Hasil untuk "Veterinary medicine"

Pamela M. Chiroque-Solano, M Lee Van Horn, Thomas Jaki

Precision medicine seeks to match patients with treatments that produce the greatest benefit. The Predicted Individual Treatment Effect (PITE)-the difference between predicted outcomes under treatment and control-quantifies this benefit but is difficult to estimate due to unobserved counterfactuals, high dimensionality, and complex interactions. We compared 30+ modeling strategies, including penalized and projection-based methods, flexible learners, and tree-ensembles, using a structured simulation framework varying sample size, dimensionality, multicollinearity, and interaction complexity. Performance was measured using root mean squared error (RMSE) for prediction accuracy and directional accuracy (DIR) for correctly classifying benefit versus harm. Internal validation produced optimistic estimates, whereas external validation with distributional shifts and higher-order interactions more clearly revealed model weaknesses. Penalized and projection-based approaches-ridge, lasso, elastic net, partial least squares (PLS), and principal components regression (PCR)-consistently achieved strong RMSE and DIR performance. Flexible learners excelled only under strong signals and sufficient sample sizes. Results highlight robust linear/projection defaults and the necessity of rigorous external validation.

Zewen Sun, Ruoxiang Huang, Jiahe Feng et al.

Enhancing interrogation capabilities in Traditional Chinese Medicine (TCM) diagnosis through multi-turn dialogues and knowledge graphs presents a significant challenge for modern AI systems. Current large language models (LLMs), despite their advancements, exhibit notable limitations in medical applications, particularly in conducting effective multi-turn dialogues and proactive questioning. These shortcomings hinder their practical application and effectiveness in simulating real-world diagnostic scenarios. To address these limitations, we propose DoPI, a novel LLM system specifically designed for the TCM domain. The DoPI system introduces a collaborative architecture comprising a guidance model and an expert model. The guidance model conducts multi-turn dialogues with patients and dynamically generates questions based on a knowledge graph to efficiently extract critical symptom information. Simultaneously, the expert model leverages deep TCM expertise to provide final diagnoses and treatment plans. Furthermore, this study constructs a multi-turn doctor-patient dialogue dataset to simulate realistic consultation scenarios and proposes a novel evaluation methodology that does not rely on manually collected real-world consultation data. Experimental results show that the DoPI system achieves an accuracy rate of 84.68 percent in interrogation outcomes, significantly enhancing the model's communication ability during diagnosis while maintaining professional expertise.

Gustavo Correia, Victor Alves, Paulo Novais

Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes. This article provides a review of the current applications of AI in emergency imaging studies, focusing on the last five years of advancements. AI technologies, particularly machine learning and deep learning, are pivotal in interpreting complex imaging data, offering rapid, accurate diagnoses and potentially surpassing traditional diagnostic methods. Studies highlighted within the article demonstrate AI's capabilities in accurately detecting conditions such as fractures, pneumothorax, and pulmonary diseases from various imaging modalities including X-rays, CT scans, and MRIs. Furthermore, AI's ability to predict clinical outcomes like mechanical ventilation needs illustrates its potential in crisis resource optimization. Despite these advancements, the integration of AI into clinical practice presents challenges such as data privacy, algorithmic bias, and the need for extensive validation across diverse settings. This review underscores the transformative potential of AI in emergency settings, advocating for a future where AI and clinical expertise synergize to elevate patient care standards.

Gernot Fiala, Markus Plass, Robert Harb et al.

A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.

Mengzhou Sun, Sendong Zhao, Jianyu Chen et al.

Evidence-based medicine (EBM) research has always been of paramount importance. It is important to find appropriate medical theoretical support for the needs from physicians or patients to reduce the occurrence of medical accidents. This process is often carried out by human querying relevant literature databases, which lacks objectivity and efficiency. Therefore, researchers utilize retrieval-augmented generation (RAG) to search for evidence and generate responses automatically. However, current RAG methods struggle to handle complex queries in real-world clinical scenarios. For example, when queries lack certain information or use imprecise language, the model may retrieve irrelevant evidence and generate unhelpful answers. To address this issue, we present the PICOs-RAG to expand the user queries into a better format. Our method can expand and normalize the queries into professional ones and use the PICO format, a search strategy tool present in EBM, to extract the most important information used for retrieval. This approach significantly enhances retrieval efficiency and relevance, resulting in up to an 8.8\% improvement compared to the baseline evaluated by our method. Thereby the PICOs-RAG improves the performance of the large language models into a helpful and reliable medical assistant in EBM.

Mengling Zhang, Mengling Zhang, Yunlei Li et al.

