CDC ZOHU Call September 2, 2026

Hi, everyone. Thanks for joining. Let's give some people time to join. Hi, everyone. Just giving it another moment before we start. Good afternoon. I'm Keisha Bohannon from CDC's One Health Office in the National Center for Emerging and Zoonotic Infectious Diseases. On behalf of our office, welcome to the September 2026 Zoonoses and One Health Updates Call. Next slide, please. This webinar is designed for veterinarians, physicians, epidemiologists, and other public and animal health professionals in federal, state, and local positions, but all are welcome. Today's webinar will be recorded. If you'd rather not be included, feel free to disconnect. Links to resources from each presentation are available on our website at cdc.gov slash Sohu dash call slash webinars slash September dash 26 dot html. Next slide, please. Today's presentations will address the following five objectives. One, identify at least one impact the One Health issue has on human, animal, or environmental health. Two, identify at least one One Health approach strategy to help prevent a public health threat. Three, identify at least one One Health approach strategy to help detect a public health threat. Four, identify at least one One Health approach strategy to help respond to or prepare for a public health threat. Five, list two ways to improve collaborative practice across the public health care team. Next slide, please. In compliance with continuing education requirements, all planners and presenters must disclose all financial relationships in any amount with ineligible companies over the previous 24 months, as well as any use of unlabeled products or products under investigational use. CDC, our planners and presenters, wish to disclose they have no financial relationships with ineligible companies whose primary business is producing, marketing, selling, reselling, or distributing health care products used by or on patients. Content will not include any discussion of the unlabeled use of a product or a product under investigational use. CDC did not accept commercial or in-kind support for this activity. Next slide, please. Instructions for receiving free continuing education are available at cdc.gov slash ZOHU dash call slash about slash ce dot html. To receive free CE for today's webcast, sign in or create an account at train.org by October 5th, 2026. The registration code for this course on CDC Train is ZOHU webcast. A captioned video of today's webinar will be posted on the September call page within 30 days. Free CE for the web on-demand video of today's webinar will be available until October 6th, 2029. Next slide, please. Before we begin today's presentations, CDC's One Health Office Veterinary Medical Officer, Dr. Natalie Wendling, will share some news and updates. Thank you, Keisha. Good afternoon, everyone, and thank you for joining the September ZOHU call today. As Keisha mentioned, we'd like to share some resources and updates. You can find links to everything we highlight in today's ZOHU call email newsletter. If you aren't subscribed to the newsletter, you can quickly sign up using the link on our main ZOHU call webpage. CDC is working closely with the U.S. Department of Agriculture, the Department of the Interior, and many other governmental and non-governmental partners to respond to an outbreak of New World Screwworm in Central America, Mexico, and the United States. New World Screwworm can affect livestock, companion animals, wildlife, and people. Updated information is available on USDA and CDC websites, as well as on the whole-of-government website, screwworm.gov. Next slide, please. In today's newsletter, we highlight several recent publications, including Metagenomics Surveillance of Tick-Borne Pathogens and Microbiomes in Huntington County, Pennsylvania, using participatory epidemiology to understand differences in tick-borne disease perceptions from homeowners in the Ohio River Valley, and the effect of diagnostic test type on detection of salmonella infections. Next slide, please. The newsletter also highlights recent announcements and resources, including a page recognizing a decade of CDC's Antimicrobial Resistance Solutions Initiative, with highlights from a recent AMR exchange where panelists discussed how the initiative has helped build national infrastructure for antimicrobial resistance detection and rapid response in healthcare and communities, a Malaria 101 training for healthcare providers, a toxicologic video series that provides healthcare professionals with clinical information about environmental hazards and how to address patient exposures, and additional webpages and announcements that are listed here. Next slide, please. Highlighted events and observances for the month of September include National Preparedness Month, Fungal Diseases Awareness Week, September 14th to the 18th, World Rabies Day on September 28th, and the continuation of the four-part Bridging Public Health and Beef Production webinar series, hosted by the National Institute for Animal Agriculture Antibiotics Council. This One Health webinar series is dedicated to strengthening dialogue between public health leaders in the U.S. beef industry. The next webinar is scheduled at 3 p.m. today, following the ZOHU call, and will focus on public health and the feed yard sector. Next slide, please. Finally, we also share updates on ongoing outbreak investigations, including a salmonella outbreak linked to jalapeños, a salmonella outbreak linked to turtles, a