Search Results (222)

Background/Objectives: Osteoporosis (OP) is a frequent complication of rheumatoid arthritis (RA), driven by chronic inflammation and subsequent bone destruction. While deer bone is recognized for its potential bone-health benefits, the therapeutic efficacy of its fermented products on RA-induced OP remains unclear. Methods: This study investigated the protective effects of Lactobacillus reuteri-fermented deer bone water extract (LR-DBW) against OP in an adjuvant arthritis (AA) rat model. Firstly, LC-MS/MS was employed to analyze the differential peptide profiles between LR-DBW and non-fermented deer bone water extract (DBW). Secondly, experiments such as Micro-CT, histological staining, and Western Blot were conducted to detect the improvement effect of LR-DBW on OP. Results:In vivo, LR-DBW administration significantly alleviated arthritis symptoms, increased bone mineral density (BMD), and improved bone microarchitecture in AA rats. In vitro, LR-DBW inhibited RANKL-induced osteoclastogenesis and actin ring formation in RAW264.7 cells. Mechanistically, LR-DBW inhibits the phosphorylation of ERK, JNK and p38 proteins. Conclusions: These research results indicate that one of the mechanisms by which inhibiting osteoclast differentiation into LR-DBW can alleviate osteoporosis caused by rheumatoid arthritis is through down-regulating the phosphorylation expression of ERK, JNK and p38 proteins. This highlights its potential as a functional food component for treating inflammatory bone diseases. Full article

Wildlife health surveillance is a vital element of disease prevention, biodiversity conservation, and public health protection, especially as most emerging infectious diseases originate from wildlife. In Slovenia, long-term passive surveillance based on necropsy data has yielded valuable insights into wildlife mortality patterns over the past three decades, despite inherent limitations such as carcass detectability, reporting bias, scavenging, and decomposition. Ongoing cooperation among governmental institutions, veterinary services, hunters, and wildlife management organisations has enabled the effective operation of this system, although passive surveillance remains subject to spatial, temporal, and species-specific biases. Necropsy data show that infectious diseases, particularly parasitic infections, are the main causes of mortality in key species such as roe deer and chamois, reflecting both their population abundance and targeted monitoring. In contrast, carcasses of species such as wild boar, red deer, small mammals, and birds are underrepresented due to ecological factors, biosecurity constraints, or low detectability. Overall, while passive wildlife surveillance does not provide representative population-level mortality estimates, it remains a reliable tool for identifying the presence or absence of significant diseases and for understanding broad mortality patterns when interpreted in the context of known methodological and ecological limitations. Full article

Background: The nasopharyngeal microbiome is crucial for respiratory health in mammals, yet it remains poorly characterized in the endangered forest musk deer (Moschus berezovskii), particularly in the context of disease. Methods: We compared the bacterial (16S rRNA) and fungal (ITS2) communities in the nasopharynx of healthy (n = 6) and clinically diseased (n = 6) individuals. Results: Although alpha diversity did not differ significantly, beta diversity (PCoA) analysis revealed distinct bacterial (PERMANOVA, R² = 0.165, p = 0.014) and fungal (R² = 0.577, p = 0.003) community structures between groups. The diseased group exhibited a significant increase in the bacterial phylum Proteobacteria (70.97% vs. 46.27%), primarily driven by the genera Bibersteinia and Pseudomonas. Fungal communities in the diseased group were dominated by a higher relative abundance of Ascomycota and Basidiomycota, with significant enrichment of Wallemia and Aspergillus. LEfSe analysis identified Pseudomonas and multiple fungal taxa (e.g., Wallemia, Aspergillus) as biomarkers for the diseased group. PICRUSt2 prediction indicated enrichment of pathways related to carotenoid biosynthesis and sphingolipid metabolism in the diseased state, while FUNGuild analysis suggested a higher abundance of animal/plant pathogen-related fungi. Conclusions: Symptomatic respiratory infections in forest musk deer are associated with significant dysbiosis of the nasopharyngeal microbiome, characterized by the marked enrichment of potential bacterial opportunists (e.g., Pseudomonas) and specific fungal taxa (e.g., Wallemia, Aspergillus), alongside distinct functional shifts in the microbiome. These findings provide the first integrated bacterial–fungal profile of the nasopharyngeal microbiome in this endangered species, and highlight potential microbial biomarkers associated with respiratory disease. Full article

