Hasil untuk "Veterinary medicine"

J. Zinsstag, E. Schelling, D. Waltner-Toews et al.

Faced with complex patterns of global change, the inextricable interconnection of humans, pet animals, livestock and wildlife and their social and ecological environment is evident and requires integrated approaches to human and animal health and their respective social and environmental contexts. The history of integrative thinking of human and animal health is briefly reviewed from early historical times, to the foundation of universities in Europe, up to the beginning of comparative medicine at the end of the 19th century. In the 20th century, Calvin Schwabe coined the concept of “one medicine”. It recognises that there is no difference of paradigm between human and veterinary medicine and both disciplines can contribute to the development of each other. Considering a broader approach to health and well-being of societies, the original concept of “one medicine” was extended to “one health” through practical implementations and careful validations in different settings. Given the global health thinking in recent decades, ecosystem approaches to health have emerged. Based on complex ecological thinking that goes beyond humans and animals, these approaches consider inextricable linkages between ecosystems and health, known as “ecosystem health”. Despite these integrative conceptual and methodological developments, large portions of human and animal health thinking and actions still remain in separate disciplinary silos. Evidence for added value of a coherent application of “one health” compared to separated sectorial thinking is, however, now growing. Integrative thinking is increasingly being considered in academic curricula, clinical practice, ministries of health and livestock/agriculture and international organizations. Challenges remain, focusing around key questions such as how does “one health” evolve and what are the elements of a modern theory of health? The close interdependence of humans and animals in their social and ecological context relates to the concept of “human-environmental systems”, also called “social-ecological systems”. The theory and practice of understanding and managing human activities in the context of social-ecological systems has been well-developed by members of The Resilience Alliance and was used extensively in the Millennium Ecosystem Assessment, including its work on human well-being outcomes. This in turn entails systems theory applied to human and animal health. Examples of successful systems approaches to public health show unexpected results. Analogous to “systems biology” which focuses mostly on the interplay of proteins and molecules at a sub-cellular level, a systemic approach to health in social-ecological systems (HSES) is an inter- and trans-disciplinary study of complex interactions in all health-related fields. HSES moves beyond “one health” and “eco-health”, expecting to identify emerging properties and determinants of health that may arise from a systemic view ranging across scales from molecules to the ecological and socio-cultural context, as well from the comparison with different disease endemicities and health systems structures.

W. P. Thomas, C. Gaber, G. Jacobs et al.

S. Nguyen, D. Thamm, D. Vail et al.

Michael Cuccarese

Activity cliff prediction - identifying positions where small structural changes cause large potency shifts - has been a persistent challenge in computational medicinal chemistry. This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change. Position-level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families, revealing that two questions have fundamentally different answers. "Which positions vary most?" is answered by scaffold size alone (NDCG@3 = 0.966), requiring no machine learning. "Which are true activity cliffs?" - where small modifications cause disproportionately large effects, as captured by SALI normalization - requires an 11-feature model with 3D pharmacophore context (NDCG@3 = 0.910 vs. 0.839 random), generalizing across all six protein families, novel scaffolds (0.913), and temporal splits (0.878). The model identifies the cliff-prone position first 53% of the time (vs. 27% random - 2x lift), reducing positions a chemist must explore from 3.1 to 2.1 - a 31% reduction in first-round experiments. Predicting which modification to make is not tractable from structure alone (Spearman 0.268, collapsing to -0.31 on novel scaffolds). The system is released as open-source code and an interactive webapp.

Shubham Vatsal, Harsh Dubey, Aditi Singh

Large Language Model (LLM)-based agents that plan, use tools and act has begun to shape healthcare and medicine. Reported studies demonstrate competence on various tasks ranging from EHR analysis and differential diagnosis to treatment planning and research workflows. Yet the literature largely consists of overviews which are either broad surveys or narrow dives into a single capability (e.g., memory, planning, reasoning), leaving healthcare work without a common frame. We address this by reviewing 49 studies using a seven-dimensional taxonomy: Cognitive Capabilities, Knowledge Management, Interaction Patterns, Adaptation & Learning, Safety & Ethics, Framework Typology and Core Tasks & Subtasks with 29 operational sub-dimensions. Using explicit inclusion and exclusion criteria and a labeling rubric (Fully Implemented, Partially Implemented, Not Implemented), we map each study to the taxonomy and report quantitative summaries of capability prevalence and co-occurrence patterns. Our empirical analysis surfaces clear asymmetries. For instance, the External Knowledge Integration sub-dimension under Knowledge Management is commonly realized (~76% Fully Implemented) whereas Event-Triggered Activation sub-dimenison under Interaction Patterns is largely absent (~92% Not Implemented) and Drift Detection & Mitigation sub-dimension under Adaptation & Learning is rare (~98% Not Implemented). Architecturally, Multi-Agent Design sub-dimension under Framework Typology is the dominant pattern (~82% Fully Implemented) while orchestration layers remain mostly partial. Across Core Tasks & Subtasks, information centric capabilities lead e.g., Medical Question Answering & Decision Support and Benchmarking & Simulation, while action and discovery oriented areas such as Treatment Planning & Prescription still show substantial gaps (~59% Not Implemented).

