Hasil untuk "Veterinary medicine"

H. H. Petersen, J. Nielsen, P. Heegaard

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).

Jihoon Jeong

Small language models (SLMs) in the 100M-10B parameter range increasingly power production systems, yet whether they possess the internal emotion representations recently discovered in frontier models remains unknown. We present the first comparative analysis of emotion vector extraction methods for SLMs, evaluating 9 models across 5 architectural families (GPT-2, Gemma, Qwen, Llama, Mistral) using 20 emotions and two extraction methods (generation-based and comprehension-based). Generation-based extraction produces statistically superior emotion separation (Mann-Whitney p = 0.007; Cohen's d = -107.5), with the advantage modulated by instruction tuning and architecture. Emotion representations localize at middle transformer layers (~50% depth), following a U-shaped curve that is architecture-invariant from 124M to 3B parameters. We validate these findings against representational anisotropy baselines across 4 models and confirm causal behavioral effects through steering experiments, independently verified by an external emotion classifier (92% success rate, 37/40 scenarios). Steering reveals three regimes -- surgical (coherent text transformation), repetitive collapse, and explosive (text degradation) -- quantified by perplexity ratios and separated by model architecture rather than scale. We document cross-lingual emotion entanglement in Qwen, where steering activates semantically aligned Chinese tokens that RLHF does not suppress, raising safety concerns for multilingual deployment. This work provides methodological guidelines for emotion research on open-weight models and contributes to the Model Medicine series by bridging external behavioral profiling with internal representational analysis.

Konstantinos Sechidis, Cong Zhang, Sophie Sun et al.

Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area.

Jack Foxabbott, Arush Tagade, Andrew Cusick et al.

The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.

Bo Yang, Zhi Biao Nan, Yan Zhong Li

IntroductionStanding milkvetch (Astragalus adsurgens) is widely distributed in the wild in Eurasia and North America and has been bred for cultivated forage in China. Yellow stunt and root rot disease caused by Alternaria gansuense is the primary disease of standing milkvetch. A. gansuense promotes the production of swainsonine in the plant. This study aimed to determine the safety of standing milkvetch that is infected with A. gansuense as forage for animals.MethodsTwo-week-old specific pathogen-free (SPF) male white mice were fed a commercial mouse feed (CMF), healthy plant feed (HPF) and diseased plant feed (DPF) for 3 or 6 weeks. We observed histological changes in the liver and kidney tissues of the mice and measured their daily feed intake, daily water intake, body weight, feed utilization, organ coefficients, and activities of serum enzymes.ResultsThe results showed that the daily feed intake of the mice that were fed DPF and HPF was significantly higher (p < 0.05) than those fed CMF at 3 and 6 weeks. The highest increase was observed in the daily water intake of the mice fed HPF (p < 0.05) followed by DPF and CMF. However, the mice fed DPF gained the least weight (p < 0.05). There was a significantly higher percentage of liver weight to body weight of the mice fed DPF (p < 0.05) than those fed HPF for 3 weeks and those fed CMF for 3 and 6 weeks. There were significantly higher levels of concentrations of alanine aminotransferase in the mice fed DPF and HPF than those fed CMF for 3 weeks (p < 0.05) and 6 weeks (p < 0.01). However, there was no significant difference in the mice fed HPF than those fed DPF. There were significantly higher of lactate dehydrogenase concentration (p < 0.001), while the blood urea nitrogen was lower in the mice fed DPF than those fed HPF and CMF at 3 weeks. There was a significantly higher percentage of numbers of lymphocytes in the blood of the mice fed DPF (p < 0.05) than those fed HPF, but the percentages of monocytes and eosinophils were significantly lower. Comparatively, there were more apparent pathological changes in the liver and kidney tissues of the mice fed with DPF than in those fed with HPF.DiscussionThese findings indicate that standing milkvetch was toxic to white mice, and infection with A. gansuense increased its toxicity. Therefore, we conclude that standing milkvetch plants infected by A. gansuense must never be used as animal feed under any circumstances. Additionally, the amount of healthy standing milkvetch fed to animals should be appropriate, avoiding long-term or excessive feeding.

Yakup Yıldırım, Seval Bilge Dağalp, Gökhan Bozkurt et al.

