Existing public health surveillance systems that rely on predefined symptom categories, or syndromes, are effective at monitoring known illnesses, but there is a critical need for innovation in “presyndromic” surveillance that detects biothreats with rare or previously unseen symptomology. We introduce a data-driven, automated machine learning approach for presyndromic surveillance that learns newly emerging syndromes from free-text emergency department chief complaints, identifies localized case clusters among subpopulations, and incorporates practitioner feedback to automatically distinguish between relevant and irrelevant clusters, thus providing personalized, actionable decision support. Blinded evaluations by New York City’s Department of Health and Mental Hygiene demonstrate that our approach identifies more events of public health interest and achieves a lower false-positive rate compared to a state-of-the-art baseline. Machine learning methods enable a new type of public health surveillance that acts as a “safety net” for public health. To offer a rapid, targeted, and effective response to emerging biothreats, public health officials must be able to detect a huge variety of emerging events. Recent, high-profile events highlight the diversity of situations that can affect public health: In February 2020, 50+ residents of a nursing home in Kirkland, Washington were part of one of the first coronavirus disease 2019 (COVID-19) outbreaks in the United States; in October 2019, 100+ people contracted Legionnaires’ disease and Pontiac fever from hot tub displays at the North Carolina state fair; and in March 2018, 90+ people presented to emergency departments in five states with unexplained bleeding that was eventually traced to the use of synthetic marijuana laced with brodifacoum, or rat poison.While existing, widely used disease surveillance systems such as the Centers for Disease Control and Prevention’s National Syndromic Surveillance Program have proven to be effective in detecting outbreaks of known illnesses, or those with common symptomology , these systems are not optimized for early detection of rare events or novel (previously unseen) biothreats. Syndromic surveillance systems typically monitor emergency department (ED) chief complaint data: free-text symptom data reported by each ED patient, recorded by a triage nurse, and sent to local public health organizations from hospitals in their jurisdiction. Chief complaints are typically short free-text strings, for example, “headache and pain in rt arm” or “cough and nasal congestion × 2 days.” These data are mapped to syndromes, like respiratory, fever, and gastrointestinal illness, and from there, spatial cluster detection methods, such as spatial scan statistics . MUSES offers three notable methodological advances:• MUSES eliminates the need for predefined syndromes by learning syndrome categories, including those that characterize rare or novel health threats and occur over a small number of cases, directly from free-text ED data.• MUSES identifies localized case clusters through multidimensional spatial scan statistics, enabling detection of emerging biothreats that may be isolated to a certain hospital or spatial region or to a certain demographic group of patients.• MUSES uses a practitioner in the loop approach to incorporate user feedback, “zoom in” on relevant patterns, reduce false positives, and provide local users with actionable insights based on their own criteria for what is, and is not, relevant.An overview of MUSES is shown in MUSES uses a new variant of topic modeling to learn syndrome categories directly from the data. Topic models are a set of algorithms that automatically summarize the main themes, or topics, contained in large collections of documents. The most widely used topic modeling approach, latent Dirichlet allocation (LDA), models topics as probability distributions over words and documents as probability distributions over these topics . This dataset has more than 28 million ED cases from 53 NYC hospitals during 2010–2016. For each hospital, we have data on the patients’ free-text chief complaint, date and time of arrival, age group, gender, and discharge International Classification of Disease–9 (ICD-9) diagnosis code. Public health practitioners at NYC’s Department of Health and Mental Hygiene (DOHMH) performed a blinded evaluation of the top 500 highest-scoring clusters detected over the 6-year time period by our method and by a competing, state-of-the-art, keyword-based approach. For each of these clusters, the evaluators indicated if the cluster (i) represents a meaningful collection of cases and (ii) is, in their judgement, highly relevant to public health . For example, clusters related to “bacterial meningitis” and “synthetic drug use” were rated as highly relevant, clusters related to “motor vehicle accidents” were rated as meaningful but not highly relevant, and clusters resulting from misspellings or common words (such as “left”) were rated as not meaningful. We note that the evaluation was blinded (cluster lists for the two methods were merged and shuffled) so that the public health practitioner was not aware which method reported a given cluster or where that cluster ranked on its top 500 list.The blinded evaluation by DOHMH demonstrated that our method correctly identifies a larger number of events of interest to public health departments than the baseline method. We observe that 320 (64%) of the top 500 results from MUSES corresponded to meaningful health events, while the keyword-based method only detected 246 such events (49.2%). In addition, to determine how our approach might provide situational awareness of emerging health concerns following a natural disaster, we examined the clusters identified by our approach in the week following 29 October 2012, when Hurricane Sandy struck NYC and caused a historic level of damage. These results show a temporal progression of detected clusters from acute cases related to falls and shortness of breath, to mental health issues like depression and anxiety, to chronic health issues that require maintenance procedures, like dialysis and methadone distribution. Such procedures are typically handled in outpatient clinics but were displaced to EDs when clinics were closed because of storm damage and power outages. We note that DOHMH epidemiologists manually inspected hospital ED data immediately following Hurricane Sandy; noticed an increase in the words “methadone,” “dialysis,” and “oxygen”; and created a “needs medication” syndrome  approach. During the initial evaluation, DOHMH practitioners indicated that 102 of MUSES’ 200 highest-scoring clusters represented events that were meaningful collections of cases but not specifically relevant to their jurisdiction’s needs for the system, including 28 clusters related to motor vehicle accidents, 12 related to medical evaluations or clearances, and 8 related to alcohol intoxication. With a PITL approach, the public health user could mark the first identified occurrence of a “motor vehicle accident” as irrelevant to that particular health department, enabling the system to ignore or de-emphasize future instances of such clusters and to focus attention on known and relevant event types, as well as those that correspond to novel, previously unseen events. Human in the loop topic modeling has also been shown to increase topics’ interpretability, improve users’ ability to find information in a large corpus, and encode expert knowledge  The PITL model outperforms the fixed model with respect to precision and the number of detected events of interest; (ii) the performance gap between the PITL and fixed models increases monotonically as a function of the number of labeled clusters used as training data by the PITL model; (iii) after an initial labeling of a cluster as a topic to be ignored, the PITL model will avoid presenting similar clusters to the user in the future, thus reducing false positives as compared to the fixed model; and (iv) after an initial labeling of a cluster as a relevant topic to be monitored, the PITL model will have higher power to detect future instances of that topic. During the 19 periods included in the experiment, the PITL model detected 49 highly relevant clusters, a 53% increase over the 32 detected by the fixed model. Because the fixed model could relearn an emerging topic that is similar to a monitored topic incorporated into the PITL model, if the cluster’s signal is sufficiently strong, then both models will detect the event. Nevertheless, as shown in Thus, we observe that the PITL MUSES model, incorporating monitored and ignored topics learned from user feedback, has improved ability to detect novel events, reduced false-positive rate, and can present users with more examples of clusters in which they have previously expressed high interest. Because PITL iteratively adds ignored and monitored topics to the model, and the emerging topics are chosen to be maximally different from these, it is able to better distinguish novel events from the known syndromes and patterns in the data. Furthermore, if the words in a cluster of documents are well represented by an ignored topic, they have a high likelihood of being assigned to this existing topic. Since we do not scan over subsets that include the ignored static topics, these clusters are unlikely to be shown to users, which reduce the false-positive rate. Conversely, because any documents that are closely aligned to one of the monitored topics have a high likelihood of being assigned to this monitored topic, fewer cases will be required to detect and report these events.The first