Oropouche virus (OROV) is an emerging zoonotic arthropod-borne virus of public health importance. The host range of OROV is largely unknown, but antibody evidence suggests that wildlife and livestock species could be susceptible hosts. To identify potential North American mammalian reservoir hosts, OROV replication curves were generated using eight cell lines derived from livestock, wildlife, and domestic animal species (cow, sheep, bison, white-tailed deer, elk, pig, horse, and dog). The virus replicated in all cell lines by 48 h post infection, except for the horse cells. OROV replication success was greatest in the bison cells followed by pig and dog cells. Moderate replication was achieved in the deer, elk, sheep, and cow cells. These results indicate that numerous animal species may be susceptible hosts for OROV, including important agricultural and wildlife species, but pathogenesis studies are required to confirm this finding. Identifying the reservoir hosts for OROV will allow livestock producers, veterinarians, and public health officials to prepare appropriate vector and disease control measures should the virus initiate an outbreak in the United States. Full article

The Carpathian Basin is a coherent biogeographic unit whose wildlife populations and pathogen dynamics are increasingly reshaped by administratively fragmented governance, land-use change and linear infrastructure. This review synthesizes evidence that the permeability patterns governing host movement also structure the transboundary exchange of genes and infections, creating a connectivity substrate for conservation genetics and One Health risk. Focusing on wild boar (Sus scrofa), red deer (Cervus elaphus), roe deer (Capreolus capreolus), fallow deer (Dama dama) and the expanding golden jackal (Canis aureus), we integrate population genetic inferences with wildlife epidemiology to examine how highways, border fences and asymmetric management (e.g., supplemental, feeding practices, hunting pressure and surveillance regimes) can generate biological asymmetries across boundaries. We highlight African swine fever as an emblematic disturbance in wild boar populations, discuss cervid risks including tick-borne pathogens and chronic wasting disease (CWD) preparedness and evaluate zoonotic threats associated with carnivore expansion (e.g., Echinococcus spp.). We propose a Carpathian Basin-level monitoring and data-sharing architecture, coupling harmonized passive surveillance, strategic active surveillance for priority pathogens, and standardized genetic marker panels supported by interoperable metadata. A Basin-scale One Health approach is a pragmatic prerequisite for the coordinated prevention, early detection and resilient management of cross-border epizootics and zoonotic threats. Full article

We report the isolation and coding complete genome sequences of a new CHeRI orbivirus from the spleen of a dead farmed white-tailed deer in Florida whose death was attributed to an infection by mule deerpox virus. Phylogenetic and genetic analyses support this new virus as the fifth strain of the CHeRI orbivirus 3 species, and we designated it CHeRI orbivirus 3–5. While our previous detections and isolations of CHeRI orbiviruses were from deer spleens that also contained epizootic hemorrhagic disease virus-2, or in one case, Hardee County ephemerovirus 1, no deerpox virus was isolated from the spleen of the animal in this report, marking the first time we have isolated a CHeRI orbivirus without a co-infecting agent. Full article

Fascioloides magna, an invasive trematode introduced to Europe in the 19th century, persists in two main foci: the Danube basin and La Mandria Natural Park (LMNP) in northern Italy. This study assessed whether the parasite has spread beyond LMNP and evaluated environmental and host-related risk factors. Between 2012 and 2023, 331 wild ruminant livers were examined, and faecal samples were analysed for fluke eggs. Gastropods from the LMNP were sampled using a predictive habitat suitability model and screened for F. magna DNA. Camera traps monitored ungulate movements across LMNP boundaries. Results confirmed the parasite’s presence in red and fallow deer within LMNP and sporadic cases in roe deer, but no evidence of infection in wildlife or gastropods outside the park. Molecular screening detected F. magna DNA in 9.2%% of snails inside LMNP only. Despite occasional crossings by potential definitive hosts, ecological conditions outside LMNP appear unsuitable for sustaining the parasite’s life cycle. These findings suggest a low current risk of spread but highlight the need for continued surveillance and barrier reinforcement. The integrated approach combining parasitology, molecular diagnostics, and GIS-based risk mapping provides a valuable framework for managing invasive parasitic diseases in wildlife. Full article

