Innovative VOLUME 13 ISSUE 4
VETERINARY CARE
TREATING CHRONIC BRONCHITIS IN DOGS CORTICOSTEROIDS ARE THE TREATMENT OF CHOICE FOR THIS CHALLENGING PROBLEM, BUT A VARIETY OF ALTERNATIVE OPTIONS CAN BE USED IN CONJUNCTION WITH THESE DRUGS. — P. 40
HOW AI IS IMPACTING VETERINARY EPIDEMIOLOGY Because AI can provide valuable insights into protecting animal health, it will play a big role in the future of veterinary medicine and epidemiology. — p. 8
USING AN INTEGRATIVE APPROACH TO PAIN MANAGEMENT IN CATS A treatment plan that uses both traditional and alternative therapies is the most effective way to manage pain in your feline clients. — p. 18
FALL 2023
www.IVCJournal.com
5 WAYS VETERINARY PROFESSIONALS CAN MAKE MONEY ONLINE
SHOULD YOU CONSIDER MCT OIL FOR YOUR PATIENTS?
NEW IDEAS ABOUT HEMANGIOSARCOMA IN DOGS
DETOXIFICATION IN EQUINES — AN INTEGRATIVE PROGRAM
From e-books to courses to podcasts…find out how you can start earning a second income online. — p. 23
Why our understanding of this cancer is improving our ability to diagnose and treat it. — p. 26
Medium chain triglycerides (MCTs) have been shown to help improve a variety of medical conditions in pets — from respiratory disease to seizures. — p. 42
Our equine patients are exposed to many toxins that can overwhelm their livers’ ability to deal with them. Here’s why a gentle detoxification program is beneficial. — p. 48
contents FEATURES
IN ARTIFICIAL 8 ADVANCES INTELLIGENCE (AI) AND VETERINARY EPIDEMIOLOGY
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PETS INGEST POISON — 30 WHEN COMMON HOUSEHOLD TOXINS AND REMEDIES
by Emily Singler, VMD
by Mari Delaney, DVM
Because of its potential for creating valuable insights into protecting animal health, AI will be a big part of the future of veterinary medicine and epidemiology.
Toxin ingestion among pets is common and can quickly become life-threatening. Saving lives relies on prompt action both at home and in the veterinary clinic setting.
STATE OF TELEMEDICINE IN 34 THE THE VETERINARY INDUSTRY
NUTRITION NOOK
CHINESE FOOD THERAPY FOR UPPER RESPIRATORY TRACT DISEASES IN DOGS
by Hannah Godfrey, BVETMED MRCVS
Veterinary telemedicine is likely here to stay. This article takes an in-depth look at its pros and cons, and how it could be improved.
by Judy Morgan, DVM, CVA, CVCP, CVFT In TCVM, three patterns lead to upper respiratory tract diseases in dogs. Based on a patient’s pattern, Chinese food therapy can help treat the problem and resolve symptoms.
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TREATMENT FOR 40 INTEGRATIVE CHRONIC BRONCHITIS IN DOGS by W. Jean Dodds, DVM
AN INTEGRATIVE APPROACH TO PAIN MANAGEMENT IN FELINE PATIENTS
Although corticosteroids remain the treatment of choice for chronic bronchitis in dogs, a variety of alternative options can be used in conjunction with these drugs.
by Angie Krause, DVM, CVA, CCRT Helping clients understand the signs of pain in cats, with an integrative approach to pain management that uses traditional and alternative therapies, is the most effective solution.
OIL AND ITS USE IN 42 MCT ANIMAL PATIENTS
by Jared Mitchell, DVM, CVMA, CVA, CVFT
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30
Medium chain triglycerides (MCTs) can be used to help improve the health of animal patients with a variety of medical conditions — from respiratory disease to seizures.
