Hasil untuk "Veterinary medicine"

D. C. Blood, O. Radostits, J. Arundel

Alice Caneschi, A. Bardhi, A. Barbarossa et al.

As warned by Sir Alexander Fleming in his Nobel Prize address: “the use of antimicrobials can, and will, lead to resistance”. Antimicrobial resistance (AMR) has recently increased due to the overuse and misuse of antibiotics, and their use in animals (food-producing and companion) has also resulted in the selection and transmission of resistant bacteria. The epidemiology of resistance is complex, and factors other than the overall quantity of antibiotics consumed may influence it. Nowadays, AMR has a serious impact on society, both economically and in terms of healthcare. This narrative review aimed to provide a scenario of the state of the AMR phenomenon in veterinary medicine related to the use of antibiotics in different animal species; the impact that it can have on animals, as well as humans and the environment, was considered. Providing some particular instances, the authors tried to explain the vastness of the phenomenon of AMR in veterinary medicine due to many and diverse aspects that cannot always be controlled. The veterinarian is the main reference point here and has a high responsibility towards the human–animal–environment triad. Sharing such a burden with human medicine and cooperating together for the same purpose (fighting and containing AMR) represents an effective example of the application of the One Health approach.

Juan Carlos Hernández-González, Abigail Martínez-Tapia, Gebim Lazcano-Hernández et al.

Simple Summary Antibiotic resistance is a growing threat; its indiscriminate use has led to management restrictions in humans and animals. Bacteriocins are powerful antimicrobial peptides that have great potential in the prevention and treatment of diseases in animals. Their antimicrobial activity is rapid, and they show a lower propensity to develop resistance than conventional antibiotics. Currently, their main application is in food preservation systems. However, several studies show their bioactive role as antimicrobials, probiotics, and immunomodulators in animals. Therefore, bacteriocins are an excellent alternative to be applied in several areas of veterinary medicine. Abstract In the search for an alternative treatment to reduce antimicrobial resistance, bacteriocins shine a light on reducing this problem in public and animal health. Bacteriocins are peptides synthesized by bacteria that can inhibit the growth of other bacteria and fungi, parasites, and viruses. Lactic acid bacteria (LAB) are a group of bacteria that produce bacteriocins; their mechanism of action can replace antibiotics and prevent bacterial resistance. In veterinary medicine, LAB and bacteriocins have been used as antimicrobials and probiotics. However, another critical role of bacteriocins is their immunomodulatory effect. This review shows the advances in applying bacteriocins in animal production and veterinary medicine, highlighting their biological roles.

I. Schmerold, I. V. van Geijlswijk, R. Gehring

Antimicrobial resistance endangers the successful combat of bacterial infections in humans and animals. The common use of antibiotic classes including those of high clinical value in human as well as veterinary medicine is a critical factor contributing to or suspected to promote the emergence of antibiotic resistance. New legal provisions laid down in veterinary drug legislations and related guidelines and advice are in force in the European Union to safeguard the effectiveness, accessibility and availability of antibiotics. Categorisation of antibiotics in classes of importance for treatment of infections of humans by the WHO was one of the first steps. This task is also undertaken for antibiotics for treatment of animals by the EMA's Antimicrobial Advice Ad Hoc Expert Group. The new veterinary Regulation (EU) 2019/6 has extended restrictions for use of some antibiotics in animals to a full ban of certain antibiotics. While some (but not all) antibiotic compounds not being authorized in veterinary medicine may still be used in companion animals more strict provisions were already applicable for treatment of food producing animal species. Distinct regulations are in place for the treatment of animals kept in large numbers in flocks. Initial regulations focussed on the protection of consumers from residues of veterinary drugs in food commodities, new regulations address prudent (not routinely) and responsible selection, prescription and use of antibiotics, and have improved the practicality for cascade use outside the terms of marketing authorisation. Mandatory recording of use of veterinary medicinal products for food safety reasons is extended to rules for veterinarians and owners or holders of animals to regularly report the use of antibiotics for the purpose of official surveillance of consumption. National sales data of antibiotic veterinary medicinal products have been collected on a voluntary basis until 2022 by ESVAC, which has created awareness of major differences between EU member states. A significant decline in sales was reported for third and fourth generation cephalosporines, polymyxins (colistin), and (fluoro)quinolones since the initiation in 2011.