wave of the COVID-19 pandemic hit NYC in March and April 2020 with catastrophic public health impacts. The city’s first COVID case was confirmed on 1 March, and by 30 April, there were over 174,000 confirmed cases and nearly 15,000 deaths, approximately one-fourth of the entire U.S. death toll to that point  and maps those cases to the emerging topics, enabling the cluster to be detected.As a robustness check, we evaluated MUSES with three different numbers of static topics  and compared the clusters detected (both for emerging topics and monitored static topics) in each case. The three variants produced extremely similar sets of detected clusters: The top 15 clusters for 10 and 25 static topics matched exactly, with minor differences in score (and thus some reordering of the cluster ranking), as well as minor differences in the age ranges included (and thus some differences in the precise set of cases included in each cluster). Fourteen of the top 17 clusters for 50 static topics also matched these 15 clusters, again with minor differences in score and cluster composition. While these results demonstrate that the number of static topics did not substantially affect MUSES’ ability to detect novel emerging clusters, some differences were observed in the clusters identified by scanning over monitored static topics. For 25 and 50 static topics, the same cluster of influenza-like respiratory illness on 31 March scored in the top 30 clusters, while for 10 static topics, no monitored static clusters scored even remotely close to the top 30. This difference was most likely due to the more focused topic distributions learned for larger numbers of static topics: Influenza-like respiratory illness formed its own topic for 25 and 50 static topics, while for 10 static topics, it was merged into a single topic with flu-like symptoms, cough, and fever. In addition, for 50 static topics, two other monitored clusters scored in the top 30: one cluster of 26 cases of flu-like symptoms in a 2-hour period on 27 March, and one cluster of 18 cough and fever cases in a 3-hour period on 21 March. Each of these two clusters was concurrent with a large, high-scoring novel cluster in the same hospital. While the assignment of COVID cases to static topics substantially reduced the score of the novel cluster for 1 hour of data, MUSES identified those cases as part of the larger novel cluster in the following hour, and thus neither timeliness nor accuracy of detection was significantly affected.While the above results demonstrate the potential utility of MUSES for identifying rare and novel events of public health interest, we now consider various limitations of the method that might reduce its ability to facilitate targeted and timely public health interventions. First, lags in data collection, preprocessing, analysis, or communication of results may affect timeliness, and thus effective use of MUSES depends both on a well-developed data infrastructure and the availability of public health practitioners to respond rapidly to the detected clusters. Second, false-positive clusters could result from repeated typographical errors by a particular triage nurse, hospital EHR changes and upgrades, or the use of new or unusual terminology to describe cases within a given hospital. While such clusters can easily be ignored, they may increase public health practitioners’ workload and potentially cause more relevant clusters to be overlooked. Thus, we have implemented data cleaning, including correction of common misspellings, as a preprocessing step for MUSES (as described below). Terminology changes could be incorporated either by adding them as ignored topics in the PITL model or by recomputing the static topics at regular intervals, as discussed below. Third, false negatives  could result from multiple sources of error. Chief among these are sampling bias and recording bias, since not everyone who has a particular symptom presents at the ED; those patients who do present may describe their symptoms differently, and different triage nurses may record them differently. ED usage may differ substantially between patient subpopulations depending on geographic, demographic, and socioeconomic characteristics, including factors such as access to care and insurance coverage. Given these biases, MUSES should not be used to perform population-level inferences like burden-of-disease estimation, nor should absence of a detected cluster be construed as an indicator that an event is not present. Rather, MUSES should be used as an exploratory data analysis tool, to identify potentially relevant case clusters that public health practitioners may otherwise have missed.sic]), they are likely to get grouped together into a single topic, mitigating this loss of detection power. In addition, an emerging pattern of cases may be grouped into an existing static topic rather than forming its own emerging topic, if it has too much overlap (in the sets of words used) with the existing topics. This incorrect grouping will lead to false negatives since the static topics are typically not included in the scan step. Possible solutions include using presyndromic surveillance as a complement to existing syndromic surveillance systems, which can pick up patterns of known syndromes, scanning over static as well as emerging topics, or using the PITL approach to designate certain static topics to be monitored rather than ignored. Last, our contrastive topic modeling approach is a randomized rather than deterministic algorithm, and thus MUSES is not guaranteed to identify identical clusters each time it is run on the same or similar data. However, we find in practice that the topics, and the resulting detected clusters, are highly consistent and robust to random variation when the amount of training data is large  and when the signal is strong . For example, we ran MUSES on a 90% subsample of the original data from March to April 2020 and compared the top 30 clusters in the original data with the top 30 clusters in the subsampled data. We observed that 22 of 30 clusters in each list matched a cluster in the other list , while the remaining clusters either narrowly missed the top 30 in the other list, or were narrowly beaten by a different cluster during that hour.While MUSES does not assume or require a representative sample for detection, its statistical power to detect a given event will depend greatly on whether the affected subpopulation is overrepresented or underrepresented in the ED data, as well as the extent to which the resulting ED chief complaints use similar sets of words to describe each case. For example, if a newly emerging event is referred to using different terminology in different hospitals, then cases from different hospitals may be assigned to different emerging topics, potentially leading to both missed cases (since each topic’s case cluster will omit the cases assigned to other topics) and reduced detection power (since fewer cases will result in a lower score). Similar losses of power may occur due to typographical errors (though preprocessing will correct some of these) or inconsistent usage within a single hospital. However, if the distinct terms co-occur with each other  or co-occur with the same other terms  from hospital EDs to health departments are necessary for both syndromic and presyndromic surveillance and are already in place for many jurisdictions. For example, NYC DOHMH’s Syndromic Surveillance Unit in their Bureau of Communicable Disease collects data on a daily basis from all 53 NYC EDs and monitors case counts of common syndromes such as influenza-like illness, respiratory illness, and gastrointestinal illness. For presyndromic surveillance, the critical data fields for each hospital to collect and send to their local health department are the date, time, and free-text chief complaint for each ED case; additional data such as ICD codes, and demographics such as age and gender, can enhance both detection and follow-up investigation of clusters. An automated process could run MUSES daily on the most recent 24 hours of ED visit data, typically requiring no more than 20 to 30 min for the contrastive topic modeling and scanning steps and have results ready for practitioners to analyze each morning. Practitioners could peruse the top-scoring clusters, and ideally provide feedback, through the visualization interface each day, or only on days when the score exceeded some fixed threshold. This process would take no more than a few minutes, unless a cluster was deemed worthy of follow-up investigation. While monitored and ignored topics would be updated automatically from user feedback, it would also be desirable to update the static topics  at regular intervals, e.g., once every few months, to account for changes in case distribution or data entry practices. Last, we recommend using MUSES as a complement, rather than a substitute, to existing practices such as notifiable disease reporting and syndromic surveillance, as these existing approaches would be more effective for identifying patterns of known disease types and commonly occurring syndromes respectively. The relative portions of the public health workflow devoted to these tasks would be situationally dependent. For example, during the peak of the COVID pandemic, public health resources were almost entirely devoted to COVID response, while presyndromic surveillance could have been used in a more limited and focused way to identify newly emerging symptom patterns among patients with COVID.MUSES builds upon new methodological approaches for syndrome discovery, cluster detection, and learning from user feedback to offer an