5 WAYS VETERINARY PROFESSIONALS CAN MAKE MONEY ONLINE by Megan Kelly, BVSc, CCRP From e-books to courses to podcasts, there are several ways veterinary professionals can start earning a second income online.
TREATMENT FOR 46 INTEGRATIVE LUNGWORM IN DOGS
by Nancy Brandt, DVM, MSOM, DIP. OM, CVC, CVA, CVMA
IDEAS ABOUT 26 NEW HEMANGIOSARCOMA IN DOGS
From environmental controls and supplements, to ozone or deep inhalation therapy, a variety of integrative treatments are effective against lungworm infections in dogs.
by Kelly Diehl, DVM, MS DIPL. ACVIM
Outcomes for dogs diagnosed with hemangiosarcoma have not changed in decades, but our understanding of this cancer is steadily improving our ability to diagnose and treat it.
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42 48
DETOXIFICATION IN EQUINES — AN INTEGRATIVE APPROACH by Joyce D. Harman, DVM Our equine patients are exposed to many toxins, and this can overwhelm their livers’ ability to process and eliminate them. A gentle detoxification program is beneficial.
COLUMNS & DEPARTMENTS
7 Editorial 13 Profitable Practice — NutriSource 25 From the AVH 29 From the AHVMA 39 Industry Innovations 45 From the VMAA 51 From the AATCVM & WATCVM 53 From the VBMA IN THE NEWS:
38 Study reveals regional variability in vector-borne infections
38 West Virginia abandons plan for new veterinary school
advisory board Dr. Richard Palmquist, DVM GDipVCHM(CIVT) CVCHM (IVAS), graduated from Colorado State University in 1983. He is chief of integrative health services at Centinela Animal Hospital in Inglewood, California, former president and research chair of the AHVMA, and an international speaker in integrative veterinary medicine. Dr. Palmquist is a consultant for the Veterinary Information Network (VIN) and a past president of the AHVM Foundation. He has published two books, one for conventional veterinarians and a second for clients discussing how integrative thinking works.
Michelle J. Rivera, MT, VDT, is an instructor at the University of Wisconsin and The Healing Oasis Wellness Center, a post-graduate educational institution offering state-approved programs. She is coowner of The Healing Oasis Veterinary Hospital, offering massage, rehabilitation, chiropractic and Chinese and Western Herbology. Michelle completed the Chinese Herbal Medicine program from the China Beijing International Acupuncture Training Center, and is certified in Chinese Medicine by the Wisconsin Institute of Chinese Herbology.
Dr. Joyce Harman, DVM, MRCVS, graduated in 1984 from Virginia Maryland Regional College of Veterinary Medicine. Her practice is 100% holistic, using acupuncture, chiropractic, herbal medicine and homeopathy to treat horses to enhance performance and those with a variety of chronic conditions, with an emphasis on Lyme Disease. Her publications include the Pain Free Back and Saddle Fit Books, and numerous articles in lay and professional magazines. She maintains an informative website: www.harmanyequine.com. Dr. Steve Marsden, DVM, ND, MSOM, Lac. Dipl.CH, CVA, AHG lectures for IVAS, the AHVMA, the AVMA, and numerous other organizations. He is co-founder of the College of Integrative Veterinary Therapies and is a director emeritus of the National University of Natural Medicine in Portland OR. He authored the Manual of Natural Veterinary Medicine (Mosby); and Essential Guide to Chinese Herbal Formulas (CIVT). Dr. Marsden is extensively trained in alternative medicine, including Chinese herbology, acupuncture and naturopathic medicine. He has holistic veterinary and naturopathic medical practices in Edmonton, Alberta. In 2010, Dr. Marsden was named Teacher of the Year by the AHVMA; and Small Animal Veterinarian of the year by the CVMA in 2009. Dr. Jean Dodds, DVM, received her veterinary degree in 1964 from the Ontario Veterinary College. In 1986, she moved to Southern California to establish Hemopet, the first non-profit national blood bank program for animals. Dr. Dodds has been a member of many national and international committees on hematology, animal models of human disease, veterinary medicine, and laboratory animal science. She received the Holistic Veterinarian of the Year Award from the AHVMA in 1994.
