search strategy has been used in a study conducted by Armenta et al. .(TITLE-ABS-KEY (bioinformatics) OR TITLE-ABS-KEY ) OR TITLE-ABS-KEY  OR TITLE-ABS-KEY ) OR TITLE-ABS-KEY ) OR TITLE-ABS-KEY ) OR TITLE-ABS-KEY ) OR TITLE-ABS-KEY ) OR TITLE-ABS-KEY (“Systems biology”) AND AFFIL (Iran))(1)PreprocessingRemoval of Empty Records. Abstract and title records as NULL were removed at this stagePunctuation Removal. In this step, punctuation marks were removed since some word-embedding models do not support themTokenization. This is a method to break texts into smaller units, known as tokensStop Word Removal. These words do not add much value to the meaning of the content. Therefore, they were removed in the preprocessing stageLemmatization. This step reduces conjunction words and contributes to retrieving or fetching valid and essential words [al words . At thisThe documents needed to be preprocessed to review a large set of texts to store the information in an appropriate data structure for further processing. This stage involved the following steps for preprocessing of abstracts and titles of papers. (2)Conversion of texts to numeric vectorsThe next step is vectorization because most machine learning techniques only work with numerical data, so in order to use and apply these techniques to textual data, it is necessary to convert the texts into a set of numbers. In this step, the TF-IDF converts the raw textual data into a matrix-based data set.(3)Topic modelingTopic modeling is an unsupervised machine learning strategy with the potential to examine a set of documents, identify words, and discover patterns within them; thereby, it can cluster words and comparative phrases. Accordingly, it can distinguish a set of sets in an appropriate manner. In summary, topic modeling algorithms produce a set of expressions and words that are believed to be relevant, allowing the reader to comprehend the relationships while classifying the topics.This research was performed in three steps, as explained in the following:The input from the TF IDF in the previous step was given to the LDA algorithm in this step so it can model the topics. LDA is a probabilistic approach to topic modeling. In other words, it is a hierarchical Bayesian probability generation model for discrete data collection that relies on the assumption that the document's words and topics are consistent. The subsequent topics are displayed as a discrete distribution of the entire phrase in the document, and it models the body as a discrete distribution of the entire subject. Since the equilibrium is determined at the document level, this model is regarded as being superior to others that also use competitive dispersion. Gensim, a Python library used for topic modeling, document indexing, and similarity retrieval with large corpora, was used in this investigation to implement LDA.It should be noted that the Pandas, Gensim, Numpy, NLTK, and Sklearn packages were employed to code this research project in Python using the Colab environment.A total of 3899 papers were retrieved based on the search made in the Scopus database. The NULL data were removed from the dataset. The title fields were not null in any of the records, and 24 abstract fields were null, which were deleted; thus, 3875 records were investigated for analysis.According to the results obtained from the analysis of bioinformatics papers with the organizational affiliation limited to Iran in the Scopus database, the words “protein,” “expression,” “mirna,” “mutation,” and “drug” were identified as the five words with higher frequencies. The data in Moreover, The data in Moreover, according to Afterward, the author's keywords were used as features, and their correlation in each topic was determined using the BOW method. As mentioned before , in the The next stage involved obtaining Iranian researchers' papers published in the field of bioinformatics. Most papers in this field were published in 2020, as shown in All theoretical and computational methods used to model or mimic the behavior of molecules are considered molecular modeling . MoleculA wide range of important biological processes, including tissue homeostasis, immunity, cellular stress responses, organism growth, and cell differentiation, depend critically on the control of gene expression. In the past few years, significant progress has been made in our understanding of the molecular mechanisms underlying transcription, the function of chromatin and its modifications, the role of enhancers, and the role of noncoding RNAs . The proApplications for biomarkers may include early disease detection, tracking the course of the disease, and selecting the most effective course of therapy. Disease-related and drug-related biomarkers are the broad categories into which biomarkers can be broadly divided. The need to identify the unique characteristics of the patient is growing as personalized treatment strategies for patients become popular. These details enable medical professionals to appropriately schedule their patients' care and administrate accurate care .By causing the COVID-19 illness, the newly discovered pathogen SARS-CoV-2 has sparked a global pandemic crisis. The unique and newly-seen genetic makeup of SARS-CoV-2 has made biological research face some obstacles. However, utilizing bioinformatics tools and techniques, researchers have successfully interpreted this viral genomic architecture. Such interpretations appear essential for ongoing research on the COVID-19 pandemic and the introduction of a SARS-CoV-2 vaccine that could protect public health .Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. This concept represents the use of computational methods and resources for understanding immunological information. Immunoinformatics not only helps deal with vast amounts of data but also plays a significant role in defining new hypotheses related to immune responses . ImmunogCancer bioinformatics is a critical part of the systems clinical medicine in cancer and the core tool and approach to carry out cancer investigations in systems clinical medicine. The approach to studying the genetic basis of cancer is undergoing a revolution. Rather than focusing on individual genes, scientists are now exploring substantial components of the expressed genome. The wealth of molecular information being generated from the laboratory as well as the volume of data being stored in the patient record is continuing to increase at an astounding rate. Finding new ways to integrate these data has been crucial to developing novel insights into cancer genetics. Therefore, bioinformatics, the convergence of biology, information science, and computation, is continuing to emerge as a crucial component of cancer biology research .With 1110 records, the largest cluster relates to the field of systems biology. Model, algorithm, proposal, method, data, network, feature, information, classification, etc. are among the terms with significant weight in this cluster. Due to the enormous amount of data obtained through biotechnology, the computational and mathematical analysis and modeling of biological systems are highly challenging. Research on intricate interactions between biological systems is performed in this interdisciplinary area of biology-based studies. In contrast to the conventional reductionist approach, it takes a holistic approach to biological research .The main objectives of this study included topic modeling methods based on finding latent topics, topic-based document interpretation, and ultimately organizing and categorizing the texts under study. With these objectives in mind, the current study used the topic modeling method to evaluate the scientific outputs of Iranian researchers in the field of bioinformatics using the LDA and TF IDF algorithms.The findings of the study showed that the five terms “protein,” “gene,” “method,” “model,” and “application” were used the most frequently in the bioinformatics studies carried out by Iranian researchers. In terms of TF-IDF weights, the words “MIR,” “model,” “gene,” “vaccine,” and “binding” had the highest weights in the body of the papers. As can be seen, the words “gene” and “model” are among the most frequent and heavily weighted