ABSTRACT Background The orf virus (ORFV) is a viral pathogen that primarily causes contagious ecthyma in humans and different ruminants. The infection, which is common worldwide, causes large‐scale economic losses to animal breeders. Objective and Methods In this study, tissue samples collected from eight randomly selected goats with dermatological lesions on the teats were examined in different goat herds. B2L gene‐specific primer pairs (PP1, PP3 and PP4) were used to reveal the presence of ORFV by molecular methods and for phylogenetic analysis. Results Viral DNA was detected in four of eight tissues using the semi‐nested PCR method. In addition, the data obtained by performing sequence analyses of the amplicons with positive results were compared with the information of different ORFV isolates registered in the GenBank database. Based on the sequence analysis of the field isolates obtained in our study, it was found that the nucleotide similarities among these isolates and those from Asian countries were 100%. Furthermore, ORFV isolates collected from different species and produced in Türkiye over various periods exhibited homologous nucleotide sequences with similarities ranging from 98.1% to 98.8%. In the phylogenetic tree drawn based on the B2L genomic region, it was observed that our field isolates were classified in Group I together with other Turkish and Asian strains. Conclusion As a result, while other pathogenic agents are considered the cause of disease in goats with dermatological lesions on their mammary tissue, the ORFV should also be evaluated, and protection and control programs should be prepared accordingly.

Md Abrar Jahin, Md. Akmol Masud, M. F. Mridha et al.

Heart failure is a leading cause of global mortality, necessitating improved diagnostic strategies. Classical machine learning models struggle with challenges such as high-dimensional data, class imbalances, poor feature representations, and a lack of interpretability. While quantum machine learning holds promise, current hybrid models have not fully exploited quantum advantages. In this paper, we propose the Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network (KACQ-DCNN), a novel hybrid architecture that replaces traditional multilayer perceptrons with Kolmogorov-Arnold Networks (KANs), enabling learnable univariate activation functions. Our KACQ-DCNN 4-qubit, 1-layer model outperforms 37 benchmark models, including 16 classical and 12 quantum neural networks, achieving an accuracy of 92.03%, with macro-average precision, recall, and F1 scores of 92.00%. It also achieved a ROC-AUC of 94.77%, surpassing other models by significant margins, as validated by paired t-tests with a significance threshold of 0.0056 (after Bonferroni correction). Ablation studies highlight the synergistic effect of classical-quantum integration, improving performance by about 2% over MLP variants. Additionally, LIME and SHAP explainability techniques enhance feature interpretability, while conformal prediction provides robust uncertainty quantification. Our results demonstrate that KACQ-DCNN improves cardiovascular diagnostics by combining high accuracy with interpretability and uncertainty quantification.

Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez et al.

The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlations or shortcuts, to obtain high performance on benchmark datasets but fail in real-world scenarios. In this work, we challenge the capacity of convolutional neural networks (CNN) to classify chest X-rays and eye fundus images while masking out clinically relevant parts of the image. We show that all models trained on the PadChest dataset, irrespective of the masking strategy, are able to obtain an Area Under the Curve (AUC) above random. Moreover, the models trained on full images obtain good performance on images without the region of interest (ROI), even superior to the one obtained on images only containing the ROI. We also reveal a possible spurious correlation in the Chaksu dataset while the performances are more aligned with the expectation of an unbiased model. We go beyond the performance analysis with the usage of the explainability method SHAP and the analysis of embeddings. We asked a radiology resident to interpret chest X-rays under different masking to complement our findings with clinical knowledge. Our code is available at https://github.com/TheoSourget/MMC_Masking and https://github.com/TheoSourget/MMC_Masking_EyeFundus

Younes Boulaguiem, Luca Insolia, Maria-Pia Victoria-Feser et al.

Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2α$)\% confidence intervals for the difference in means between the two conditions of interest lie within pre-defined lower and upper equivalence limits. This procedure, known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate $α$-TOST, that consists in a correction of $α$, the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define $α^{\star}$, the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between $α^{\star}$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate $α$-TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses -- i.e.,~relatively small sample sizes, unknown and heterogeneous variances, and different correlation structures -- and show the superior finite-sample properties of the multivariate $α$-TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters.

Qian Wen, Jun Wang, Lihui Dai et al.

This study was aimed at investigating the viral diversity associated with marine organisms in the South China Sea, to improve understanding of the region’s viral ecosystems. Viruses profoundly influence aquatic ecosystems, by affecting marine biogeochemical cycles and posing threats to marine organisms. Nonetheless, a comprehensive study of marine organisms’ viral diversity in the South China Sea remains lacking. We collected gill and viscera tissue samples from three marine phyla ( Chordata , Arthropoda , and Mollusca ) along the South China Sea coast. High-throughput sequencing and bioinformatics analyses were conducted to identify and characterize viral communities within these samples, with a focus on both viral composition and potential zoonotic threats. We observed distinct viral composition patterns across tissues and phyla, notably involving Adintoviridae and viruses within the Herpesviridae and Dicistroviridae . The presence of zoonotic viruses in economically important aquatic organisms suggests potential risks. This study contributes to broader understanding of viral diversity, by suggesting potential epidemic causes and illustrating genetic relationships among viruses associated with marine organisms. By extending the virus distribution map for this region, our findings underscore the need to consider the viral microenvironments surrounding marine species, and their implications for marine and human health.