52 Rapid diagnostic technique identifies bacterial infections
54 Drug-resistant hookworms spreading among dogs
Dr. Barbara Fougere, DVM, CVAA graduated in 1986, and was named the American Holistic Veterinary Medical Association Educator for 2011. Dr. Fougere is the principal and one of the founders of the College of Integrative Veterinary Therapies. She has continued studying over the last 26 years, and has three Bachelor degrees, two Masters degrees, three post Graduate Diplomas, several Certifications and numerous other courses under her belt.
Dr. Christina Chambreau, DVM, CVH, graduated from the University of Georgia Veterinary College in 1980. She is a founder of the Academy of Veterinary Homeopathy, was on the faculty of the National Center for Homeopathy Summer School and has been the holistic modality adjunct faculty liaison for the Maryland Veterinary Technician Program and is the former Associate Editor of IVC Journal. Dr. Chambreau teaches classes in homeopathy for animals, lectures on many topics, speaks on Radio and TV, and is the author of the Healthy Animal’s Journal among other titles. She is now on the faculty of the Holistic Actions Academy, which empowers members to keep their animals healthy with weekly live webinars.
IVC Fall 2023
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ADVANCES IN ARTIFICIAL INTELLIGENCE (AI) AND VETERINARY EPIDEMIOLOGY BY EMILY SINGLER, VMD
Because of its potential for creating valuable insights into protecting animal health, artificial intelligence (AI) will be a big part of the future of veterinary medicine and epidemiology. This means experts in veterinary medicine, epidemiology, and AI will need to work together to ensure the technology is used responsibly and interpreted correctly.
Almost every facet of everyday life has been affected by artificial intelligence (AI), or will be in the near future. From autocorrect features on smart devices to photo editing apps and the increasingly popular ChatGPT, AI is transitioning from a poorly understood topic of science fiction stories to an integral feature of daily life. In many industries, AI has become an essential tool for improving workplace efficiency and advancing new discoveries. It has already started to leave its mark on the fields of veterinary medicine and animal health. In clinical practice, for example, AI is being used to predict the likelihood of renal disease in cats based on serial lab work results.1 AI-assisted radiographic interpretation, called radiomics, has been used in the analysis of digital radiographs and ultrasound images.2 And practitioners are touting ChatGPT as a productivity aid to speed the writing of their medical note templates. Apart from uses in clinical practice, AI has also become an increasingly effective tool in veterinary epidemiology, which is what this article will focus on.
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AI AND BIG DATA Veterinary epidemiology is defined as “a study of the distribution and determinants of animal health-, welfare-, and production-related states or events.”3 Veterinary epidemiology can be helpful in promoting health and well being at the level of the individual, the herd, and the community at large (both animal and human). With the rise of big data in veterinary medicine, AI has become increasingly relevant and necessary. Big data is defined as “large, hard-to-manage volumes of data — both structured and unstructured — that inundate business on a day-to-day basis.”3 Veterinary big data can include animal demographics and lifespan, rates of death or disease, and other factors such as weather patterns, animal movement, and geography.3,4 Sources of veterinary big data include the Dog Aging Project, the Golden Retriever Lifetime Study, and the Veterinary Companion Animal Surveillance System
(VetCompass), among many others.5 AI has the potential to unlock previously unknown insights from this data, to highlight areas where further study is needed, and predict future challenges. AI’s superiority over other forms of data analysis lies in its ability to consume data in large quantities, and draw conclusions from it, without a human having to direct every step the algorithm takes. These advantages can result in faster results, lower costs, and new insights into problems that were previously difficult or cumbersome to address. One form of commonly-used AI is machine learning (ML), defined as “a subfield of AI in which algorithms are trained to perform tasks by learning patterns from data rather than by explicit programming.”5,6 ML can be divided into several different categories depending on the type of data being used, how much is known about the data, and what the goals of the analysis are. Within these different types of ML are various models that can be used individually to evaluate the data.
