Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

How transdisciplinarity can help biotech-driven biodiversity research

The Kunming-Montreal Global Biodiversity Framework marks a significant step toward conserving genetic diversity on a global scale. Sequencing advancements have broadened biodiversity studies by enabling the mapping of species distributions, increasing understanding of ecological interactions, and monitoring genetic diversity. However, these tools are hindered by inequalities and biases, particularly in biodiversity-rich developing countries. To navigate these challenges, we propose strategies using the existing biotechnological toolbox to make biodiversity data more accessible and useful for research and development. This includes increasing funding for database curation, improving metadata standards, addressing inequalities in technological capacity, and supporting holistic capacity-building programmes. Implementing these strategies can unlock new opportunities for biodiversity research aligned with sustainable development principles and can contribute to improved conservation outcomes.

A framework for managing infectious diseases in rural areas in low- and middle-income countries in the face of climate change-East Africa as a case study

Climate change is having unprecedented impacts on human health, including increasing infectious disease risk. Despite this, health systems across the world are currently not prepared for novel disease scenarios anticipated with climate change. While the need for health systems to develop climate change adaptation strategies has been stressed in the past, there is no clear consensus on how this can be achieved, especially in rural areas in low- and middle-income countries that experience high disease burdens and climate change impacts simultaneously. Here, we highlight the need to put health systems in the context of climate change and demonstrate how this can be achieved by taking into account all aspects of infectious disease risk (i.e., pathogen hazards, and exposure and vulnerability to these pathogen hazards). The framework focuses on rural communities in East Africa since communities in this region experience climate change impacts, present specific vulnerabilities and exposure to climate-related hazards, and have regular exposure to a high burden of infectious diseases. Implementing the outlined approach can help make health systems climate adapted and avoid slowing momentum towards achieving global health grand challenge targets.

Bridging Biodiversity and Health: The Global Biodiversity Information Facility's initiative on open data on vectors of human diseases

There is an increased awareness of the importance of data publication, data sharing, and open science to support research, monitoring and control of vector-borne disease (VBD). Here we describe the efforts of the Global Biodiversity Information Facility (GBIF) as well as the World Health Special Programme on Research and Training in Diseases of Poverty (TDR) to promote publication of data related to vectors of diseases. In 2020, a GBIF task group of experts was formed to provide advice and support efforts aimed at enhancing the coverage and accessibility of data on vectors of human diseases within GBIF. Various strategies, such as organizing training courses and publishing data papers, were used to increase this content. This editorial introduces the outcome of a second call for data papers partnered by the TDR, GBIF and GigaScience Press in the journal GigaByte. Biodiversity and infectious diseases are linked in complex ways. These links can involve changes from the microorganism level to that of the habitat, and there are many ways in which these factors interact to affect human health. One way to tackle disease control and possibly elimination, is to provide stakeholders with access to a wide range of data shared under the FAIR principles, so it is possible to support early detection, analyses and evaluation, and to promote policy improvements and/or development.

Global Mapping and Visualization Analysis of One Health Knowledge in the COVID-19 Context

Globally, the COVID-19 pandemic had a significant impact on the health, social, and economic systems, triggering lasting damage and exposing the complexity of the problem beyond just being a health emergency. This crisis has highlighted the need for a comprehensive and collaborative strategy to successfully counter infectious diseases and other global challenges. With the COVID-19 pandemic pushing One Health to the forefront of global health and sustainable development agendas, this concept has emerged as a potential approach for addressing these challenges. In the context of COVID-19, this study investigates global knowledge about One Health by examining its state, significant contributions, and future directions. It seeks to offer an integrated framework of insights guiding the development of well-informed decisions. A comprehensive search using the Scopus database was conducted, employing specific terms related to One Health and COVID-19. VOSviewer 1.6.19 software was used to generate network visualization maps. Countries' research output was adjusted based on their gross domestic product (GDP) and population size. The study identified a total of 527 publications. The United States led with 134 documents (25.4%), but India topped the adjusted ranking. One Health journal stood as the most common outlet for disseminating knowledge (49 documents; 9.3%), while Centers for Disease Control and Prevention (CDC), the United States emerged as the most prolific institution (13 documents; 2.5%). Key topics were related to the virus transmission mechanisms, climate change impacts, antimicrobial resistance, ecosystem health, preparedness, collaboration, community engagement, and developing of efficient surveillance systems. The study emphasizes how critical it is to capitalize on the present momentum of COVID-19 to advance One Health concepts. Integrating social and environmental sciences, and a variety of professions for better interaction and collaboration is crucial. Additionally, increased funding for developing countries, and legislative empowerment are vital to advance One Health and boost disease prevention.

Integrating databases for spatial analysis of parasite-host associations and the novel Brazilian dataset

Incomplete information on parasites, their associated hosts, and their precise geographical location hampers the ability to predict disease emergence in Brazil, a continental-sized country characterised by significant regional disparities. Here, we demonstrate how the NCBI Nucleotide and GBIF databases can be used as complementary databases to study spatially georeferenced parasite-host associations. We also provide a comprehensive dataset of parasites associated with mammal species that occur in Brazil, the Brazilian Mammal Parasite Occurrence Data (BMPO). This dataset integrates wild mammal species' morphological and life-history traits, zoonotic parasite status, and zoonotic microparasite transmission modes. Through meta-networks, comprising interconnected host species linked by shared zoonotic microparasites, we elucidate patterns of zoonotic microparasite dissemination. This approach contributes to wild animal and zoonoses surveillance, identifying and targeting host species accountable for disproportionate levels of parasite sharing within distinct biomes. Moreover, our novel dataset contributes to the refinement of models concerning disease emergence and parasite distribution among host species.

Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient's medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.

Nondestructive Methods of Pathogen Detection: Importance of Mosquito Integrity in Studies of Disease Transmission and Control

Mosquitoes are vectors of many pathogens, including viruses, protozoans, and helminths, spreading these pathogens to humans as well as to wild and domestic animals. As the identification of species and the biological characterization of mosquito vectors are cornerstones for understanding patterns of disease transmission, and the design of control strategies, we conducted a literature review on the current use of noninvasive and nondestructive techniques for pathogen detection in mosquitoes, highlighting the importance of their taxonomic status and systematics, and some gaps in the knowledge of their vectorial capacity. Here, we summarized the alternative techniques for pathogen detection in mosquitoes based on both laboratory and field studies. Parasite infection and dissemination by mosquitoes can also be obtained via analyses of saliva- and excreta-based techniques or of the whole mosquito body, using a near-infrared spectrometry (NIRS) approach. Further research should be encouraged to seek strategies for detecting target pathogens while preserving mosquito morphology, especially in biodiversity hotspot regions, thus enabling the discovery of cryptic or new species, and the determination of more accurate taxonomic, parasitological, and epidemiological patterns.