Although studies have investigated Solanum nigrum L. (SNL) in mice, its effects on broilers remain unclear. This study examined how dietary SNL influences growth performance, antioxidant capacity, ileal transcriptome, and gut microbiota in broilers. A total of 200 one-day-old healthy Wuhua yellow-feathered chickens were randomly divided into four groups of five replicates (10 birds each). The groups received: a basal diet (CON), a basal diet with 500 mg/kg amoxicillin (AMO), a basal diet with 1000 mg/kg SNL grass meal (0.1% SNL), and a basal diet with 2000 mg/kg SNL grass meal (0.2% SNL). The experiment lasted 35 days. SNL supplementation modestly improved feed efficiency and jejunal villus height (p = 0.019). It also altered cecal microbiota by increasing Bacteroidetes, Bacteroides, and Faecalibacterium, while decreasing Firmicutes and Oscillibacter. Ileal transcriptomics identified multiple differentially expressed genes (DEGs) across comparisons, which were enriched in intestinal immune network pathways for IgA production. Correlation analysis linked cecal microbiota changes to ileal gene expression. In conclusion, SNL exhibits the potential as an alternative to antibiotics in chickens, and this study provides empirical support for its broader adoption in poultry industry.

Hancai Jiang, Xiaoxian Xu, Xinhui Song et al.

Currently, most studies on lactation-related traits and gene expression rely on invasive techniques to obtain mammary tissue. These methods are not only difficult to perform but also limit the availability of samples. Therefore, this study aimed to utilize whole transcriptome sequencing to investigate the gene expression profiles of Golden hamsters (Gh, n = 5) and Kunming mice (Km, n = 5). It compared the transcriptome expression between milk fat globules (MFG) and the mammary gland (MG), identified candidate genes and pathways associated with lactation traits, and assessed the potential of MFG as an effective alternative to MG. The data showed that a total of 21,360 genes were identified in the Gh group, with 66.5% of the mRNAs showing no differential expression between MG and MFG. In the Km group, a total of 44,248 genes were identified, with non-differentially expressed genes (NDEGs) accounting for 58.8%. Additionally, the majority of ncRNA data consisted of NDEGs. In both groups, approximately 80% of miRNA data were NDEGs. Notably, the proportion of NDEGs in circRNA data approached 100%. Enrichment analysis revealed that NDEGs from both groups were significantly enriched in several pathways, including the MAPK signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, and prolactin signaling pathway, all of which are closely associated with lactation traits and the lactation process. Furthermore, we identified various ncRNAs that regulate the expression of target genes either directly or indirectly, thereby influencing the lactation process. This study validates MFG as a reliable substitute for MG, with potential applications in improving dairy science. By identifying key genes and pathways, it provides new insights for optimizing genetic selection and breeding strategies. It also supports the improvement of dairy animal management practices.

Enrico Gugliandolo, Bilal Mghili, Francesca Fabrizi et al.

This study examines the occurrence of bacteria resistant to antibiotics and heavy metals in Terra Nova Bay, a coastal area of the Ross Sea in Antarctica that is increasingly recognised as vulnerable to human influence. During the 37th Italian Antarctic Expedition (2021–2022), researchers collected seawater, sediment, and fish samples from the notothenioid species <i>Trematomus bernacchii</i> to evaluate microbial resistance in an environment once considered largely pristine. Fifty heterotrophic bacterial isolates were obtained and tested against twenty-eight antibiotics, revealing a notable presence of multidrug resistance. These multidrug-resistant isolates were then assessed for their tolerance to eight heavy metal salts to understand whether resistance traits extended beyond antimicrobials. Twelve isolates showing resistance to both antibiotics and metals were selected for further genetic screening, targeting key resistance genes linked to tetracycline, vancomycin, sulphonamides, and other antimicrobial classes. The detection of multiple resistance genes in genera such as <i>Pseudomonas</i>, <i>Pseudoalteromonas</i>, and <i>Psychrobacter</i> indicates that both natural selective pressures and local, human-related contamination may be shaping resistance patterns in this region. Overall, the study demonstrates that even remote Antarctic marine ecosystems can host bacteria with complex resistance profiles. While these ecosystems are largely isolated, human activities such as scientific research, tourism, and the introduction of pollutants may contribute to the dissemination of antibiotic resistance genes, raising important ecological and potential public health considerations regarding the spread of resistance in polar environments.

Victor Temoche, Irene Acosta, Pablo Gonzales et al.

