Hasil untuk "Veterinary medicine"

Humera Hamid, Yao Jun Liu, Wen Xiang Li et al.

ABSTRACT Non‐alcoholic fatty liver disease (NAFLD) is a growing concern in both human and animal health, with nutritional strategies playing a key role in its management. This study used a laying hen model to evaluate the effects of two high‐fat diets, one containing margarine (MAR) and the other natural milk cream (NC) on hepatic health, egg production and gut microbiota composition. Both diets were formulated with 8% fat but differed in fatty acid profiles: MAR was rich in lauric and stearic acids, whereas NC contained more palmitic and oleic acids. Compared to NC, MAR‐fed hens showed significantly higher total cholesterol, low‐density lipoprotein cholesterol (LDL‐cholesterol), liver fat, relative liver weight, abdominal fat and serum markers of liver damage (aspartate aminotransferase [AST], alkaline phosphatase [ALP], gamma‐glutamyl transferase [GGT] and adiponectin) (p < 0.05). Microbiota analysis revealed that although Firmicutes and Bacteroidetes dominated in both groups, MAR‐fed hens had lower microbial diversity (Shannon index) and altered relative abundance of Verrucomicrobia, Peptostreptococcaceae and Turicibacter (p < 0.05), indicating microbial dysbiosis. These findings demonstrate that the type of dietary fat—independent of total fat content—strongly influences liver function and gut microbial balance in poultry. The novelty of this study lies in showing how different fat sources, despite equal inclusion levels, can distinctly modulate the gut–liver axis. This provides practical feed formulation insights and advances understanding of the gut–liver axis in animal health.

Elena Lazzaro, Elena Lazzaro, Dessislava Dimitrova et al.

African Swine Fever (ASF) represents a significant threat to global pig production, due to its high lethality rate and the ability of the African Swine Fever Virus (ASFV) to persist in wild boar populations and the environment. In areas where small-scale pig farming is an important economic activity and a diverse source of protein, the disease also significantly affects nutritional security, food sovereignty and self-sufficiency. This study, conducted in Bulgaria, investigated the impact of ASF on small-scale pig farmers and East Balkan Swine farmers. A mixed-methods approach was employed, combining semi-structured interviews (n = 30), structured questionnaires (n = 10), and discussions with relevant authorities (n = 7), including farmers, health authorities and local veterinarians. The results highlight the vulnerability of traditional pig farming methods, with a significant focus on the East Balkan Swine, the last native pig breed in Bulgaria, whose population has been heavily affected by the disease.

Mahmoud Alwakeel, Aditya Nagori, Vijay Krishnamoorthy et al.

Objectives: To evaluate the current limitations of large language models (LLMs) in medical question answering, focusing on the quality of datasets used for their evaluation. Materials and Methods: Widely-used benchmark datasets, including MedQA, MedMCQA, PubMedQA, and MMLU, were reviewed for their rigor, transparency, and relevance to clinical scenarios. Alternatives, such as challenge questions in medical journals, were also analyzed to identify their potential as unbiased evaluation tools. Results: Most existing datasets lack clinical realism, transparency, and robust validation processes. Publicly available challenge questions offer some benefits but are limited by their small size, narrow scope, and exposure to LLM training. These gaps highlight the need for secure, comprehensive, and representative datasets. Conclusion: A standardized framework is critical for evaluating LLMs in medicine. Collaborative efforts among institutions and policymakers are needed to ensure datasets and methodologies are rigorous, unbiased, and reflective of clinical complexities.

Gina D'Angelo, Xiaowen Tian, Chuyu Deng et al.

Precision medicine is an evolving area in the medical field and rely on biomarkers to make patient enrichment decisions, thereby providing drug development direction. A traditional statistical approach is to find the cut-off that leads to the minimum p-value of the interaction between the biomarker dichotomized at that cut-off and treatment. Such an approach does not incorporate clinical significance and the biomarker is not evaluated on a continuous scale. We are proposing to evaluate the biomarker in a continuous manner from a predicted risk standpoint, based on the model that includes the interaction between the biomarker and treatment. The predicted risk can be graphically displayed to explain the relationship between the outcome and biomarker, whereby suggesting a cut-off for biomarker positive/negative groups. We adapt the TreatmentSelection approach and extend it to account for covariates via G-computation. Other features include biomarker comparisons using net gain summary measures and calibration to assess the model fit. The PRIME (Predictive biomarker graphical approach) approach is flexible in the type of outcome and covariates considered. A R package is available and examples will be demonstrated.

