Hasil untuk "Veterinary medicine"

Keer Lu, Zheng Liang, Da Pan et al.

Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. Despite their potential, existing works face challenges when applying LLMs to medical settings. Strategies relying on training with medical datasets are highly cost-intensive and may suffer from outdated training data. Leveraging external knowledge bases is a suitable alternative, yet it faces obstacles such as limited retrieval precision and poor effectiveness in answer extraction. These issues collectively prevent LLMs from demonstrating the expected level of proficiency in mastering medical expertise. To address these challenges, we introduce Med-R^2, a novel LLM physician framework that adheres to the Evidence-Based Medicine (EBM) process, efficiently integrating retrieval mechanisms as well as the selection and reasoning processes of evidence, thereby enhancing the problem-solving capabilities of LLMs in healthcare scenarios and fostering a trustworthy LLM physician. Our comprehensive experiments indicate that Med-R^2 achieves a 13.27\% improvement over vanilla RAG methods and even a 4.55\% enhancement compared to fine-tuning strategies, without incurring additional training costs. Furthermore, we find that our LLaMA3.1-70B + Med-R$^2$ surpasses frontier models, including GPT-4o, Claude3.5-Sonnet and DeepSeek-V3 by 1.05\%, 6.14\% and 1.91\%. Med-R$^2$ effectively enhances the capabilities of LLMs in the medical domain.

Xinhan Zheng, Huyu Wu, Ruotai Li et al.

Traditional Chinese medicine (TCM) exhibits remarkable therapeutic efficacy in disease treatment and healthcare through patienti-specific formulas. However, current AI-based TCM formula recommendation models and methods mainly focus on data-based textual associations between symptoms and herbs, and have not fully utilized their features and relations at different scales, especially at the molecular scale. To address these limitations, we propose the Fusion of Multiscale Associations of Symptoms and Herbs (FMASH), an novel framework that effectively combines molecular-scale features and macroscopic properties of herbs with clinical symptoms, and provides the refined representation of their multiscale associations, enhancing the effectiveness of TCM formula recommendation. This framework can integrate molecular-scale chemical features and macroscopic properties of herbs, and capture complex local and global relations in the heterogeneous graph of symptoms and herbs, providing the effective embedding representation of their multiscale features and associations in a unified semantic space. Based on the refined feature representation, the framework is not only compatible with both traditional unordered formula recommendation task and the ordered herb sequence generation task, but also improves model's performance in both tasks. Comprehensive evaluations demonstrate FMASH's superior performance on the TCM formula recommendation over the state-of-the-art (SOTA) baseline, achieving relative improvements of 9.45\% in Precision@5, 12.11% in Recall@5, and 11.01% in F1@5 compared to the SOTA model on benchmark datasets. This work facilitates the practical application of AI-based TCM formula recommendation system.

Xiaoyang Wang, Christopher C. Yang

The rapid growth of healthcare data and advances in computational power have accelerated the adoption of artificial intelligence (AI) in medicine. However, AI systems deployed without explicit fairness considerations risk exacerbating existing healthcare disparities, potentially leading to inequitable resource allocation and diagnostic disparities across demographic subgroups. To address this challenge, we propose FairGrad, a novel gradient reconciliation framework that automatically balances predictive performance and multi-attribute fairness optimization in healthcare AI models. Our method resolves conflicting optimization objectives by projecting each gradient vector onto the orthogonal plane of the others, thereby regularizing the optimization trajectory to ensure equitable consideration of all objectives. Evaluated on diverse real-world healthcare datasets and predictive tasks - including Substance Use Disorder (SUD) treatment and sepsis mortality - FairGrad achieved statistically significant improvements in multi-attribute fairness metrics (e.g., equalized odds) while maintaining competitive predictive accuracy. These results demonstrate the viability of harmonizing fairness and utility in mission-critical medical AI applications.

Rui Yang, Matthew Yu Heng Wong, Huitao Li et al.

The rapid growth of medical knowledge and increasing complexity of clinical practice pose challenges. In this context, large language models (LLMs) have demonstrated value; however, inherent limitations remain. Retrieval-augmented generation (RAG) technologies show potential to enhance their clinical applicability. This study reviewed RAG applications in medicine. We found that research primarily relied on publicly available data, with limited application in private data. For retrieval, approaches commonly relied on English-centric embedding models, while LLMs were mostly generic, with limited use of medical-specific LLMs. For evaluation, automated metrics evaluated generation quality and task performance, whereas human evaluation focused on accuracy, completeness, relevance, and fluency, with insufficient attention to bias and safety. RAG applications were concentrated on question answering, report generation, text summarization, and information extraction. Overall, medical RAG remains at an early stage, requiring advances in clinical validation, cross-linguistic adaptation, and support for low-resource settings to enable trustworthy and responsible global use.