Goat production in the dry forest of northern Peru is essential for rural livelihoods but remains poorly characterized regarding its productivity and sustainability. This study used multivariate techniques—a multiple correspondence analysis (MCA), principal component analysis (PCA), factor analysis of mixed data (FAMD), and hierarchical cluster analysis (HCA)—to analyze data from 284 producers in Tumbes, Piura, and Lambayeque. Surveys captured 48 variables (41 qualitative, seven quantitative) on productivity, socioeconomics, and management. The MCA explained 22.07% of the variability in two dimensions, while the PCA accounted for 63.9%, focusing on productivity and diversification. The FAMD integrated these variables, explaining 51.12% of variability across five dimensions, emphasizing socioeconomic and management differences. The HCA identified three clusters: cluster 1 featured intensive systems with advanced management and commercial focus, cluster 2 included extensive systems limited by water scarcity, and cluster 3 reflected semi-intensive systems with irrigation and diversified production. These findings provide a detailed understanding of goat systems in northern Peru, identifying opportunities to improve resource use and tailor strategies to enhance sustainability. The multivariate analysis proved effective in capturing the complexity of these systems, supporting productivity and improving livelihoods in rural areas.

Xiaoye Wang, Nicole Xi Zhang, Hongyu He et al.

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have unlocked significant potential to enhance the quality and efficiency of medical care. By introducing a novel way to interact with AI and data through natural language, LLMs offer new opportunities for medical practitioners, patients, and researchers. However, as AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified. These concerns about AI safety have emerged as the most significant obstacles to the adoption of AI in medicine. In response, this review examines emerging risks in AI utilization during the LLM era. First, we explore LLM-specific safety challenges from functional and communication perspectives, addressing issues across data collection, model training, and real-world application. We then consider inherent safety problems shared by all AI systems, along with additional complications introduced by LLMs. Last, we discussed how safety issues of using AI in clinical practice and healthcare system operation would undermine trust among patient, clinicians and the public, and how to build confidence in these systems. By emphasizing the development of safe AI, we believe these technologies can be more rapidly and reliably integrated into everyday medical practice to benefit both patients and clinicians.

Chaoyi Wu, Pengcheng Qiu, Jinxin Liu et al.

In this study, we present MedS-Bench, a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. Unlike existing benchmarks that focus on multiple-choice question answering, MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation, among others. We evaluated six leading LLMs, e.g., MEDITRON, Mistral, InternLM 2, Llama 3, GPT-4, and Claude-3.5 using few-shot prompting, and found that even the most sophisticated models struggle with these complex tasks. To address these limitations, we developed MedS-Ins, a large-scale instruction tuning dataset for medicine. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks. To demonstrate the dataset's utility, we conducted a proof-of-concept experiment by performing instruction tuning on a lightweight, open-source medical language model. The resulting model, MMedIns-Llama 3, significantly outperformed existing models across nearly all clinical tasks. To promote further advancements in the application of LLMs to clinical challenges, we have made the MedS-Ins dataset fully accessible and invite the research community to contribute to its expansion.Additionally, we have launched a dynamic leaderboard for MedS-Bench, which we plan to regularly update the test set to track progress and enhance the adaptation of general LLMs to the medical domain. Leaderboard: https://henrychur.github.io/MedS-Bench/. Github: https://github.com/MAGIC-AI4Med/MedS-Ins.

Jiawei Wu, Jun Wen, Mingyuan Yan et al.

Medicinal synergy prediction is a powerful tool in drug discovery and development that harnesses the principles of combination therapy to enhance therapeutic outcomes by improving efficacy, reducing toxicity, and preventing drug resistance. While a myriad of computational methods has emerged for predicting synergistic drug combinations, a large portion of them may overlook the intricate, yet critical relationships between various entities in drug interaction networks, such as drugs, cell lines, and diseases. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. We introduce a salient deep hypergraph learning method, namely, Heterogeneous Entity Representation for MEdicinal Synergy prediction (HERMES), to predict anti-cancer drug synergy. HERMES integrates heterogeneous data sources, encompassing drug, cell line, and disease information, to provide a comprehensive understanding of the interactions involved. By leveraging advanced hypergraph neural networks with gated residual mechanisms, HERMES can effectively learn complex relationships/interactions within the data. Our results show HERMES demonstrates state-of-the-art performance, particularly in forecasting new drug combinations, significantly surpassing previous methods. This advancement underscores the potential of HERMES to facilitate more effective and precise drug combination predictions, thereby enhancing the development of novel therapeutic strategies.

Kathleen R. Mullen, Imke Tammen, Nicolas A. Matentzoglu et al.

Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Objectives: A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Methods: The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results: VBO is an open, community-driven ontology representing over 19,500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance: The adoption of VBO as a source of standard breed names in databases and veterinary electronic health records can enhance veterinary data interoperability and computability.

Valencia V. Ndlangamandla, Adeola Salawu-Rotimi, Vuyiswa S. Bushula-Njah et al.