salmonella outbreak linked to shell eggs, and other active investigations for salmonella, cyclospora, and E. coli outbreaks that are listed here. The outbreak pages also include tips to help you stay safe. You can visit CDC's Healthy Pets, Healthy People website for a list of ongoing past U.S. outbreaks of zoonotic diseases, and today's newsletter also includes information about additional ongoing U.S. outbreaks. Please continue to send suggestions for future presenters and topics, as well as news from your organization, to ZOHUCallatCDC.gov. We encourage you to share ZOHUCall website with colleagues in public health, agriculture, wildlife, plant health, environmental health, and other fields and sectors. This website provides information about upcoming live webinars, recordings of past webinars, and free continuing education offered through the ZOHUCall. Next slide, and back over to you, Keisha. Thank you. Thank you, Natalie. You may submit questions at any time using Teams Q&A feature. Please include the topic or presenter's name. The Q&A session will follow the final presentation, if time permits. You may also email questions to today's presenters. We've included their email addresses on the slide and in today's email newsletter. Next slide, please. Our first presentation, You Can't Manage What You Don't Measure, Utilization of CasNet, a national electronic database to measure antimicrobial prescribing in companion animals, will be shared by Dr. Jennifer Granik. Please begin when you're ready. Thank you. Next slide. So, we care about measuring antibiotic use in dogs and cats because we care about antibiotic resistance. We know that antibiotics are critical for human and animal health, but their judicious use is required to slow emergence of antibiotic resistance and maintain the efficacy of these drugs. Next slide. To this end, the American Veterinary Medical Association has created five core principles of antimicrobial stewardship, including number four, evaluate antimicrobial drug use practices. And this includes measurement at both the veterinary practice and aggregated levels, providing feedback of this data back to veterinarians and preserving the veterinarian client confidentiality while we're doing all of this. Next slide. So, why track antibiotic use in dogs and cats? Certainly, dogs and cats are part of the antimicrobial resistance story. And as veterinarians, we can't manage what we don't measure, meaning that we can't improve our prescribing unless we know what we're doing. So, we collect this data to establish baseline measures, to identify and guide goal setting, and then to measure progress over time towards those goals. So, what can we measure? We can measure all antimicrobial use in dogs and cats. We can focus in on select antimicrobials, like second or third line agents, or we can focus on specific conditions for which antimicrobial prescribing is common. Next slide. So, at the University of Minnesota, we've created the Companion Animal Veterinary Surveillance Network, or CAVSNet, with a goal of collating veterinary medical practice data from across the U.S. into a common data model for research and analysis to improve pet health. One of our specific goals is to describe treatment practices and outcomes. Next slide. So, how do we do this? So, we have been lucky enough to identify data providers that share our values and goals of improving animal health. We have striven to establish and maintain relationships with our data providers to create a balance between data provider concerns and then our research objectives. So, we've created data use agreements in line with antitrust guidelines and built trust with our data providers and negotiated these data use agreements. So, in order to do that, we have to be transparent about what data we need to achieve our goals and what that data will be used for. We also need to ensure data security. So, we've established secure file transfer protocols and created a landing spot for this data in a secure computing environment at the University of Minnesota. We also allow our data providers to determine what data they feel comfortable sharing. So, in addition to data from structured fields, there's variability among our data providers about whether or not they share all free text fields, no free text fields, or some free text fields, because those are fields that may include some personal identifying information. We also clarify very clearly what the permitted use of the data in our data use agreements. So, for our purposes, that's research and not commercialization of that data. And then if we engage with external research partners, the data use agreements that we have between them and us need to be beholden to our original data use agreements with our data providers. Next slide. So, CAVSNAT receives data from three external data providers, which represents about 1,200 U.S. veterinary clinics that utilize 28 different electronic health record systems. There's no standardization among these electronic health record systems, or EHRs, or between veterinary clinics, so that means that our data is really messy and highly variable. Next slide. So, that makes our challenge standardizing this data to make it analyzable. And the way that we do that is by utilization of OMOP, or the Observational Medical Outcomes Partnership, as our common data model. We, within OMOP, we utilize standard vocabulary, so all of our data, our source data, gets mapped to standardized vocabularies and standardized codes. The