Hasil untuk "Veterinary medicine"

Tianai Huang, Lu Lu, Jiayuan Chen et al.

Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introduce TCM3CEval, a benchmark assessing LLMs in TCM across three dimensions: core knowledge mastery, classical text understanding, and clinical decision-making. We evaluate diverse models, including international (e.g., GPT-4o), Chinese (e.g., InternLM), and medical-specific (e.g., PLUSE). Results show a performance hierarchy: all models have limitations in specialized subdomains like Meridian & Acupoint theory and Various TCM Schools, revealing gaps between current capabilities and clinical needs. Models with Chinese linguistic and cultural priors perform better in classical text interpretation and clinical reasoning. TCM-3CEval sets a standard for AI evaluation in TCM, offering insights for optimizing LLMs in culturally grounded medical domains. The benchmark is available on Medbench's TCM track, aiming to assess LLMs' TCM capabilities in basic knowledge, classic texts, and clinical decision-making through multidimensional questions and real cases.

Alice Tang, Maria Wei, Anna Haemel et al.

Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.

Arti Virkud, Jessie K. Edwards, Michele Jonsson Funk et al.

Identifying optimal medical treatments to improve survival has long been a critical goal of pharmacoepidemiology. Traditionally, we use an average treatment effect measure to compare outcomes between treatment plans. However, new methods leveraging advantages of machine learning combined with the foundational tenets of causal inference are offering an alternative to the average treatment effect. Here, we use three unique, precision medicine algorithms (random forests, residual weighted learning, efficient augmentation relaxed learning) to identify optimal treatment rules where patients receive the optimal treatment as indicated by their clinical history. First, we present a simple hypothetical example and a real-world application among heart failure patients using Medicare claims data. We next demonstrate how the optimal treatment rule improves the absolute risk in a hypothetical, three-modifier setting. Finally, we identify an optimal treatment rule that optimizes the time to outcome in a real-world heart failure setting. In both examples, we compare the average time to death under the optimized, tailored treatment rule with the average time to death under a universal treatment rule to show the benefit of precision medicine methods. The improvement under the optimal treatment rule in the real-world setting is greatest (additional ~9 days under the tailored rule) for survival time free of heart failure readmission.

Hyungjun Park, Chang-Yun Woo, Seungjo Lim et al.

Objective To develop an LLM based realtime compound diagnostic medical AI interface and performed a clinical trial comparing this interface and physicians for common internal medicine cases based on the United States Medical License Exam (USMLE) Step 2 Clinical Skill (CS) style exams. Methods A nonrandomized clinical trial was conducted on August 20, 2024. We recruited one general physician, two internal medicine residents (2nd and 3rd year), and five simulated patients. The clinical vignettes were adapted from the USMLE Step 2 CS style exams. We developed 10 representative internal medicine cases based on actual patients and included information available on initial diagnostic evaluation. Primary outcome was the accuracy of the first differential diagnosis. Repeatability was evaluated based on the proportion of agreement. Results The accuracy of the physicians' first differential diagnosis ranged from 50% to 70%, whereas the realtime compound diagnostic medical AI interface achieved an accuracy of 80%. The proportion of agreement for the first differential diagnosis was 0.7. The accuracy of the first and second differential diagnoses ranged from 70% to 90% for physicians, whereas the AI interface achieved an accuracy rate of 100%. The average time for the AI interface (557 sec) was 44.6% shorter than that of the physicians (1006 sec). The AI interface ($0.08) also reduced costs by 98.1% compared to the physicians' average ($4.2). Patient satisfaction scores ranged from 4.2 to 4.3 for care by physicians and were 3.9 for the AI interface Conclusion An LLM based realtime compound diagnostic medical AI interface demonstrated diagnostic accuracy and patient satisfaction comparable to those of a physician, while requiring less time and lower costs. These findings suggest that AI interfaces may have the potential to assist primary care consultations for common internal medicine cases.

Wenxuan Wang, Zizhan Ma, Zheng Wang et al.

Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks. This survey provides a comprehensive review of LLM-based agents in medicine, examining their architectures, applications, and challenges. We analyze the key components of medical agent systems, including system profiles, clinical planning mechanisms, medical reasoning frameworks, and external capacity enhancement. The survey covers major application scenarios such as clinical decision support, medical documentation, training simulations, and healthcare service optimization. We discuss evaluation frameworks and metrics used to assess these agents' performance in healthcare settings. While LLM-based agents show promise in enhancing healthcare delivery, several challenges remain, including hallucination management, multimodal integration, implementation barriers, and ethical considerations. The survey concludes by highlighting future research directions, including advances in medical reasoning inspired by recent developments in LLM architectures, integration with physical systems, and improvements in training simulations. This work provides researchers and practitioners with a structured overview of the current state and future prospects of LLM-based agents in medicine.