With ensemble learning, multiple models are used in synchrony to improve the accuracy and consistency of the results.7 Other forms of AI include data mining (searching through large amounts of data for patterns), natural language processing (AI algorithms that are trained to “understand” written and/or spoken text, such as in medical records), and signal processing (analysis of images, audio or video, and other types of information).5 The use of ML and other forms of AI can significantly improve our ability to diagnose disease, assess patient morbidity IVC Fall 2023
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and mortality risk, predict and monitor disease outbreaks, and plan health policy in both human and animal health.8 We’ll now review some of the current and potential uses of AI in veterinary epidemiology, along with some of the obstacles that AI faces in becoming a more useful tool in veterinary medicine.
COMPANION ANIMAL MEDICINE Fetch Pet Insurance recently reported their use of AI to analyze a pool of more than 750,000 insured dogs and 2.4 million claims collected from these dogs over 17 years. The data included breed, age, sex, geographical location, history of previous diseases, and any treatments the dogs received. Using ensemble ML, researchers were able to identify 45 disease groups whose risk of occurring in the next year can be predicted in individual dogs with a high degree of
accuracy. These include 710 specific diseases (82.5% of the claims submitted). Examples of predicted conditions include arthritis, disc diseases, diabetes, thyroid disorders, adrenal gland disorders, and certain cancers. These ML models can be helpful for pet insurance companies to predict the risk of certain conditions, and therefore provide better preventative recommendations to help clients reduce the risk for their dogs. This data may also be useful for other research studies aimed at preventing and treating diseases in dogs.9 Studies have demonstrated the effectiveness of AI in identifying patients who should be tested for Addison’s disease or Cushing’s syndrome.10,11 AI with natural language processing software has also been programmed to “read” medical records and codify them so they are easier to track and draw conclusions from in the future.12
CURRENT OBSTACLES TO AI USE IN VETERINARY MEDICINE Since treatment decisions in veterinary medicine can affect the health and
1.
even survival of our patients, it is essential for veterinary professionals to have confidence in the feedback they receive from any AI technology. In human medicine, AI use is regulated by the FDA, but there is no such oversight for its use in veterinary medicine. Because of the lack of
regulation, there is no requirement for transparency about the source and quality of data, and the models used to generate conclusions. This can leave the door open for misuse of algorithms and the utilization of poor-quality models. The quality of the data being fed to an AI algorithm can greatly affect any conclusions it draws. Therefore, incomplete or imbalanced data may give results that are biased.3 When AI is utilized in the diagnosis or treatment of animals, it’s important to inform clients of its use so that they are aware of the role that AI may play in their animals' treatment outcome.6
2.
Other concerns include risks surrounding security and confidentiality, especially with respect to animal owner information. Costs associated with obtaining the data, selecting appropriate algorithms, maintaining the necessary hardware and software, and hiring the appropriate individuals to evaluate the results can be limiting factors as well.
3.