vocabularies that we use are SNOMED and SNOMED Veterinary Extension, which are used primarily for transforming our source values to standardized diagnostic and procedure concepts. We also use RxNORM and RxNORM Extension for prescriptions and medications. Because the ODYSSEY OMOP common data model was developed for use with human health records, there are a group of folks utilizing OMOP for veterinary records that have come together in the Electronic Animal Health Records Working Group, so that we can strategize how we apply OMOP to veterinary records. Next slide. So, this is the CAVSNET common data model. So, we receive data, as I said, from three external data providers. Veterinarians just enter their data into the medical record, and these external data providers collate that data and provide it to us. Specific data that we ask for includes signalment, diagnoses, procedures, diagnostics, vaccinations, prescriptions, and then, in some cases, free text data, for example, doctor's notes. That data is securely transferred to the University of Minnesota and stored in a HIPAA-compliant database at the University of Minnesota. And then, it's our task to take all of that data and standardize it. As an example, a real simple example, clinics may call a dog a dog, or a canine, or a capital K, number nine, and all of that needs to mean domestic dog within our common data model. So, we map it to the SNOMED concept code that means domestic dog. So, that way, when we're searching for a cohort of dogs, we can look only for one concept instead of various concepts. After we have mapped all of our source data to standard vocabularies, then we are ready to aggregate that data and do some analysis. Next slide. So, of course, there are many challenges, and we've come up with some solutions for working with this data. There is no standard medical coding in veterinary medicine that's not required by insurance companies or any federal agency. So, as an example, cefavacin is an injectable antibiotic. It comes in one formulation, but across our 1,200 clinics that is written in over 1,100 different ways in our source data. So, we need to find all of those different ways and map them to one single concept code. There's also variable use of structured fields within the electronic health records. So, sometimes diagnoses, structured fields are utilized, prescription fields are utilized, and sometimes they're not. So, we can use transaction data to fill some of those gaps, but a caveat of using transaction data is that it can overestimate the number of patients seen at a clinic. For example, there might be a transaction that is just associated with an owner picking up a bag of dog food or bringing in a dog for a nail trim, and there's no opportunity in those transactions for a patient to encounter a veterinarian and potentially have an antibiotic prescription. So, we need to come up with a reliable denominator so that we can evaluate our data in terms of prevalence of prescribing or prevalence of disease. So, one solution is to find proxies for veterinary visits. So, we've done that by looking through our data to find notations of exams, surgeries, anesthesia, dental procedures, hospitalization, all things that require a veterinarian to be attending. Another challenge is the cost of maintaining a large database. So, opportunities for solutions to that, for that is to identify partnerships for exploration of shared research questions with industry. And then another big and final challenge is that data science expertise is needed to create, maintain, and utilize this database. One thing that could make it easier for us to utilize electronic health record data and companion animal practice in the future is to encourage adoption of standard medical coding by both electronic health record companies and veterinarians and practices. Next slide. So, we have been able to identify some actionable antimicrobial stewardship insights already from the data that we've analyzed. Next slide. So, when we look at national prescribing of antibiotics for both dogs and cats, we can see that third-generation cephalosporins are the second most prescribed drugs for both dogs and cats, and there's an opportunity for education of veterinarians to take a tiered prescribing approach and utilize first-line agents ahead of these third-generation cephalosporins in their practice. Next slide. We can also look at this data over time to see trends in the data. Between 2021 and 2024, we've seen a decrease in metronidazole prescribing in dogs, and this is likely related to the publication of guidelines for prescribing for canine acute diarrhea and an increased awareness and education about the lack of need for use of antimicrobials for most cases of canine acute diarrhea. At the same time, we have seen an increase in cephavesin prescribing, which is an injectable third-generation cephalosporin among cats in the United States between 2021 and 2024. So, again, there's an opportunity for educational interventions to find alternatives to this prescribing. Next slide. We can also use this data to look at national trends for specific conditions. So, for example, canine lower urinary tract infections, prescribing guidelines recommend the use of either amoxicillin or trimethoprim sulfas as first-line agents for canine UTIs. But we can see from our data that there's very little use of trimethoprim sulfas. Additionally, there's very little use of urine cultures to guide antimicrobial prescribing for UTIs. So, in terms of antimicrobial