Bartosz Orzechowski, Jan Miciński, Katarzyna Ząbek et al.

With the aim to investigate the immunomodulatory potential of selenitetriglycerides (SeTG), a new lipophilic Se (IV) compound, 30 sheep (15 sheep/treatment) were used in a completely random design to receive the SeGT supplement as follows: (1) no SeGT supplement (Control) and (2) daily dosage of 2 mL of SeGT (equivalent to 1 mg Se/kg BW) during the first 7 days of the evaluation, which lasted 28 d. Individually, blood samples were collected on days 0, 14, and 28 to measure and assess parameters of innate cellular and humoral immunity, including respiratory burst activity (RBA) and potential killing activity (PKA) of monocytes and granulocytes, proliferative response of lymphocytes stimulated with ConA (Concovalin A) and LPS (lipopolisaccharidde), lysozyme activity, ceruloplasmin activity, and gamma globulin levels. From the 14th day, supplemental SeTG saw significant increases (<i>p</i> ≤ 0.001) in RBA and PKA parameters, as well as enhanced proliferative responses of lymphocytes compared with controls. Both innate humoral immunity (elevated lysozyme activity) and adaptive humoral immunity (increased gamma globulin levels) were positively influenced (<i>p</i> ≤ 0.01), whereas ceruloplasmin activity remained unchanged. Under the conditions in which the current experiment was carried out, SeGT showed good promise to modulate immunity in a short period (28 d). Further research should explore experiments with a greater number of animals over long-term periods of evaluation under production system conditions.

Sook-Jin Lee, An-Na Lee, Eun-Bin Shin et al.

Sarcoptic mange is a highly contagious and often lethal parasitic skin disease caused by the mite <i>Sarcoptes scabiei</i>, which is frequently reported in red foxes (<i>Vulpes vulpes</i>). However, there is a lack of documented cases and treatment strategies for sarcoptic mange in reintroduced red fox populations, particularly in South Korea. This study describes 27 cases of sarcoptic mange outbreaks in 26 red foxes reintroduced into South Korea between 2019 and 2024. Of these, 15 foxes were rescued alive, while 12 were found dead. Blood tests of the surviving animals (<i>n</i> = 15) showed significant leukocytosis, anemia, decreased albumin levels, increased globulin levels, elevated blood urea nitrogen levels, and decreased creatinine levels, indicative of a chronic wasting infection. Treatment with ivermectin or fluralaner resulted in complete recovery in 12 of 15 animals, while three animals died during treatment. Necropsy of the deceased animals (<i>n</i> = 12) revealed characteristic skin lesions, such as alopecia, hyperkeratosis, epidermal hyperplasia, and dermatitis. The study findings highlight the importance of long-term monitoring and active, continuous treatment of sarcoptic mange, a major threat, for the stable re-establishment of reintroduced foxes in South Korea.

K. Anderson, D. O'Neill, D. Brodbelt et al.

Osteoarthritis is the most common joint disease diagnosed in veterinary medicine and poses considerable challenges to canine welfare. This study aimed to investigate prevalence, duration and risk factors of appendicular osteoarthritis in dogs under primary veterinary care in the UK. The VetCompassTM programme collects clinical data on dogs attending UK primary-care veterinary practices. The study included all VetCompassTM dogs under veterinary care during 2013. Candidate osteoarthritis cases were identified using multiple search strategies. A random subset was manually evaluated against a case definition. Of 455,557 study dogs, 16,437 candidate osteoarthritis cases were identified; 6104 (37%) were manually checked and 4196 (69% of sample) were confirmed as cases. Additional data on demography, clinical signs, duration and management were extracted for confirmed cases. Estimated annual period prevalence (accounting for subsampling) of appendicular osteoarthritis was 2.5% (CI95: 2.4–2.5%) equating to around 200,000 UK affected dogs annually. Risk factors associated with osteoarthritis diagnosis included breed (e.g. Labrador, Golden Retriever), being insured, being neutered, of higher bodyweight and being older than eight years. Duration calculation trials suggest osteoarthritis affects 11.4% of affected individuals’ lifespan, providing further evidence for substantial impact of osteoarthritis on canine welfare at the individual and population level.