Haoran Li, Xingye Cheng, Ziyang Huang et al.

Traditional Chinese Medicine diagnosis and treatment principles, established through centuries of trial-and-error clinical practice, directly maps patient-specific symptom patterns to personalised herbal therapies. These empirical holistic mapping principles offer valuable strategies to address remaining challenges of reductionism methodologies in modern biomedicine. However, the lack of a quantitative framework and molecular-level evidence has limited their interpretability and reliability. Here, we present an AI framework trained on ancient and classical TCM formula records to quantify the symptom pattern-herbal therapy mappings. Interestingly, we find that empirical TCM diagnosis and treatment are consistent with the encoding-decoding processes in the AI model. This enables us to construct an interpretable TCM embedding space (TCM-ES) using the model's quantitative representation of TCM principles. Validated through broad and extensive TCM patient data, the TCM-ES offers universal quantification of the TCM practice and therapeutic efficacy. We further map biomedical entities into the TCM-ES through correspondence alignment. We find that the principal directions of the TCM-ES are significantly associated with key biological functions (such as metabolism, immune, and homeostasis), and that the disease and herb embedding proximity aligns with their genetic relationships in the human protein interactome, which demonstrate the biological significance of TCM principles. Moreover, the TCM-ES uncovers latent disease relationships, and provides alternative metric to assess clinical efficacy for modern disease-drug pairs. Finally, we construct a comprehensive and integrative TCM knowledge graph, which predicts potential associations between diseases and targets, drugs, herbal compounds, and herbal therapies, providing TCM-informed opportunities for disease analysis and drug development.

Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee et al.

Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.

Aliaksandr Marozau, Barbara Karpowicz, Tomasz Kowalewski et al.

Mixed Reality (MR) technologies such as Virtual and Augmented Reality (VR, AR) are well established in medical practice, enhancing diagnostics, treatment, and education. However, there are still some limitations and challenges that may be overcome thanks to the latest generations of equipment, software, and frameworks based on eXtended Reality (XR) by enabling immersive systems that support safer, more controlled environments for training and patient care. Our review highlights recent VR and AR applications in key areas of medicine. In medical education, these technologies provide realistic clinical simulations, improving skills and knowledge retention. In surgery, immersive tools enhance procedural precision with detailed anatomical visualizations. VR-based rehabilitation has shown effectiveness in restoring motor functions and balance, particularly for neurological patients. In mental health, VR has been successful in treating conditions like PTSD and phobias. Although VR and AR solutions are well established, there are still some important limitations, including high costs and limited tactile feedback, which may be overcome with implementing new technologies that may improve the effectiveness of immersive medical applications such as XR, psychophysiological feedback or integration of artificial intelligence (AI) for real-time data analysis and personalized healthcare and training.

Tianai Huang, Jiayuan Chen, Lu Lu et al.

Large language models (LLMs) have demonstrated exceptional capabilities in general domains, yet their application in highly specialized and culturally-rich fields like Traditional Chinese Medicine (TCM) requires rigorous and nuanced evaluation. Building upon prior foundational work such as TCM-3CEval, which highlighted systemic knowledge gaps and the importance of cultural-contextual alignment, we introduce TCM-5CEval, a more granular and comprehensive benchmark. TCM-5CEval is designed to assess LLMs across five critical dimensions: (1) Core Knowledge (TCM-Exam), (2) Classical Literacy (TCM-LitQA), (3) Clinical Decision-making (TCM-MRCD), (4) Chinese Materia Medica (TCM-CMM), and (5) Clinical Non-pharmacological Therapy (TCM-ClinNPT). We conducted a thorough evaluation of fifteen prominent LLMs, revealing significant performance disparities and identifying top-performing models like deepseek\_r1 and gemini\_2\_5\_pro. Our findings show that while models exhibit proficiency in recalling foundational knowledge, they struggle with the interpretative complexities of classical texts. Critically, permutation-based consistency testing reveals widespread fragilities in model inference. All evaluated models, including the highest-scoring ones, displayed a substantial performance degradation when faced with varied question option ordering, indicating a pervasive sensitivity to positional bias and a lack of robust understanding. TCM-5CEval not only provides a more detailed diagnostic tool for LLM capabilities in TCM but aldso exposes fundamental weaknesses in their reasoning stability. To promote further research and standardized comparison, TCM-5CEval has been uploaded to the Medbench platform, joining its predecessor in the "In-depth Challenge for Comprehensive TCM Abilities" special track.