stewardship interventions, we can educate veterinarians about the true risk of trimethoprim sulfa side effects in dogs. We can increase awareness of guidelines, and we can understand barriers to utilization of urine cultures in this setting. Next slide. We've also looked at canine bacterial pneumonia and prescribing for that condition. About a third of these cases are prescribed a fluoroquinolone. And there, again, is very little utilization of antimicrobial susceptibility testing. Culture and susceptibility testing would allow veterinarians to de-escalate antimicrobials that are used for bacterial pneumonia and provide better targeting of drugs for this condition. So, education to make veterinarians more comfortable with the procedure of airway cultures is another opportunity for antimicrobial stewardship intervention. Next slide. We can also use this data at an individual hospital level. So, we've done that at our University of Minnesota Veterinary Medical Center using CAVSNet data from our own hospital to try to intervene in terms of metronidazole use for canine acute diarrhea. In 2018, this drug made up over 20% of all prescriptions in the hospital. But through various educational interventions and providing alternative treatments available in the pharmacy, in the after-hours pharmacy, we've been able to decrease metronidazole prescriptions to about 1% of all drugs used in the hospital. Next slide. So, in summary, CAVSNet has demonstrated feasibility of collection of patient-level electronic health and treatment data from primary care practices, the creation of a nationally representative data set, and the development of public-private partnerships related to companion animal health data. But we certainly do have challenges that remain, including technical challenges to reduce the friction between collecting the data and being able to utilize it, as well as the challenge of sustainable and sufficient funding. Next slide. So, thanks for allowing us to come and present. And if you have questions, please check out our website or email us directly. Thank you. Thanks so much, Dr. Granick. Our next presentation, a multicenter iatrogenic outbreak of purpurosilium septic arthritis in horses from contaminated injected pharmaceuticals. This will be shared by Mr. Douglas Pluta. Please begin when you're ready. Thanks, Keisha, and thanks so much for having me. So, just for the non-horse people in the call, it's pretty standard for high-performance horses to be given hyaluronic acid injections in the joints to restore joint lubrication, prevent osteoarthritis, and to reduce inflammation during training and competition. Most of the time, these injections are given in high-motion joints during training and competition, it's not abnormal for minor post-joint injection flares to occur, usually presenting with minor lameness and inflammation and swelling. But true septic arthritis and synovitis is typically rare. Next slide, please. Over the past about year, we've seen an outbreak of purpurosilium septic arthritis in a number of horses. At a glance, we've seen 127 known cases of this septic arthritis with 23 confirmed by culture or other molecular methods, 104 of the 127 cases with a known exposure to the affected lot of medication with compatible disease. Forty-one horses have been euthanized secondary to the septic arthritis, 36 days average from joint injection to report of symptoms to the veterinarian. A case definition was created of being exposed to the affected lot of the product with compatible signs of new or worsening synovitis or septic arthritis with or without a confirmation of purpurosilium because a lot of the veterinarians were either not submitting to a diagnostic lab for confirmation or they just did not know that this was potentially an outbreak. As you can see on the bottom, a lot of the cases that we were seeing were happening either in Puerto Rico or Florida with a number of cases happening in Arizona, New Jersey, New York, and Pennsylvania. One of the limitations that we've run into so far is just simply communication of this outbreak. So there are a number of cases likely out there in the rest of the country that we just haven't been able to collect. Additionally, within these states that we know of that, again, just haven't been able to get to us. So these numbers are preliminary at this point, but these are the numbers that we know as of right this minute. Next slide. The timeline of events, October of 2025 was the first time that veterinarians identified adverse events related to these joint injections. Not necessarily infections, simply severe joint flares related to the injections. And in December of 2025, individual clinicians had gotten together at a major equine industry conference and kind of identified that there were adverse events related to the injections at multiple different practices. So a pattern was established, and a number of cases in New York were identified related to the injections of purpuricillium isolated from these horses. And in January of 2026, a consortium was created of academic researchers, diagnosticians, including microbiologists and pathologists, as well as private practice clinicians. And purpuricillium fungus was isolated from additional horses, as well as from the product vials themselves. The product was recalled in late January 26. And as you can see on the right, we can see in the blue that is the dates when horses were injected, and in the orange is when we identified flares, according to medical