There are also concerns that AI might be considered a substitute for veterinarians and other animal health professionals in the future. While some of this fear may be rooted in science fiction, where AI became sentient, some is also likely based on the vast and comprehensive information that can be quickly evaluated and/ or generated using AI today. While it will revolutionize the way we practice
veterinary medicine, most experts agree that AI will never replace humans in the practice of veterinary medicine. They do predict that AI will become so intertwined in the various ways veterinary medicine is practiced that it will be virtually impossible to not use it, and the insights gained from it, when treating animals or studying animal diseases.6
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For dogs with neoplasia, AI is helping researchers examine new ways to administer radiation therapy that results in less damage to healthy tissue. Following advances made in human radiation oncology, veterinary researchers are studying ways to use AI technology such as ML and deep learning (DL — a specific type of ML), to clean up the “noise” on CT images used for planning radiation treatment in animals. AI technology is also being combined with information about genetic markers for cancer to more accurately and efficiently create radiation treatment plans with fewer toxic side effects to healthy tissue, a field known as radiogenomics.13
PRODUCTION ANIMAL MEDICINE AI also has many uses for improving the health and production of livestock species. Since measures taken to treat livestock species are usually done at the herd level, AI’s ability to process large amounts of data and incorporate multiple variables is ideal. AI algorithms have been constructed to consider weather patterns, movement of animals, and current disease incidence in a particular geographical location. These algorithms are then used to predict the risk of future disease in populations of animals, and help producers pinpoint which steps would be most appropriate to reduce the risk. One such study in India developed a “disease-climate relationship model” using AI and GIS (geographic information system) to predict 13 “economically important” livestock disease outbreaks.14
Early Warning Systems (EWS) can also be developed using AI and remote surveillance to detect changes in the environment that can negatively affect animal nutrition and health. This can include tracking drought conditions, measuring the amount of water present in a watering hole, and measuring and predicting the growth of vegetation upon which grazing animals depend for food.14,15 Other reported uses of AI include predicting lameness in dairy cows based on milk production and conformation traits; estimating the location, size, and type of poultry operations in the US using drone images; early detection of emerging animal diseases; and identifying predictive factors for farms becoming positive for bovine tuberculosis.9 In addition to disease prediction, AI is a useful tool for livestock disease surveillance in what would otherwise be a very labor-intensive and costly process. Instead of humans visiting or otherwise communicating with producers and recording information on animal population numbers, locations, and disease rates, drones and remote sensors can be used to capture images
and other information that can be analyzed using AIbacked photo recognition software and other programs. This type of photo surveillance is also useful for detecting lesions in animals in slaughterhouses that can serve as sentinels of disease in the animal population.7
ADDITIONAL USES FOR AI IN VETERINARY EPIDEMIOLOGY • AI can be very helpful for answering questions about wildlife health and disease, which can be inherently harder to study. Some
As we continue to use AI more and more in veterinary medicine, it will be essential to maintain reasonable expectations for the AI modality being used. While it is possible to ask certain AI programs like ChatGPT to create a comprehensive list of differential diagnoses for a case based on clinical signs and physical exam findings, this is not the same as predictive modeling. This is just one example of AI technology that has some usefulness for veterinary and other healthcare professionals, but was not created specifically for this purpose and should be utilized with caution.
studies have used it to determine which species of carnivores and bats have the potential to become reservoirs for rabies virus in the future, even if they have not previously been identified as rabies reservoirs. • There is also interest in using AI to identify species of bats that may act as reservoirs for coronaviruses, of which the Sars-CoV2 virus that causes COVID-19 is one.8 • A I has been used to estimate the effects of environmental lead contamination in bald eagle deaths,16 and to determine the impacts of parasites on wild moose populations.17 • In equine medicine, researchers are using AI to try and predict
With so much potential for creating valuable insights that can protect animal health, AI is guaranteed to be part of the future of veterinary medicine. It will be incumbent upon experts in the fields of veterinary medicine, epidemiology, and AI to work together to ensure this technology is used responsibly and interpreted meaningfully.
which racehorses are at higher risk for “catastrophic” breakdown on the racetrack, based on computed tomography images of their legs.18 • AI is also being used in the search for new animal drugs and vaccines by speeding up the evaluation of various potential candidates and for analyzing multidrug res-
Burns K. Creating brighter futures for cats with chronic kidney disease. Published February 1, 2021. Accessed July 12, 2023.
istance in bacteria.19
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Hespel AM, Zhang Y, Basran PS. Artificial intelligence 101 for veterinary diagnostic imaging. Vet Radiol Ultrasound. 2022 Dec;63 Suppl 1:817-827.