Soumen Sinha, Tanisha Rana, Susmita Ghosh et al.

In this article, we propose a novel approach for plant hierarchical taxonomy classification by posing the problem as an open class problem. It is observed that existing methods for medicinal plant classification often fail to perform hierarchical classification and accurately identifying unknown species, limiting their effectiveness in comprehensive plant taxonomy classification. Thus we address the problem of unknown species classification by assigning it best hierarchical labels. We propose a novel method, which integrates DenseNet121, Multi-Scale Self-Attention (MSSA) and cascaded classifiers for hierarchical classification. The approach systematically categorizes medicinal plants at multiple taxonomic levels, from phylum to species, ensuring detailed and precise classification. Using multi scale space attention, the model captures both local and global contextual information from the images, improving the distinction between similar species and the identification of new ones. It uses attention scores to focus on important features across multiple scales. The proposed method provides a solution for hierarchical classification, showcasing superior performance in identifying both known and unknown species. The model was tested on two state-of-art datasets with and without background artifacts and so that it can be deployed to tackle real word application. We used unknown species for testing our model. For unknown species the model achieved an average accuracy of 83.36%, 78.30%, 60.34% and 43.32% for predicting correct phylum, class, order and family respectively. Our proposed model size is almost four times less than the existing state of the art methods making it easily deploy able in real world application.

Luohong Wu, Nicola A. Cavalcanti, Matthias Seibold et al.

Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, and acoustic shadowing, which make interpretation difficult. Existing deep learning models for bone segmentation rely primarily on costly manual labeling by experts, limiting dataset size and model generalizability. Additionally, the complexity of ultrasound physics and acoustic shadow makes the images difficult for humans to interpret, leading to incomplete labels in anechoic regions and limiting model performance. To advance ultrasound bone segmentation and establish effective model benchmarks, larger and higher-quality datasets are needed. We propose a methodology for collecting ex-vivo ultrasound datasets with automatically generated bone labels, including anechoic regions. The proposed labels are derived by accurately superimposing tracked bone CT models onto the tracked ultrasound images. These initial labels are refined to account for ultrasound physics. A clinical evaluation is conducted by an expert physician specialized on orthopedic sonography to assess the quality of the generated bone labels. A neural network for bone segmentation is trained on the collected dataset and its predictions are compared to expert manual labels, evaluating accuracy, completeness, and F1-score. We collected the largest known dataset of 100k ultrasound images of human lower limbs with bone labels, called UltraBones100k. A Wilcoxon signed-rank test with Bonferroni correction confirmed that the bone alignment after our method significantly improved the quality of bone labeling (p < 0.001). The model trained on UltraBones100k consistently outperforms manual labeling in all metrics, particularly in low-intensity regions (320% improvement in completeness at a distance threshold of 0.5 mm).

Diego Pulido Vega, Jérémie Ficheroulle, Jérémie Ficheroulle et al.

IntroductionPrediction of renal recovery after surgical management of feline unilateral ureteral obstruction (UO) is crucial to guide therapeutic decisions, but predictors of this outcome are still lacking. Despite the functional importance of the contralateral kidney, there is currently no precise description of its ultrasonographic (US) features. In addition, US parameters of both the renal collecting system and the renal parenchyma have been identified in human medicine as prognostic factors in the case of UO but have not been described in veterinary medicine. The aim of this study was to evaluate an association between preoperative structural US renal parameters and long-term International Renal Interest Society (IRIS) stage after successful renal decompression with subcutaneous ureteral bypass (SUB) device in cats with unilateral UO.MethodsThis retrospective study included 60 cats with unilateral UO and evaluated preoperative US parameters of both kidneys, including measurements of parenchymal and pelvic areas as well as a renal score. Cats were divided according to their serum creatinine at 3 months postoperatively into group A (IRIS stages I and II) and group B (IRIS stages III and IV).ResultsA higher US chronic kidney disease (US-CKD) score of the kidney contralateral to the UO was associated with long-term IRIS stages III and IV. It also appeared as a fair discriminator of long-term IRIS stage IV, with an area under the curve of 0.74. The optimal cutoff value for accurately identifying cats with long-term IRIS stage IV was a US-CKD score &gt; 7, with a specificity of 98%, a sensitivity of 25%, and a positive likelihood ratio of 12.75. No preoperative US parameters regarding the obstructed kidney, including parenchymal and pelvic areas, were significantly associated with long-term creatinine.ConclusionUltrasonographic scoring of contralateral chronic kidney disease abnormalities is associated with IRIS stage following treatment of feline unilateral UO with a SUB device and serves as a specific indicator of cats presenting with long-term IRIS stage IV.