Cutaneous papillomaviruses (PVs) are host-specific DNA viruses that cause papillomas in many wild cervids, including red deer, moose, roe deer, white-tailed deer, and reindeer. Species-specific PVs such as CePV1 and AaPV1 typically induce rough, verrucous skin and mucosal lesions that, while usually benign, can impair feeding, movement, vision, or mating. A high prevalence—especially in young or immunocompromised animals—may affect population health. Transmission occurs through contact, skin microtrauma, or possibly ectoparasites. PV lesions can resemble more serious diseases, complicating diagnostics. Understanding PV diversity and ecology is important for wildlife health monitoring, conservation planning, and assessing cross-species transmission risks. Full article

Background/Objectives: Diabetes is one of the most familiar and common diseases among people currently, and is a type of metabolic disease that is caused due to high levels of sugar in the blood for longer periods of time. If the disease is predicted at an earlier stage, the severity and risks associated with diabetes are significantly reduced, which helps to save the lifespan of people. In earlier investigations, various kinds of automated models based on artificial intelligence (AI) were developed for this purpose. However, key issues still revolve around the lack of robustness, dependability, and precise prediction. The motivation behind the proposed study is to design and develop an automated tool for the diagnosis of chronic disease with the use of novel AI methodology. Methods: For this purpose, a new detection framework is introduced, known as the Brass Optimized Learning-Based Diabetes Prediction (BOLD) model for remote healthcare applications. By using this kind of optimization-integrated deep learning technique, the overall performance and efficiency of the diabetes detection system are maximized. This framework preprocesses the input diabetes dataset before performing the data splitting, normalization, and cleaning activities. Next, the best attributes for improving the prognostic performance of the classifier are chosen using the Brassy Pelican Optimization (BPO) procedure. The Hunting Optimized Recurrent Neural Network—Long Short-Term Memory (RNN-LSTM) method is used to categorize the people into those who are diabetic and those who are not based on the chosen attributes. The approach employs a Deer Hunting Optimization (DHO) method to choose the hyperparameters needed to make an informed choice. A variety of parameters have been employed to confirm the results, which are evaluated for performance verification using the PIDD, Indonesia diabetic database, and kidney disease dataset. Results: The BOLD framework is successful to the extent that it has been able to achieve several metrics of comparably good results, such as an RMSE value of 0.015, a Cohen’s Kappa measure of 0.99, a precision of 0.991, a recall of 0.99, an accuracy equal to 0.996, and an AUC equal to 0.99. Conclusions: It is also remarkable that a very short time of 0.8 s was enough for it to deliver this kind of performance, making it a neat combination of both time and power efficiency. Full article

Epizootic haemorrhagic disease virus serotype 8 (EHDV-8) emerged in southern Europe in 2022–2023, but clinical and pathological characterization in free-ranging wildlife remains limited. This study investigated EHDV-8-associated morbidity and mortality in wild ruminants in a 2023 outbreak in Sierras de Cazorla, Segura y Las Villas Natural Park (Jaén, Andalusia, Spain). Moribund animals demonstrated a consistent acute neuro-respiratory syndrome characterized by weakness, ataxia, nystagmus and severe dyspnoea with frothy oral discharge. On the carcasses of 39 red deer, two fallow deer, and one mouflon, necropsy was performed and subsequently histopathology and a real-time polymerase chain reaction (RT-PCR) on the collected samples. Gross lesions included marked pulmonary oedema, tracheal foam and widespread congestion, while histopathology revealed lymphoid depletion, pulmonary haemorrhage, vascular injury and renal tubular necrosis. All animals tested positive for EHDV-8 with low RT-qPCR cycle threshold values, indicating high viral loads. This series provides the first confirmed clinical, pathological, and molecular evidence of EHDV-8 infection in fallow deer and mouflon in Europe. The observations demonstrate that EHDV-8 causes a peracute systemic haemorrhagic disease in susceptible wild ruminants and underline the importance of integrated wildlife surveillance and timely diagnostic sampling during peak vector activity. Full article