Ashraf M. Nazem, Eman K. Abo Shaala, Sameh A. Awad

Background: Nanoparticles are regarded as magical bullets because of their exclusive features. Recently, the usage of nanoparticles has progressed in almost all aspects of science and technology due to its ability of revolutionizing certain fields. In the field of food science and technology, the application of nanoparticles is being researched in many various areas thus provide the dairy industry a variety of new attitudes for developing the quality, prolong shelf life, ensure safety and healthiness of foods. Aim: This study aimed to focus on the application of some inorganic metal oxide nanoparticles (zinc oxide (ZnO), magnesium oxide (MgO), and calcium oxide (CaO)) to control E. coli in raw milk and ensure its safety. Methods: The antibacterial action of certain nanoparticles (ZnO, MgO, and CaO) with multiple concentrations (0.1, 0.05, 0.025, 0.0125, 0.006, and 0.003 mg/ml) was evaluated against E. coli strains in UHT milk samples. As well, storage temperature and storage period effects were studied. Results: The findings of the current research revealed that inorganic metal oxide nanoparticles had a significant antibacterial role against E. coli, at the following order; ZnO, MgO, and CaO, respectively. The antibacterial effect of inorganic metal oxide nanoparticles is more noticed at lower temperatures. Conclusion: Inorganic metal nanoparticles can be used in the food industry for the purpose of the control of E. coli, and extension of the shelf life of the dairy products. [Open Vet J 2024; 14(1.000): 545-552]

J. Duncan, K. Prasse, E. Mahaffey

Aina Tersol Montserrat, Alexander R. Loftus, Yael Daihes

In machine learning, classification tasks serve as the cornerstone of a wide range of real-world applications. Reliable, trustworthy classification is particularly intricate in biomedical settings, where the ground truth is often inherently uncertain and relies on high degrees of human expertise for labeling. Traditional metrics such as precision and recall, while valuable, are insufficient for capturing the nuances of these ambiguous scenarios. Here we introduce the concept of aberrant predictions, emphasizing that the nature of classification errors is as critical as their frequency. We propose a novel, efficient training methodology aimed at both reducing the misclassification rate and discerning aberrant predictions. Our framework demonstrates a substantial improvement in model performance, achieving a 20\% increase in precision. We apply this methodology to the less-explored domain of veterinary radiology, where the stakes are high but have not been as extensively studied compared to human medicine. By focusing on the identification and mitigation of aberrant predictions, we enhance the utility and trustworthiness of machine learning classifiers in high-stakes, real-world scenarios, including new applications in the veterinary world.

Santiago Hernández-Orozco, Abicumaran Uthamacumaran, Francisco Hernández-Quiroz et al.

For more than two decades, advances in personalised medicine and precision healthcare have largely been based on genomics and other omics data. These strategies aim to tailor interventions to individual patient profiles, promising greater treatment efficacy and more efficient allocation of healthcare resources. Here, we show that widely collected common haematologic markers can reliably predict and discriminate individual chronological age and health status from even noisy sources. Our analysis includes synthetic and real retrospective patient data, including medically relevant and extreme cases, and draws on more than 100\,000 complete blood count records over 13 years from the United States Centers for Disease Control and Prevention's National Health and Nutrition Examination Survey (CDC NHANES). We combine fully explainable risk assessment scores with machine and deep learning techniques to focus on clinically significant patterns and characteristics without functioning purely as a ''black-box model allowing interpretation and control. We validated the results with the UK Biobank, a larger cohort independent of the CDC NHANES and with very different collection techniques, the former a survey and the second a longitudinal study. Unlike current biological ageing indicators, this approach may offer rapid, and scalable implementations of personalised, precision and predictive approaches to healthcare and medicine without or before requiring other specialised, uncommon or costly tests.

Amanda S. Latham, Amanda S. Latham, Julie A. Moreno et al.