innovative, presyndromic surveillance system that facilitates early detection and investigation of events of public health concern. Evaluation results from NYC DOHMH demonstrate the power of our detection methodology for accurately identifying clusters that are meaningful and relevant to local public health users, substantially improving the accuracy and specificity of detection as compared to existing state-of-the-art approaches. With the potential to enhance day-to-day situational awareness, to enable early detection of emerging biothreats during an emergency, and to provide a “safety net” to identify and investigate newly emerging and previously unseen events that existing systems would fail to detect, presyndromic surveillance is a critical next step for improved public health practice.To clean the data, we first used the Emergency Medical Text Processor (EMT-P), an open-source, natural language preprocessing system , learned from the data.MUSES uses a contrastive topic model to learn syndromes that correspond to rare diseases or novel emerging biothreats. This extension of the LDA topic model learns two sets of topics: a set of k’s distribution over words, φk, and each document d’s distribution over the KS static topics, θd, are drawn from Dirichlet distributions with hyperparameters β and α, respectively. The Dirichlet is a distribution over the probability simplex and is the conjugate prior of the multinomial distribution. From there, each word n in each document d is assumed to be generated by first drawing a topic assignment zd,n ~ multinomial(θd) and then a word assignment wd,n ~ multinomial .We learn the static topics by fitting a topic model to historical data using the standard LDA approach. To define a statistical model of the topics’ distributions over words and the documents’ distributions over topics, LDA makes a set of assumptions about how the data were generated. The standard generative model used in LDA assumes that each topic k and document-topic distributions θd are latent (unobserved) and must be inferred from the data through the conditional distribution P. While this posterior cannot be found through exact methods, there are a variety of methods that can be used to perform inference. MUSES uses collapsed Gibbs sampling, a common Markov Chain Monte Carlo approach, which is widely used for LDA models +Ksαwhere nd that is observed and remains fixed at the levels learned by the static LDA model.1) Each static topic φV) that is given by 2) Each emerging topic d has a distribution θd over static and emerging topics (a vector of length KS + KE) that is given by θd ~ Dirichlet(α).3) Each document wd,n in document d, a topic assignment zd,n is drawn from Multinomial(θd). If the topic assignment corresponds to a static topic, a word assignment is drawn from Multinomial. If the topic assignment corresponds to an emerging topic, a word assignment is drawn from 4) For each word Note that the presence of the fixed static topics distinguishes this model and set of assumptions from standard topic modeling. Given these generative assumptions, the joint distribution isK = KS + KE topics. Typically, we assume KS = 25 static topics and KE = 25 emerging topics. The initial topic assignments are based on the topics φ learned by performing inference on the historical corpus and the most recent collection of documents. That is, wd,n is the nth word in document d, zd,n is the topic assignment of wd,n, and φk is determined as follows: If k is a historical static topic, φk was learned by applying standard LDA to a large set of historical data, and if k is an emerging topic, φk was learned by applying standard LDA to the most recent set of data. After making the initial topic assignments, we proceed with the Gibbs sampling procedure described in the previous section, using the combined set of static and emerging topics, but only update φk using k is an emerging topic, thus keeping all static topics fixed throughout the learning process. When this inference is complete, we refer to the second set of re-optimized φ′ as emerging topics.To learn the emerging topics’ distribution over words and the documents’ distributions over static and emerging topics, we perform Gibbs sampling with a few modifications. Here, we consider a total of We note that time is not explicitly represented in the Emerging Topic LDA Model . Rather,k has a distribution over words It is straightforward to incorporate ignored and monitored static topics into the Emerging Topic Model. We assume that each ignored and monitored static topic SAfter learning newly emerging syndromes from the chief complaint data, we use a multidimensional extension of spatial scan statistics to identify localized case clusters corresponding to these topics. Kulldorff’s spatial scan approach considers circular geographic search regions that are centered at each monitored location and have varying radii, allowing for detection of both spatially compact and dispersed clusters. Extensions to this method consider a variety of types of geographic search regions, including rectangles, ellipses, and more general search regions over subsets of the data , has demographic characteristics included in {δ1,1, δ1,2, …, δp1,} × … × {δj,1, δj,2, …, δj,s}, and has a chief complaint that maps to a given emerging topic or monitored static topic . To map each chief complaint to one of the learned syndromes, we follow  is that there is a cluster of some (emerging or monitored static) topic affecting search group S. We assume that if there is no cluster, count ci,j,k of this topic at time i, location j, and among demographic group k will be distributed according to a Poisson distribution ci,j,k ~ Poisson , where bi,j,k is a baseline or expected count for this topic during the same time frame and among the same demographic group of patients. If these patients are affected by a cluster, we expect that there will be a multiplicative increase in counts as compared to the baseline; i.e., the count will be distributed as ci,j,k ~ Poisson  for some constant q > 1, where q is estimated by maximum likelihood. Given these assumptions, the formula for the log-likelihood ratio score F(S) simplifies toFor each search group, we compute the log-likelihood ratio scorei is given by ib = (ACh + ACoh)/2, where ACh is the average hourly case count over the last 28 days during the same hour as the hour in time period i, and ACoh is the average hourly case count over the last 28 days during all other hours of the day. We use F(S) to identify the highest-scoring clusters of cases that should be reported to users as potential events of interest.Here, We use the log-likelihood ratio scan statistics to identify anomalous clusters in both MUSES and the keyword-based comparison method. While keyword-based approaches have used a variety of statistical methods for this task, this allows us to isolate and study the impact of learning syndromes versus considering each keyword individually.For the first blinded user study, we ran both MUSES (fixed model) and the competing keyword-based approach for each hour of data over the entire 6-year period from 2010 to 2016. For a given hour of data, we used a 3-hour moving window (that hour and the two previous hours) to learn emerging topics and scanned over clusters from 1 to 3 hours in duration. The static model for the first case study was learned from a 10% sample of the entire 6 years of data. For this user study, we learned separate topic models for each NYC hospital, with 25 static and 25 emerging topics for most of the hospitals, and up to 50 static topics for hospitals with particularly large patient populations.For the second blinded user study, we ran both the fixed and PITL models for each hour of data in 2-week increments from 1 January through 30 September 2016. For a given hour of data, we used a 3-hour moving window (that hour and the two previous hours) to learn emerging topics and scanned over clusters from 1 to 3 hours in duration. The initial static topic model (for both fixed and PITL models) was learned from the previous year of data, 1 January through 31 December 2015. For this user study, we learned a single topic model across all NYC hospitals, using 25 static and 25 emerging topics. The PITL model’s static topics were updated after each 2-week period (adding monitored and ignored static topics as labeled by the user), while the fixed model’s static topics were not updated.x is given byc is a cluster, iI is the set of clusters detected by the model and labeled “to ignore” by the practitioner in period i, t is a topic, ISTi is the set of ignored static topics at the start of period i, similarity is a similarity measure, and τ is a similarity threshold. Here, we measure the similarity between a detected cluster c (with a topic ct) and an ignored topic w in the cluster’s topic and the ignored topic, respectively. For example, the similarity between a detected cluster with a topic that places probabilities of  on the words  and an ignored topic that places probabilities of  on the words  would be min + min  + min  + min  = 0.66. A similarity threshold of τ = 0.6 was used for this analysis.To understand why the PITL model offers improvements over the fixed model, we first consider our third hypothesis, which predicted that after an initial labeling of a cluster as a topic to be ignored, the PITL model will avoid presenting similar clusters to the user in the future, thus reducing false positives as compared to the fixed model. We evaluate this hypothesis by measuring the total number of clusters detected by the model in each period that have topics similar to a topic that was previously detected and labeled “to ignore” by the public health practitioner. That is, the number of previously incorporated ignored (PII) topics shown to the user in period iM is the set of clusters detected by the model and labeled “to monitor” by the practitioner in period i, MSTi is the set of monitored static topics at the start of period i, and all other values are defined as in Our final hypothesis predicts that after an initial labeling of a cluster as a relevant topic to be monitored, the PITL model will have higher power to detect future instances of that topic. We evaluate whether the PITL model is able to identify more clusters that are similar to those the practitioner has labeled “to monitor” than the fixed model by measuring