Ahmed Kabil, Ghada Khoriba, Mina Yousef et al.

Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model generalizability across diverse imaging modalities. Furthermore, a specialized case study on lumbar spine segmentation is presented, offering insights into the challenges and advancements in this relatively underexplored anatomical region. Despite significant progress in the field, critical challenges persist, including dataset bias, domain adaptation, interpretability of deep learning models, and integration into real-world clinical workflows.

Yehan Yang, Tianhao Ma, Ruotai Li et al.

The practice of Traditional Chinese Medicine (TCM) requires profound expertise and extensive clinical experience. While Large Language Models (LLMs) offer significant potential in this domain, current TCM-oriented LLMs suffer two critical limitations: (1) a rigid consultation framework that fails to conduct comprehensive and patient-tailored interactions, often resulting in diagnostic inaccuracies; and (2) treatment recommendations generated without rigorous syndrome differentiation, which deviates from the core diagnostic and therapeutic principles of TCM. To address these issues, we develop \textbf{JingFang (JF)}, an advanced LLM-based multi-agent system for TCM that facilitates the implementation of AI-assisted TCM diagnosis and treatment. JF integrates various TCM Specialist Agents in accordance with authentic diagnostic and therapeutic scenarios of TCM, enabling personalized medical consultations, accurate syndrome differentiation and treatment recommendations. A \textbf{Multi-Agent Collaborative Consultation Mechanism (MACCM)} for TCM is constructed, where multiple Agents collaborate to emulate real-world TCM diagnostic workflows, enhancing the diagnostic ability of base LLMs to provide accurate and patient-tailored medical consultation. Moreover, we introduce a dedicated \textbf{Syndrome Differentiation Agent} fine-tuned on a preprocessed dataset, along with a designed \textbf{Dual-Stage Recovery Scheme (DSRS)} within the Treatment Agent, which together substantially improve the model's accuracy of syndrome differentiation and treatment. Comprehensive evaluations and experiments demonstrate JF's superior performance in medical consultation, and also show improvements of at least 124% and 21.1% in the precision of syndrome differentiation compared to existing TCM models and State of the Art (SOTA) LLMs, respectively.

Andreea Pușcaș, Anda Elena Tanislav, Andruța Elena Mureșan et al.

Hydrogels (Hy) were obtained with a ternary system of proteins (pea (P) or soy isolate (S) 2%), guar (0.5%), and xanthan gums (0.5%) and were subjected to thermal treatment (70 °C/20 min or 85 °C/15 min, or not) prior to structure formation. The FTIR spectra of the hydrogels and the turbidity test (spectrophotometrically red at 600 nm) were used for studying protein–polysaccharide interactions. Amplitude sweeps (0.01–100%) and flow behavior tests (0.1–100 s⁻¹) were conducted for structure analysis. Emulgels were obtained by emulsification of the Hy with 40% or 60% sunflower oil. The centrifugal stability and texture (TPA test) of the emulgels were assessed and SND_40% exhibited the highest hardness (5.30 ± 0.23 N). Based on the results, SND_40%, PND_40%, SD70_40%, and PD_70% were chosen as fat-replacing systems in biscuit formulation. The textural, color, and stability attributes of the reformulated samples were compared with a reference containing margarine. Increased hardness and fracturability were determined for the emulgel-based biscuits, while the color parameters were statistically similar to the reference. Thermal treatments applied to enhance protein–polysaccharide interactions increased the structural performances of some emulgels, while their application as fat-replacing systems should be further evaluated since no statistical differences were recorded in the sensory evaluation of the reference and reformulated biscuits. Emulgels with tuned technological properties have the potential to replace saturated fats in foods.