Neuroinflammation is a universal characteristic of brain aging and neurological disorders, irrespective of the disease state. Glial inflammation mediates this signaling, through astrocyte and microglial polarization from neuroprotective to neurotoxic phenotypes. Glial reactivity results in the loss of homeostasis, as these cells no longer provide support to neurons, in addition to the production of chronically toxic pro-inflammatory mediators. These glial changes initiate an inflammatory brain state that injures the central nervous system (CNS) over time. As the brain ages, glia are altered, including increased glial cell numbers, morphological changes, and either a pre-disposition or inability to become reactive. These alterations induce age-related neuropathologies, ultimately leading to neuronal degradation and irreversible damage associated with disorders of the aged brain, including Alzheimer’s Disease (AD) and other related diseases. While the complex interactions of these glial cells and the brain are well studied, the role additional stressors, such as infectious agents, play on age-related neuropathology has not been fully elucidated. Both biological agents in the periphery, such as bacterial infections, or in the CNS, including viral infections like SARS-CoV-2, push glia into neuroinflammatory phenotypes that can exacerbate pathology within the aging brain. These biological agents release pattern associated molecular patterns (PAMPs) that bind to pattern recognition receptors (PRRs) on glial cells, beginning an inflammatory cascade. In this review, we will summarize the evidence that biological agents induce reactive glia, which worsens age-related neuropathology.

Paula Tilley, Joana Simões, José Paulo Sales Luis

From previous studies, the International Society for Equitation Science has advised that further research be conducted on the physiological/psychological effects of less-exacerbated poll flexion angles. We aimed to evaluate the effects of two riding poll flexion positions with a difference of only 15° on the respiratory systems and behaviour of horses through an evaluation of dynamic airway collapse via over-ground endoscopy, the pharyngeal diameter, pleural pressure, arterial oxygenation and lactate, HR/RR, and the occurrence of conflict behaviours. Twenty high-level dressage and twenty show-jumping horses underwent a 40 min ridden test at a ground angle of 85°; 3 weeks later, they underwent a ridden test at a 100° ground angle (the angle between the ground and the line from the forehead to the muzzle) and in a cross-over design. Using a mixed model for repeated measures, Wilcoxon/Friedman tests were carried out according to the experimental design and/or error normality. For both groups, at 100°, conflict behaviours and upper airway tract abnormalities were significantly more frequent, and the pleural pressure was higher, and the pharyngeal diameter was lower. At 85°, relaxation behaviours were significantly more frequent. Lactate was significantly higher at 100° only in the dressage horses. Compared to the first test at 85°, the HR/RR were significantly lower at the beginning of the second test (at 100°) but higher at the end. The significant differences identified in these dressage and show-jumping horses support the idea that an increase of just 15° in riding poll flexion can have negative effects on the respiratory system and behaviour of a horse and therefore on its welfare.

Sekobane Daniel Kolobe, Tlou Grace Manyelo, Emmanuel Malematja et al.

Common food sources including meat, fish and vegetables are the main source of fats and fatty acids required by human body. Edible insects such as worms, locusts, termites, crickets and flies have also been identified as a potential source of essential fatty acids since they are highly documented to be rich in unsaturated fatty acids such as α-linolenic and linoleic acids which are vital for the normal functioning of the body. The approval of insects as edible food by the European Union has sparked research interest in their potential to form part of human and animal diets due to their abundant protein, amino acids, fats, and minerals. However, little attention has been given to the importance and health benefits of lipids and fatty acids present in edible insects consumed by human and animals. This article aims to review the biological significance of essential fatty acids found in edible insects. The accumulation of fats and essential fatty acids present in edible insects were identified and described based on recommended levels required in human diets. Furthermore, the health benefits associated with insect oils as well as different processing techniques that could influence the quality of fats and fatty acid in edible insects were discussed.

Mahmood Alzubaidi, Marco Agus, Khalid Alyafei et al.

Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM Library, Google Scholar, and the Web of Science. We retrieved studies published between 2010 to 2021. Data extracted from studies were synthesized using a narrative approach. Out of 1269 retrieved studies, we included 107 distinct studies from queries that were relevant to the topic in the survey. We found that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound images (n=19). Classification is the most used method (n=42), followed by segmentation (n=31), classification integrated with segmentation (n=16) and other miscellaneous such as object-detection, regression and reinforcement learning (n=18). The most common areas within the pregnancy domain were the fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen (n=10), and lastly the fetus face (n=10). In the most recent studies, deep learning techniques were primarily used (n=81), followed by machine learning (n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI techniques played a crucial role in predicting fetal diseases and identifying fetus anatomy structures during pregnancy. More research is required to validate this technology from a physician's perspective, such as pilot studies and randomized controlled trials on AI and its applications in a hospital setting.

Luis R. Soenksen, Yu Ma, Cynthia Zeng et al.

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N=34,537 samples) containing 7,279 unique hospitalizations and 6,485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48-hour mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.