Antimicrobial resistance (AMR) is continuing to grow across the world. Though often thought of as a mostly public health issue, AMR is also a major agricultural and environmental problem. As such, many researchers refer to it as the preeminent One Health issue. Aerial transport of antimicrobial-resistant bacteria via bioaerosols is still poorly understood. Recent work has highlighted the presence of antibiotic resistance genes in bioaerosols. Emissions of AMR bacteria and genes have been detected from various sources, including wastewater treatment plants, hospitals, and agricultural practices; however, their impacts on the broader environment are poorly understood. Contextualizing the roles of bioaerosols in the dissemination of AMR necessitates a multidisciplinary approach. Environmental factors, industrial and medical practices, as well as ecological principles influence the aerial dissemination of resistant bacteria. This article introduces an ongoing project assessing the presence and fate of AMR in bioaerosols across Canada. Its various sub-studies include the assessment of the emissions of antibiotic resistance genes from many agricultural practices, their long-distance transport, new integrative methods of assessment, and the creation of dissemination models over short and long distances. Results from sub-studies are beginning to be published. Consequently, this paper explains the background behind the development of the various sub-studies and highlight their shared aspects.  Rising levels of antimicrobial resistance (AMR) have caused great concern amongst policymakers, doctors, and governments in recent years. In 2019, an estimated 4.95 million deaths were associated with AMR globally, of which approximately 1.27 million were directly attributable to resistant bacteria  and healThis reality has led scientists and policymakers to consider AMR as the preeminent One Health question . Much atIndeed, Huijbers and collaborators  reportedUnderstanding the role bioaerosols play in ARG transmission is critical to addressing AMR in the environment. Yet this requires a multidisciplinary approach that can integrate data from diverse systems, including indoor  and outdoor environments  or remote areas  and produce data that can be integrated into the current understanding of AMR. To this end, we have launched a multi-year research program dedicated to the airborne dissemination of ARGs. The project is funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers opportunity, specifically targeting AMR in the environment (2019 competition), as a major research topic of the Canada Research Chair on Bioaerosols and a network of Canadian and international collaborators. Here, we outline the rationale for this undertaking and describe the ongoing research topics. The purpose of this article is to present (i) the current knowledge gaps in the roles of bioaerosols in AMR; (ii) our ongoing work to address them; (iii) stimulating discussion and interest amongst the broader scientific community to further shed light on the roles of bioaerosols in disseminating ARGs; and (iv) open doors for new collaborations.Bioaerosols are particles suspended in the air upon which, microorganisms, living or dead, microbial fragments, and viruses may be found. Such biological material can be transported over many kilometres in the environment. Bioaerosols are defined as particles of less than 100 μm and may remain airborne indefinitely, depending on air currents and turbulence . The aerBioaerosols are representative of their sources and together form a combined sample of multiple origins, although a recent hypothesis tends to support the existence of a specific atmospheric microbiota . FollowiLegionella [Staphylococcus [Often researchers rely on modeling approaches or database references to infer the source community of bioaerosols . Since vgionella ,29 and Slococcus ,30 usingBioaerosols may play an active role in the dissemination of ARGs in the environment. In Colorado, concentrated animal feeding operations were shown to emit ARGs detectable over 2 km from buildings . FurtherParticulate matter (PM) present in pollution events harbours ARGs in greater concentrations than under ambient conditions . SeveralRecent research has shown that ARGs are present in the indoor air of wastewater treatment facilities  and livetetM and tetO have been found in the air of agricultural buildings and farms [tetW gene was common in health clinics and a homeless shelter in Colorado, USA [ermB and ermC were highly abundant [Nevertheless, the above examples provide evidence that a significant number of ARGs are present in bioaerosols. For instance, in their worldwide sampling of dust collected from vehicle air filters, Li et al. detected ARGs against aminoglycosides, beta-lactams, macrolides, quinolones, sulfonamides, tetracyclines, and vancomycin . Bioaerond farms ,37. Furtado, USA . In a Soabundant . Yet, thDr. Caroline Duchaine has spearheaded a multi-year project funded by an NSERC Discovery Frontiers program to study ARGs in bioaerosols to address these fundamental questions. The overarching project incorporates studies of indoor environments, their emissions into the environment and their long-distance transport, novel ARG tracking and surveillance methods, selective culture approaches, and animal models that can be incorporated into exposure and risk assessment models . The aspInstitut Universitaire de Cardiologie et Pneumologie de Québec, Université Laval in Quebec City, QC, Canada. A multidisciplinary team of collaborators at Université Laval, across Canada  and internationally  are participating in the project. They bring expertise in human, veterinary and aquacultural disease, antibiotics, virology, bioaerosols, culture approaches, geography, modeling, bioinformatics, and artificial intelligence.This project is led by the Canada Research Chair in Bioaerosols based in the bioaerosol laboratory, Together, our team proposes to estimate the contribution of agricultural and sanitation activities to ARG dispersal across Canada and the potential for long-distance transfers through a program addressing the following objectives: (i) assess ARG dispersion and associated bacterial diversity across Canada and their relationships to land use via vehicle cabin filters; (ii) assess ARG dispersion and associated bacterial diversity of bioaerosols in representative source locations by using high-volume air samplers; (iii) determine the subsequent fate of airborne ARGs; (iv) determine the potential for long-distance transport of ARGs; and (v) adding the role of bioaerosols to an integrated assessment model on AMR. The findings will significantly enhance the understanding of ARGs in bioaerosols and provide a framework for future research. It is hoped that the project will stimulate other researchers to pursue interdisciplinary approaches to studying bioaerosols.Evaluating the contributions of various bioaerosol sources is key to exposure and mitigation strategies. Hospitals, wastewater treatment plants, fish farms, livestock barns —particularly those of swine and poultry—and manure spreading are being evaluated in this research. Assessing the emissions of bioaerosols within and around these locations requires the use of multiple sampling methodologies involving a range of air samplers . These iWhile frequently used in indoor environments, the deployment of such samplers outdoors presents challenges. Decisions in deployment location (both on the ground and at height) and duration may introduce sampling biases into collection. Sampling is also limited to relatively short periods, which may not align with ideal weather conditions for bioaerosol collection. Standardized sampling approaches have been implemented by all investigators to reduce bias throughout the project. The SASS3100  electret sampler is part of most of the sampling campaigns. However, it is unsuitable for culture techniques and does not maintain microbial viability. For culture and viability purposes, liquid samplers will be added to protocols, such as the SASS2300 and Coriolis μ , where appropriate.Institut de Recherche et de Développement en Agroenvironnement (IRDA) farm at Saint-Lambert-de-Lauzon, Quebec. Several Quebec fish farms, including the Laboratoire Aquatique de Recherche enSsciences Environnementales et Médicales (LARSEM) will also be studied. Air will be sampled in hospitals in Toronto, ON, Canada. In terms of study locations, these sampling campaigns will be undertaken in several locations across the Canadian provinces of Quebec, Ontario, and Saskatchewan. Several wastewater treatment plants have been selected for study. The presence of ARGs in bioaerosols was shown by previous work from our research team in swine barns ,41 and pAssessing the long-distance transport of ARGs is a critical objective of this project. We will address this question using a variety of complementary approaches. The aerial