words in both cases, indicating the importance of these words in the field of bioinformatics. Some definitions claim that informatics emerged as a result of the enormous amount of genomic data that has been generated and the need to properly store, retrieve, and analyze it. Gene discovery, genome assembly, etc. may be cited as some of the most notable research efforts in the field of bioinformatics. The steps in the bioinformatics method known as building a computational model and solving a computational modeling problem after data collection are known as essential steps.MicroRNA and gene expression were among the subjects with the highest frequency rates, according to research .In addition, the LDA topic modeling algorithm was used in this paper to identify seven topics, including coronavirus, gene expression, systems biology, molecular modeling, cancer bioinformatics, and immunoinformatics.The fields of biology, applied statistics and mathematics, computer science, and chemistry are all clearly used in the field of bioinformatics, as can be seen in the clusters. In other words, bioinformatics is used to analyze biological issues using statistical and mathematical methods within a computer.In terms of the share of productions devoted to each topic, systems biology and immunoinformatics have experienced the highest frequency rates, whereas coronavirus has experienced the lowest frequency rates. The study area of bioinformatics focuses on the computational analysis of biological data. Systems biology is a field of study that focuses on comprehending entire biological systems like protein complexes, metabolic pathways, or gene regulatory networks, as opposed to bioinformatics, which focuses on single genes or proteins. As a result, it is referred to as a branch of bioinformatics. According to literature , the sysThe field of immunoinformatics accounted for the highest proportion of productions and the topic's highest growth trend. Computational immunology, also known as immunoinformatics, has recently emerged as a significant and cutting-edge field in the sciences of analysis, modeling, immune system function prediction, vaccine design, allergenicity research, and pharmaceutical discoveries. Due to its interaction with the human genome project and other organisms, this science has not only aided in the acceleration of scientific research but also resulted in the achievement of a great deal of information pertaining to immunology. In actuality, immunoinformatics serves as a link between computational methods and experimental experiments. Because of this field's direct connection to global health and the fact that it speeds up and lowers the cost of scientific research from a different angle, its successes are of high strategic importance.Since the coronavirus outbreak began in 2019, papers in this area have also started to be written as of that time. Therefore, it is not surprising that there are fewer papers on this topic than on others.The COVID-19 pandemic's emergence, rapid geographic spread (to more than 200 countries), and the WHO's designation of the outbreak as a public health emergency of international concern have prompted researchers to take advantage of bioinformatics tools and techniques in order to quickly develop new diagnostics and vaccines. As a result, the coronavirus appears to form a distinct cluster in the field of bioinformatics due to the significance of this issue.This study identified the bioinformatics topic clusters among Iranian researchers using text-mining techniques and topic modeling tools. These subjects include systems biology, immunoinformatics, cancer bioinformatics, biomarkers, coronavirus, gene expression, and molecular modeling. The biggest and smallest clusters belonged to the topics of systems biology and coronavirus, respectively. The clustering of texts requires the extraction and preservation of semantic information, and one of the characteristics of this extraction is the use of features that have this attribute. As a result, the latent Dirichlet allocation (LDA) model was employed in this paper as a semantic analysis method for feature extraction. The classification of topics in this field has shown a passable performance for the LDA algorithm. The extracted topic classes are well contained and have strong subject connections. Despite the fact that the words in each topic heading may not be identical, they are undoubtedly related. A topic model, in contrast to conventional clustering, enables one to obtain data from multiple clusters rather than just one. These characteristics might be advantageous in the field of bioinformatics.In general, the findings of this research may, on the one hand, provide a better context for developing research programs and policy, and, on the other hand, they may lead to further recognition and use of topics that have been given more thought.The intellectual structure of the bioinformatics field in Iran can be inferred from the outputs indexed in the global database and used to predict future research topics and trends by identifying the outstanding topics, which in some way indicate the main trends of the research in a subject area. The gaps in the literature can also be found. In addition, the modeling of topics of bioinformatics articles worldwide and comparing another algorithm with that of the present study is suggested for future research.


National surveillance of antimicrobial resistance (AMR) is an important public health function. Published national AMR surveillance data from the Middle East/North Africa (MENA) region is scarce. This paper describes the early implementation phase of establishing AMR surveillance in the United Arab Emirates (UAE).Building on the existing AMR surveillance system in the Emirate of Abu Dhabi, and adopting the WHO-GLASS methodology, the UAE Ministry of Health and Prevention (MOHAP) established the national AMR Surveillance program in 2015, in collaboration with regional health authorities and healthcare providers. Main objectives of this program are to (a) set AMR surveillance standards, (b) collect and analyze AMR surveillance data for common bacterial and fungal infections, (c) report on AMR levels and trends in the UAE, (d) strengthen local and national capacity for AMR surveillance, and (e) support AMR prevention and control strategies in the UAE. AMR surveillance data is collected through a network of 317 surveillance sites , and 45 microbiology labs across all seven Emirates of the UAE.Surveillance of antimicrobial resistance has been successfully established since 2010 in the UAE. A national AMR surveillance protocol has been developed, adopting the WHO GLASS protocol. Extensive capacity-building and training activities have strengthened the local and national capacity for AMR surveillance. Between 2010 and 2021, a network of 317 surveillance sites and 45 laboratories have reported a total of 1,277,080 isolates from 662,065 non-duplicate patients to the national level. AMR data is reported annually by MOHAP through a National AMR surveillance report. National AMR data is utilized for informing the development of standard treatment guidelines at national level.National surveillance of antimicrobial resistance has been successfully established in the United Arab Emirates, allowing to monitor levels and trends of antimicrobial resistance for common bacterial and fungal pathogens, and detecting emerging resistance. The availability of such national AMR surveillance data allows for the first time to inform the development of national standard treatment guidelines for empiric treatment of common bacterial and fungal infections in the UAE. AMR impacts human health due to increased length of stay, treatment failures, and significant human suffering and deaths, as well as leading to increased healthcare costs and indirect costs. Globally, an estimated 700,000 deaths annually are currently attributable to antimicrobial resistance, and this number is expected to increase to 10,000,000 deaths by 2050, with an associated estimated loss to global gross domestic product of up to 100 trillion US dollar per year  is the ability of a microorganism to resist the action of one or more antimicrobial agents. The consequences