Search Results (1,594)

The Chinese red panda (Ailurus styani) is a rare and endangered animal in China; the increase in global temperature and the interference of human activities have caused irreversible effects on the suitable habitat of wild red pandas and threatened their survival. Therefore, it is necessary to carry out scientific research and protection for Chinese red pandas. In this study, the MaxEnt model was used to predict and analyze the suitable habitats of Chinese red pandas in the large and small Xiangling Mountains. The results showed that the main ecological factors affecting the suitable habitat distribution of Chinese red pandas in the Daxiangling Mountains are the average slope (45.6%, slope), the distance from the main road (24.2%, road), and the average temperature in the coldest quarter (11%, bio11). The main ecological factors affecting the suitable habitat distribution of Chinese red pandas in the Xiaoxiangling Mountains are bamboo distribution (67.4%, bamboo), annual temperature range (20.7%, bio7), and the average intensity of human activities (8.7%, Human Footprint). The predicted suitable habitat area of the Daxiangling Mountains is 123.835 km², and the predicted suitable habitat area of the Xiaoxiangling Mountains is 341.873 km². The predicted suitable habitat area of the Daxiangling Mountains accounts for 43.45% of the total mountain area, and the predicted suitable habitat area of the Xiaoxiangling Mountains accounts for 71.38%. The suitable habitat area of the Xiaoxiangling Mountains is nearly three times that of the Daxiangling Mountains, and the proportion of suitable habitat area of the Xiaoxiangling Mountains is much higher than that of the Daxiangling Mountains. The suitable habitat of Chinese red pandas in the Daxiangling Mountains is mainly distributed in the southeast, and the habitat is coherent but fragmented. The suitable habitat of Chinese red panda in Xiaoxiangling Mountains is mainly distributed in the east, and the habitat is more coherent. The results of this study can provide a scientific basis for the protection of the population and habitat of Chinese red pandas in Sichuan. Full article

Quercetin is a biologically active flavonoid compound that exerts numerous beneficial effects in humans and animals, including anti-diabetic activity. Its action has been explored in rodent models of type 1 and type 2 diabetes. It was revealed that quercetin mitigated diabetes-related hormonal and metabolic disorders and reduced oxidative and inflammatory stress. Its anti-diabetic effects were associated with advantageous changes in the relevant enzymes and signaling molecules. Quercetin positively affected, among others, superoxide dismutase, catalase, glutathione peroxidase, glucose transporter-2, glucokinase, glucose-6-phosphatase, glycogen phosphorylase, glycogen synthase, glycogen synthase kinase-3β, phosphoenolpyruvate carboxykinase, silent information regulator-1, sterol regulatory element-binding protein-1, insulin receptor substrate 1, phosphoinositide 3-kinase, and protein kinase B. The available data support the conclusion that the action of quercetin was pleiotropic since it alleviates a wide range of diabetes-related disorders. Moreover, no side effects were observed during treatment with quercetin in rodents. Given that human diabetes affects a large part of the population worldwide, the results of animal studies encourage clinical trials to evaluate the potential of quercetin as an adjunct to pharmacological therapies. Full article

Xylosyltransferase-I (XT-I) plays a crucial role in skeletal development and cartilage integrity. An XT-I deficiency is linked to severe bone disorders, such as Desbuquois dysplasia type 2. While animal models have provided insights into XT-I’s role during skeletal development, its specific effects on adult bone homeostasis, particularly in human mesenchymal stem cell (hMSC) differentiation, remain unclear. This study investigates how XT-I deficiency impacts the differentiation of hMSCs into chondrocytes, osteoblasts, and adipocytes—key processes in bone formation and repair. The aim of this study was to elucidate for the first time the molecular mechanisms by which XT-I deficiency leads to impaired bone homeostasis. Using CRISPR-Cas9-mediated gene editing, we generated XYLT1 knockdown (KD) hMSCs to assess their differentiation potential. Our findings revealed significant disruption in the chondrogenic differentiation in KD hMSCs, characterized by the altered expression of regulatory factors and extracellular matrix components, suggesting premature chondrocyte hypertrophy. Despite the presence of perilipin-coated lipid droplets in the adipogenic pathway, the overall leptin mRNA and protein expression was reduced in KD hMSCs, indicating a compromised lipid metabolism. Conversely, osteogenic differentiation was largely unaffected, with KD and wild-type hMSCs exhibiting comparable mineralization processes, indicating that critical aspects of osteogenesis were preserved despite the XYLT1 deficiency. In summary, these results underscore XT-I’s pivotal role in regulating differentiation pathways within the bone marrow niche, influencing cellular functions critical for skeletal health. A deeper insight into bone biology may pave the way for the development of innovative therapeutic approaches to improve bone health and treat skeletal disorders. Full article