microbiome of remote locations in the Canadian North will be characterized. Arctic bioaerosols are a critical blind spot in our understanding of airborne biodiversity , which eThe resistome of clouds will also be characterized. Clouds can be considered as an oases for microorganisms, providing them with more favourable conditions such as water or shading against UV radiation . Such siwww.blue-observer.com). It will consist of a 7000 km trip from Brest, France to Woods Hole, MA, USA, with daily air sampling using two different types of air collectors. A SASS3100 extractor will be mounted on the mast of a sailing ship (8–10 m high) and deployed daily for 1 h each night and each day. In parallel, three filter holders connected to individual pumps (7 L min−1) will be deployed continuously to perform 24 h sampling.The potential for long-distance transport of ARGs will be complemented by a 15-day low altitude transatlantic survey of airborne bioaerosols aboard a sailboat. Monitoring ARGs at sea constitutes a novel and innovative approach to better characterize ARG transport and identify the marine contribution to ARG emission worldwide. The campaign will be performed with the collaboration of the Blue Observer organization  carrying ARGs. Culture can retrieve less abundant bacteria which is one of the major biases of culture-independent methods . This isUnderstanding the fate of ARGs and potential risks to human health is a key to this project. However, bacterial communities are shaped by a complex array of evolutionary, ecological, and environmental factors. As such, it is difficult to predict the fate of ARGs in environmental samples. The lack of functional demonstrations for ARGs in environmental metagenomes is a considerable limitation when characterizing the environmental resistome and assessing of its clinical relevance. Therefore, a selective culture approach will be employed to enrich bacterial species from selected samples collected at emission sources . The ARGs and or MGEs present in these isolates will be analyzed.Furthermore, animal models will be developed to assess the risk of ARG transfer in vivo. Briefly, C57BL/6 mice will be treated with a mixture of antibiotics to perturb their gut microbiota over a period of five days. Liquid cultures of sorbitol peptone broth and bile salts media derived from air samples from selected environments will be introduced to the mouse gut once daily over the course of the eight-week experiment to mimic chronic exposure to potentially harmful bioaerosols. Faecal samples will be collected before, during, and after this process. DNA will be extracted from these samples and will be processed through the metagenomics pipeline described earlier.An atmospheric pollutant dispersion model will evaluate the dispersion and fate of bioaerosols and ARGs emitted from specific activities. Atmospheric dispersion models have been used extensively for both research and regulatory purposes. Previous research demonstrates the efficacy of dispersion models to estimate the risk of infection from bioaerosol exposure for residents near manure application sites . Given tMaps of ARG and bacterial concentrations will also be produced to provide examples of plume development in a given direction to demonstrate how concentrations are reduced with distance away from sources. The same concentration maps will also be used to evaluate dispersion from short-term emissions, for example, from manure spreading. Results from this aspect of this research project will contribute to the development of information to inform policy decisions regarding safe distances from ARG sources to prevent their transmission to humans and domestic animals.The roles of exposure pathways, bacterial relationships, co-selective pressures, ARG transmission, and the combined and cumulative effects of antimicrobial use need to be brought together in a One Health context. The One Health ethos posits that the interconnectedness of human, animal, and environmental activities are so great that they must be addressed together in an interdisciplinary manner to ensure health across systems and scales . IntegraTo date, the iAM.AMR framework has been populated with a significant body of information on AMR and antimicrobial use from the scientific literature and surveillance data. Model development has focused on specific antimicrobial/bacteria/pathway combinations related to the food chain, particularly the poultry, swine, and beef production chains . Our resGiven the interdisciplinary nature of this project, shared data collection and management practices must be implemented. The project will use a shared ARG panel  and metaAll researchers will upload their data to a secure, shared data portal. Data entry will conform to a standardized format to be accessible for all team members. This shared format will also permit the integration of different sampling campaigns into model design. These data can be selected or removed from models as needed by the lead statisticians without lengthy delays in contacting researchers for their data and reformatting them for specific uses.Presently, the spread of ARGs in bioaerosols is not understood. This research program has the power to change paradigms by comprehensively addressing ARG emissions, exposure, and risk models, to inform policy for antimicrobial management and address AMR in the context of the environment. Antibiotic use and association with bioaerosols and geographical distribution of ARG across landscapes will be important knowledge produced from this work. Proof-of-concept for developing surveillance networks in natural and man-made environments using new rapid detection systems will be presented. Modeling will allow activity-based estimations of distribution. In addition, studying the contribution of ARG sources in bioaerosols will better contextualize our understanding of the contributions of activities to ARGs. The potential routes for transmission of antimicrobial-resistant bacteria and AMR genes between animals and people are complex and include many environmental routes beyond direct contact and the food chain. Thus, all data generated in this project will be made available to inform and expand an integrated exposure model on AMR.The scientific data and models produced in this project will be accessible. This way, it is hoped our designs may be applicable to other countries and climates. The program addresses the complete ARG transport chain from sources, emissions, dispersal, and long-distance transport to models of exposure potential and risk in humans and animal models to elucidate the roles of bioaerosols in their transmission and detection of ARGs. These results have the potential for a far-reaching impact across Canada and internationally through collaborative team efforts, developing expertise and promoting the study of airborne ARGs to other anthropogenic and natural activities. Results will be important to policymakers, stakeholders, and communities to understand and develop strategies for mitigating ARG emissions. Ultimately, it is hoped that this project will serve as a blueprint for developing strategies to study bioaerosols and address AMR in the environment.


Nature Communications [Kidney transplantation is a life-saving procedure for patients with end-stage renal failure, but unfortunately, only a limited number of patients benefit from this procedure due to organ shortage. Moreover, even after transplantation, the graft can be lost due to antibody-mediated rejection (AMR). A recent study by Schmitz et al. in ications  providesAfter kidney transplantation, graft function can be impaired by various injurious processes. HLA incompatibility between the donor and the recipient contributes to the development of AMR. Events preceding organ removal from the donor, being living-related, living-unrelated, or deceased, and the length of ischemia followed by reperfusion can affect the immediate outcome . AdditioComplement activation in the graft may occur during ischemia, resulting in extensive endothelial cell damage. Prolonged ischemia also leads to complement C3 production by tubular cells, which correlates with postischemic graft failure in a mouse model . In addiOne of the most common causes of graft deterioration is AMR. AMR is associated with the presence of DSAs in the circulation of sensitized graft recipients . These aThe role of complement activation, specifically of C3, in AMR was highlighted in the study by Schmitz et al., in which a C3 inhibitory peptide, Cp40, was administered to HLA-sensitized primates to prevent AMR after kidney transplantation . SensitiThis is a very interesting proof-of-concept paper showing that C3 inhibition has a place in the treatment of AMR. The authors suggest that increasing the dose of Cp40 may be able to overcome the effects of IgM DSAs and prevent early rejection.CP40 is a peptidic C3 inhibitor of the compstatin family . CompstaOther complement blocking strategies are also currently available in clinical practice. Inhibition of the terminal complement pathway by the C5 antibody eculizumab has been used for the treatment of AMR in kidney transplantation with some success in patients with evidence for complement-dependent AMR ; howeverA clinical trial of C3 inhibition in AMR will show whether this treatment strategy provides new hope for patients with kidney graft rejection. The transplantation field has the unmet need to find the right complement inhibitor and the appropriate combination therapy to prevent rejection not only of kidney allografts but also of other organs and even xenografts, as we have recently witnessed the first pig-to-human heart transplantation.