can be severe, as prompt treatment with effective antimicrobials is the most important intervention to reduce the risk of poor outcome of serious infections. Development of AMR is a natural phenomenon caused by mutations in bacterial genes targeted by antimicrobials, or by acquisition of exogenous resistance genes carried by mobile genetic elements that can spread horizontally between bacteria. Bacteria can acquire multiple resistance mechanisms and hence become resistant to several, or even all, antimicrobial agents used to treat them, which is particularly problematic as it may severely limit the available treatment alternatives for the infection.The major drivers behind the occurrence and spread of AMR are the use of antimicrobial agents and the transmission of antimicrobial-resistant microorganisms between humans; between animals; and between humans, animals and the environment. While antimicrobial use exerts ecological pressure on bacteria and contributes to the emergence and selection of AMR, poor infection prevention and control practices favor the further spread of these bacteria.Public health surveillance can be defined as the continuous and systematic collection, analysis, interpretation and dissemination of health-related data needed for the planning, implementation, and evaluation of public health practice .The purpose of public health surveillance can be to estimate the burden of a disease, describe and characterize the problem, identify risk factors, monitor trends, and assess the effectiveness of interventions, and inform public health policy and decision making.Hospitals, centers, clinics, and clinical microbiology labs in the United Arab Emirates (UAE) and elsewhere are generating and collecting many clinical and AMR data as part of their routine patient care. This data can be utilized for local monitoring of antimicrobial resistance and stewardship activities (at the facility level), as well as for public health surveillance of antimicrobial resistance .Surveillance of antimicrobial resistance (AMR) is not only important to better understand the epidemiology of antimicrobial resistance in a country or region; this data can also be utilized to (a) detect and predict trends of resistance, (b) generate cumulative antibiograms (routine and enhanced antibiograms), (c) detect and identify clusters and potential outbreaks of community-associated (CA) and healthcare-acquired infections (HAI), (d) inform and guide, and monitor the effectiveness of interventions, e.g., antimicrobial stewardship programs (ASP), (e) inform the development of empiric antibiotic treatment guidelines for common bacterial and fungal infections, and (f) assist health professionals with empiric antimicrobial treatment choices, tailored to the antibiotic resistance epidemiology in the patient’s geographic region and setting.Published national AMR surveillance data from the Middle East and North Africa (MENA) region and in particular from the Arab peninsula is scarce. This paper describes the rationale and objectives for establishing AMR surveillance in the UAE, the challenges faced in the early implementation phase, and how they were overcome, characteristics of the network of participating surveillance sites and labs, capacity building and training activities, as well as the concepts, methods and protocols utilized for the generation, collection, cleaning, quality control, analysis, reporting and utilization of national AMR surveillance data in the UAE.22.1The Department of Health Abu Dhabi  established in 2010 the first antimicrobial resistance surveillance program in the United Arab Emirates, as part of a strategic initiative to tackle the globally growing problem of antimicrobial resistance. The rationale behind this decision was to allow the government of Abu Dhabi to monitor trends of antimicrobial resistance, identify newly emerging resistance, and monitor the effectiveness of interventions. The Abu Dhabi AMR surveillance program enrolled initially 22 surveillance sites from the public sector (2010), which increased to 42, 44, and 64 sites in 2011, 2012, and 2013, respectively. Since 2012 also sites from the private sector in Abu Dhabi joined the program. DoH issued in 2011 a standard, mandating healthcare facilities to monitor and report AMR data to DoH.In 2014, the Ministry of Health and Prevention launched an initiative to address AMR on a national level, and established in 2015 a Higher Committee for AMR, as well as three working groups . The national working group on AMR surveillance was a few years later renamed to become the National Sub-Committee for AMR surveillance, and given the mandate to oversee and coordinate all national AMR surveillance activities, including (a) developing the rationale, strategies, and action plans for national AMR surveillance, (b) conduct a situational analysis on AMR monitoring and surveillance practices and capacities, (c) review international AMR surveillance guidelines, best practice examples, and global trends for AMR surveillance, (d) develop or promote methods, forms, tools, etc. for national AMR surveillance, (e) establish standards for surveillance methods, research institutes, and other institutions, (g) provide technical support, and facilitate collection, analysis, and sharing of AMR data and statistics, and (h) conduct awareness, training, and capacity building activities for AMR surveillance  Member States adopted the Global Action Plan on AMR (GAP-AMR). The UAE also participated in the development of the GCC (Gulf Cooperation Council) Strategic Plan for Combating AMR , led by Jens Thomsen, started working on developing the UAE national AMR Surveillance System. The working group first conducted a situational analysis, reviewed international guidelines and best practice examples, including the newly launched World Health Organization Global AMR and Use Surveillance System (GLASS) , and theSurveillance sites  and labs are reporting phenotypical AMR surveillance data and related information (meta data) since 2014 from all seven Emirates via their concerned regulatory authority  to the UAE Sub-Committee for AMR surveillance, which is acting as the national coordinating body for AMR surveillance .During 2010 to 2021, the national AMR surveillance program was expanded continuously and significantly. As of 31 December 2021, it includes 317 surveillance sites , and 45 clinical microbiology laboratories across all seven Emirates .The national AMR surveillance program covers all relevant regions and cities in the UAE, including remote, rural areas. Privately owned health care facilities are mostly concentrated in the major cities, whereas public health care facilities are in cities as well as more rural areas .2.2According to WHO GLASS, when selecting a potential AMR surveillance site, the following criteria should be considered and were applied :support from the central and local management, and the motivation of local staff to participate in surveillance, to comply with case definitions and protocols for collecting specimens, and to generate the necessary clinical, demographic and epidemiological data,availability of and accessibility to a laboratory with the capacity and capability to perform microbiological diagnostic testing, adequate staffing levels, equipment and a reliable supply chain,logistical feasibility to routinely collect and transport clinical specimens,ability to manage and report surveillance data, including denominator data ,capacity and support to connect to the national network and report data to NCC,relative cost efficiency of conducting surveillance activities compared with other possible sites,sufficient number of patients and volume of laboratory diagnostic activity to allow a meaningful analysis of surveillance data,ability to mentor and support capacity building at subsequent sites,demographic, socioeconomic and geographic representativeness,representation of different levels of health care.2.3As part of the enrollment process local management approval was obtained, and focal points for AMR surveillance were nominated for each site (or group of sites) , and BD Phoenixn = 12, 27%), and MicroScann = 1, 2%). One lab used the Sensititre systemn = 1, 2%) relies on