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Background: Zoonotic diseases originating in animals pose a significant threat to global public health. Recent outbreaks, such as coronavirus disease 2019 (COVID-19), have caused widespread illness, death, and socioeconomic disruptions worldwide. To cope with these diseases effectively, it is crucial to strengthen surveillance capabilities and establish rapid response systems. Aim: The aim of this review to examine the modern technologies and solutions that have the potential to enhance zoonotic disease surveillance and outbreak responses and provide valuable insights into how cutting-edge innovations could be leveraged to prevent, detect, and control emerging zoonotic disease outbreaks. Herein, we discuss advanced tools including big data analytics, artificial intelligence, the Internet of Things, geographic information systems, remote sensing, molecular diagnostics, point-of-care testing, telemedicine, digital contact tracing, and early warning systems. Results: These technologies enable real-time monitoring, the prediction of outbreak risks, early anomaly detection, rapid diagnosis, and targeted interventions during outbreaks. When integrated through collaborative partnerships, these strategies can significantly improve the speed and effectiveness of zoonotic disease control. However, several challenges persist, particularly in resource-limited settings, such as infrastructure limitations, costs, data integration and training requirements, and ethical implementation. Conclusion: With strategic planning and coordinated efforts, modern technologies and solutions offer immense potential to bolster surveillance and outbreak responses, and serve as a critical resource against emerging zoonotic disease threats worldwide.

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The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.

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This study aimed to evaluate the effects of dietary embelin supplementation during late gestation (from days 60 to 110) on performance and maternal-fetal glucose metabolism of pigs. Sixty sows (Duroc × Yorkshire × Landrace; parity = 1.68 ±â€…0.03; N = 20) were randomly divided into three gestation (day 60 of pregnancy) treatments, Control pigs (CON) were fed a basal diet, and the other animals were fed a basal diet supplemented with 200 or 600 mg/kg embelin per kg of feed. The body weight, backfat thickness and litter size of the sows, and birth weight and mortality of piglets were recorded. Sows' blood and piglets' umbilical cord blood were collected for the measurements of hematological parameters and anti-oxidative and immune indexes, and maternal-fetal glucose metabolism parameters, respectively. The colostrum and milk and fecal samples of the sows were also collected for analysis of milk composition and apparent total tract nutrient digestibility. Dietary embelin had no effect on the BW and backfat thickness of the sows but significantly increased the birth weight of piglets (P < 0.05) and decreased the mortality (P < 0.05). Moreover, the white blood cell counts (day 90), neutrophil count and mean cell hemoglobin (day 110), total anti-oxidant capacity (T-AOC), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) content of the sows were increased significantly (P < 0.05) in the embelin groups than that in the CON group, whereas the malondialdehyde (MDA) content was decreased (P < 0.05). Embelin significantly increased immunoglobulin A (IgA) and immunoglobulin G (IgG) content in plasma of piglets as well as those in colostrum and milk of sows than the CON treatment (P < 0.05). In addition, dry matter, ash, and ether extract in the colostrum were similar between groups (P > 0.05), whereas the embelin significantly increased the crude protein in the milk. The apparent total tract nutrient digestibility was similar between treatments (P > 0.05). The embelin treatment significantly increased the glucose levels and lactate dehydrogenase B (LDHB) activity in sows plasma, and decreased the lactate levels in both sows and fetuses plasma (P < 0.05). Collectively, this study indicates that sows fed with embelin in mid-to-late gestation showed improved maternal health and anti-oxidative status, milk protein content, and maternal-fetal glucose metabolism, showing promise in natural plant extract nutrition for sows.

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Abnormal glucose metabolism in sows in late gestation can lead to incapacity of sow production, and even reproductive disorders. It has been confirmed that inefficient glucose utilization and oxidative damage are intimately related. Thus, studies about alleviating oxidative stress and facilitating glucose metabolism in pregnant sows can be relevant. As an excellent anti-oxidative plant extract, embelin has been widely used in dietary supplementation of rodents, however, the effect of dietary supplementation with embelin on the performance of sows and newborn piglets, as well as on the glucose metabolism has rarely been reported. The present study provides the first evidence that dietary supplementation with embelin during mid-to-late gestation improved maternal immune and oxidative status, the milk quality as well as the glucose metabolism of both sows and piglets, suggesting that embelin may be a promising natural plant extractive of nutrition for sows especially during mid-to-late pregnancy and lactation.

Asunto(s)

Calostro , Lactancia , Embarazo , Porcinos , Animales , Femenino , Peso al Nacer , Calostro/metabolismo , Suplementos Dietéticos , Dieta/veterinaria , Paridad , Inmunoglobulina G/análisis , Sangre Fetal , Glucosa/metabolismo , Alimentación Animal/análisis

RESUMEN

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.