WeiChun Huang, Mathieu Victor Paulin, Elisabeth C. R. Snead

Abstract Background Although dyslipidemia is commonly reported in dogs, comparative data on the magnitude of serum cholesterol disturbances have not been reported. We aimed to describe the severity of hyper- and hypocholesterolemia in dogs with common endocrinopathies and to evaluate its association with common laboratory parameters. Medical records were reviewed over a decade (2011–2022) for dogs with hypothyroidism, diabetes mellitus (DM), hyperadrenocorticism (HAC), or hypoadrenocorticism (HA), and included signalment, common laboratory and diagnostic imaging parameters, comorbidities, and medications. This retrospective study included 53 dogs with hypothyroidism, 54 with DM, 62 with HAC, and 79 with HA. Results Medians [range] of serum cholesterol concentration ([Chol]s) for dogs with hypothyroidism, DM, HAC, and HA were 492 [174–1829], 321 [116–928], 309 [151–630], and 112 mg/dL [31–309], and hypercholesterolemia was reported in 91%, 85%, 81%, and 9% for each disorder, respectively. Median [Chol]s was significantly higher in hypothyroid dogs with a serum thyroxine concentration < 0.47 (A = 607) vs. ≥0.47 ug/dL (B = 324 mg/dL) (B-A = -299 mg/dL; 95.21% CI of difference = [-433; -166]; p < .0001), and significantly lower in HAC dogs with serum ALP activity < 1,000 U/L (A = 275) vs. ≥1,000 (B = 360 mg/dL) (B-A = + 74 mg/dL; 95.14% CI of difference = [+ 25; +121], p = .006). Comparison among all studied endocrinopathies showed that median [Chol]s was significantly higher in hypothyroid dogs and significantly lower in HA dogs, whereas median [Chol]s was similar in HAC and DM dogs. Conclusions Serum cholesterol concentration can serve as a valuable tool to suspect certain canine endocrinopathies.

Youpeng Su, Siqi Xu, Yilei Ma et al.

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences about the effects of modifiable risk factors on diseases from observational data. One of the major challenges in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the risk factor of interest, a setting known as many weak instruments. Many existing methods, such as the popular inverse-variance weighted (IVW) method, could be biased when the instrument strength is weak. To address this issue, the debiased IVW (dIVW) estimator, which is shown to be robust to many weak instruments, was recently proposed. However, this estimator still has non-ignorable bias when the effective sample size is small. In this paper, we propose a modified debiased IVW (mdIVW) estimator by multiplying a modification factor to the original dIVW estimator. After this simple correction, we show that the bias of the mdIVW estimator converges to zero at a faster rate than that of the dIVW estimator under some regularity conditions. Moreover, the mdIVW estimator has smaller variance than the dIVW estimator.We further extend the proposed method to account for the presence of instrumental variable selection and balanced horizontal pleiotropy. We demonstrate the improvement of the mdIVW estimator over the dIVW estimator through extensive simulation studies and real data analysis.

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson et al.

Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is necessary to incorporate the particular characteristics of these images. We hypothesize that the similarity of medical images hinders the success of contrastive learning in the medical imaging domain. To this end, we investigate different strategies based on deep embedding, information theory, and hashing in order to identify and reduce redundancy in medical pre-training datasets. The effect of these different reduction strategies on contrastive learning is evaluated on two pre-training datasets and several downstream classification tasks. In all of our experiments, dataset reduction leads to a considerable performance gain in downstream tasks, e.g., an AUC score improvement from 0.78 to 0.83 for the COVID CT Classification Grand Challenge, 0.97 to 0.98 for the OrganSMNIST Classification Challenge and 0.73 to 0.83 for a brain hemorrhage classification task. Furthermore, pre-training is up to nine times faster due to the dataset reduction. In conclusion, the proposed approach highlights the importance of dataset quality and provides a transferable approach to improve contrastive pre-training for classification downstream tasks on medical images.