manual (API) systems only for identification.Phenotypical AMR surveillance data is generated as part of routine patient care by participating sites and clinical laboratories. Forty-four (44) out of 45 (98%) participating microbiology laboratories use at least one commercial, automated system for identification of bacteria and/or yeast, including VITEK-22.5n = 1, 2%) uses manual testing methods (disc diffusion/Kirby Bauer). Selected organisms  are routinely tested by manual methods (disc diffusion), as per CLSI guideline recommendations (Forty-four out of 45 98%) microbiology laboratories now use at least one commercial, automated system for routine antimicrobial susceptibility testing, one laboratory , otherwise the locally interpreted AST result .AST core data routinely submitted to the national AMR surveillance program includes information on the organism’s name, specimen type, specimen collection and/or testing date, antibiotic name, AST test method used, as well as the measured and/or interpreted AST test results. Wherever available and technically feasible, the measured, numericalClinical and demographic data for each isolate is extracted from hospital/laboratory information systems (HIS/LIS) wherever available and technically feasible . This includes information on, e.g., patient date of birth, age, gender, nationality, location, location type, clinical specialty/department, date of admission/discharge, health outcome, etc. See 2.6All participating microbiology laboratories are:operated by a licensed healthcare provider, i.e., licensed by MOHAP, DoH, or DHA,lab-accredited (ISO 15189 or CAP),headed by a licensed clinical pathologist or clinical microbiologist,expected to conduct routine  internal quality control testing (ATCC); andsuccessfully participating in at least one internationally recognized, external quality assurance program (EQAS), i.e., College of American Pathologists Proficiency Testing (CAP Pt), American College of Physicians - Medical Laboratory Evaluation (ACP-MLE), or Regional External Quality Assessment Scheme (REQAS).Only final and validated antimicrobial susceptibility testing results are reported for AMR surveillance. As of June 2023, all 45 (100%) of participating microbiology labs are lab-accredited, by either College of American Pathologist (CAP), or International Organization for Standardization (ISO) Standard 15,189, or both. At least 70 out of 84 (83.3%) of participating hospitals are accredited by Joint Commission International (JCI).2.7Since the start of the UAE AMR surveillance system in 2010, the number of bacterial and fungal isolates reported by participating surveillance sites has increased significantly, from 21,866 isolates in 2010, to 261,224 isolates in 2022 .1 = 1,277,080 isolates were reported to the national AMR surveillance Sub-Committee.For the reporting period 2010 to 2021, a total of Nn = 22,335 isolates) of the total reported isolates. Screening and quality control (QC) isolates are then routinely excluded from statistical analysis and reporting, leaving N2 = 1,254,745 isolates for analysis and reporting.Although surveillance sites were requested to not submit data for screening and quality control isolates, for technical reasons the exclusion of such data was not always possible at the local level, and screening and quality control data accounted for 1.75% , equivalent to 46.4% of total reported isolates (N1). These copy strains are also routinely excluded from statistical analysis and reporting, leaving a total of N3 = 662,065 non-duplicate, diagnostic isolates (=patients) for analysis .The NThe UAE national AMR surveillance system collects information on all bacteria and fungi grown by cultural methods in participating healthcare facilities as part of daily patient routine.For analysis and public health reporting, the program focuses on the UAE AMR priority pathogens, including the following bacterial and fungal priority pathogens of public health and clinical importance:Escherichia coli (E. coli)Klebsiella pneumoniae (K. pneumoniae)Salmonella spp. Pseudomonas aeruginosa (P. aeruginosa)Acinetobacter spp.Staphylococcus aureus (S. aureus)Streptococcus pneumoniae (S. pneumoniae)Enterococcus faecalis Enterococcus faecium (E. faecium)Candida spp., andMycobacterium tuberculosis.2.8After submission of AMR data to the national AMR surveillance program, the data is initially checked at the central level for plausibility, quality, and completeness; and feedback is communicated to the AMR focal point at the surveillance site. If needed, AMR focal points are asked to verify and resubmit the data. At central level the AMR raw data files are then cleaned, and identifiable quality control and screening data is removed.The AMR raw data is then converted to the WHONET data base format (SQLite), using the BacLink tool . WHONET 2.9Data analysis is conducted with the WHONET Software for Laboratory Database Management . The folInternal quality control isolates ,External quality control isolates ,Isolates labeled as “screening,” “validation,” “verification,” “proficiency testing,” or similar,Suspected screening isolates, e.g.:S. aureus isolates from axilla, nose, groin, umbilicus and perineum,S. agalactiae isolates from vagina,Duplicate isolates (copy strains), i.e., only the first isolate per patient, specimen type and species during the reporting period (1 year) is considered,Isolates from primarily contaminated specimen types ,Other non-diagnostic isolates ,Species for which less than 10 isolates are available for analysis,Antimicrobial agents that are selectively/not routinely tested .2.9.1As recommended by CLSI guideline M39-ED5:2022 , multiplE. coli isolates resistant to ciprofloxacin is divided by the total number of E. coli isolates in which susceptibility to this antibiotic was tested.For reporting of AMR data, antimicrobial susceptibility testing results are presented as the proportion of isolates of a specific microorganism that are susceptible (S), intermediate (I), resistant (R), or non-susceptible  to a specific antimicrobial agent. For example, the number of n = 37/45 labs, 82%), or, if this was technically not feasible, obtained from labs in form of already locally interpreted (S/I/R) results . For reporting, percent RIS (%RIS) interpretations are based on the most recent CLSI interpretation standard for bacterial isolates  and CLSI interpretation standard M27M44S-ED3:2022 for yeast  were used  isolates is either interpreted at the national level  or cefoxitin (FOX), or both.MRSA: Enterococcus faecalis or Enterococcus faecium, resistant to vancomycin (VAN).VRE: CRE: Enterobacterales, resistant to any carbapenem , or carbapenemase-positive .MDR (multidrug resistance) was defined as acquired non-susceptibility to at least one agent in three or more antimicrobial classes, as suggested by Magiorakos et al. .M. tuberculosis to both, isoniazid (INH) and rifampin (RIF).MDR-TB was defined as combined resistance of italics):XDR/PDR: Magiorakos’ et al. definitions for extensively drug-resistant (XDR) and pandrug-resistant (PDR) organisms could not be strictly applied as only a limited number of antibiotic classes were routinely tested by clinical labs, and MDR isolates were not routinely sent to a reference lab. As such, the following modified definitions were used for “possible XDR” and “possible PDR” isolates .“Possible XDR”: Non-susceptibility to at least one agent routinely tested by clinical labs in all antimicrobial categories .