Jihoon Jeong

Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.

Brooklyn L. Laubinger, Kelsey M. Harvey, William Isaac Jumper

Gastrointestinal nematodes (GINs) remain a significant challenge to productivity and sustainability in beef cattle systems in the United States, contributing to subclinical reductions in growth, reproductive performance, and overall herd health across production stages. Control programs have historically relied on routine anthelmintic use; however, increasing reports of anthelmintic resistance highlight the need for alternative management strategies. This narrative review synthesizes peer-reviewed literature identified through targeted searches of major scientific databases spanning approximately seven decades, with articles selected for relevance to GIN epidemiology, diagnostics, and control in beef cattle. Particular emphasis is placed on life stage-specific susceptibility, host immune development, and the role of diagnostic tools in guiding evidence-based interventions. The review further examines non-anthelmintic strategies such as grazing management, nutritional supplementation, selective breeding, and integrated parasite management practices adapted from small ruminant systems. Across studies, young and immunologically developing cattle experience the greatest productivity losses, while mature animals contribute disproportionately to pasture contamination, reinforcing the importance of targeted control measures. Overall, the literature supports a transition toward integrated, diagnostics-driven parasite control programs that sustain productivity and animal well-being while preserving long-term anthelmintic efficacy.

Tyler J Poore, Christopher J Pinard, Aleena Shabbir et al.

Large language models (LLMs) are increasingly used in clinical settings, yet their performance in veterinary medicine remains underexplored. We evaluated three commercially available veterinary-focused LLM summarization tools (Product 1 [Hachiko] and Products 2 and 3) on a standardized dataset of veterinary oncology records. Using a rubric-guided LLM-as-a-judge framework, summaries were scored across five domains: Factual Accuracy, Completeness, Chronological Order, Clinical Relevance, and Organization. Product 1 achieved the highest overall performance, with a median average score of 4.61 (IQR: 0.73), compared to 2.55 (IQR: 0.78) for Product 2 and 2.45 (IQR: 0.92) for Product 3. It also received perfect median scores in Factual Accuracy and Chronological Order. To assess the internal consistency of the grading framework itself, we repeated the evaluation across three independent runs. The LLM grader demonstrated high reproducibility, with Average Score standard deviations of 0.015 (Product 1), 0.088 (Product 2), and 0.034 (Product 3). These findings highlight the importance of veterinary-specific commercial LLM tools and demonstrate that LLM-as-a-judge evaluation is a scalable and reproducible method for assessing clinical NLP summarization in veterinary medicine.

Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou et al.

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Shumin Li, Xiaoyun Lai

While the global integration of artificial intelligence (AI) into veterinary medicine is accelerating, its adoption dynamics in major markets such as China remain uncharacterized. This paper presents the first exploratory analysis of AI perception and adoption among veterinary professionals in China, based on a cross-sectional survey of 455 practitioners conducted in mid-2025. We identify a distinct "adoption paradox": although 71.0% of respondents have incorporated AI into their workflows, 44.6% of these active users report low familiarity with the technology. In contrast to the administrative-focused patterns observed in North America, adoption in China is practitioner-driven and centers on core clinical tasks, such as disease diagnosis (50.1%) and prescription calculation (44.8%). However, concerns regarding reliability and accuracy remain the primary barrier (54.3%), coexisting with a strong consensus (93.8%) for regulatory oversight. These findings suggest a unique "inside-out" integration model in China, characterized by high clinical utility but restricted by an "interpretability gap," underscoring the need for specialized tools and robust regulatory frameworks to safely harness AI's potential in this expanding market.

Takehiro Furukawa, Fumihiro Yamane, Takuji Okumura et al.