Davide Belluomo, Tiziana Calamoneri, Giacomo Paesani et al.

We present a new unified graph-based representation of medical data, combining genetic information and medical records of patients with medical knowledge via a unique knowledge graph. This approach allows us to infer meaningful information and explanations that would be unavailable by looking at each data set separately. The systematic use of different databases, managed throughout the built knowledge graph, gives new insights toward a better understanding of oncology medicine. Indeed, we reduce some useful medical tasks to well-known problems in theoretical computer science for which efficient algorithms exist.

Guangzhi Xiong, Qiao Jin, Zhiyong Lu et al.

While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinations and outdated knowledge. Retrieval-augmented generation (RAG) is a promising solution and has been widely adopted. However, a RAG system can involve multiple flexible components, and there is a lack of best practices regarding the optimal RAG setting for various medical purposes. To systematically evaluate such systems, we propose the Medical Information Retrieval-Augmented Generation Evaluation (MIRAGE), a first-of-its-kind benchmark including 7,663 questions from five medical QA datasets. Using MIRAGE, we conducted large-scale experiments with over 1.8 trillion prompt tokens on 41 combinations of different corpora, retrievers, and backbone LLMs through the MedRAG toolkit introduced in this work. Overall, MedRAG improves the accuracy of six different LLMs by up to 18% over chain-of-thought prompting, elevating the performance of GPT-3.5 and Mixtral to GPT-4-level. Our results show that the combination of various medical corpora and retrievers achieves the best performance. In addition, we discovered a log-linear scaling property and the "lost-in-the-middle" effects in medical RAG. We believe our comprehensive evaluations can serve as practical guidelines for implementing RAG systems for medicine.

Oleksandr Bilokon, Nataliya Bilokon, Paul Bilokon

AIAltMed is a cutting-edge platform designed for drug discovery and repurposing. It utilizes Tanimoto similarity to identify structurally similar non-medicinal compounds to known medicinal ones. This preprint introduces AIAltMed, discusses the concept of `AI-driven alternative medicine,' evaluates Tanimoto similarity's advantages and limitations, and details the system's architecture. Furthermore, it explores the benefits of extending the system to include PubChem and outlines a corresponding implementation strategy.

Guoxing Yang, Jianyu Shi, Zan Wang et al.

Pre-training and fine-tuning have emerged as a promising paradigm across various natural language processing (NLP) tasks. The effectiveness of pretrained large language models (LLM) has witnessed further enhancement, holding potential for applications in the field of medicine, particularly in the context of Traditional Chinese Medicine (TCM). However, the application of these general models to specific domains often yields suboptimal results, primarily due to challenges like lack of domain knowledge, unique objectives, and computational efficiency. Furthermore, their effectiveness in specialized domains, such as Traditional Chinese Medicine, requires comprehensive evaluation. To address the above issues, we propose a novel domain specific TCMDA (TCM Domain Adaptation) approach, efficient pre-training with domain-specific corpus. Specifically, we first construct a large TCM-specific corpus, TCM-Corpus-1B, by identifying domain keywords and retreving from general corpus. Then, our TCMDA leverages the LoRA which freezes the pretrained model's weights and uses rank decomposition matrices to efficiently train specific dense layers for pre-training and fine-tuning, efficiently aligning the model with TCM-related tasks, namely TCM-GPT-7B. We further conducted extensive experiments on two TCM tasks, including TCM examination and TCM diagnosis. TCM-GPT-7B archived the best performance across both datasets, outperforming other models by relative increments of 17% and 12% in accuracy, respectively. To the best of our knowledge, our study represents the pioneering validation of domain adaptation of a large language model with 7 billion parameters in TCM domain. We will release both TCMCorpus-1B and TCM-GPT-7B model once accepted to facilitate interdisciplinary development in TCM and NLP, serving as the foundation for further study.

Giulia Marchionne, Emili Alcoverro, Simone Spinillo et al.