“Possible PDR”: Non-susceptibility to all agents Antibiotics reported in the national AMR Surveillance report are important for antimicrobial resistance surveillance purposes. They may or may not be first-line options for susceptibility testing or for patient treatment and should not be interpreted as such.2.10In 2021, the 1st national AMR surveillance report has been published by the Ministry of Health and Prevention , followed in 2022 by the 2nd national AMR surveillance report (reporting on 2010–2020 data), published by MOHAP in September 2022 . A 3rd nNational AMR surveillance data is also frequently reported in the form of presentations at national and international conferences, e.g., the UAE International Conference on Antimicrobial Resistance (ICAMR), Dubai, UAE.Furthermore, from 2017 onwards, each year the national AMR surveillance data has been reported to the global AMR Surveillance system. Historical AMR data (2010–2016) was also uploaded to the GLASS platform (WHO GLASS)  isolates, and copy strains, 662,065 (51.8%) non-duplicate patients/isolates are available for analysis.For the reporting period 2010 to 2021 (12 years), phenotypical data for a total of Nn = 173,351 diagnostic, non-duplicate isolates from n = 317 surveillance sites are available for analysis. For 2021, the top five reported AMR priority pathogens were E. coli (27.8%), followed by S. aureus (11.7%), K. pneumoniae (11.4%), Candida spp. (7.6%), and P. aeruginosa (5.9%) (For the reporting period 2021 (1 year), a (5.9%) . The disSalmonella spp. and S. pneumoniae, as expected, being more prevalent in the children’s age group. M. tuberculosis is found almost exclusively in adults. All age groups  are included.The data shows a typical age group distribution, with E. coli and K. pneumoniae being more prevalent in the female gender, which can be explained by the higher prevalence of urinary tract infections in females (E. coli and K. pneumoniae are the leading pathogens isolated from urinary tract). M. tuberculosis is found predominantly in males.Distribution by gender is largely balanced, with the exception of M. tuberculosis, which is predominantly (95%) found in Expatriates. However, UAE nationals represent a significantly higher proportion in the reported data (23.7%) than in the general UAE population (estimated 10%), which could be explained by the higher rate of healthcare utilization by UAE nationals. Internal analysis of expatriates by nationality show that most nationalities (n > 164) are represented in the data and reflecting the typical distribution of nationalities found in the UAE (data not shown).Distribution by nationality status shows a balanced distribution between UAE nationals and expatriates for most pathogens, except for E. coli, K. pneumoniae and enterococci are predominantly isolated from urine, Salmonella spp. from stool, Streptococcus pneumoniae from respiratory tract, S. aureus from wound/pus, whereas P. aeruginosa and Acinetobacter spp. are mostly found in urine, pus, and the respiratory tract.Distribution by isolate source shows the typical and expected patterns of specimen sources: E. faecium and M. tuberculosis which are predominantly observed in inpatients. All relevant location types are included in good numbers .Distribution by location type shows that the data is largely balanced between outpatient and inpatients, except for Distribution by department/clinical specialty shows a good mix of all relevant clinical specialties, including internal medicine, surgery, intensive care, emergency medicine, pediatrics and neonatology, obstetrics and gynecology, hematology and oncology, and other specialties.M. tuberculosis.Distribution by Emirate shows that patients from all seven Emirates are represented in the database. The data are slightly skewed towards Abu Dhabi Emirate, whereas patients from the northern Emirates are slightly underrepresented, especially from the private sector, and for 3.2n = 662,065) during a relatively long time period  from all seven Emirates.The data is largely representative of the whole UAE population, with a few important limitations. This report presents the, by far, largest data set and best currently available diagnostic, non-duplicate AMR data on a very large number of patients ,highly representative for private sector healthcare facilities in the UAE, except for the Emirates Ajman, UAQ and Fujairah, from which private healthcare facilities are not yet participating in sufficient numbers ,highly representative for inpatients and ICU patients, with now 88 out of 151 (58.3%) hospitals participating in the system, andn = 231; 8.5%) of the approximately n = 2,730 relevant ambulatory healthcare clinics/centers in the UAE are participating in the national AMR surveillance program.moderately representative for outpatients: results for outpatients need to be interpreted with some caution, as an increasing, but still relatively small fraction , and AMR team leads in concerned health authorities.The AMR Surveillance network is further enhanced by the 80+ members of the UAE AMR Surveillance consortium, which includes AMR surveillance leadership and team leads, AMR focal points, AMR researchers, clinical microbiologists, ID physicians, pharmacists, public health professionals, and others.Clinical and AMR surveillance data for 1.2 m + pathogens has been reported to the program, including data from over 600,000+ non-duplicate patients during 2010–2021.The national AMR surveillance program and the availability of national AMR surveillance data allows the UAE for the first time to:Identify and assess the AMR problem in the UAE, and describe its characteristics,Develop a national cumulative antibiogram,Publish a national AMR surveillance report,Monitor AMR levels and trends over time,Assess and describe the burden of MDR, XDR, and PDR pathogens in the UAE,Detect newly emerging trends of resistance, e.g., Candida auris,Report AMR surveillance data to the global AMR surveillance platform (GLASS),Support the development of national standard treatment guidelines for empiric treatment of common bacterial and fungal infections in the UAE.National AMR surveillance data has been utilized in the UAE to inform the development of several empirical antimicrobial treatment or prophylaxis guidelines so far, including national guidelines on the empiric antibiotic treatment of urinary tract infections, respiratory tract infections, skin and soft tissue infections, and intraabdominal infections, as well as guidelines for the prophylaxis of surgical site infections.Several challenges were and are still faced during the early implementation phase of the national AMR surveillance program. These challenges include the lack of awareness, lack of technical and human capacity, technical issues, lack of a national reference lab for AMR (NRL-AMR), and lack of funding for AMR surveillance.4.1In the early years of AMR surveillance in the UAE, AMR surveillance was not a well-known concept at all levels . AMR surveillance was not a public health priority for many years, and it was not before 2019 that it became part of a national 5-year public health strategy and action plan to combat AMR . The ini4.2A lack of trained and skilled human resources for AMR surveillance at the local, sub-national, and national level has been an important observation and was a challenge for several years. To overcome this challenge, considerable time and effort was spent on technical training and capacity building, which came in form of, e.g., training courses for AMR surveillance  for clinical staff, as well as for public health officials. Over time, a relatively large professional community with an interest in AMR surveillance and research has developed, leading now to the formation of a national AMR surveillance consortium with currently 80+ members.4.3Across the UAE, there is a large diversity of IT systems (HIS/LIS) as well as automated susceptibility testing (AST) systems used at healthcare provider and laboratory level, which was a challenge for harmonizing and standardizing the AMR surveillance data across the systems and platforms to allow for standardized data analysis and reporting. There is a large variety of codes used by healthcare providers for, e.g., pathogens, antibiotics, specimen types and patient locations. Furthermore, the AMR surveillance data submitted by surveillance sites might still contain some quality control data, screening data, and duplicate isolates, which should be removed before data analysis and reporting. The WHONET software, in particular the BacLink tool, proved invaluable to overcome this challenge, by enabling us to harmonize, and convert all data with protocols and data dictionaries specific for each site. Free tools, such as DB Browser for SQLite allowed for further easy cleaning and editing of the data.Another technical challenge lies in the fact that automated AST systems in microbiology labs are not routinely and fully interfaced with HIS/LIS systems of surveillance