Insulin-like androgenic gland factor (IAG) is considered a key regulator of male sexual differentiation and maturation in decapod crustaceans. In several species, <i>IAG</i> expression is thought to be negatively regulated by the eyestalk, as demonstrated by eyestalk ablation (ESA) experiments. In the kuruma prawn <i>Marsupenaeus japonicus</i>, however, the upstream regulatory mechanisms of <i>IAG</i> (<i>Maj-IAG</i>) remain largely unclear. In the present study, males of different body sizes were subjected to ESA to elucidate these mechanisms. Bilateral ESA induced upregulation of <i>Maj-IAG</i> expression from day 7 onward, whereas unilateral ESA did not. Moreover, enhanced development of male reproductive organs and hypertrophy of the androgenic gland were observed from day 7 after bilateral ESA. These findings indicate that <i>Maj-IAG</i> is regulated by eyestalk-derived factor(s), supporting the presence of an eyestalk–androgenic gland endocrine axis in <i>M. japonicus</i>. By contrast, the expression of <i>Maj-Dsx2</i>, a homolog of doublesex (<i>Dsx</i>) that has recently been proposed as an upstream regulator of IAG, did not show a consistent increase following bilateral ESA across all experiments, suggesting that the involvement of <i>Maj-Dsx2</i> in this axis remains unclear. Overall, this study provides fundamental insights into the regulatory mechanisms of decapod male reproduction.

Susheel Kumar Nethi, Venugopal Gunda, Nagabhishek Sirpu Natesh et al.

Summary: Pancreatic cancer (PC) exhibits profound metabolic adaptations that support tumor progression, survival, and therapy resistance. Hypoxia-inducible factor-1α (HIF-1α) is a key regulator of these processes, promoting metabolic reprogramming and chemoresistance. Given that mitochondrial metabolites modulate HIF-1α stability, targeting mitochondrial metabolism offers a promising therapeutic strategy. Niclosamide (Nic), a clinically approved anthelmintic, disrupts mitochondrial function but is limited by poor bioavailability. To overcome this, we developed polyanhydride-based Nic nanoparticles (NicNps) to enhance bioavailability and efficacy. NicNps impaired mitochondrial function, suppressed metabolism, downregulated HIF-1α, and inhibited growth of PC cells and orthotopic gemcitabine (Gem)-resistant mouse tumor models. Notably, NicNps combined with Gem overcame therapy resistance by synergistically reducing tumor hypoxia and HIF-1α-driven metabolic reprogramming. These findings highlight NicNps as a mitochondria-targeted, nanoparticle-based therapy that enhances Nic’s bioavailability while suppressing HIF-1α-driven adaptations. NicNps in combination with Gem offer a promising strategy to overcome therapy resistance and improve treatment outcomes in patients with pancreatic cancer.

Candice P. Chu

ChatGPT, the most accessible generative artificial intelligence (AI) tool, offers considerable potential for veterinary medicine, yet a dedicated review of its specific applications is lacking. This review concisely synthesizes the latest research and practical applications of ChatGPT within the clinical, educational, and research domains of veterinary medicine. It intends to provide specific guidance and actionable examples of how generative AI can be directly utilized by veterinary professionals without a programming background. For practitioners, ChatGPT can extract patient data, generate progress notes, and potentially assist in diagnosing complex cases. Veterinary educators can create custom GPTs for student support, while students can utilize ChatGPT for exam preparation. ChatGPT can aid in academic writing tasks in research, but veterinary publishers have set specific requirements for authors to follow. Despite its transformative potential, careful use is essential to avoid pitfalls like hallucination. This review addresses ethical considerations, provides learning resources, and offers tangible examples to guide responsible implementation. A table of key takeaways was provided to summarize this review. By highlighting potential benefits and limitations, this review equips veterinarians, educators, and researchers to harness the power of ChatGPT effectively.

Roberto Bava, F. Castagna, V. Musella et al.