Cardiac arrhythmias remain a major source of morbidity and mortality, often stemming from molecular and structural abnormalities that are insufficiently addressed by current pharmacologic and interventional therapies. Gene therapy has emerged as a transformative approach, offering precise and durable interventions that directly target the arrhythmogenic substrate. Across the spectrum of inherited and acquired arrhythmias—including long QT syndrome, Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, atrial fibrillation, and post-infarction ventricular tachycardia—gene-based strategies such as allele-specific silencing, gene replacement, CRISPR-mediated editing, and suppression-and-replacement constructs are showing growing translational potential. Advances in delivery platforms, including cardiotropic viral vectors, lipid nanoparticle-encapsulated mRNA, and non-viral reprogramming tools, have further enhanced the specificity and safety of these approaches. Additionally, innovative applications such as biological pacemaker development and mutation-agnostic therapies underscore the versatility of genetic modulation. Nonetheless, significant challenges remain, including vector tropism, immune responses, payload limitations, and the translational gap between preclinical models and human electrophysiology. Integration of patient-derived cardiomyocytes, computational simulations, and large-animal studies is expected to accelerate clinical translation. This review provides a comprehensive synthesis of the mechanistic rationale, therapeutic strategies, delivery platforms, and translational frontiers of gene therapy for cardiac arrhythmias. Full article

Background/Objectives: Autism spectrum disorder (ASD) encompasses several neurodevelopmental disorders, whose onset is correlated to genetic and environmental factors. Although the etiopathogenesis is not entirely clear, the involvement of inflammatory processes, the endocannabinoid system, and alterations in the permeability and composition of the intestinal microbiota are known to occur. Methods: This review systematically explores the literature available to date on the most widely used murine models for the study of ASD, the main biomarkers investigated for the diagnosis of ASD, and the therapeutic potential of probiotics, with a particular focus on the use of strains of Lactiplantibacillus (Lpb.) plantarum in in vivo models and clinical trials for ASD. Results: Several studies have demonstrated that targeting multifactorial biomarkers in animal models and patients contributes to a more comprehensive understanding of the complex mechanisms underlying ASD. Moreover, accumulating evidence supports the beneficial effect of probiotics, including Lpb. plantarum, as a promising alternative therapeutic strategy, capable of modulating gut–brain axis communication. Conclusions: Probiotic supplementation, particularly with selected Lpb. plantarum strains, is emerging as a potential complementary approach for ameliorating ASD-related gastrointestinal and behavioral symptoms. However, further large-scale clinical studies are essential to validate their efficacy and determine optimal treatment protocols and dietary strategies. Full article

The gut microbiota and its interaction with the nervous system through the gut–brain axis (MGB) have been the subject of growing interest in biomedical research. It has been proposed that modulation of microbiota using probiotics could offer a promising therapeutic alternative for mood regulation and the treatment of anxiety and depression disorders. The findings indicate that several probiotic strains, such as Lactobacillus and Bifidobacterium, have demonstrated anxiolytic and antidepressant effects in pre and clinical studies. These effects seem to be mediated by the regulation of the hypothalamic–pituitary–adrenal axis (HPA), the synthesis of neurotransmitters such as serotonin (5-HT) and Gamma-amino-butyric acid (GABA), as well as the modulation of systemic inflammation. However, the lack of standardization in dosing and strain selection, in addition to the scarcity of large-scale clinical studies, limit the applicability of these findings in clinical therapy. Additional research is required to establish standardized therapeutic protocols and better understand the role of probiotics in mental health. The aim of this narrative review is to discuss the relationship between the gut microbiota and the MGB axis in the context of anxiety and depression disorders, the underlying neurobiological mechanisms, as well as the preclinical evidence for the effect of probiotics in modulating these disorders. In this way, an exhaustive search was carried out in scientific databases including PubMed, ScienceDirect, Scopus, and Web of Science. Preclinical research evaluating the effects of different probiotic strains in animal models during chronic treatment was selected, excluding those studies that did not provide access to the full text. Full article