Objectives: This study aims to explore the therapeutic effect and mechanism of stir-roasted deer velvet antler with ghee (ZLR) on Non-Alcoholic Fatty Liver Disease (NAFLD). Methods: This study used proteomics to analyze the protein composition of roasted deer antler velvet. It established a high-fat diet (HFD)-induced NAFLD rat model and evaluated the therapeutic effects of different dosage groups, including liver injury, oxidative stress, glucose metabolism, steatosis, and insulin homeostasis (via fasting glucose tolerance). Transcriptomics explored the mechanism. Gene expression and Western blot detected lipid metabolism-related gene expression. In vivo experiments validated that ZLR-containing serum alleviates NAFLD and reduces reactive oxygen species levels. Results: The results indicated that ZLR could significantly reduce the body weight, liver weight and degree of hepatic steatosis in HFD rats, improve glycolipid metabolism and insulin sensitivity, and alleviate oxidative stress damage. The mechanism involves activating the adenosine monophosphate-activated protein kinase/peroxisome proliferator-activated receptor (AMPK/PPAR) signaling pathway, regulating the expression of lipid metabolism-related genes, promoting fatty acid oxidation, and reducing fat deposition. The results of in vitro experiments show that ZLR-containing serum can effectively reduce lipid droplet production in liver cells and effectively alleviate oxidative stress damage in liver cells. Conclusions: The traditional Chinese medicine processed product ZLR can regulate lipid metabolism in the body and alleviate the degree of NAFLD by activating the AMPK and PPAR signaling pathways. It provides new ideas for the clinical treatment of NAFLD. Full article

Astroviruses are non-enveloped, positive-sense single-stranded RNA viruses known to infect various mammals and birds, including humans, often causing gastrointestinal disorders. In recent years, astroviruses have also been linked to neurological and respiratory diseases across several species, including ruminants, mink, deer, and other mammals. Notably, astrovirus infections in goats have been documented in countries such as Switzerland and China, where novel genotypes have been identified in fecal samples. However, their role in the context of disease remains unclear, and reports focusing solely on goat astrovirus in the United States have not been published. A necropsy case of a Boer goat kid with a history of diarrhea was submitted for investigation following death in January 2025. Fresh tissues were received and used for histopathology and enteric pathogen testing, including parasitic, bacterial, and viral workups. Metagenomic-based next-generation sequencing (mNGS) was also applied for this case. Histological examination revealed severe necrotizing enterocolitis. The small intestine exhibited epithelial ulcerations, villus atrophy, hyperplastic and dilated crypts with necrotic debris, few intraenterocytic coccidian parasites, and increased inflammatory cells in the lamina propria. The large intestine showed similar findings with pleomorphic crypt enterocytes. Standard enteric pathogen tests were negative except for aerobic culture that identified Escherichia.coli and Enterococcus hirae. mNGS and bioinformatic analysis identified a novel astrovirus in the intestinal content that showed the highest nucleotide identity (86%) to the sheep strain Mamastrovirus 13 sheep/HA3 from China based on BLAST analysis. Phylogenetic analysis indicated that the newly identified caprine astrovirus IL90175 clustered with astrovirus strains from small ruminants in Asia and Europe. This research reports the discovery, histopathologic features, and genetic characteristics of a gastrointestinal disease-causing astrovirus in a goat kid, which had not been previously described in the United States. Full article