records provided to us by veterinarians. So we can see a pretty clear staggering of when horses were injected and then when flares were reported. We still did have a couple of horses injected after the recalls were put out, but we can see, again, a pretty clear staggering of when horses were injected and then an average of 36 days after injection to flare. Next slide, please. Clinical presentation, we saw a pretty severe degradation and destruction of periarticular structures, leading to more severe lameness than what would be expected after a typical joint injection. So you can see the interstesmoidean ligament in this specific horse completely destroyed, leading to, in this specific case, the horse being completely unable to walk or perform, which is what led to many of the euthanasias in these cases in terms of horses being unable to perform their normal duties in terms of racing or riding or working on the farm. We also saw a lot of non-degenerate neutrophilic inflammation, which is atypical for septic arthritis presentation in horses. A lot of times, septic arthritis in horses is bacterial, which would be accompanied by degenerate neutrophilic inflammation, which was, again, atypical. We also saw a lot of fibrinous exudate. So on gross pathology, we saw large, essentially chunks of fibrin in the joint space. And as you can see on the histopathology, we saw, again, large tears within the tissue, leading to poor outcomes for a lot of these horses. And, yeah, again, just really poor outcomes, severe lameness. Next slide. Microbiological findings in some of the vials that were affected, we saw macroscopic aggregates of what would be later discovered to be the fungus itself. Not every single vial in the lot exhibited this, but many of the vials that later grew the fungus that you can see in the picture on the bottom exhibited these findings. When we first received vials, we first did a calciflore white stain, which is a fluorescent stain that can identify chitin in the cell walls of fungus. And that is what we can see on the top right, and that fluoresces blue there. We confirmed that with a cytology of PAS and GMS, which is the middle, which is the PAS, and the bottom is the GMS, showing the fungal hyphae in the product itself, which is the first signal that showed us that there was fungi in the actual product. The colonies themselves grew a venacious tomov in color with radial zonation, which is consistent with purpuricillium. And these matched with the patient samples, as well as the vials themselves. Next slide. And we also can see the fungus growing out of tissue samples themselves. If we just plated the tissue onto microbiological agar, which shows that the fungus had invaded the tissue itself. It's hard to see on the picture, but you can see kind of a fuzzy outline on the tissue, which means it's growing directly up from the tissue. The microscopic findings are pretty consistent with purpuricillium, as well. But this was a challenge in terms of diagnosing this, because microscopically, many fungi tend to look pretty similar, as evidenced by some of the early samples that showed up to diagnostic labs were misidentified as acrimonium or penicillium, which became a challenge when trying to correlate findings between diagnostic labs. Because in terms of identifying an outbreak, it's difficult to call it an outbreak when different labs are identifying the same fungus as different species. Next slide. Purpuricillium itself is a naturally-occurring mold in nature. It is cosmopolitan distribution all over the place in the soil. It is primarily an entomopathogen infecting insects. It is FDA-approved in the U.S. as a bio-nematicide. It is used to kill nematodes in agricultural applications. The predominant species in soil is purpuricillium lilacinum, and that is typically the pathogen that we would run into in animals and humans. It causes hyalohyphomycosis and has been known to cause disease in soft tissues, the lungs, or eyes. And its identification is complicated by morphological similarities with Pacellomyces, Aspergillus, Penicillium, as well as others. And it is a relatively common laboratory contaminant, which, again, complicates identification, because if it comes up as an identified fungus in the laboratory, it is difficult to determine whether it is causing a disease or whether it's a contaminant. Next slide. We did whole-genome sequencing on a number of isolates because initial ITS sequencing, which is the typical barcode for fungal identification, only came up 96% similarity with published purpuricillium lilacinum sequences. So, whole-genome sequencing and average nucleotide identity showed a pretty consistent cluster among the outbreak-associated isolates. We compared it with publicly available sequences of other purpuricillium lilacinum isolates that also clustered separately. So, this big red square in the middle is all of our outbreak-associated isolates. And average nucleotide identity just tells us that these are similar genetically across the entire genome. This doesn't really tell us anything about their phylogeny or their ancestry or anything like that. This just tells us that across the entire genome, they are very similar. But it is helpful in the sense that they are all approximately the same genome. Next slide. In terms of phylogeny and ancestry, we did do a single-nucleotide polymorphism analysis. So, across all of these genomes, we looked at the actual sequence of nucleotides, and we analyzed how many nucleotides different each sequence