Mohammad Amaan Sayeed, Mohammed Talha Alam, Raza Imam et al.

Centuries-old Islamic medical texts like Avicenna's Canon of Medicine and the Prophetic Tibb-e-Nabawi encode a wealth of preventive care, nutrition, and holistic therapies, yet remain inaccessible to many and underutilized in modern AI systems. Existing language-model benchmarks focus narrowly on factual recall or user preference, leaving a gap in validating culturally grounded medical guidance at scale. We propose a unified evaluation pipeline, Tibbe-AG, that aligns 30 carefully curated Prophetic-medicine questions with human-verified remedies and compares three LLMs (LLaMA-3, Mistral-7B, Qwen2-7B) under three configurations: direct generation, retrieval-augmented generation, and a scientific self-critique filter. Each answer is then assessed by a secondary LLM serving as an agentic judge, yielding a single 3C3H quality score. Retrieval improves factual accuracy by 13%, while the agentic prompt adds another 10% improvement through deeper mechanistic insight and safety considerations. Our results demonstrate that blending classical Islamic texts with retrieval and self-evaluation enables reliable, culturally sensitive medical question-answering.

Hyunjae Kim, Jiwoong Sohn, Aidan Gilson et al.

Large language models (LLMs) are transforming the landscape of medicine, yet two fundamental challenges persist: keeping up with rapidly evolving medical knowledge and providing verifiable, evidence-grounded reasoning. Retrieval-augmented generation (RAG) has been widely adopted to address these limitations by supplementing model outputs with retrieved evidence. However, whether RAG reliably achieves these goals remains unclear. Here, we present the most comprehensive expert evaluation of RAG in medicine to date. Eighteen medical experts contributed a total of 80,502 annotations, assessing 800 model outputs generated by GPT-4o and Llama-3.1-8B across 200 real-world patient and USMLE-style queries. We systematically decomposed the RAG pipeline into three components: (i) evidence retrieval (relevance of retrieved passages), (ii) evidence selection (accuracy of evidence usage), and (iii) response generation (factuality and completeness of outputs). Contrary to expectation, standard RAG often degraded performance: only 22% of top-16 passages were relevant, evidence selection remained weak (precision 41-43%, recall 27-49%), and factuality and completeness dropped by up to 6% and 5%, respectively, compared with non-RAG variants. Retrieval and evidence selection remain key failure points for the model, contributing to the overall performance drop. We further show that simple yet effective strategies, including evidence filtering and query reformulation, substantially mitigate these issues, improving performance on MedMCQA and MedXpertQA by up to 12% and 8.2%, respectively. These findings call for re-examining RAG's role in medicine and highlight the importance of stage-aware evaluation and deliberate system design for reliable medical LLM applications.

H. Endtz, G. Ruijs, B. van Klingeren et al.

V. I. Koshevoy, S. V. Naumenko, I. I. Bespalova et al.

Creating a new drug for animals requires detailed preclinical studies of its active ingredient. The problem of mineral element deficiency in animals and poultry, on the one hand, is due to their nutritional deficiency, and on the other hand, is associated with the low bioavailability of compounds presented on the pharmaceutical market. Nanotechnologically synthesized substances are widely introduced in the world, which not only significantly increase the bioavailability of such compounds, but also reduce their toxicity in the macroform. Among them, the most common is zinc oxide – its nanoparticles (NPs), obtained by various methods, are successfully used as an effective source of zinc in poultry diets, with pronounced antioxidant, immunomodulatory and anti-inflammatory properties. However, most zinc compounds in nanoform still have a toxic effect on the body, especially with chronic intake. To solve this problem, we developed zinc hydrocarbonate (ZnCN) nanocrystals synthesized by the coprecipitation method, these NPs did not show acute toxicity and were classified as class VI. Further studies are aimed at determining the specific toxicity of ZnCN, including nephrotoxicity, which was the goal of this work. When ZnCN (25–200 mg/kg b. w.) was administered, no signs of intoxication were observed during the experiment. The level of biochemical markers of kidney damage was characterized by a compensatory increase during the administration of the studied compound, and after its cessation in rats of experimental groups 1–3 was at the level of the control group, and in experimental group 4 it had higher values. The urea content and the amount of creatinine in the blood plasma underwent a dose-dependent increase when administering lower doses of 25–50 mg/kg b. w. (experimental groups 1 and 2) these indicators had a slight increase during the study, and at the end of the experiment there were no significant differences from the control group. When administering higher doses of 100–200 mg/kg b. w. in rats of experimental groups 3 and 4 there was an intensification of urea formation and an increase in creatinine levels, which was obviously evidence of the rate of elimination of ZnCN in the animal body. The content of uric acid in the blood of animals of experimental groups 1 and 2 did not show an increase, and in experimental groups 3 and 4 it was higher than the control data throughout the entire period of the study. In general, no signs of pronounced nephrotoxicity of the studied NPs in the studied dosages were noted. Further studies will be aimed at determining the effects of ZnCN on the immune system, antioxidant status, and hormonal balance in animals.