Pteridium aquilinum (bracken fern) poses a global threat to biodiversity and to the health of both animals and humans due to its toxic metabolites and aggressive ecological expansion. In northern Spain, particularly in regions of intensive livestock farming, these risks may be exacerbated, calling for urgent assessment and monitoring strategies. In this study, we implemented a multidisciplinary approach to evaluate the toxicological and ecological relevance of P. aquilinum through four key actions: (a) quantification of pterosins A and B in young fronds (croziers) using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS); (b) analysis of in vivo genotoxicity of aqueous extracts using Drosophila melanogaster as a model organism; (c) a large-scale survey of local livestock farmers to assess awareness and perceived impact of bracken; and (d) the development and field application of a drone-based mapping tool to assess the spatial distribution of the species at the regional level. Our results confirm the consistent presence of pterosins A and B in croziers, with concentrations ranging from 0.17 to 2.20 mg/g dry weight for PtrB and 13.39 to 257 µg/g for PtrA. Both metabolite concentrations and genotoxicity levels were found to correlate with latitude and, importantly, with each other. All tested samples exhibited genotoxic activity, with notable differences among them. The farmer survey (n = 212) revealed that only 50% of respondents were aware of the toxic risks posed by bracken, indicating a need for targeted outreach. The drone-assisted mapping approach proved to be a promising tool for identifying bracken-dominated areas and provides a scalable foundation for future ecological monitoring and land management strategies. Altogether, our findings emphasize that P. aquilinum is not merely a local concern but a globally relevant toxic species whose monitoring and control demand coordinated scientific and policy-based efforts. Full article

Tea is one of the most consumed beverages in the world, belonging to the category of compounds known as tannins and flavonoids. One of the polyphenols found in large amounts in green tea leaves (Camellia sinensis) is epigallocatechin-3-O-gallate (EGCG). Though EGCG has shown some pharmacological effects, to date, it has not been utilised as a therapeutic agent. This is attributed to the fact that EGCG lacks adequate stability, and it is known to degrade through epimerization or auto-oxidation processes, especially when it is exposed to light, temperature fluctuations, some pH values, or the presence of oxygen. Consuming green tea with EGCG can alleviate the effects of bone diseases, such as osteoporosis, and support faster bone regeneration in the case of fractures. Therefore, this review focuses on the current state of research, highlighting the effects of EGCG on bone biology, such as enhancing osteoblast differentiation, promoting bone mineralisation, improving bone microarchitecture, and inhibiting osteoclastogenesis through the modulation of the RANK/RANKL/OPG pathway. Additionally, EGCG exerts antioxidant, anti-inflammatory, and dose-dependent effects on bone cells. It also downregulates inflammatory markers (TNF-α, IL-1β, and COX-2) and reduces oxidative stress via the inhibition of reactive oxygen species generation and the activation of protective signalling pathways (e.g., MAPK and NF-κB). Studies in animal models confirm that EGCG supplementation leads to increased bone mass and strength. These findings collectively support the further exploration of EGCG as an adjunct in the treatment and prevention of metabolic bone diseases. The authors aim to present the relationship between EGCG and bone health, highlighting issues for future research and clinical applications. Full article

White-tailed deer (Odocoileus virginianus) in the wild and on cervid farms have drawn the attention of state wildlife agencies and animal health agencies as Chronic Wasting Disease (CWD) has spread across North America. Deer farm regulations have been implemented to reduce direct contact between wild and farmed cervids; however, evidence suggests that indirect contact to infectious prions passed through the alimentary tracts of scavengers may be an important transmission pathway. The objective of this study was to characterize mammalian scavenger and wild deer activities associated with deer farms and link these activities with site-specific spatial covariates utilizing a network of camera traps, mounted to farm perimeter fences. We monitored each of 14 farms in Minnesota, Wisconsin, and Pennsylvania for two weeks during the summer, with a subset of farms also monitored in the winter and fall. Across all sites and seasons, we captured 749 observations of wildlife. In total, nine species were captured, with wild white-tailed deer accounting for over three quarters of observations. Despite the large number of wild deer observed, we found that interactions between wild and farmed deer at the fence line were infrequent (six direct contacts observed). In contrast, mammalian scavengers were frequently observed inside and outside of the fence. Supplementary cameras placed on deer feeders revealed higher observation rates of scavengers than those placed along fence lines, highlighting the potential for transmission of CWD through indirect contact via scavenger excreta. To evaluate associations between the number of observations of focal species with land-cover characteristics, two mixed-effects regression models were fitted, one model for scavengers and one for wild deer. Contrary to our hypothesis, landscape context did not have a strong impact on wildlife visitation. This suggests that farm location is less important than management practices, highlighting the need for future research into how farming practices impact rates of wildlife visitation onto cervid farms. Full article

Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial. Full article

Background/Objectives: Artificial intelligence (AI) is beginning to be applied in wound ballistics, showing preliminary potential to improve the accuracy and objectivity of forensic analyses. This review explores the current state of AI applications in forensic firearm wound analysis, emphasizing its potential to address challenges such as subjective interpretations and data heterogeneity. Methods: A systematic review adhering to PRISMA guidelines was conducted using databases such as Scopus and Web of Science. Keywords focused on AI and GSW classification identified 502 studies, narrowed down to 4 relevant articles after rigorous screening based on inclusion and exclusion criteria. Results: These studies examined the role of deep learning (DL) models in classifying GSWs by type, shooting distance, and entry or exit characteristics. The key findings demonstrated that DL models like TinyResNet, ResNet152, and ConvNext Tiny achieved accuracy ranging from 87.99% to 98%. Models were effective in tasks such as classifying GSWs and estimating shooting distances. However, most studies were exploratory in nature, with small sample sizes and, in some cases, reliance on animal models, which limits generalizability to real-world forensic scenarios. Conclusions: Comparisons with other forensic AI applications revealed that large, diverse datasets significantly enhance model performance. Transparent and interpretable AI systems utilizing techniques are essential for judicial acceptance and ethical compliance. Despite the encouraging results, the field remains in an early stage of development. Limitations highlight the need for standardized protocols, cross-institutional collaboration, and the integration of multimodal data for robust forensic AI systems. Future research should focus on overcoming current data and validation constraints, ensuring the ethical use of human forensic data, and developing AI tools that are scientifically sound and legally defensible. Full article

Endophytic microorganisms inhabiting plant tissues constitute a unique and largely untapped reservoir of bioactive metabolites, including phenolics, terpenoids, alkaloids, polysaccharides, and anthraquinones, among others. This review focuses on the potential of these compounds to modulate the complex processes of wound repair, such as hemostasis, inflammation, proliferation, and remodeling. Uniquely, this review delineates the specific mechanisms supported not only by indirect evidence but by primary research directly linking endophytic metabolites to wound repair. We synthesized and evaluated evidence from 18 studies, of which over 75% directly assessed wound healing effects through in vitro and in vivo models. Metabolites from endophytic microorganisms promoted wound contraction, suppressed biofilm formation by key pathogens (e.g., MRSA, P. aeruginosa), and accelerated tissue re-epithelialization in animal models. Other compounds demonstrated >99% wound closure in rats, while several extracts showed anti-inflammatory and cytocompatible profiles. Nevertheless, the majority of studies applied unstandardized methods and used crude extracts, hindering precise structure–activity assessment. The originality of this review lies in drawing attention to direct evidence for wound healing from diverse endophytic sources and systematically identifying gaps between preclinical promise and clinical translation, positioning endophytes as a sustainable platform for next-generation wound therapeutics. Full article

Developing a new drug costs approximately one to three billion dollars and takes around ten years; however, this process has only a ten percent success rate. To address this issue, new technologies that combine artificial intelligence (AI) and quantum computing can be leveraged in the pharmaceutical industry. The RSA cryptographic algorithm, developed by Rivest, Shamir, and Adleman in 1977, is one of the most widely used public-key encryption schemes in modern digital security. Its security foundation lies in the computational difficulty of factoring the product of two large prime numbers, a problem considered intractable for classical computers when the key size is sufficiently large (e.g., 2048 bits or more). A future application of using a detailed structural model of a protein is that digital drug design can be used to predict potential drug candidates, thereby reducing or eliminating the need for time-consuming laboratory and animal testing. Knowing the molecular structure of a possible candidate drug can provide insights into how drugs interact with targets at an atomic level, at significantly lower expenditures, and with maximum effectiveness. AI and quantum computers can rapidly screen out potential new drug candidates, determine the toxicity level of a known drug, and eliminate drugs with high toxicity at the beginning of the drug development phase, thereby avoiding expensive laboratory and animal testing. The Food and Drug Administration (FDA) and other regulatory bodies are increasingly supporting the use of in silico to in vitro/in vivo validation methods and assessments of drug safety and efficacy. Full article

Axis definition plays a key role in the establishment of animal body plans, both in normal development and regeneration. The cnidarian Hydra can re-establish its simple body plan when regenerating from a random cell aggregate or a sufficiently small tissue fragment. At the beginning of regeneration, a hollow cellular spheroid forms, which then undergoes symmetry breaking and de novo body axis definition. In the past, we have published related work in a physics journal, which is difficult to read for scientists from other disciplines. Here, we review our work for readers not so familiar with this type of approach at a level that requires very little knowledge in mathematics. At the same time, we present a few aspects of Hydra biology that we believe to be linked to our work. These biological aspects may be of interest to physicists or members of related disciplines to better understand our approach. The proposed theoretical model is based on fluctuations of gene expression that are triggered by mechanical signaling, leading to increasingly large groups of cells acting in sync. With a single free parameter, the model quantitatively reproduces the experimentally observed expression pattern of the gene ks1, a marker for ‘head forming potential’. We observed that Hydra positions its axis as a function of a weak temperature gradient, but in a non-intuitive way. Supposing that a large fluctuation including ks1 expression is locked to define the head position, the model reproduces this behavior as well—without further changes. We explain why we believe that the proposed fluctuation-based symmetry breaking process agrees well with recent experimental findings where actin filament organization or anisotropic mechanical stimulation act as axis-positioning events. The model suggests that the Hydra spheroid exhibits huge sensitivity to external perturbations that will eventually position the axis. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article