was from another. So, within our outbreak group, the minimum pairwise distance from one to another, the closest distance was only four nucleotides. The median distance from one to another was 116 nucleotides. The mean distance was 138.5. And the most divergent pair within the outbreak group was only 443 nucleotides. Now, the closest neighbor outside of the outbreak group was 135,105 nucleotides, which means that we have a very clear separation between our outbreak group and the nearest non-outbreak purpuricillium isolate that we compared it to, indicating that our outbreak group forms a very distinct clade genetically and phylogenetically from any other purpuricillium isolate that we compared these to, indicating that these all almost certainly indicate a common source outbreak isolate group. These also indicate that our closest or our most divergent pair within the outbreak group are, excuse me, the difference between the most, the closest non-outbreak isolate and the most divergent within outbreak pair is 300 times more divergent. So, again, just very, very clear differences between the outbreak group and the non-outbreak group. So, again, just very clear differences. We have a very nicely tightly packed outbreak group indicating a single point source outbreak situation. Next slide, please. One of the challenges we've run into with this outbreak is that treatment has been extremely challenging for our veterinarians. We've run a number of antifungal susceptibility tests and a number of challenges have presented. So, a lot of the veterinarians we've worked with have initiated treatment with corticosteroids before they even knew this was a fungal issue, which is a challenge because corticosteroids are contraindicated in fungal infection, which has kind of created a cyclical recovery pattern in these horses where they'll recover and they'll be put back to work and then they'll go lame again, which has kind of set them back in treatment even once they are placed onto antifungal treatment. We can also see, based on our antifungal susceptibility testing, that isofuconazole and posaconazole both show very strong in vitro antifungal susceptibility, yet there is no pharmacokinetic or pharmacodynamic or safety data on those two drugs in horses, which means that they're not feasible options for our veterinarians to prescribe to our horses. Additionally, caspofungin has shown differing antifungal susceptibility data depending on which lab we got those tested at, which means it's become very difficult for us to interpret that data and very difficult for us to recommend to veterinarians whether we can prescribe that to our horses. Boriconazole became our first-line therapeutic option, but boriconazole in horses is expensive, prolonged, recovery is very slow, and our horse owners must weigh whether that kind of expensive prolonged treatment is necessary and worth it when the prognosis of return to function, return to competition is possible. Next slide. So, zooming out a little bit from the actual outbreak, we've kind of identified some challenges to early outbreak recognition and investigation. In terms of fungal outbreaks, and especially in terms of septic arthritis, it's not something we would necessarily suspect clinically. Fungal septic arthritis is very rare, not something we would initially test for. Clinically, there's a long interval between the exposure, and we've seen this with about 36 days between exposure and clinical presentation, and the empiric treatment may precede fungal culture, which then might create a negative fungal culture when it is ordered. And then diagnostics, when a mold is recovered from a synovial fluid or tissue, the question remains, is it a contaminant or a true pathogen? In terms of identifying the mold that does appear, the similarities on microscopic morphology complicate whether we can accurately identify the fungus and then collaborate between labs to identify an outbreak if we're getting the same answers. Methods, databases, and validation vary between laboratories, especially in the veterinary space. And then systems, limited centralized surveillance for many unusual and non-reportable fungal and bacterial infections remain limited, especially in veterinary spaces, wide distribution of cases across practices and institutions, both geographically and regionally, make it difficult to have crosstalk between diagnostic labs, clinicians, and other institutions to identify outbreaks. And communication is critically important to identify and respond to these outbreaks. And like I said earlier, additional cases may remain unrecognized when there is no mechanism to link these cases and incorporate them into the response plan. And even beyond outbreak recognition, identification of molds is still a critical challenge in all diagnostic labs, when working with tissue samples and fluid samples, fungal burden remains low in many samples, and routine workups may not include fungal testing at all until or unless other testing is negative, growth is slow of many molds, and prior treatment can affect recovery as well as shipment conditions. Interpretation of environmental molds is complicated, ID methods vary between laboratories, and again, validation is a challenge in many places, and performance of proteomic and molecular methods relies on robust databases, which are still being built out and researched. So being able to reliably and accurately identify molds is still a real challenge for many diagnostic labs, especially in low resource