Gema J. Cabañero-Resta, Bárbara Sánchez-Dengra, Alejandro Ruiz-Picazo et al.

Itraconazole is a drug used in veterinary medicine for the treatment of different varieties of dermatophytosis at doses between 3–5 mg/kg/day in cats. Nevertheless, in Spain, it is only available in the market as a 52 mL suspension at 10 mg/mL. The lack of alternative formulations, which provide sufficient formulation to cover the treatment of large animals or allow the treatment of a group of them, can be overcome with compounding. For this purpose, it has to be considered that itraconazole is a weak base, class II compound, according to the Biopharmaceutics Classification System, that can precipitate when reaching the duodenum. The aim of this work is to develop alternative oral formulations of itraconazole for the treatment of dermatophytosis. Several oral compounds of itraconazole were prepared and compared, in terms of dissolution rate, permeability, and stability, in order to provide alternatives to the medicine commercialized. The most promising formulation contained hydroxypropyl methylcellulose and β-cyclodextrin. This combination of excipients was capable of dissolving the same concentration as the reference product and delaying the precipitation of itraconazole upon leaving the stomach. Moreover, the intestinal permeability of itraconazole was increased more than two-fold.

Guoxing Liu, Guoxing Liu, Sen Zhang et al.

Vaccination is a widely adopted measure to prevent diseases, but the process of immunization can induce a substantial stress response. This study aimed to investigate the impact of a combined Mycoplasma bovis-BoHV-1 vaccine on the upper respiratory tract microbiome and BRD-associated pathogens in calves, as well as to evaluate the effects of potential interventions. The results showed that the percentage of Pasteurella species in the upper respiratory tract was elevated in calves after vaccination without intervention, and Pasteurella multocida was activated and proliferated. Interestingly, none of the three interventions (Sodium selenite-vitamin E, Astragalus polysaccharide and Ceftiofur sodium) affected antibody production after immunization. The administration of sodium selenite-vitamin E and astragalus polysaccharide reduced serum levels of cortisol and malondialdehyde, increased glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD), and alleviated the proliferation of Pasteurella multocida. Furthermore, the use of ceftiofur sodium almost completely inhibited the proliferation of Pasteurella multocida induced by immune stress. These findings provide a reference for mitigating the negative impacts associated with vaccination and highlight the potential benefits of using targeted nutritional and antimicrobial interventions to optimize immune responses and maintain a stable respiratory microbiome in calves.

Marie Abitbol, Caroline Dufaure de Citres, Gabriela Rudd Garces et al.

During the last twenty years, minimal white spotting associated with blue eyes was selected by feline breeders to create the Altai, Topaz, and Celestial breeds. Additionally, certain breeders introduced this trait in their lineages of purebred cats. The trait has been called “dominant blue eyes (DBE)” and was confirmed to be autosomal dominant in all lineages. DBE was initially described in outbred cats from Kazakhstan and Russia and in two purebred lineages of British cats from Russia, as well as in Dutch Maine Coon cats, suggesting different founding effects. We have previously identified two variants in the <i>Paired Box 3 (PAX3)</i> gene associated with DBE in Maine Coon and Celestial cats; however, the presence of an underlying variant remains undetermined in other DBE breeding lines. Using a genome-wide association study, we identified a single region on chromosome C1 that was associated with DBE in British cats. Within that region, we identified <i>PAX3</i> as the strongest candidate gene. Whole-genome sequencing of a DBE cat revealed an RD-114 retrovirus LTR (long terminal repeat) insertion within <i>PAX3</i> intron 4 (namely NC_018730.3:g.206975776_206975777insN[433]) known to contain regulatory sequences. Using a panel of 117 DBE cats, we showed that this variant was fully associated with DBE in two British lineages, in Altai cats, and in some other DBE lineages. We propose that this NC_018730.3:g.206975776_206975777insN[433] variant represents the <i>DBE^ALT</i> (<i>Altai Dominant Blue Eye</i>) allele in the domestic cat. Finally, we genotyped DBE cats from 14 lineages for the three <i>PAX3</i> variants and showed that they were not present in four lineages, confirming genetic heterogeneity of the DBE trait in the domestic cat.