It is manifested that the construction of CuOx/Cu1.5Mn1.5O4 nanocomposite can produce abundant surface CuOx species and a number of oxygen vacancies, resulting in substantially enhanced CO oxidation activity. The CO is completely converted to carbon dioxide (CO2) at 75 °C when CuOx/Cu1.5Mn1.5O4 nanocomposites were involved, which is higher than individual CuOx, MnOx, and Cu1.5Mn1.5O4. Density function theory (DFT) calculations suggest that CO and O2 are adsorbed on CuOx/Cu1.5Mn1.5O4 surface with relatively optimal adsorption energy, which is more beneficial for CO oxidation activity. This work presents an effective way to prepare heterogeneous metal oxides with promising application in catalysis.Carbon monoxide (CO) oxidation performance heavily depends on the surface-active species and the oxygen vacancies of nanocomposites. Herein, the CuO Transition metal oxide catalysts for eliminating carbon monoxide (CO) at lower temperatures have attracted enormous attention in the past decades for their inexpensive cost and wide applications in catalytic applications and environmental protection ,2,3. Man2 [2 [3O4 [2O3 [3O4-CeO2−x [3O4-CeO2 nanocomposite, which showed good catalytic activity due to its special hollow multishell structure and the interaction between the two components. Chen [x-CeO2 nanorods and explained the relationship between reduction treatment and catalytic activity, indicating that reduction treatment accelerates the generation of active sites. In our previous works, we fabricated a CuOx-CeO2 catalyst via the solid-state method [x on CeO2 surface, resulting in enhanced catalytic performance. Copper-manganese mixed oxide catalyst is a typical transition metal-based catalyst in the CO oxidation reaction, which is known for its high activity at high temperature and low cost [In the past few decades, many transition metal oxide catalysts have been developed, mainly including CeO2 ,21,22, M2 [2 ,24,25, C [2 [3O4 ,27,28, C [2 [3O4 ,30,31, F3O4 [2O3 ,33,34 et4-CeO2−x ,35, Cu-M4-CeO2−x , Ce-Cu [4-CeO2−x ,38, and 4-CeO2−x ,40 compo4-CeO2−x  synthesits. Chen  construclow cost ,42. Howelow cost ,44. The low cost ,46, havelow cost ,48. Solilow cost ,51,52,53x/Cu1.5Mn1.5O4, which was implemented by the straightforward grinding of copper salt, manganese salt, and potassium hydroxide at ambient conditions. The metal oxide catalysts fabricated by solid-state synthesis are considered a simple and economical approach because they are without complicated procedures and organic solvents. The as-prepared CuOx/Cu1.5Mn1.5O4 exhibit significant advantages compared to other methods. The catalytic performance was obviously promoted, which can be attributed to the surface CuOx species and the number of oxygen vacancies. More importantly, this work presents us with an effective way to prepare heterogeneous metal oxides with outstanding catalytic performance.Herein, a solid-state synthesis was developed to fabricate the CuOx/Cu1.5Mn1.5O4 is schematically illustrated in x/Cu1.5Mn1.5O4 can be efficiently synthesized by the solvent-free strategy. The corresponding X-ray powder diffraction (XRD) patterns of CuOx/Cu1.5Mn1.5O4 are exhibited in 1.5Mn1.5O4 spinel solid solution (JCPDS No:70-0262). The diffraction peak at 38.92 degrees attributes to the isolated CuO phases. These results demonstrate the successful synthesis of CuOx/Cu1.5Mn1.5O4 nanocomposites by solid-state strategy. As shown in x, as shown in 2+/Cu+ in Cu2O/CuO can be controlled by adjusting the types of copper salts. The energy-dispersive X-ray spectrum (EDS) mapping analyses was implemented to identify the elementary composition of the CuOx/Cu1.5Mn1.5O4. The Cu/Mn molar ratio in the as-prepared samples is close to 1.0  image  and transmission electron microscopes (TEM) images as shown in M) image i, corresacquired , displayx/Cu1.5Mn1.5O4 were firstly evaluated. As shown in x/Cu1.5Mn1.5O4 with Cu/Mn molar ratio of 1:1 calcined at 400 °C. The CuOx/Cu1.5Mn1.5O4 can completely convert CO to CO2 at 75 °C, especially at low temperatures. T50 (the temperature of 50% of CO conversion) is only 41 °C. The CO2 yield in the CO oxidation has been shown in 100 (the temperature of 100% of CO conversion) and T50 (the temperature of 50% of CO conversion) in 2O-CuO, and MnOx samples at 140 °C, 130 °C, and 200 °C, respectively. The individual CuOx and MnOx particles show poorer performance than CuOx/Cu1.5Mn1.5O4, implying that the synergistic effect between CuOx and MnOx may promote its catalytic activity that is not presented in the individual components. As shown in x + MnOx was also prepared, which exhibits the poor catalytic performance for CO oxidation. The stability of the CuOx/Cu1.5Mn1.5O4 was also tested at 60 °C. The negligible decline of activity can be observed during 30 h testing from x/Cu1.5Mn1.5O4 for CO oxidation reaction.The CO catalytic performance of the as-obtained CuO+ or Cu° due to the fact that their binding energies are basically the same [x/Cu1.5Mn1.5O4 nanocomposites were acquired after being calcined at high temperature in air. Therefore, the peak is assigned to Cu+ because of the successful synthesis of CuOx/Cu1.5Mn1.5O4 nanocomposites. The two main peaks have small shoulder peaks that appeared at 933.3 and 953.4 eV, relating to the Cu2+ [+ and Cu2+ coexist on the surfaces of the CuOx/Cu1.5Mn1.5O4. As shown in x catalysts could be fitted into four components based on their binding energies. The binding energies of 640.2 eV, 641.2 eV, 642.5 eV, and 646.0 eV correspond to Mn2+, Mn3+, Mn4+ species, and the satellite peak, respectively [α), surface adsorbed oxygen (Oβ), and adsorbed molecular water species (Oγ), respectively [x/Cu1.5Mn1.5O4 demonstrates the highest surface adsorbed oxygen, which is beneficial to the adsorption of O2 molecules, and thus help to improve catalytic performance.The X-ray photoelectron spectra (XPS) were used to investigate the chemical states of samples. The XPS spectrum in the same ,54. Cu° the Cu2+ ,56,57,58ectively . The O 1ectively ,60,61,622-TPR). A peak at 200 °C–400 °C was presented for an individual CuO sample, attributing to the gradual reduction of copper oxide [2 reduction peaks at 245 °C and 364 °C occurred for an MnOx sample, which correspond to the gradual reduction of MnO2 → Mn3O4 → MnO [x/Cu1.5Mn1.5O4 nanocomposite, the first peak at below 158 °C refers to the reduction of fine CuO to Cu or MnO2 to Mn3O4. Other peaks at 174 °C and 201 °C correspond to the gradual reduction of Cu1.5Mn1.5O4 oxides [x/Cu1.5Mn1.5O4 compared with individual CuOx and MnOx indicates the strong synergistic effect between CuOx and MnOx. The strong interactions in CuOx/Cu1.5Mn1.5O4 are often related to the abundant oxygen vacancies, which can promote catalytic performance. The oxygen storage capacity (OSC) of catalysts was assessed by oxygen temperature-programmed desorption (O2-TPD). In β) [α). The high-temperature zone at above 550 °C is related to the bulk lattice oxygen species [x/Cu1.5Mn1.5O4 shows the highest amount of adsorbed oxygen species compared with other samples, confirming the higher oxygen capacity that is conducive to the promotion of catalytic performance.As shown in er oxide . In addiO4 → MnO ,39,59. F4 oxides . The low species . The CuOx species and the oxygen vacancies in CuOx/Cu1.5Mn1.5O4 in detail, the Cu1.5Mn1.5O4 was fabricated by changing the types of copper salt in the synthesis process to investigate the structure-activity relationships. The Cu2+ and Mn4+ proportion was evaluated by XPS  in the synthetic process. Therefore, the different surface types in Cu1.5Mn1.5O4 depend on the types of copper salts  in the synthetic process used. The higher content of CuO in CuOx/Cu1.5Mn1.5O4 can significantly enhance redox reaction between