Yueyang Li, Weiming Zeng, Wenhao Dong et al.

Background: Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. Methods: We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction. MHNet has two branches: the Euclidean Space Features Extraction (ESFE) module and the Non-Euclidean Space Features Extraction (Non-ESFE) module, followed by a Feature Fusion-based Classification (FFC) module for NDD identification. ESFE includes a Functional Connectivity Generation (FCG) module and a High-order Convolutional Neural Network (HCNN) module to extract local and high-order features from BFNs in Euclidean space. Non-ESFE comprises a Generic Internet-like Brain Hierarchical Network Generation (G-IBHN-G) module and a High-order Graph Neural Network (HGNN) module to capture topological and high-order features in non-Euclidean space. Results: Experiments on three public datasets show that MHNet outperforms state-of-the-art methods using both AAL1 and Brainnetome Atlas templates. Extensive ablation studies confirm the superiority of MHNet and the effectiveness of using multi-view fMRI information and high-order features. Our study also offers atlas options for constructing more sophisticated hierarchical networks and explains the association between key brain regions and NDD. Conclusion: MHNet leverages multi-view feature learning from both Euclidean and non-Euclidean spaces, incorporating high-order information from BFNs to enhance NDD classification performance.

Moritz Blumenthal, Chiara Fantinato, Christina Unterberg-Buchwald et al.

Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via non-linear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the FoV and to focus the k-space based SSDU loss on the region of interest. NLINV-Net based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. Results: NLINV-Net based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focussed loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. Conclusion: NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

Emilie A Paterson, Carly I O’Malley, Dawn M Abney et al.

Primates are important species for biomedical research and ensuring their good welfare is critical for research translatability and ethical responsibility. Systematic animal welfare assessments can support continuous programme improvements and build institutional awareness of areas requiring more attention. A multi-facility, collaborative project aimed to develop and implement a novel primate welfare assessment tool (PWAT) for use with research macaques. PWAT development involved: establishing an internal focus group of primate subject matter experts, identifying animal welfare categories and descriptors based on literature review, developing a preliminary tool, beta-testing the tool to ensure practicality and final consensus on descriptors, finalising the tool in a database with semi-automated data analysis, and delivering the tool to 13 sites across four countries. The tool uses input- and outcome-based measures from six categories: physical, behavioural, training, environmental, procedural, and culture of care. The final tool has 133 descriptors weighted based upon welfare impact, and is split into three forms for ease of use (room level, site level, and personnel interviews). The PWAT was trialled across facilities in March and September 2022 for benchmarking current macaque behavioural management programmes. The tool successfully distinguished strengths and challenges at the facility level and across sites. Following this benchmarking, the tool is being applied semi-annually to assess and monitor progress in behavioural management programmes. The development process of the PWAT demonstrates that evidence-based assessment tools can be developed through collaboration and consensus building, which are important for uptake and applicability, and ultimately for promoting global improvements in research macaque welfare.

Eric N. Ponnampalam, Eric N. Ponnampalam, Michelle Kearns et al.

Optimising resource use efficiency in animal- agriculture-production systems is important for the economic, environmental, and social sustainability of food systems. Production of foods with increased health enhancing aspects can add value to the health and wellbeing of the population. However, enrichment of foods, especially meat with health enhancing fatty acids (HEFA) increases susceptibility to peroxidation, which adversely influences its shelf life, nutritional value and eating quality. The meat industry has been challenged to find sustainable strategies that enhance the fatty acid profile and antioxidant actions of meat while mitigating oxidative deterioration and spoilage. Currently, by-products or co-products from agricultural industries containing a balance of HEFA and antioxidant sources seem to be a sustainable strategy to overcome this challenge. However, HEFA and antioxidant enrichment processes are influenced by ruminal lipolysis and biohydrogenation, HEFA-antioxidant interactions in rumen ecosystems and muscle biofortification. A deep understanding of the performance of different agro-by-product-based HEFA and antioxidants and their application in current animal production systems is critical in developing HEFA-antioxidant co-supplementation strategies that would benefit modern consumers who desire nutritious, palatable, safe, healthy, affordable, and welfare friendly meat and processed meat products. The current review presents the latest developments regarding discovery and application of novel sources of health beneficial agro-by-product-based HEFA and antioxidants currently used in the production of HEFA-antioxidant enriched ruminant meats and highlights future research perspectives.

D. Gunn-Moore