Yunfei Xie, Juncheng Wu, Haoqin Tu et al.

Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key aspects: understanding, reasoning, and multilinguality. Specifically, our evaluation encompasses 6 tasks using data from 37 medical datasets, including two newly constructed and more challenging question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. These datasets offer greater clinical relevance compared to standard medical QA benchmarks such as MedQA, translating more effectively into real-world clinical utility. Our analysis of o1 suggests that the enhanced reasoning ability of LLMs may (significantly) benefit their capability to understand various medical instructions and reason through complex clinical scenarios. Notably, o1 surpasses the previous GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two newly created complex QA scenarios. But meanwhile, we identify several weaknesses in both the model capability and the existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. We release our raw data and model outputs at https://ucsc-vlaa.github.io/o1_medicine/ for future research.

Bongsu Kang, Jundong Kim, Tae-Rim Yun et al.

We propose a natural language prompt-based retrieval augmented generation (Prompt-RAG), a novel approach to enhance the performance of generative large language models (LLMs) in niche domains. Conventional RAG methods mostly require vector embeddings, yet the suitability of generic LLM-based embedding representations for specialized domains remains uncertain. To explore and exemplify this point, we compared vector embeddings from Korean Medicine (KM) and Conventional Medicine (CM) documents, finding that KM document embeddings correlated more with token overlaps and less with human-assessed document relatedness, in contrast to CM embeddings. Prompt-RAG, distinct from conventional RAG models, operates without the need for embedding vectors. Its performance was assessed through a Question-Answering (QA) chatbot application, where responses were evaluated for relevance, readability, and informativeness. The results showed that Prompt-RAG outperformed existing models, including ChatGPT and conventional vector embedding-based RAGs, in terms of relevance and informativeness. Despite challenges like content structuring and response latency, the advancements in LLMs are expected to encourage the use of Prompt-RAG, making it a promising tool for other domains in need of RAG methods.

Ahmed M Salih

Data pre-processing is a significant step in machine learning to improve the performance of the model and decreases the running time. This might include dealing with missing values, outliers detection and removing, data augmentation, dimensionality reduction, data normalization and handling the impact of confounding variables. Although it is found the steps improve the accuracy of the model, but they might hinder the explainability of the model if they are not carefully considered especially in medicine. They might block new findings when missing values and outliers removal are implemented inappropriately. In addition, they might make the model unfair against all the groups in the model when making the decision. Moreover, they turn the features into unitless and clinically meaningless and consequently not explainable. This paper discusses the common steps of the data preprocessing in machine learning and their impacts on the explainability and interpretability of the model. Finally, the paper discusses some possible solutions that improve the performance of the model while not decreasing its explainability.

Juan Miguel Lopez Alcaraz, Hjalmar Bouma, Nils Strodthoff

Background: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including raw waveform signals, remains underexplored in clinical decision support. Methods: We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care. Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration. Results: The diagnostic model achieves area under the receiver operating curve (AUROC) scores above 0.8 for 609 out of 1,428 conditions, covering both cardiac (e.g., myocardial infarction) and non-cardiac (e.g., renal disease, diabetes) diagnoses. The deterioration model attains AUROC scores above 0.8 for 14 out of 15 targets, accurately predicting critical events such as cardiac arrest, mechanical ventilation, ICU admission, and mortality. Conclusions: Our study highlights the positive impact of incorporating raw waveform data into decision support models, improving predictive performance. By introducing a unique, publicly available dataset and baseline models, we provide a foundation for measurable progress in AI-driven decision support for emergency care.

K. Br