Cu and Mn species, promoting charge transfer in nanocomposites, and thus achieving a stronger interaction. From x/Cu1.5Mn1.5O4 shows a lower reduction temperature than Cu1.5Mn1.5O4, implying the better reducibility. The peak areas for different samples were estimated from the H2-TPR results. In x/Cu1.5Mn1.5O4, indicating the ratio of Cu2+ and Mn4+ in CuOx/Cu1.5Mn1.5O4, which is consistent with XPS results. Therefore, we can confirm that CO oxidation activity is heavily dependent on the surface CuOx species in CuOx/Cu1.5Mn1.5O4. In addition, as shown in β) over the obtained CuOx/Cu1.5Mn1.5O4 are greatly changed by adjusting the surface CuOx species. The CuOx/Cu1.5Mn1.5O4 shows the highest amount of adsorbed oxygen species compared with Cu1.5Mn1.5O4, confirming the higher oxygen capacity that is conducive to the promotion of catalytic performance. The H2-TPR analysis results indicate that the interaction of nanocomposites could be manipulated by changing the surface compositions of CuOx/Cu1.5Mn1.5O4. The O2-TPD results further identify the presence of abundant surface-adsorbed oxygen on the surface of CuOx/Cu1.5Mn1.5O4. Therefore, the construction of CuOx/Cu1.5Mn1.5O4 with abundant surface CuOx species not only strengthens the interactions in CuOx/Cu1.5Mn1.5O4, but also facilitates the absorption and activation of surface oxygen species.As shown in e to 1.0 , which home CuOx b, while ous work , the ind2 adsorption and the subsequent oxidation process for CuOx/Cu1.5Mn1.5O4. The adsorption configurations of CO and O2 molecules on the CuO, Cu1.5Mn1.5O4, and CuOx/Cu1.5Mn1.5O4 are shown in 1.5Mn1.5O4 with the adsorption energy of −0.316 eV and −0.164 eV, respectively. The adsorption energy of CO molecules adsorbed at the CuOx/Cu1.5Mn1.5O4 surface is −1.346 eV, which are lower than that on CuO and Cu1.5Mn1.5O4. In addition, the adsorption energy of O2 molecules on CuOx/Cu1.5Mn1.5O4 surface is −1.018 eV, which is much lower than the 0.026 eV and −0.962 eV for CuO and Cu1.5Mn1.5O4. The lower adsorption energy indicates that gas molecules are easier to adsorb on the surface of CuOx/Cu1.5Mn1.5O4. In summary, DFT calculation showed that construction of CuOx/Cu1.5Mn1.5O4 nanocomposite with abundant surface CuOx species and oxygen vacancies significantly improved the adsorption capacity of CO and O2 molecules, and thus is more beneficial for CO oxidation activity.Density functional theory (DFT) calculations were implemented to understand the intrinsic reason about the mechanism of CO and Ox species and the oxygen vacancies in composite oxide can be clarified based on the above results. In x/Cu1.5Mn1.5O4 is improved compared to Cu1.5Mn1.5O4, which confirms the important role of surface CuOx species and oxygen vacancies. After construction of CuOx/Cu1.5Mn1.5O4 nanocomposite with abundant surface CuOx species and oxygen vacancies, the abundant Cu2+ and Mn4+ proportions in CuOx/Cu1.5Mn1.5O4 are higher than in Cu1.5Mn1.5O4, which facilitated the formation of more (Cu2+-O2−-Mn4+) entities at the two interfaces. In addition, the construction of CuOx/Cu1.5Mn1.5O4 nanocomposites is beneficial for enhancing the synergetic interaction between MnOx species and CuOx species, which promotes the massive production of surface adsorbed oxygen species [x species and oxygen vacancies play significant roles in catalytic activity. The abundant surface CuOx species and oxygen vacancies could preferentially adsorb CO and O2 molecules [2 reacts with CO to form CO2, which ultimately enhances catalytic activity.The effects of surface CuO species . In CO oolecules , and the2, AR), cuprous (I) chloride , manganese (II) chloride , and potassium hydroxide  were purchased from Tianjin Zhiyuan Chemical Reagents Co., Ltd. , which were used without further refinement.Copper (II) chloride (CuCl2 (10 mmol) and 1.25 g of MnCl2 (10 mmol) were mixed well in an agate mortar by grinding. Then 4.49 g of KOH (60 mmol) was added into the agate mortar. After continuous grinding for about 1 h, the resulting solid products were sufficiently washed with deionized water and anhydrous ethanol to clear the residual Cl or K species, and then dried at ambient temperature overnight. The final CuOx/Cu1.5Mn1.5O4 nanocomposites were acquired after calcining the mixtures in the air at 400 °C for 2 h (5 °C/min). In addition, the CuOx/Cu1.5Mn1.5O4 with different Cu/Mn mole ratios (Cu/Mn = 1:2 and 2:1) were calcined at 300 °C or 500 °C.As shown in 1.5Mn1.5O4 nanocomposite (containing some CuO) was also obtained, and only the CuCl2 was replaced by CuCl during the solvent-free synthesis route.The Cux particles were also fabricated by straightforward grinding MnCl2 with KOH under a similar process.As a comparison, the individual MnO2O/CuO nanocomposite was fabricated according to our previous work [x/Cu1.5Mn1.5O4 nanocomposite above.The Cuous work . The 0.92 and KOH in the agate mortar. The sample of physical mixing of CuOx + MnOx was also prepared by straightforward grinding CuO and MnOx, and then calcining the mixtures in the air at 400 °C for 2 h (5 °C/min).In addition, the CuO was also prepared by mixing CuCl2-TPR, and O2-TPD were implemented to investigate the morphology and structure of CuOx/Cu1.5Mn1.5O4 nanocomposites. Detailed characterization and testing processes are presented in the XRD, SEM, HRTEM, EDS, XPS, H1.5Mn1.5O4 with different surface CuOx types were fabricated by altering the types of copper salts  in the synthetic process. The higher content of CuO in CuOx/Cu1.5Mn1.5O4 can significantly enhance redox reaction between Cu and Mn species, promoting charge transfer in nanocomposites, thus achieving a stronger interaction. In addition, the higher ratio of Cu2+ and Mn4+ is beneficial to the formation of Cu2+-O2−-Mn4+ entities at the two-phase interface, which produced abundant surface CuOx species and oxygen vacancies. DFT calculations suggest that CO and O2 molecules are adsorbed on the CuOx/Cu1.5Mn1.5O4 surface with relatively optimal adsorption energy, resulting in the highest CO oxidation activity. The as-synthesized CuOx/Cu1.5Mn1.5O4 delivers excellent CO catalytic performance compared with individual CuOx and MnOx particles. The CO is completely converted to CO2 at 75 °C when CuOx/Cu1.5Mn1.5O4 is involved. This work opens new avenues for the efficient and sustainable production of heterogeneous metal oxides with an outstanding catalytic performance.In summary, the Cu


Currently, there is a lot of public interest in naturally occurring substances with medicinal properties that are minimally toxic, readily available and have an impact on health. Over the past decade, molecular hydrogen has gained the attention of both preclinical and clinical researchers. The death of pyramidal neurons in especially the CA1 area of the hippocampus, increased permeability of the blood-brain barrier, neuroinflammation, amyloid accumulation, tau protein dysfunction, brain atrophy, cognitive deficits and dementia are considered an integral part of the phenomena occurring during brain neurodegeneration after ischemia. This review focuses on assessing the current state of knowledge about the neuroprotective effects of molecular hydrogen following ischemic brain injury. Recent studies in animal models of focal or global cerebral ischemia and cerebral ischemia in humans suggest that hydrogen has pleiotropic neuroprotective properties. One potential mechanism explaining some of the general health benefits of using hydrogen is that it may prevent aging-related changes in cellular proteins such as amyloid and tau protein. We also present evidence that, following ischemia, hydrogen improves cognitive and neurological deficits and prevents or delays the onset of neurodegenerative changes in the brain. The