sites, which may result in a loss of information . We were overcoming this issue by advocating for, and requesting the interfacing of systems where feasible.4.4A serious limitation for AMR surveillance in the UAE is the lack of a national reference laboratory. Such a NRL-AMR would serve multiple purposes including, but not limited to:Setting national laboratory standards for identification and susceptibility testing of AMR priority pathogens;Setting quality control standards for participating clinical laboratories and providing external quality assurance (EQAS) services as a nationwide coordinated service;Providing reference lab services for participating clinical laboratories, for further molecular and genetic characterization of AMR priority pathogens;Providing technical training and capacity building activities for clinical laboratories;Providing epidemiological support for outbreak investigations;Establishing a biorepository for relevant strains; and coordinate, and participate in, national studies and research on AMR.4.5AMR surveillance in the UAE has always been a non-budgeted activity, and the lack of funding for the national AMR surveillance program has limited achieving its full potential. The Global Action Plan on AMR (GAP-AMR) recommends WHO Member States to establish a National Coordinating Center for AMR surveillance (NCC-AMR), with a clear mandate, delegated authority, full-time dedicated and trained staff, and an annual budget. This would help to institutionalize AMR surveillance and to ensure continuity and sustainability of the program for the future. Several important components of the AMR surveillance program can be implemented without a cost; however others do require a budget. For example, AMR surveillance data is generated as part of routine patient care and submitted to governmental health authorities free of charge based on their mandate for public health. Data processing and analysis tools are available for free from the internet . Other important components, however, do require a budget. This includes for example a national reference lab, external quality assurance services, lab accreditation, outbreak analysis, biorepository of isolates, hiring competent staff, conducting workshops, etc. The lack of funding was partially overcome with the help of sponsors from the private sector, where needed, e.g., for awareness activities.The Global Action Plan for AMR (GAP-AMR), and the continuous commitment of the UAE leadership to implement this plan in the UAE since 2015, was the critical step forward and provided the necessary senior management support and facilitated acceptance by the concerned healthcare facilities to develop and implement the national AMR surveillance program. The development of the UAE National Strategy and Action Plan to combat AMR (2019–2023) further helped to specify goals and objectives for national AMR surveillance .This was only possible because of the following:Senior management and leadership support and commitment from MOHAP and other concerned health authorities , and participating entities (surveillance sites and laboratories).Guidelines and recommendations for AMR surveillance being available through WHO-GLASS.AMR surveillance data being generated at surveillance sites and labs through routine patient care and available in an electronic format for governmental public health surveillance activities at no cost.Software and IT tools needed for AMR surveillance, e.g., WHONET, BacLink, database tools , and statistical packages  can be The central core team was able to provide numerous awareness and technical training workshops and sessions for AMR surveillance at no cost.Having nominated AMR focal points at each surveillance site (or group of sites) who facilitated data collection and reporting.Data cleaning, analysis and reporting was done in-house at the central level at no cost.There are some limitations of the current national AMR surveillance program. The current focus on collection of phenotypical data, and, although in line with the adopted WHO-GLASS protocol, this does not allow for further characterization on the molecular level, e.g., by NGS (next generation sequencing). Main reason for this limitation is the lack of a national reference lab. This could be partially overcome by using existing phenotypical isolate resistance profiles, as well as phenotypical biochemical profiles of isolates as a substitute, however this is not well established in the literature. The national AMR surveillance program would certainly benefit significantly from the routine establishment of molecular and genetic methods at central level, such as molecular markers and NGS (next generation sequencing) to allow for further describing the characteristics, and the local and regional epidemiology of antimicrobial resistance, and support outbreak detection as well. Another limitation is the significant reliance on manual steps for data collection, data cleaning, data conversion and harmonization, data analysis, and reporting of findings. Automation could potentially help here in future, especially if combined with data mining and artificial intelligence tools. However, automation can also bring new challenges, and the added value of automation is likely to be limited due to the generally high complexity of AMR surveillance, the diverse landscape of HIS/LIS and AST systems, technical limitations , and other factors, e.g., the difficulty to automate or incorporate the clinical microbiological expertise required. For some steps, automation tools are available , and could be explored to be implemented.5National surveillance of antimicrobial resistance is an important concept and public health tool for the global and national response to antimicrobial resistance. The development and implementation of the national AMR surveillance system in the United Arab Emirates enabled concerned public health authorities and healthcare professionals for the first time to monitor levels and trends of antimicrobial resistance in the UAE, detect emerging resistance, publish annual AMR surveillance reports, report AMR surveillance data to WHO-GLASS, and inform local and national antibiotic stewardship policies and activities, such as the development of empirical antimicrobial treatment guidelines for common bacterial and fungal infections. National AMR surveillance in the UAE will further be strengthened by establishment of a national reference lab that could provide technical support for characterizing isolates on the molecular/genetic level (NGS) and providing further services such as outbreak analysis support and external quality assurance services (EQAS).https://mohap.gov.ae/).The national AMR Surveillance database managed by the UAE Ministry of Health and Prevention (MOHAP) contains confidential health information, and as such can only be made available upon reasonable request from the UAE Ministry of Health and Prevention , Dubai Scientific Research Ethics Committee (DSREC-GL17-2023), and Abu Dhabi Health Research and Technology Ethics Committee (DOH/ZHCD/2023/1316). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.JT, NA, and HA: conceptualization and manuscript review and editing. JT, NA, and The UAE AMR Surveillance Consortium: data collection. JT and NA: formal analysis, data interpretation, manuscript preparation. All named authors have read and agreed to the published version of the manuscript.Ahmed Elhag Ahmed, UAE University, College of Medicine and Health Sciences, Al Ain; Ahmed F. Yousef, Department of Biology, Center for Membranes and Advanced Water Technology, Khalifa University, Abu Dhabi; Amna AlBlooshi, Purelab, Al Ain; Dr. Adnan Alatoom, Sheikh Shakhbout Medical City (SSMC), Abu Dhabi; Dr. Ahmed Abdulkareem Al Hammadi, Tawam Hospital, Al Ain; Dr. Alaa MM Enshasy, Dubai Health Authority, Dubai; Dr. Amal Mubarak Madhi, Abu Dhabi Public Health Center, Abu Dhabi; Dr. Anju Nabi, Dubai Academic Health Corporation (DAHC), Dubai; Dr. Anup Shashikant Poddar, Al Sharq Hospital, Fujairah; Dr. Arun Kumar Jha, Danat Al Emarat Hospital, Abu Dhabi; Dr. Ayesha Abdulla Al Marzooqi, Abu Dhabi Public Health Center, Abu Dhabi; Dr. Bashir Aden, Khalifa University, Abu Dhabi; Dr. Deeba Jafri, Purelab, Sheikh Khalifa Medical City, Ajman; Dr. Duckjin Hong, Sheikh Khalifa Specialty Hospital (SKSH) RAK; Dr. Farah Ibrahim Al-Marzooq, United Arab Emirates University, Al Ain; Dr. Fatima Al Dhaheri, United Arab Emirates University, Al Ain; Dr. Ghada Abdel Wahab, Abu Dhabi Agriculture and Food Safety Authority, Abu Dhabi; Dr. Ghalia Abdul Khader Khoder, University of Sharjah, Sharjah; Dr. Gitanjali Avishkar Patil, NMC Specialty Hospital, Abu Dhabi; Dr. Hafiz Ahmad, RAK Hospital, Ras Al Khaimah; Dr. Hazim Khalifa, Department of Veterinary Medicine, UAE University, Al Ain; Dr. Husein Alzabi, Sheikh Khalifa General Hospital, Umm al Quwain; Dr. Ibrahim Alsayed Mustafa Alhashami, Purelab, Al Qassimi Hospital, Sharjah; Dr. Irfaan Akthar, Mediclinic City Hospital, Dubai; Dr. Jens Thomsen, Abu Dhabi Public Health Center, Abu Dhabi; Dr. John Stelling, WHONET, Boston, USA; Dr. Kavita Diddi, Prime Hospital, Dubai; Dr. Krishnaprasad Ramabhadran, Burjeel Hospital, Abu Dhabi; Dr. Laila Al Dabal, Dubai Academic Health Corporation ; Dr. Madikay Senghore, Khalifa University, Abu Dhabi; Dr. Manal Abdel Fattah Ahmed, PureLab, Ras Al Khaimah; Dr. Maya Habous, Rashid Hospital, Dubai Academic Health Corporation, Dubai; Dr. Moeena Zain, American Hospital Dubai; Dr. Monika Maheshwari, Al Zahra Hospital, Dubai; Dr. Monika Maheshwari, Medeor 24x7 Hospital, Dubai; Dr. Mubarak Saif Alfaresi, Zayed Military Hospital, Abu Dhabi; Dr. Mushtaq Khan, United Arab Emirates University, Al Ain; Dr. Najiba Abdulrazzaq, Al Kuwait Hospital, Emirates Health Services Establishment, Dubai; Dr. Nehad Nabeel Al Shirawi, Al Fujairah Hospital; Dr. Nesrin Helmy, Mediclinic Al Noor Hospital - Khalifa Street, Abu Dhabi; Dr. Prashant Nasa, NMC Specialty Hospital Al Nahda, Dubai; Dr. Rajeshwari T. A. Patil, Burjeel Medical City, Abu Dhabi; Dr. Ratna A. Kurahatti, NMC Royal Hospital Khalifa City A, Abu Dhabi; Dr. Riyaz Amirali Husain, Dubai Hospital, Dubai Academic Health Corporation, Dubai; Dr. Robert Lodu Serafino Wani Swaka, Sheikh Shakhbout Medical City, Abu Dhabi; Dr. Savitha Mudalagiriyappa, University Hospital Sharjah, Sharjah; Dr. Seema Oommen, Burjeel Medical City, Abu Dhabi; Dr. Shaikha Ghannam Alkaabi, Abu Dhabi Public Health Center, Abu Dhabi; Dr. Simantini Jog, Fakeeh University Hospital, Dubai; Dr. Simantini Jog, King’s College Hospital London Dubai Hills, Dubai; Dr. Siobhan O‘Sullivan, Khalifa University, Abu Dhabi; Dr. Somansu Basu, NMC Specialty Hospital, Al Ain; Dr. Yassir Mohammed Eltahir Ali, Animal Wealth Sector, Abu Dhabi Agriculture and Food Safety Authority, Abu Dhabi; Dr. Yousuf Mustafa Naqvi, Department of Health Abu Dhabi (DoH), Abu Dhabi; Dr. Zulfa Omar Al Deesi, Latifa Maternity & Pediatric Hospital, Dubai; Emmanuel Fru Nsutebu, Sheikh Shakhbout Medical City, Abu Dhabi; Fouzia Jabeen, Purelab, Sheikh Khalifa Hospital, Abu Dhabi; Francis Amirtharaj Selvaraj, Sheikh Khalifa Medical City (SKMC), Abu Dhabi; Hadayatullah Ghulam Muhammad, Emirates International Hospital, Al Ain; Imene Lazreg, University of Sharjah, Sharjah; Kaltham Ali Kayaf, Ministry of Climate Change & Environment (MOCCAE), Dubai; Laura Thomsen, University of Freiburg, Germany; Leili Chamani-Tabriz, Clemenceau Medical Center, Dubai; Pamela Fares Mrad, Abu Dhabi Public Health Center (ADPHC), Abu Dhabi; Pascal Frey, Berne University Hospital, Berne, Switzerland; Prof. Abiola Senok, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai; Prof. Agnes-Sonnevend-Pal, University of Pécs, Pécs, Hungary; Prof. Andreas Podbielski, University Hospital Rostock, Rostock, Germany; Prof. Carole Ayoub Moubareck, College of Natural and Health Sciences, Zayed University, Dubai; Prof. Dean Everett, Department of Pathology and Infectious Diseases, College of Medicine, Khalifa University, Abu Dhabi; Prof. Godfred A. Menezes, Department of Medical Microbiology and Immunology, RAK Medical and Health Sciences University, Ras Al Khaimah; Prof. Hala Ahmed Fouad Ismail, PureLab, Al Qassimi Hospital, Sharjah; Prof. Mohamud M. Sheek-Hussein, United Arab Emirates University, Al Ain; Prof. Peter Nyasulu, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa; Prof. Sameh Soliman, University of Sharjah, Sharjah; Prof. Tibor Pal, University of Pécs, Pécs, Hungary; Rania El Lababidi, Department of Pharmacy Services, Cleveland Clinic Abu Dhabi; Saeed Hussein, Erada Center for Treatment and Rehabilitation, Dubai; Stefan Weber, Purelab, Abu Dhabi; Sura Khamees Majeed, Al Gharbia Hospitals - Madinat Zayed Hospital; Syed Irfan Hussein Rizvi, Mediclinic City Hospital, Dubai; Timothy Anthony Collyns, Tawam Hospital, Al Ain; Zahir Osman Babiker, Sheikh Shakhbout Medical City (SSMC), Abu Dhabi.


Dopamine was first synthesized in 1910. It was initially named 3-hydroxytyramine and gained little interest in the science community. About a half-century later, neuroscientists started to realize that dopamine is a neurotransmitter and plays a key role in Parkinson's disease (PD). It was then formally renamed “dopamine” by Sir Henry Dale at the Physiology Society Meeting at Cambridge  that are the main source of hyperactivity in dopamine-depleted striatum. This consists with decades of clinical observations that when striatal dopamine level drops, acetylcholine level rises, and anticholinergic drugs were used to treat the motor symptoms of PD in the pre-levodopa era. The authors further showed that changes in both intrinsic and synaptic properties contribute to CIN hyperactivity. Among the synaptic inputs, not only glutamatergic, but also GABAergic and nicotinic transmission drive the pathological hyperactivity in CINs. These different mechanisms that all contribute to CIN hyperactivity during parkinsonism provide new directions for future investigations on basal ganglia hyperactivity and therapeutic targets for treating PD.In PD, midbrain dopaminergic neurons innervating the basal ganglia are lost. This results in an increased neuronal activity, particularly exaggerated beta oscillations, in the basal ganglia , exacerbating incentive motivation and choice impulsivity. To better understand the neurocognitive mechanisms underlying ICBs in PD, Hölscher reviewed studies on glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) receptor agonists that are ready to cross the blood-brain barrier for the protection of PD and AD. In PD mouse models, GLP-1 and GIP receptor agonists enhanced dopamine levels in the striatum, protected dopaminergic neuronal loss, and improved motor functions. Similarly, these agonists also enhanced synaptic transmission, protected synaptic plasticity, and facilitated learning and memory in AD mouse models. Various clinical trials of different GLP-1 and GIP agonists have also shown protective effects in PD and AD patients. Ongoing and future trials, particularly of dual receptor agonists, are promising in developing effective and safe treatments for PD and AD. This is even more exciting given the recent FDA approval of dual receptor agonists tirzepatide that is superior to GLP-1 agonist semaglutide in treating diabetes at much higher doses but with similar mild to moderate side effects , another common neurodegenerative disorder, is characterized by the loss of glutamatergic and cholinergic neurons in the forebrain as well as other regions. Growth factors that have neural protective effects in principle should protect neuronal loss and maintain synaptic transmission. A key issue with most growth factors is the ability to cross the blood-brain barrier. Arvidsson et al. used both NgR1-overexpression and NgR1 knockout mice to study how NgR1 affects dopamine release. The authors found NgR1 overexpression reduced dopamine release while NgR1 knockout increased dopamine release. While dopamine may induce global synaptic plasticity changes via broad dopaminergic projections, NgR1 modules local structural changes. This work suggests a mechanism where neuronal activity temporally down-regulates NgR1 expression, which leads to an increase in dopamine release, together these create a window where local and global synaptic plasticity regulating systems may work in synergy for learning and memory consolidation.Dopamine release is spatiotemporally regulated by multiple mechanisms, including tonic and phasic, axonal and somatodendritic, somatic