Case summary A 15-year-old female spayed domestic shorthair cat underwent an emergency craniotomy to remove an intracranial meningioma causing marked midline shift, caudal transtentorial and foramen magnum herniation. Because intracranial structures are enclosed in the cranium, any volume-occupying lesions might raise intracranial pressure (ICP), compromising cerebral perfusion. Relevance and novel information This case report discusses the anaesthetic management of a cat that presented with marked bradycardia and concomitant hypotension. Cushing’s reflex (CR) is a well-recognised cardiovascular reflex following sudden ICP increase, and it features an irregular breathing pattern and increased arterial blood pressure with reflex bradycardia. However, CR is reported to have a low sensitivity for the detection of raised ICP in humans with traumatic brain injury. In a previous study reporting seven cats undergoing surgical removal of intracranial meningioma, ICP was measured in four cases and, in these patients, CR was not observed during surgery. Because bradycardia was not secondary to hypertension, in this case, it might have been the result of direct compression of the nucleus of the vagus nerve. Based on the literature search, there is paucity of reports of cardiovascular changes in cats with increased ICP and their perianaesthetic management.

Deny Jennes, Soumya Ramankutty, S. Anoop et al.

Platelet rich plasma (PRP) therapy is an integral part of regenerative medicine as the platelets possess a good healing capacity owing to the presence of a wide variety of growth factors in the platelet granules found in the cytoplasm of the platelet. Autologous PRP was prepared from the blood of the patient itself, without any preservatives. Storage of PRP was one of the main hurdles of the treatment modality. During storage, the platelet counts may get reduced, undergo activation or get contaminated with bacteria as no preservatives are used in the preparation of autologous PRP. Cytological changes and microbial quality of the PRP during storage at 4oC and -20oC for seven days were analysed in this study. Reduction in platelet count and the chance of microbial contamination were less when autologous PRP was stored at -20°C compared to 4°C.

Péter Nagy

Shengyang Xu, Chong Jiao, Qiyu Diao et al.

Abstract Background Rumen bacterial groups can affect growth performance, such as average daily gain (ADG), feed intake, and efficiency. The study aimed to investigate the inter-relationship of rumen bacterial composition, rumen fermentation indicators, serum indicators, and growth performance of Holstein heifer calves with different ADG. Twelve calves were chosen from a trail with 60 calves and divided into higher ADG (HADG, high pre- and post-weaning ADG, n = 6) and lower ADG (LADG, low pre- and post-weaning ADG, n = 6) groups to investigate differences in bacterial composition and functions and host phenotype. Results During the preweaning period, the relative abundances of propionate producers, including g_norank_f_Butyricicoccaceae, g_Pyramidobacter, and g_norank_f_norank_o_Clostridia_vadinBB60_group, were higher in HADG calves (LDA > 2, P < 0.05). Enrichment of these bacteria resulted in increased levels of propionate, a gluconeogenic precursor, in preweaning HADG calves (adjusted P < 0.05), which consequently raised serum glucose concentrations (adjusted P < 0.05). In contrast, the relative abundances of rumen bacteria in post-weaning HADG calves did not exert this effect. Moreover, no significant differences were observed in rumen fermentation parameters and serum indices between the two groups. Conclusions The findings of this study revealed that the preweaning period is the window of opportunity for rumen bacteria to regulate the ADG of calves.

W. Jansen, Jobke van Hout, Jeanine Wiegel et al.

Simple Summary The antibiotic colistin has been used for Gram-negative bacterial infections in veterinary medicine since the 1950s, with resistance rates remaining low. However, due to the discovery of resistance via transmissible plasmids in 2015, the use of colistin for animals became a subject of discussion, and actions taken resulted in a pan-European reduction of colistin sales for animal use of 76.5% between 2011 and 2020. Nevertheless, few studies evaluated real-world field data on the use patterns of veterinary use of colistin in different countries and species in Europe. A survey among 662 veterinary practitioners revealed that the majority did not use or ceased colistin use (51.9%), 33.4% had decreased their use, 10.4% stabilised their use, and 2.7% increased use. The most important indications for use of colistin were gastrointestinal diseases followed by septicaemia. Governmental/industry restrictions regarding colistin use were reported by 16% of the responding veterinarians, and of them, the most common was “use only after susceptibility testing” (57%). Abstract Polymyxin E (colistin) is a medically important active substance both in human and veterinary medicine. Colistin has been used in veterinary medicine since the 1950s. Due to the discovery of the plasmid-borne mcr gene in 2015 and the simultaneously increased importance in human medicine as a last-resort antibiotic, the use of colistin for animals was scrutinised. Though veterinary colistin sales dropped by 76.5% between 2011 to 2020, few studies evaluated real-world data on the use patterns of colistin in different European countries and sectors. A survey among veterinarians revealed that 51.9% did not use or ceased colistin, 33.4% decreased their use, 10.4% stabilised their use, and 2.7% increased use. The most important indications for colistin use were gastrointestinal diseases in pigs followed by septicaemia in poultry. A total of 106 (16.0%) responding veterinarians reported governmental/industry restrictions regarding colistin use, most commonly mentioning “use only after susceptibility testing” (57%). In brief, colistin was perceived as an essential last-resort antibiotic in veterinary medicine for E. coli infections in pigs and poultry, where there is no alternative legal, safe, and efficacious antimicrobial available. To further reduce the need for colistin, synergistic preventive measures, including improved biosecurity, husbandry, and vaccinations, must be employed.