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• O ther studies are analyzing Listeria contamination in
Peake T. Artificial Intelligence in Veterinary Medicine Raises Ethical Challenges. NC State University. Published December 14, 2022. Accessed June 27, 2023. https://news.ncsu.edu/2022/12/ artificial-intelligence-in-veterinary-medicine-raises-ethical-challenges/.
food packing plants, and
Paynter AN, Dunbar MD, Creevy KE, Ruple A. Veterinary Big Data: When Data Goes to the Dogs. Animals (Basel). 2021 Jun 23;11(7):1872.
and find ways to reduce the
3
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Appleby RB, Basran, PS. Artificial intelligence in veterinary medicine. JAVMA Volume 260 Issue 8.
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Guitian J, Arnold M, Chang Y, Snary EL. Applications of machine learning in animal and veterinary public health surveillance. Rev Sci Tech. 2023 May;42:230-241.
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Kundu R. The Complete Guide to Ensemble Learning. V7 Labs. Published March 1, 2022. Accessed June 27, 2023. www.v7labs.com/blog/ensemble-learning.
using AI to look for patterns risk of contamination and resulting food-borne illness.20
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Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020 May 16;395(10236):1579-1586.
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Debes C, Wowra J, Manzoor S, Ruple A. Predicting health outcomes in dogs using insurance claims data. Sci Rep 13, 9122 (2023)
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Matere J, Simpkin P, Angerer J, Olesambu E, Ramasamy S, Fasina F. Predictive Livestock Early Warning System (PLEWS): Monitoring forage condition and implications for animal production in Kenya, Weather and Climate Extremes, Volume 27, 2020, 100209
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Hanley BJ, Dhondt AA, Forzán MJ, Bunting EM, Pokras MA, Hynes KP, Dominguez-Villegas E, Schuler KL. 2022. Environmental lead reduces the resilience of bald eagle populations. Journal of Wildlife Management 86:e22177.
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Zhao Y. 4 Uses of AI in Veterinary Medicine. AI Time Journal. Published August 8, 2022. Accessed June 27, 2023. www.aitimejournal.com/uses-of-ai-in-veterinary-medicine/.
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Schofield I, Brodbelt DC, Kennedy N, Niessen SJM, Church DB, Geddes RF, O'Neill DG. Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice. Sci Rep. 2021 Apr 27;11(1):9035.
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Nie A, Zehnder A, Page RL, Zhang Y, Pineda AL, Rivas MA, Bustamante CD, Zou J. DeepTag: inferring diagnoses from veterinary clinical notes. NPJ Digit Med. 2018 Oct 24;1:60.
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Leary D, Basran P. The role of artificial intelligence in veterinary radiation oncology. Vet Radiol Ultrasound. 2022 Dec; Suppl 1:903-912.
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Suresh KP, Dhemadri K Rashmi, Dheeraj R, Roy, P Application of Artificial Intelligence for livestock disease prediction. Indian Farming 69(03): 60-62; March 2019.
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Cornell University. MoosePOPd: Population Dynamics in the Presence of Lethal Parasites. Accessed July 22, 2023. https://cwhl.vet.cornell.edu/tools_and_resources/moosepopd-population-dynamics-presencelethal-parasites. Basran PS, McDonough S, Palmer S, Reesink HL. Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using μCT. Animals (Basel). 2022 Nov 4;12(21):3033 Cazer C, Westblade L, Simon M, Magleby R, Castanheira M, Booth J, Jenkins S, Grohn Y. Analysis of Multidrug Resistance in Staphylcoccus aureus with a Machine Learning-Generated Antibiogram. Antimicrobial Agents and Chemotherapy. 2021 Mar 18; 65(4). Barnett-Neefs C, Sullivan G, Zoellner C, Wiedmann M, Ivanek R (2022) Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLoS ONE 17(3): e0265251.
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