areas, as well as ones that don't see many molds come through their lab. Despite that challenge, the diagnostic lab remains kind of the cornerstone of outbreak recognition and response, and we wanted to highlight the role the diagnostic lab plays in outbreak response and recognition. When a clinician sees an unusual case, many times that sample is going to a diagnostic lab, who is going to identify an unusual organism, and oftentimes will identify a weird pattern. The diagnostic lab is also potentially going to be the place where a pattern is going to be identified, and the pattern and the findings and exposures are going to be linked across potentially very different submissions from very different places. The diagnostic lab and the clinical microbiologist will also likely be the one that will escalate those findings beyond the institution, beyond the lab, to governmental agencies, to reference labs, to regulatory institutions, and then also back to the clinicians to notify them to keep an eye out for additional cases. So, we wanted to highlight that the clinical microbiologists and the diagnostic labs are your friends. When you have an interesting case, or a weird exposure, or a weird organism that you're not expecting to find somewhere, consult with your clinical microbiologist. Consult with your diagnostic lab, because they're oftentimes the ones that can put you in touch with someone who knows more about that organism, or put you in touch with somebody who can tell you if there's a known outbreak, because they're the ones linking everything together. Next slide. And altogether, outbreak recognition and response is everybody's responsibility. Clinicians are the ones seeing the patients, seeing the presentations, and seeing what things look like on the ground. You're the ones seeing the unusual disease patterns. You're the ones, you know, when a patient says that they've had a weird exposure, you're the ones who are going to hear that, and write it down, and communicate that. You're the ones that are going to maintain suspicion for atypical etiologies, and sample appropriately, and communicate that with the diagnostic labs. The diagnostic labs are going to detect unusual findings, and connect the cases, and communicate that. Academia can play a role, because there's probably somebody that is studying a weird pathogen, or who has experience with an outbreak of that weird pathogen. Government and public health are the ones who can help with investigations, and mobilize resources that might not otherwise be available, and can link cases across jurisdictions, and across institutions. And then industry manufacturers have to be involved, too, because if it's, like in this case, a contaminated pharmaceutical product, we want to get those people involved, instead of leaving them out, because they can trace affected products and lots, investigate the contamination, implement corrective action, and hopefully prevent additional patients from getting sick. Next slide, please. Also, this is, I think, a very One Health kind of situation. Although this outbreak happened in courses, we believe that this outbreak is very reminiscent of the 2012 Xerohylum steroid outbreak, because contaminated pharmaceutical products can affect anybody. That one was in humans, this one was in horses, the next one can be in dogs, this can affect anybody. Emerging threats may come from unexpected pathogens, it can affect unexpected hosts, it can come from an unexpected environmental reservoir. This incorporates every aspect of the One Health philosophy. Early recognition and effective response depend on communication and collaboration across disciplines, organizations, including academia, government, industry, everybody. So, although, again, this happened in horses, with a fungus, we all have to be aware that the next outbreak can happen from anywhere, to anyone, and we all need to be ready to predict it, respond to it, and communicate to improve the systems necessary to prevent harm in the future. Next slide, please. I just wanted to include some acknowledgments, including our consortium, my lab, and some other collaborators within and beyond Virginia Tech, and I'm happy to answer any questions. Thank you. Thank you so much, Mr. Pluta, for highlighting this outbreak and insights. Okay, we can stay on this slide. We have time for questions. As a reminder, if you have a question, please use the Q&A feature to send them in and include the presenter's name or topic, if you can. Okay, we're going to start from one of the first questions that came in for Dr. Granick, and I think you also answered, so I'm going to read the question. How geographically representative is the database slash participating clinics? And Jennifer, you wrote that so far, you believe that you have 45 of 50 states and Puerto Rico, so feel free to add on to that if you want to. I was just going to say, Emma can correct me if I'm wrong about that. No, that sounds right. Thank you. Okay. This question is going to be for Mr. Pluta. Might fungal contaminants in medical products be trending upward, or is the appearance of an uptrend merely better promulgation of cases and clusters? Thinking back to recent years, recalls of ophthalmic products? That is an excellent question. I'm not entirely sure if it's trending upward necessarily. I do think fungal outbreaks and contaminants are getting more attention because a lot of them are coming from more abnormal pathogens. So thinking back to, again, the Axirohalum outbreak, this one