Simple Summary Bee products consist of many substances that have long been known for their medicinal and health-beneficial properties. Venom is certainly the one that has attracted the most interest due to the complexity of its chemical composition. Several types of research have been conducted utilizing biological (cellular) systems to figure out the properties of bee venom in vitro. Primarily, cell lines of various sorts and origins are used for this purpose. Afterward, experiments on murine models paved the way for clinical trials on humans. Therefore, there are numerous reviews summarising the uses of venom for human medicine, but none have focused on its use in veterinary medicine. This review aims to gather the relevant publications on the use of bee venom in veterinary medicine. Abstract Apitherapy is a branch of alternative medicine that consists of the treatment of diseases through products collected, processed, and secreted by bees, specifically pollen, propolis, honey, royal jelly, and bee venom. In traditional medicine, the virtues of honey and propolis have been well-known for centuries. The same, however, cannot be said for venom. The use of bee venom is particularly relevant for many therapeutic aspects. In recent decades, scientific studies have confirmed and enabled us to understand its properties. Bee venom has anti-inflammatory, antioxidant, central nervous system inhibiting, radioprotective, antibacterial, antiviral, and antifungal properties, among others. Numerous studies have often been summarised in reviews of the scientific literature that have focused on the results obtained with mouse models and their subsequent transposition to the human patient. In contrast, few reviews of scientific work on the use of bee venom in veterinary medicine exist. This review aims to take stock of the research achievements in this particular discipline, with a view to a recapitulation and stabilisation in the different research fields.

Emilia Trif, C. Cerbu, D. Olah et al.

Simple Summary Florfenicol is a bacteriostatic antibiotic that is primarily used in veterinary medicine to treat a range of diseases in farm and aquatic animals. This synthetic analog of thiamphenicol and chloramphenicol works by inhibiting ribosomal activity, thereby disrupting bacterial protein synthesis, and has been proven in its effectiveness against a variety of Gram-positive and Gram-negative bacterial groups. Additionally, florfenicol has been found to possess anti-inflammatory properties and reduce immune cell proliferation and cytokine production. However, the inappropriate use of florfenicol has led to concerns about resistance genes, and its low solubility in water has made it difficult to formulate aqueous solutions using organic solvents. This review aims to synthesize the various applications of florfenicol in veterinary medicine, explore the potential use of nanotechnology to improve its effectiveness and analyze the advantages and limitations of such approaches. This review draws on data from scientific articles and systematic reviews found in multiple databases. Abstract Florfenicol is a broad-spectrum bacteriostatic antibiotic used exclusively in veterinary medicine in order to treat the pathology of farm and aquatic animals. It is a synthetic fluorinated analog of thiamphenicol and chloramphenicol that functions by inhibiting ribosomal activity, which disrupts bacterial protein synthesis and has shown over time a strong activity against Gram-positive and negative bacterial groups. Florfenicol was also reported to have anti-inflammatory activity through a marked reduction in immune cell proliferation and cytokine production. The need for improvement came from (1) the inappropriate use (to an important extent) of this antimicrobial, which led to serious concerns about florfenicol-related resistance genes, and (2) the fact that this antibiotic has a low water solubility making it difficult to formulate an aqueous solution in organic solvents, and applicable for different routes of administration. This review aims to synthesize the various applications of florfenicol in veterinary medicine, explore the potential use of nanotechnology to improve its effectiveness and analyze the advantages and limitations of such approaches. The review is based on data from scientific articles and systematic reviews identified in several databases.

Ryan B. Appleby, P. Basran

Artificial intelligence (AI) is a branch of computer science in which computer systems are designed to perform tasks that mimic human intelligence. Today, AI is reshaping day-to-day life and has numerous emerging medical applications poised to profoundly reshape the practice of veterinary medicine. In this Currents in One Health, we discuss the essential elements of AI for veterinary practitioners with the aim to help them make informed decisions in applying AI technologies into their practices. Veterinarians will play an integral role in ensuring the appropriate uses and good curation of data. The expertise of veterinary professionals will be vital to ensuring good data and, subsequently, AI that meets the needs of the profession. Readers interested in an in-depth description of AI and veterinary medicine are invited to explore a complementary manuscript of this Currents in One Health available in the May 2022 issue of the American Journal of Veterinary Research.

