Science in One Health最新文献

Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.

Nanoscience and technology have shown promise in revitalizing the agricultural sector and industries. This tool has gained the interest of many researchers as it can be utilized to drive sustainable agriculture by suggesting long-lasting solutions to different problems in the agricultural space. However, there is a paucity of data on its health implications for the environment, plants, animals, and humans. This review evaluated the cost-effectiveness and productivity of nanoscience and technologies. The review highlighted the underlying health implications of nanoscience and technology from a One Health perspective.

One Health is an interdisciplinary approach that promotes the resolution of complex health issues through collaboration across multiple disciplines. In addition to accountability, the One Health governance structure fosters shared understanding, trust, and an appreciation for diverse perspectives and requirements. This article provides a comprehensive analysis of the current integration of the One Health approach within China's existing health governance framework. It also proposes strategies for further improvement, with emphasis on the level of implementation and contributions to the advancement of One Health governance through an examination of current health policies.

Background

The holistic view of the ‘The unity of man and nature’ promotes the development and application of traditional Chinese medicine (TCM). Despite the absence of modern pharmacological therapies with robust efficacy against coronavirus disease 2019 (COVID-19), TCM has exhibited potential utility for treating the disease in clinical practice.

Methods

A retrospective cohort study was conducted to investigate the therapeutic effect of TCM treatment intensity (TCMTI) in patients with mild COVID-19. A total of 6120 laboratory-confirmed patients with mild COVID-19 were recruited from temporary isolation facilities. The primary outcome measure was severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ribonucleic acid conversion time. In addition, restricted cubic spline models were employed to elucidate nonlinear relationships.

Results

The median age (range) of the study participants was 43.0 (2.0–75.0) years, with a median hospitalization duration of 9.7 (4.1–22.5) days. The median time for achieving SARS-CoV-2 ribonucleic acid negativity was 6.67 days. The restricted cubic spline models revealed a remarkable nonlinear association between TCMTI and the time-to-ribonucleic acid negativity. After adjusting for potential confounders, the high TCMTI group exhibited a markedly shorter median time to SARS-CoV-2 ribonucleic acid negativity and reduced hospitalization duration (P < 0.001) than the low TCMTI group. Moreover, the mean time to achieve SARS-CoV-2 ribonucleic acid negativity was shortened by 1.909 days (P < 0.001) in the high-TCMTI group compared to the low-TCMTI group.

Conclusion

This study suggests that early initiation and intensified use of TCM may accelerate the time required to achieve SARS-CoV-2 ribonucleic acid negativity in patients with COVID-19, bearing considerable implications for public health.

Antimicrobial resistance (AMR) is a substantial global One Health problem. This paper reports on initial, proof-of-concept development of an agent-based model (ABM) as part of wider modelling efforts to support collaborations between groups interested in policy development for animal health and food systems. The model simulates AMR in poultry production in Senegal. It simultaneously addresses current policy issues, builds on existing modelling in the domain and describes AMR in the broiler chicken production cycle as seen by producers and veterinarians. This enables implementation and assessment of producer antimicrobial use and infection prevention and control strategies in terms of immediate economic incentives, potentially helping to advance conversations by addressing national policy priorities. Our model is presented as a flexible tool with promise for extension as part of AMR policy development in Senegal and West Africa, using participatory approaches. This work indicates that ABM can potentially play a useful role in fostering counter-AMR initiatives driven by food system actor behaviour in lower- and middle-income countries more generally.

More than 3 years have passed since the outbreak of COVID-19 and yet, the origin of the causal virus SARS-CoV-2 remains unknown. We examined the evolutionary trajectory of SARS-CoV-2 by analyzing non-redundant genome sets classified based on six closely linked mutations. The results indicated that SARS-CoV-2 emerged in February 2019 or earlier and evolved into three main haplotypes (GL, DS, and DL) before May 2019, which then continued to evolve in parallel. The dominant haplotype GL had spread worldwide in the summer (May to July) of 2019 and then evolved into virulent strains in December 2019 that triggered the global pandemic, whereas haplotypes DL and DS arrived in China in October 2019 and caused the epidemic in China in December 2019. Therefore, haplotype GL neither originated in China nor from the viral strains that caused the epidemic in China. Accordingly, considering data solely from China would be inadequate to reveal the mysterious origin of SARS-CoV-2, emphasizing the necessity of global cooperation.

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.

The international journal Science in One Health (SOH), launched a year ago, is a platform to disseminate research achievements and practical lessons in the field of One Health globally and to achieve the United Nations Sustainable Development Goals by systematic thinking with multi-disciplinary approach to solve complex health problems. This paper reviews the efforts that SOH editorial board made in the first year to promote a multi-disciplinary teamwork, create a platform for exchanges of ideas, and initiate a community for One Health. SOH has received manuscripts from six continents and published 24 articles (5 in press) in the first year. SOH is devoted to providing more and more high-quality articles in One Health disciplines including One Health database, antimicrobial resistance, zoonotic diseases, food security, One Health governance and climate changes with their impacts. This editorial illustrated the power of Science in One Health.

The triple crises of the COVID-19 pandemic, conflict and climate change have severely impacted food systems, leading to socio-economic consequences and undermining food and nutrition security across Africa. To address the malnutrition and poverty affecting approximately 700 million people in Africa, there is potential for the One Health approach implementation and operationalization to bring together multidisciplinary solutions for tackling food insecurity and ensuring food safety net. However, there is limited documentation on the potential of the One Health approach system thinking implementation to guide responses to triple crises-induced food insecurity. Therefore, this article aims to systematically understand the triple crises-induced food insecurity, connect existing solutions, and explore the role of the One Health approach in strengthening food and agriculture systems in Africa.

Our finding showed the impact of triple crises exacerbating food system vulnerability in Africa and worldwide. Mitigating and resilient actions are urgently needed in tackling the emerging and persisting challenges, and infectious diseases menace and burden across Africa. We present a conceptual model illustrating the complex nature of triple crises-induced food insecurity, vulnerability areas within the food system, and actionable strategies for building community food resilience. Additionally, recommendations are provided to create an enabling environment that supports One Health approach implementation and addresses food insecurity challenges through innovative partnerships, local-led initiatives, and enhanced governance and artificial intelligence technology capacities in achieving sustainable and inclusive growth to reduce socio-economic inequalities.

Stepping up integrated, actionable, and sustainable food systems programs and innovative long-lasting solutions requires investing in promoting new partnership and research collaboration in building conflict resolution and peace towards strengthening and reshaping local and global food security related climate change adaptations actions for most vulnerable communities’ benefits. These are ingredients in fastening preparedness, prevention and control of infectious diseases prevention and control, reducing food supply chains disruption towards accelerating equitable benefits of Universal Health Coverage and Sustainable Development Goals, 2030 across Africa.

This letter draws attention to the worrying situation of heavy metal pollution in Brazil, especially concerning the Amazon's Indigenous peoples affected by mercury contamination from illegal gold mining activities. Heavy metal pollution is also an emerging problem in other Brazilian biomes besides the Amazon Forest (e.g., Pampa biome in southern Brazil), as well as in coastal ecosystems/regions and large cities. Despite being a neglected problem, Brazil's heavy metal pollution causes significant detrimental impacts on human health and ecosystems. Finally, some alternatives to overcome this problem are suggested.