JunBo Wu, Nathaniel Comfort

Public health is the most recent of the biomedical sciences to be seduced by the trendy moniker "precision." Advocates for "precision public health" (PPH) call for a data-driven, computational approach to public health, leveraging swaths of genomic "big data" to inform public health decision-making. Yet, like precision medicine, PPH oversells the value of genomic data to determine health outcomes, but on a population-level. A large historical literature has shown that over-emphasizing heredity tends to disproportionately harm underserved minorities and disadvantaged communities. By comparing and contrasting PPH with an earlier attempt at using big data and genetics, in the Progressive era (1890-1920), we highlight some potential risks of a genotype-driven preventive public health. We conclude by suggesting that such risks may be avoided by prioritizing data integration across many levels of analysis, from the molecular to the social.

Katia Pinello, Irina Amorim, Isabel Pires et al.

Analysis of canine and feline tumor malignancy data can help clinicians identify high-risk patients and make more accurate decisions. Based on a sample of 16,272 cancer records, including 3266 cats and 13,006 dogs, collected from January 2019 to December 2021 in the Vet-OncoNet Network database, this study aimed to compare the tumor malignancy profile between cats and dogs, considering animal-related factors (sex, age, and breed), topography, and geographic location using a mixed-effects logistic regression model. Cats had a higher proportion of malignant tumors (78.7%) than dogs (46.2%), and the malignancy profile was very different regarding tumors’ topographies. The mean age of malignant tumors occurred eight months later than benign ones (9.1, SD = 3.4; 9.8, SD = 3.2), in general. Species (OR = 3.96, 95%CI 3.57: 4.39) and topography (MOR = 4.10) were the two most important determinants of malignancy risk. Female dogs had a higher risk than male dogs (OR = 1.19, 95%CI 1.08: 1.31), which does not appear to be the case in cats (OR = 0.98, 95%CI 0.77: 1.23). Breed contributed significantly to differences in malignancy risk in dogs (MOR = 1.56), particularly in pit bulls and boxers. District of residence was not so relevant in predicting malignancy risk (MOR = 1.14). In both species, the risk of malignancy increased by approximately 20% every three years. It could be hypothesized that species differences in genetic structure may contribute to tumor malignancy.

José Felipe Warmling Sprícigo, Ana Luiza Silva Guimarães, Andrielle Thainar Mendes Cunha et al.

The present study aimed to determine whether cumulus cells (CC) biopsy, acquired before or after in vitro maturation (IVM), presents similar gene expression pattern and if would compromises oocyte quality. First, immature cumulus oocyte complexes (COCs) were distributed: (1) maturated in groups (control); (2) individually maturated, but not biopsied; (3) subjected to CC biopsy before maturation and individually matured; (4) individually matured and submitted to CC biopsy after maturation; (5) individually matured and CC biopsied before and after maturation. Secondly, candidate genes, described as potential markers of COCs quality, were quantified by RT-qPCR in CCs before and after IVM. After in vitro fertilization (IVF), zygotes were tracked and sorted regarding their developmental potential: fully developed to embryo, cleaved and arrested, and not-cleaved. The COC’s biopsy negatively affects embryo development (<i>p</i> < 0.05), blastocyst cell number (<i>p</i> < 0.05), and apoptotic cell ratio (<i>p</i> < 0.05), both before and after IVM. The PTGS2, LUM, ALCAM, FSHR, PGR, SERPINE2, HAS2, and PDRX3 genes were differentially expressed (<i>p</i> < 0.05) on matured CCs. Only PGR gene (<i>p</i> = 0.04) was under-expressed on matured CCs on Not-Cleaved group. The SERPINE2 gene was overexpressed (<i>p</i> = 0.01) in the Cleaved group on immature CCs. In summary, none of the selected gene studies can accurately predict COC’s fate after fertilization.