Florina DEIAC, Felix Horațiu ARION

The cluster of excellence in the agri-food sector could function as a significant driver for promoting innovation and advanced technologies throughout all aspects of the supply chain. This collaboration can facilitate the exchange of knowledge and experiences, promoting innovation and the development of efficient solutions. Innovative clusters have an impact on regional growth because they enable new technology applications, cost savings, and research. Romania's North West area is well-known for having the highest number of gold-certified clusters in a variety of disciplines, including the lone gold-certified cluster in the agro-industrial sector. For the gold clusters in the North West region, the study finds less meaningful indicators for the agro-industrial sector using the ESCA standard technique for management excellence indicator evaluation.

Abhiram B. Nair, Abhinand K., Anamika U. et al.

Sentiment analysis has become increasingly important in healthcare, especially in the biomedical and pharmaceutical fields. The data generated by the general public on the effectiveness, side effects, and adverse drug reactions are goldmines for different agencies and medicine producers to understand the concerns and reactions of people. Despite the challenge of obtaining datasets on drug-related problems, sentiment analysis on this topic would be a significant boon to the field. This project proposes a drug review classification system that classifies user reviews on a particular drug into different classes, such as positive, negative, and neutral. This approach uses a dataset that is collected from publicly available sources containing drug reviews, such as drugs.com. The collected data is manually labeled and verified manually to ensure that the labels are correct. Three pre-trained language models, such as BERT, SciBERT, and BioBERT, are used to obtain embeddings, which were later used as features to different machine learning classifiers such as decision trees, support vector machines, random forests, and also deep learning algorithms such as recurrent neural networks. The performance of these classifiers is quantified using precision, recall, and f1-score, and the results show that the proposed approaches are useful in analyzing the sentiments of people on different drugs.

Xi Chen, MingKe You, Li Wang et al.

The efficacy of large language models (LLMs) in domain-specific medicine, particularly for managing complex diseases such as osteoarthritis (OA), remains largely unexplored. This study focused on evaluating and enhancing the clinical capabilities of LLMs in specific domains, using osteoarthritis (OA) management as a case study. A domain specific benchmark framework was developed, which evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM tailored for OA management that integrates retrieval-augmented generation (RAG) and instruction prompts, was developed. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations. Results showed that general LLMs like GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements. This study introduces a novel benchmark framework which assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs.

Gary An, Chase Cockrell

On December 15, 2023, The National Academies of Sciences, Engineering and Medicine (NASEM) released a report entitled: Foundational Research Gaps and Future Directions for Digital Twins. The ostensible purpose of this report was to bring some structure to the burgeoning field of digital twins by providing a working definition and a series of research challenges that need to be addressed to allow this technology to fulfill its full potential. In the work presented herein we focus on five specific findings from the NASEM Report: 1) definition of a Digital Twin, 2) using fit-for-purpose guidance, 3) developing novel approaches to Verification, Validation and Uncertainty Quantification (VVUQ) of Digital Twins, 4) incorporating control as an explicit purpose for a Digital Twin and 5) using a Digital Twin to guide data collection and sensor development, and describe how these findings are addressed through the design specifications for a Critical Illness Digital Twin (CIDT) aimed at curing sepsis.

Parul Singh, Aniruddha Dan, Padma Priya Kannan et al.

This research work showcases a non-toxic approach to synthesize carbon nanoparticles (CNPs) from various medicinal plants namely Syzygium cumini, Holy basil, Azadirachta indica A, Psidium guajava, Mangifera indica, and Bergera koenigii using microwave approach. The optical, morphological, structural, and functional properties of obtained CNPs from all mentioned sources were investigated using UV-Vis, Scanning electron microscopy (SEM), Fourier transform infrared spectrophotometry (FTIR), dynamic light scattering (DLS), zeta potential tests and X-ray diffraction (XRD). With great water dispersibility, and photostability all the medicinal sources chosen yielded in bright red fluorescent nanoparticles under exposure to UV light, thereby giving a significant peak around 650 nm recorded in absorption spectrum. Antoxidant assay was performed on all these six different plant-derived nanoparticles with two different concentrations and all have exhibited excellent free radical (DPPH) scavenging activity, proving their role as antioxidants. This further opens up doors for various other plant and biomedical applications to be targeted using these CNPs.

Daan Schouten, Giulia Nicoletti, Bas Dille et al.

Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field's maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine.

Wenjing Yue, Xiaoling Wang, Wei Zhu et al.