C. D. Silva, A. A. D. Gomes, Thaís Rabelo dos Santos-Doni et al.

Veterinarians are commonly exposed to occupational stressors, including excessive workload and financial constraints. These stressors can lead to psychological distress, which typically results in mental health disorders such as depression, anxiety, and burnout and can even culminate in suicide attempts or suicide deaths. Risk factors associated with poor mental health and high rates of suicide in veterinary practitioners include continuous exposure to challenging scenarios, such as interpersonal conflicts, performing euthanasia, and easy access to lethal means of suicide, such as opioids and anesthetics. The previous studies highlight the urgent need for a better understanding of predisposing factors, mental health-related improvements in the professional environment, and the subsequent establishment of primary mental health-related care policies. Effective ways to promote mental health and prevent suicide may include social support, resilience, developing coping skills, promoting a healthy work environment, and discouraging perfectionist behaviors. This review aimed to summarize findings in studies that have investigated mental health and suicide in veterinarians and veterinary students and highlight measures that could be implemented as options for mental health promotion and suicide prevention.

In-Gyu Lee, Jun-Young Oh, Hee-Jung Yu et al.

Recently, with increasing interest in pet healthcare, the demand for computer-aided diagnosis (CAD) systems in veterinary medicine has increased. The development of veterinary CAD has stagnated due to a lack of sufficient radiology data. To overcome the challenge, we propose a generative active learning framework based on a variational autoencoder. This approach aims to alleviate the scarcity of reliable data for CAD systems in veterinary medicine. This study utilizes datasets comprising cardiomegaly radiograph data. After removing annotations and standardizing images, we employed a framework for data augmentation, which consists of a data generation phase and a query phase for filtering the generated data. The experimental results revealed that as the data generated through this framework was added to the training data of the generative model, the frechet inception distance consistently decreased from 84.14 to 50.75 on the radiograph. Subsequently, when the generated data were incorporated into the training of the classification model, the false positive of the confusion matrix also improved from 0.16 to 0.66 on the radiograph. The proposed framework has the potential to address the challenges of data scarcity in medical CAD, contributing to its advancement.

Chun Yin Kong, Picasso Vasquez, Makan Farhoodimoghadam et al.

In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR systems or the limited availability of IT resources. To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time.

Judit M Wulcan, Kevin L Jacques, Mary Ann Lee et al.

Large language models (LLMs) can extract information from veterinary electronic health records (EHRs), but performance differences between models, the effect of temperature settings, and the influence of text ambiguity have not been previously evaluated. This study addresses these gaps by comparing the performance of GPT-4 omni (GPT-4o) and GPT-3.5 Turbo under different conditions and investigating the relationship between human interobserver agreement and LLM errors. The LLMs and five humans were tasked with identifying six clinical signs associated with Feline chronic enteropathy in 250 EHRs from a veterinary referral hospital. At temperature 0, the performance of GPT-4o compared to the majority opinion of human respondents, achieved 96.9% sensitivity (interquartile range [IQR] 92.9-99.3%), 97.6% specificity (IQR 96.5-98.5%), 80.7% positive predictive value (IQR 70.8-84.6%), 99.5% negative predictive value (IQR 99.0-99.9%), 84.4% F1 score (IQR 77.3-90.4%), and 96.3% balanced accuracy (IQR 95.0-97.9%). The performance of GPT-4o was significantly better than that of its predecessor, GPT-3.5 Turbo, particularly with respect to sensitivity where GPT-3.5 Turbo only achieved 81.7% (IQR 78.9-84.8%). Adjusting the temperature for GPT-4o did not significantly impact classification performance. GPT-4o demonstrated greater reproducibility than human pairs regardless of temperature, with an average Cohen's kappa of 0.98 (IQR 0.98-0.99) at temperature 0 compared to 0.8 (IQR 0.78-0.81) for humans. Most GPT-4o errors occurred in instances where humans disagreed (35/43 errors, 81.4%), suggesting that these errors were more likely caused by ambiguity of the EHR than explicit model faults. Using GPT-4o to automate information extraction from veterinary EHRs is a viable alternative to manual extraction.