available evidence suggests that molecular hydrogen has neuroprotective properties and may be a new therapeutic agent in the treatment of neurodegenerative diseases such as neurodegeneration following cerebral ischemia with progressive dementia. We also present the experimental and clinical evidence for the efficacy and safety of hydrogen use after cerebral ischemia. The therapeutic benefits of gas therapy open up new promising directions in breaking the translational barrier in the treatment of ischemic stroke. Molecular hydrogen is an odorless, colorless gas that is physiologically inert. Hydrogen is the lightest and most abundant element in the Earth’s atmosphere. Furthermore, hydrogen is a biological gas that is produced in mammals by intestinal bacteria ,2,3. It Recently, it has been proposed to prevent and treat coronavirus disease by inhaling oxygen mixed with hydrogen (33.4% oxygen and 66.6% hydrogen) due to the important role of hydrogen in alleviating the worsening of lung function, emphysema, and acute or chronic inflammation ,35,36. TPublished scientific papers on the use of molecular hydrogen have been screened for in vitro, in vivo, animal and clinical investigations and side effects. Searches were performed digitally using databases including PubMed, MEDLINE, SCOPUS, Google Scholar and Science Direct to identify peer-reviewed original articles and reviews over the past twenty years (1 January 2001–31 December 2021). The search strategy was carried out using the following key words: “Molecular hydrogen therapy and brain ischemia”, “brain ischemia and molecular hydrogen therapy”, “molecular hydrogen therapy and ischemic stroke”, “ischemic stroke and molecular hydrogen therapy”, “molecular hydrogen therapy and cardiac arrest”, “cardiac arrest and molecular hydrogen therapy”, “molecular hydrogen neuroprotection and brain ischemia”, “molecular hydrogen neuroprotection and ischemic stroke”, “ischemic stroke and molecular hydrogen neuroprotection”, “molecular hydrogen therapy and amyloid”, “molecular hydrogen therapy and tau protein”, “tau protein and molecular hydrogen therapy“, “molecular hydrogen and bioavailability”, “molecular hydrogen and side effects”. The work covered by the study had to be related to the search terms and be as up-to-date as possible. The excluded works did not include hydrogen compounds, including hydrogen sulfide and numerous works by one author or from the same laboratory, in this case the latest works were taken. A total of 575 original papers and reviews were found, and 109 publications closely related to the subject of the review were used.It is now known that neurodegeneration following cerebral ischemia is caused by numerous proteomic and genomic changes that lead to neuronal death by necrosis and apoptosis, with progressive brain neuroinflammation and atrophy, ultimately leading to full-blown dementia ,47,48,49Restoration of blood flow in the brain following cerebral ischemia and ischemic stroke triggers an outbreak of reactive oxygen species, triggering a neuroinflammatory response and oxidative damage ,44,48,652+ caused by glutamate toxicity [In mice with focal cerebral ischemia with reperfusion, molecular hydrogen significantly increases SOD and GSH-Px activity, reduces malondialdehyde levels and infarct volume, relieves cerebral edema, improves neurological outcomes and alleviates cognitive deficits  19,68,6,668,69. toxicity .Subsequent studies found that rats that underwent global cerebral ischemia and were treated with hydrogen-rich saline had milder neuronal injury and a limited number of irreversibly damaged neurons in the brain . ExpressIn behavioral studies of rats after focal cerebral ischemia, administration of lactulose, which induces endogenous hydrogen production in the intestine, resulted in higher neurological scores and shorter escape latency in the Morris test . MorpholStudies that assessed the most effective timing of hydrogen administration after local cerebral ischemia in rats showed a post-ischemic time interval of up to 6 h during which significant reductions in infarct volume and brain edema were observed, and neurological outcomes improved . At thatOne study used hydrogen-saturated saline for focal cerebral ischemia in rats to test whether hydrogen-saturated saline reduces apoptosis of neuronal cells through the p38 MAPK-caspase-3 signaling pathway . The obtStudies in patients with acute focal cerebral ischemia have shown that administration of molecular hydrogen by inhalation or intravenous infusion is safe, hydrogen has been found in the blood, and there was no effect of hydrogen on physiological parameters ,25,89. PIt is now well known that ischemic neurodegeneration of the brain is caused by a set of proteomic and genetic changes that lead to the death of neuronal cells in an amyloid and tau protein dependent manner ,59,63,64The amyloid protein precursor was found to be significantly lowered in the brain in a transgenic mouse model of Alzheimer’s disease after treatment with hydrogen-rich water . In thesIn the transgenic model of Alzheimer’s disease, intracerebral administration of Pd hydride nanoparticles (a highly charged hydrogen carrier) effectively removes hydroxyl radicals, reduces amyloid production and aggregation, alleviates mitochondrial dysfunction, reverses synaptic deficits and inhibits neuronal death . In vitrThe administration of hydrogen-rich water significantly inhibited the phosphorylation of the tau protein in Ser404 and Ser422 in the transgenic model of Alzheimer’s disease . This reCurrently, there are several methods of molecular hydrogen administration, i.e., hydrogen inhalation, infusion of hydrogen-rich saline, hydrogen-rich water provided by various types of molecular hydrogen donors/suppliers, as well as functional micro/nanomaterials that increase the concentration of hydrogen administered and thus the effectiveness of treatment ,104,105.Molecular hydrogen exerts a significant neuroprotective effect on almost all animal models of cerebral ischemia studied. The neuroprotective properties of hydrogen are often documented by assessing neuronal survival and improved neurocognitive function . In addiCurrently, ischemic stroke is one of the most important causes of morbidity and mortality worldwide. At present, tissue plasminogen activator is administered to induce thrombolysis and restore cerebral blood flow following focal brain ischemia, but therapeutic benefit is only achieved in a small percentage of patients qualified for fibrinolysis. Despite the evident preclinical confirmation of the neuroprotective hydrogen treatment of local post-ischemic brain injury, it has not been successfully translated into clinical use. So, the treatment that will please most acute stroke patients remains a mystery. Currently, despite the lack of effective treatment of the consequences of ischemic stroke, research is being carried out on new methods of treating this disease, however, the use of medicinal gases, especially hydrogen, is not very popular. Nevertheless, it should be stated that gases such as normobaric and hyperbaric oxygen and hydrogen exert medical effects in preclinical studies of cerebral ischemia. There are significant advantages in using gases in terms of low cost, high quantity and ease of administration, all of these properties making them ideal candidates for the translational treatment of ischemic stroke. In conclusion, the influence of cellular gaseous mediators such as carbon monoxide, nitric oxide and hydrogen could be an interesting alternative for treating ischemic stroke ,2,3. BreThe evidence introduced in this review shows a promising neuroprotective effect of hydrogen after cerebral ischemia with recirculation . However