Large language models (LLMs) have performed remarkably well in various natural language processing tasks by benchmarking, including in the Western medical domain. However, the professional evaluation benchmarks for LLMs have yet to be covered in the traditional Chinese medicine(TCM) domain, which has a profound history and vast influence. To address this research gap, we introduce TCM-Bench, an comprehensive benchmark for evaluating LLM performance in TCM. It comprises the TCM-ED dataset, consisting of 5,473 questions sourced from the TCM Licensing Exam (TCMLE), including 1,300 questions with authoritative analysis. It covers the core components of TCMLE, including TCM basis and clinical practice. To evaluate LLMs beyond accuracy of question answering, we propose TCMScore, a metric tailored for evaluating the quality of answers generated by LLMs for TCM related questions. It comprehensively considers the consistency of TCM semantics and knowledge. After conducting comprehensive experimental analyses from diverse perspectives, we can obtain the following findings: (1) The unsatisfactory performance of LLMs on this benchmark underscores their significant room for improvement in TCM. (2) Introducing domain knowledge can enhance LLMs' performance. However, for in-domain models like ZhongJing-TCM, the quality of generated analysis text has decreased, and we hypothesize that their fine-tuning process affects the basic LLM capabilities. (3) Traditional metrics for text generation quality like Rouge and BertScore are susceptible to text length and surface semantic ambiguity, while domain-specific metrics such as TCMScore can further supplement and explain their evaluation results. These findings highlight the capabilities and limitations of LLMs in the TCM and aim to provide a more profound assistance to medical research.

Nassim Rajabzadeh, E. Fathi, R. Farahzadi

Recent developments in the stem cell biology provided new hopes in treatment of diseases and disorders that yet cannot be treated. Stem cells have the potential to differentiate into various cell types in the body during age. These provide new cells for the body as it grows, and replace specialized cells that are damaged. Since mesenchymal stem cells (MSCs) can be easily harvested from the adipose tissue and can also be cultured and expanded in vitro they have become a good target for tissue regeneration. These cells have been widespread used for cell transplantation in animals and also for clinical trials in humans. The purpose of this review is to provide a summary of our current knowledge regarding the important and types of isolated stem cells from different sources of animal models such as horse, pig, goat, dog, rabbit, cat, rat, mice etc. In this regard, due to the widespread use and lot of attention of MSCs, in this review, we will elaborate on use of MSCs in veterinary medicine as well as in regenerative medicine. Based on the studies in this field, MSCs found wide application in treatment of diseases, such as heart failure, wound healing, tooth regeneration etc.

H. Bártíková, R. Podlipná, L. Skálová

V. V. Ukhovskyi, O. M. Romanov, O. M. Chechet et al.

The article presents the results of a molecular genetic study of two isolates of the Pseudorabies virus that were isolated from pigs in Ukraine. Bioinformatic analysis of the gE gene fragment of Aujeszky's disease virus (Pseudorabies virus) isolates was carried out in order to determine the phylogenetic relationships and homology of nucleotide sequences. Fragments of the Aujeszky disease virus genome corresponding to the C-terminal region of the gE gene were selected for sequencing and further analysis. As a result of the conducted studies, it was demonstrated that the nucleotide sequences of the analyzed samples differ from each other by the presence of ACG insert in the tandem repeats region. Comparison of the studied sequences with the sequences of strains/isolates of the Aujeszky's disease virus found in Europe and Asia, presented in the GenBank database, indicates that such an insert is characteristic for the Min-A and HNJZ strains (position 1487 in the gE gene) isolated in Asia. Analysis of the homology of nucleotide sequences showed that the sequence of the gE gene fragment of sample No. 1 is 100% identical to the sequences of strains 89V87 and 00V72 isolated in Belgium. The homology of the nucleotide sequence of the gE gene fragment of sample No. 3 with strains 89V87 and 00V72 was 99.13%. In order to clarify the analyzed samples belonging to a particular genogroup (genetic cluster), a phylogenetic dendrogram was constructed. This demonstrates the phylogenetic relationships between strains/isolates of the Aujeszky's disease virus. It was found that the analyzed samples belong to the genetic cluster uniting European strains/isolates, and the studied isolates are most genetically close to strains 89V87 and 00V72.