The hypoxic, cold, and high-ultraviolet radiation environments at high altitude pose severe challenges to mammalian immune and metabolic systems. However, little is known about how captive forest musk deer adapt to high-altitude environments and their seasonal variations. This study analyzed peripheral blood transcriptomes of 33 captive forest musk deer (Moschus berezovskii) at high altitude (~3900 m) and low altitude (~1450 m) during autumn-winter and spring-summer seasons. Results revealed comprehensive immune suppression in the high-altitude group during autumn-winter (downregulation of complement system CFB/C2/C3, interferon pathway genes including FLT3, with only natural killer (NK) cell PRKCQ upregulated), coupled with energy-conserving metabolic reprogramming (altered carbohydrate metabolism, inhibited lipid synthesis, fat mobilization, suppressed protein degradation). During spring-summer, neutrophil antimicrobial responses (SLPI/NCF1/ELANE) and humoral immunity (B cell differentiation genes PAX5/RUNX1; class-switch enzyme AICDA) partially recovered while cellular immunity (IL15/B2M) remained suppressed, accompanied by enhanced anabolic metabolism and adipocyte differentiation. Notably, NK cell-mediated cytotoxicity showed selective enhancement despite comprehensive immune suppression, representing an energy-efficient innate defense strategy. This study provides the first characterization of seasonal immune dynamics in a high-altitude cervid species. These findings reveal persistent immune constraints in high-altitude populations and provide theoretical foundations for disease prevention and health management in captive forest musk deer at high altitudes. Full article

Lyme disease, caused by the Borrelia burgdorferi bacterium and transmitted through black-legged (deer) tick bites, is becoming increasingly prevalent globally. According to data from the Lyme Disease Association, the number of cases has surged by more than 357% over the past 15 years. According to the Infectious Disease Society of America, traditional diagnostic methods are often slow, potentially allowing bacterial proliferation and complicating early management. This study proposes a novel hybrid deep learning framework to classify Lyme disease rashes, addressing the global prevalence of the disease caused by the Borrelia burgdorferi bacterium, which is transmitted through black-legged (deer) tick bites. This study presents a novel hybrid deep learning framework for classifying Lyme disease rashes, utilizing pre-trained models (ResNet50 V2, VGG19, DenseNet201) for initial classification. By combining VGG19 and DenseNet201 architectures, we developed a hybrid model, SkinVisualNet, which achieved an impressive accuracy of 98.83%, precision of 98.45%, recall of 99.09%, and an F1 score of 98.76%. To ensure the robustness and generalizability of the model, 5-fold cross-validation (CV) was performed, generating an average validation accuracy between 98.20% and 98.92%. Incorporating image preprocessing techniques such as gamma correction, contrast stretching and data augmentation led to a 10–13% improvement in model accuracy, significantly enhancing its ability to generalize across various conditions and improving overall performance. To improve model interpretability, we applied Explainable AI methods like LIME, Grad-CAM, CAM++, Score CAM and Smooth Grad to visualize the rash image regions most influential in classification. These techniques enhance both diagnostic transparency and model reliability, helping clinicians better understand the diagnostic decisions. The proposed framework demonstrates a significant advancement in automated Lyme disease detection, providing a robust and explainable AI-based diagnostic tool that can aid clinicians in improving patient outcomes. Full article

Apple is an important fruit worldwide, but it is quite susceptible to various diseases. In particular, apple scab disease (Venturia Inaequalis) is a common fungal infection that causes serious yield losses in apple production. This disease causes spots on both leaves and fruits, negatively affecting product quality and marketability. Early diagnosis and management of apple diseases are critical to increase productivity in apple production. Traditional methods are usually time-consuming and costly; therefore, image processing and artificial intelligence technologies have become important tools in disease detection. In this study, a new approach is developed for the classification of healthy and scab apples by combining image processing, deep learning and optimization methods. First, the dataset is enriched using data augmentation techniques such as rotation, mirroring, zooming, shifting, brightness adjustment, and noise addition. Then, the images are analyzed with Shearlet Transform (ST), and frequency and spatial features are extracted in detail. The features obtained from the ST are reconstructed with the inverse transformation, and the original images are given as inputs to deep learning architectures, specifically AlexNet, VGG-16 and ResNet-18. In each model, deep features are extracted to classify healthy and scab apple images, and a feature pool is created by combining these features. The selection process of features that will increase performance in the classification process is carried out with the Red Deer Optimization (RDO) algorithm. This algorithm, inspired by the natural life cycle of male deer, includes the steps of determining the leader deer, creating a harem, mating and selecting the next generations. By selecting the best male leaders and optimizing the mating process, the algorithm ensures that the most effective feature combinations are chosen to enhance classification performance. As a result, this hybrid method presents an innovative approach to accurately classifying healthy and scab apple images, contributing to more efficient and reliable disease detection in apple production. Full article