<i>Cannabis sativa</i> L. is a monotypic genus belonging to the family Cannabaceae. It is one of the oldest species cultivated by humans, believed to have originated in Central Asia. In pivotal judgements in 2016 and 2018, the South African Constitutional Court legalised the use of <i>Cannabis</i> within the country for medicinal and recreational purposes, respectively. These decrees opened opportunities for in-depth research where previously there had been varying sentiments for research to be conducted on the plant. This review seeks to examine the history, genetic diversity, and chemical profile of <i>Cannabis</i>. The cultivation of <i>Cannabis</i> by indigenous people of southern Africa dates back to the eighteenth century. Indigenous rural communities have been supporting their livelihoods through <i>Cannabis</i> farming even before its legalisation. However, there are limited studies on the plant’s diversity, both morphologically and genetically, and its chemical composition. Also, there is a lack of proper documentation of <i>Cannabis</i> varieties in southern Africa. Currently, the National Centre for Biotechnology Information (NCBI) has 15 genome assemblies of <i>Cannabis</i> obtained from hemp and drug cultivars; however, none of these are representatives of African samples. More studies are needed to explore the species’ knowledge gaps on genetic diversity and chemical profiles to develop the <i>Cannabis</i> sector in southern Africa.

O. V. Krushelnytska

An important aspect of industrial fish farming is the increase in technogenic influence on the habitat of aquatic organisms, which suppresses the functions of the fish immune system or promotes the development of hypersensitivity and autoimmune reactions due to disruption of the mechanisms of immune system regulation, which leads to disruption of the homeostasis of the fish organism. Due to the tense environmental situation, including aquatic ecosystems, the search for environmentally safe immunostimulants necessary to maintain the organism's homeostasis and its correction is urgent. The search was conducted on carp (Cyprinus carpio) in aquarium conditions. The main hydrochemical parameters corresponded the fishery standards. The investigation was conducted for 5 – 10 – 15 – 20 days using the preparation in different doses: 5 – 10 – 15 mg per kilogram of fish weight. Thus, 4 groups were formed: a control and three experimental groups. The results of investigation of the influence of an immunostimulant of natural origin on immunological indicators are presented. The relationship between humoral and cellular immunity when using the immunostimulant in different doses and time ranges is searched. It was set up that the optimal dose of the preparation is 10 mg/kg of fish weight, which leads to an increase in the level of immunoglobulins, T- and B-lymphocytes without changes in the content of circulating immune complexes. Their ratio varies depending on the dose and duration of preparation use. Analyzing the data got over time, a significant increase in the number of immunocompetent cells investigated was observed on the 15th-20th day of preparation use. Researches have not revealed a negative influence of the preparation on regulatory T-cells, which indicates a normal course of immune response regulation. The activation of the biosynthesis of immunoglobulins indicates an increase in the tension of humoral immunity. However, such an increase in the humoral component of the immune response is not the result of an increasing antigenic load, since the searches were conducted in aquarium conditions. Evidence of this fact is the absence of significant changes in the level of circulating immune complexes at different doses of the immunostimulant and the period of its use, given that circulating immune complexes characterize the degree of interaction of the antigen-antibody complex in the animal organism and are directed at eliminating pathogens. The use of the immunostimulating preparation had a beneficial influence on the level of cellular and humoral immunity of fish. This was reflected in an increase in the level of total T-lymphocytes, active T-lymphocytes, B-lymphocytes and immunoglobulins. An effective immunostimulating effect was achieved at a dose of 10 mg/kg of fish weight with a duration of use of the preparation  for 15–20 days. Research into optimal preparation doses for different age groups and sizes of fish will help determine the most effective doses for different categories of carp. In addition, it is important to conduct searches on other fish species to evaluate the efficacy of the investigational preparation and its potential use in different aquaculture systems. It is also actual to further investigation the possibility of combined use of the preparation with other immunostimulants or therapeutic preparation to enhance the overall immune defense of fish. An important aspect is the research of the ecological influence of the use of the immunostimulant in fish farming, including the possibility of its accumulation in aquatic ecosystems and the influence on other aquatic organisms.

Azmat Ali Nosher, Abdul Manaf, Qaiser Hussain et al.

Lignite and sulphur are instrumental in enhancing the growth and yield-related traits of Brassica napus, commonly known as rapeseed. This study aimed to explore the effects of lignite-based sulphur fertilizers on Brassica napus production. Spanning two consecutive seasons, the experiment included treatments with a control group, three levels of elemental sulphur (30, 40, and 50 kg ha-1), and three levels of lignite-based sulphur fertilizer (30, 40, and 50 kg ha-1). Employing a randomized complete block design with four replications, the study revealed that applying lignite-based sulphur fertilizer at a rate of 50 kg ha-1 led to significant improvements in various growth parameters, such as plant height, primary and secondary branches per plant, pods per plant, pod length, seeds per pod, biological yield, seed yield, thousand seed weight, and oil yield. Notably, substantially higher seed and oil yields were achieved with the application of 50 kg ha-1 of lignite-based sulphur fertilizer. In semi-arid climates, to maximize rapeseed yield, yield components, and quality, it is advisable to utilize lignite-based sulphur fertilizer at a rate of 50 kg ha-1.