with purpuricillium, I believe last year was also purpuricillium, so we're seeing a lot more abnormal environmental molds become problematic and pathogenic. So I think they're getting more attention put on them because we're not expecting these kinds of pathogens. And also, they are likely to cause contamination events because they're environmental. They like living in these kinds of environments, room temperature kinds of things. So they just get more attention on them because we don't expect them. And we want to be able to figure out why they're able to cause disease in these kinds of situations because, again, we're not prepared to treat them or diagnose them. So again, I'm not sure if it's necessarily trending upwards, but they're getting more attention because they're just really strange pathogens, if that makes sense. Thank you. Got another question for you. Would the inclusion of a filter on the injection of this type of product prevent passage of this fungus into the joint? Yeah. So that's actually one thing we've been having a lot of trouble with in terms of recovery from the product because the hyaluronic acid product is really thick. So we've been having trouble even passing it through a sterile filter to collect fungus on a filter. So trying to filter it into, like through a syringe filter to get it into a joint would be near impossible just because of how thick the product is. So just based on the fluid dynamics of the product itself, it would not be feasible. Thank you. I think we have another question for you. This attendee is asking if the contaminated Equin product was misused or used in an off-label fashion. The medicine might have been implicated for topical use as wound therapy and not for injection into joints. Is that correct? Do you know if that's correct? I will say there is potentially one, more than one product implicated. At least one of the products is labeled for injection. Okay. Thank you for clarifying. We have questions coming in. Thank you for submitting. I'm going to read another question, I believe also for you, Mr. Pluta. Is the fungal outbreak due to not taking into account of the environmental biome, if you will? I'm not sure I understand the question. Okay. If you can maybe add more context, we might be able to answer the question. But, or if you could expand on that question a little bit more, we'll read it again if you can write in a comment. Thank you. All right. Well, I'm still looking through. I think we've answered all the questions that were submitted so far. And if, okay, we have another one coming in for Dr. Granik. Let me publish it. I'm curious how difficult the task of data entry is for the clinic. I assume clinicians can't upload directly from their veterinary records platform or scan their forms. Do you have any insight? So, in terms of the, I hope I'm interpreting the question correctly, but in terms of the data that we receive from CAVSnet, the veterinary clinicians just utilize their electronic health records as they do, like as they do, like so we don't ask anybody to do anything different than they normally do. In terms of how, like what those electronic health record systems can do and how they're utilized, they're, some will have, for example, diagnostic results data, like enter directly into, like automatically get entered into the electronic health record. Others will have to upload like a PDF to connect it to the electronic health record. So, I'm not sure if that's what you're asking. We don't receive data directly from any clinic. We go through our external data providers that kind of collate that data and provide it to us. So, hopefully that answered the question, but if it didn't, please ask for more specifics. Thank you. Okay. And regarding the first, the question, the last question that I read for you, Mr. Pluta, there's more context, this attendee is asking, is the fungal outbreak a result of failing to account for disruptions in the local microbiome? Does that help? Yeah. If I'm understanding the question correctly, I think one of the reasons why this became such an issue is because the joint is supposed to be sterile. So, purpuricillium is not typically considered a major pathogen. It is very much on the rise in invasive fungal disease. So, any time you inject a fungus into a supposedly sterile environment, it's going to cause pretty major disease. So, there really is no opportunity for a microbiome, so to speak, to kind of control for an invader because there is no microbiome in the joint, nor is there, because you're injecting it, so there's no opportunity for a microbiome to stop the fungus, if that kind of makes sense. Thank you. Okay. Well, I am looking for additional questions, and I don't see any that have come in. So, we'll give it maybe a few more seconds to see if anyone has thought of anything else they wanted to ask before we move on to closing. I don't see anything else, so just wanted to thank our presenters again. Thank you also to the attendees for your participation and for joining today. If you have any other questions, we've included the email addresses on this slide, and please look forward to the recorded video of this webinar being posted within 30 days. If you can go to the next slide, please. And please join us for next month's webinar on October 7th. If you have any topics or suggestions, we have the email on the slide for you to share, and also, you can also find previous recordings on our website. Thank you again, and that will be the end of today's webinar.