activity-driven and distal activity-driven mechanisms. Recent studies suggest Nogo receptor 1 (NgR1) may also regulate dopamine release. The article of Gerfen wrote an excellent historical review on this topic where he played the central role in not just demonstrating the receptor expression patterns in these two pathways, but also creating the first mouse lines labeling these two pathways that are most commonly used in the basal ganglia field now. Gerfen also integrated more recent findings that have challenged the classical “go/no-go” model of the direct and indirect pathways and proposed a more complex circuit organization of the basal ganglia, particularly at the globus pallidus externa.The functional circuit organization of the basal ganglia consists of two major pathways initiating from the striatal SPNs. The direct pathway SPNs project directly to the basal ganglia output structures, the substantia nigra pars reticulata and globus pallidus interna; the indirect pathway SPNs project indirectly to the output structures via the globus pallidus externa and the subthalamic nucleus. Interestingly, it was found that the direct pathway SPNs express type 1 dopamine receptors (D1Rs) while the indirect pathway SPNs express type 2 dopamine receptors (D2Rs). However, this conclusion did not come easily. McDevitt et al. investigated how morphine, a highly addictive opioid, alters the synaptic and intrinsic properties of the dopamine cellular targets: D1R- and D2R-expressing SPNs in the nucleus accumbens shell. They found morphine treatment did not alter the synaptic and intrinsic properties in D1R-SPNs. However, morphine treatment reduced the frequency of both the miniature excitatory and inhibitory postsynaptic currents in D2R-SPNs. Interestingly, they found that as the frequency of the miniature postsynaptic currents decreased, there's a concomitant increase in D2R-SPN intrinsic excitability. Notably, the functional output of synaptically-driven firing in D2R-SPNs was unchanged, suggesting a potential mechanism for homeostatic modulation of neuronal activity in D2R-SPNs upon morphine exposure.Dopamine-induced changes in the nucleus accumbens are crucial for reward learning and drug addiction. In turn, Surmeier et al. therefore raised eight problems with the classical model and provided detailed discussion on (1) the striatal circuit with an emphasis on the role of cholinergic modulations, (2) striatal adaption in PD focusing on how dopamine depletion-induced synaptic plasticity changes affect SPN ensembles essential for motor functions, (3) dopamine release beyond the striatum, particularly en passant release in nuclei downstream of striatum and dendritic release in substantia nigra pars reticulata.Another recent study led by the Surmeier group also challenged the classical model with their finding that striatal dopamine depletion is not sufficient to induce PD motor symptoms, but dopamine depletion throughout the basal ganglia is (Gonzalez-Rodriguez et al., More than a century has passed since the initial synthesis of dopamine. Still more is going to be discovered regarding dopamine's function in health and diseases. We hope you enjoy reading this Research Topic and we look forward to new advances in the dopamine field.BY: Writing—original draft, Writing—review and editing. RR: Writing—review and editing. JX: Writing—review and editing. J-PM: Writing—review and editing.


Antimicrobial resistance (AMR) poses a relentlessly growing threat to human and animal health across the globe. The inappropriate use of antimicrobials in clinical and agricultural settings has created suitable conditions for the evolution, emergence, and dissemination of AMR. Overreliance on antimicrobials has resulted in an unprecedented selection pressure that spurred the proliferation of antimicrobial-resistant microorganisms in human, animal, and environmental microbiota . These cAntimicrobial resistance and antimicrobial alternatives” hosts 11 manuscripts, including seven original articles, one systematic review, two narrative reviews, and one perspective article. The manuscripts were contributed by 76 authors from various institutions and research centers. This Research Topic is involved in addressing AMR challenges by assessing trends in antimicrobial use and the development of resistance, especially in developing countries, and exploring the antimicrobial properties of promising non-traditional products. Inconsistent antimicrobial stewardship programs and deficiencies in systemic surveillance of AMR in low- and middle-income countries (LMICs) have promoted the injudicious use of antimicrobials among humans and other animals. Consequently, multidrug-resistant (MDR) microorganisms have been steadily increasing . Interestingly, the mobile colistin resistance gene (mcr-1.26), previously isolated in a pigeon in Lebanon . The spread of MDR isolates  is of great concern, especially in LMICs  and the disenfranchised population that are experiencing compounded public health challenges. The findings corroborated those observed in China, which reported a rapid increase in the fecal carriage rate of extended-spectrum beta-lactamase (ESBL)-producing Klebsiella pneumoniae among healthy rural residents . The most common AMR determinants documented in these residents were blaM−14CTX−, followed by blaM−3CTX−, blaM−15CTX−, blaM−27CTX−, blaM−24CTX−, and blaM−65CTX− . To halt the dissemination of AMR in these socioeconomic settings, it is crucial to understand both the intrinsic and extrinsic factors that contribute to the emergence and persistence of resistance. Therefore, there is a paramount need for One Health-based surveillance studies that allow local and international stakeholders to improve AMR stewardship programs and address the calamitous problems that threaten these populations.Although the highest toll of AMR burden is thought to affect developing countries , it is w Lebanon , was repMeng et al.). Although there is a pressing demand for new antimicrobials, the use of antimicrobial alternatives  is also sorely needed, especially when noting the decline in the discovery and development of novel antimicrobial agents. It is no secret that pharmaceutical companies are reluctant to develop new drugs due to regulatory, scientific, and financial barriers (Haridas and Atreya). Furthermore, Gungordu Er et al. highlighted that graphene-based nanomaterials can be potential antiviral candidates for biomedical applications such as antimicrobial personal protective equipment. Future investigations must continue to explore more promising antimicrobial alternatives that can effectively control untreatable complicated infections, perhaps tackling etiologic agents in a variety of settings in the host and beyond. While progress is being made, the current challenge is to implement these alternative therapies in medical practices, prove their efficacy, safety, and affordability, and gradually replace/supplement conventional antimicrobial interventions.Given the rapid spread of resistance, it is imperative to seek novel antimicrobial agents and other therapeutic options to overcome increasing rates of AMR and treat life-threatening MDR infections. Eravacycline, a relatively new fluorocycline antimicrobial with broad-spectrum efficacy against common clinical pathogens, was not inferior to ertapenem and meropenem in adult patients with complicated intra-abdominal infections. Subsequently, Eravacycline might represent an excellent option for the treatment of these infections  is necessary to avoid the tsunami of AMR. Resistance is not only a local challenge, but it also affects everyone across the globe and requires holistic consideration of clinical, agricultural, and environmental practices. Special and real support must be provided to protect the most vulnerable populations in locations where AMR can take hold, amplify, and spread; causing unimaginable suffering. The manuscripts published on this Research Topic highlighted the growing problems of inappropriate use of antimicrobials and the spread of AMR and the need for effective antimicrobial alternatives.MO drafted and edited the manuscript. IA, KE, and IK edited the manuscript. All authors approved it for publication.