Ashley S Villatoro, Holly A Phelps, Justin B Ganjei

Abstract Background Bupivacaine lioposomal suspension has recently emerged in the veterinary field for local analgesia. Objective To describe the extra‐label administration of bupivacaine liposomal suspension at the incision site of dogs undergoing limb amputation and characterize any complications. Study Design Nonblinded retrospective study. Animals Client‐owned dogs undergoing limb amputation from 2016 to 2020. Methods Medical records of dogs undergoing limb amputation with concurrent use of long‐acting liposomal bupivacaine suspension were reviewed for incisional complications, adverse effects, hospitalization length, and time to alimentation. Data were compared to a control group (CG) of dogs who underwent a limb amputation procedure without concurrent use of liposomal bupivacaine suspension. Results Forty‐six dogs were included in the liposomal bupivacaine group (LBG) and 44 cases in the CG. The CG had 15 incidences of incisional complications (34%) compared to 6 within the LBG (13%). Four dogs required revisional surgery in the CG (9%) whereas none of the dogs required revisional surgery in the LBG. Time from surgery to discharge was statistically higher in the CG compared to the LBG (p = 0.025). First time to alimentation was statistically higher in the CG (p value = 0.0002). The total number of rechecks needed postoperatively revealed the CG having a statistically significant increase in recheck evaluations (p = 0.001). Conclusions Extra‐label administration of liposomal bupivacaine suspension was well‐tolerated in dogs undergoing limb amputation. Liposomal bupivacaine usage did not increase incisional complication rates and its use allowed for a quicker time to discharge. Clinical significance Surgeons should consider inclusion of extra‐label administration of liposomal bupivacaine in analgesic regimens for dogs undergoing limb amputation.

Aamer Alchalaby, Semaa AL-Abedi

The present study's objective was to evaluate the inhibitory activity of the Probiotics Lactobacillus acidophilus and Bifidobacterium (obtained from the Agriculture Research Directorate, Ministry of Science and Technology, Iraq) and a suspension of a mixture between the two mentioned probiotics with two types of Gram-negative bacteria (Pseudomonas spp and Proteus spp) and one type of Gram-positive bacteria (Streptococcus spp) in vitro. The required tests were completed to verify the probiotics' purity, and the bacterial isolates used in the current investigation were assessed using biochemical assays and selected culture medium (culture and microscopic features). In addition, the inhibitory efficacy of the investigated Probiotics in different Gram positive and negative bacteria was evaluated by drug susceptibility testing (disc diffusion test as well as agar well diffusion test). Our data of the current study confirmed an excellent inhibitory activity of each Bifidobacterium (B) and the mixture of the two probiotics (MLB) via measuring the inhibition area, they had 25, 22mm, 28,-30 mm inhibition zone for Pseudomonas spp, 23, 25 mm, 26-27mm inhibition zone for Proteus species spp, and 22,20 mm, 33,29 mm inhibition quarter for Streptococcus species, by way of the usage of disc and agar well diffusion methods respectively. Where it was once weak inhibition activity of Lactobacillus acidophilus (L)on Pseudomonas spp, 0-3 mm and Streptococcus species 1-7 mm by the usage ofthe disc and agar well diffusion respectively. On the other, hand, Probiotic(Lactobacillus acidophilus) had available zone of inhibition on the Proteus sppbacteria, which were 24, 24 mm through the disc and agar well diffusion respectively. In conclusion: the Probiotics were found to have good and active inhibitory action on Gram-positive microorganism (Streptococcus) and gram-negative microorganism (Pseudomonas and Proteus) in vitro by way of using disc and agar well diffusion test, and the combination of the two probiotics MBL of present study, had more potent inhibitory action than each one of the studied separate probiotics.

Mostafa Abdollahi, Samad Lotfollahzadeh, Sara Shokrpoor et al.

Abstract Six 100‐day‐old mixed‐breed lambs were examined in a farm with a semi‐intensive system due to neurologic signs. Cachexia, bilateral blindness, stupor, severe drowsiness and lethargy with left and right movements of the head and neck were recorded after awakening and stimulation. Lambs died 10 days after the onset of the clinical signs. The lambs were necropsied, and after routine parasitology, bacteriology and histopathology, the occurrence of acute coenurosis was confirmed due to finding multiple cystic structures in the brain tissue. All lambs of the herd were treated with albendazole (orally, 25 mg/kg, two doses with an interval of 14 days). All shepherd dogs were treated with popantel (orally, one tablet/10 kg, two doses with an interval of 14 days). The affected lambs died despite this treatment. No new case of the disease was observed after the initiation of control measures. The present study shows the importance of preventive measure against coenurosis in a semi‐intensive sheep farming system that includes implementing consistent parasite control programme in dogs being in contact with sheep.