Global One Health index for zoonoses: A performance assessment in 160 countries and territories.

The One Health (OH) approach is used to control/prevent zoonotic events. However, there is a lack of tools for systematically assessing OH practices. Here, we applied the Global OH Index (GOHI) to evaluate the global OH performance for zoonoses (GOHI-Zoonoses). The fuzzy analytic hierarchy process algorithm and fuzzy comparison matrix were used to calculate the weights and scores of five key indicators, 16 subindicators, and 31 datasets for 160 countries and territories worldwide. The distribution of GOHI-Zoonoses scores varies significantly across countries and regions, reflecting the strengths and weaknesses in controlling or responding to zoonotic threats. Correlation analyses revealed that the GOHI-Zoonoses score was associated with economic, sociodemographic, environmental, climatic, and zoological factors. Additionally, the Human Development Index had a positive effect on the score. This study provides an evidence-based reference and guidance for global, regional, and country-level efforts to optimize the health of people, animals, and the environment.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.

OBJECTIVE: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. METHOD: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed. RESULTS: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. CONCLUSION: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

Natural variables separate the endemic areas of Clonorchis sinensis and Opisthorchis viverrini along a continuous, straight zone in Southeast Asia.

BACKGROUND: Clonorchiasis and opisthorchiasis, caused by the liver flukes Clonorchis sinensis and Opisthorchis viverrini respectively, represent significant neglected tropical diseases (NTDs) in Asia. The co-existence of these pathogens in overlapping regions complicates effective disease control strategies. This study aimed to clarify the distribution and interaction of these diseases within Southeast Asia. METHODS: We systematically collated occurrence records of human clonorchiasis (n = 1809) and opisthorchiasis (n = 731) across the Southeast Asia countries. Utilizing species distribution models incorporating environmental and climatic data, coupled machine learning algorithms with boosted regression trees, we predicted and distinguished endemic areas for each fluke species. Machine learning techniques, including geospatial analysis, were employed to delineate the boundaries between these flukes. RESULTS: Our analysis revealed that the endemic range of C. sinensis and O. viverrini in Southeast Asia primarily spans across part of China, Vietnam, Thailand, Laos, and Cambodia. During the period from 2000 to 2018, we identified C. sinensis infections in 84 distinct locations, predominantly in southern China (Guangxi Zhuang Autonomous Region) and northern Vietnam. In a stark contrast, O. viverrini was more widely distributed, with infections documented in 721 locations across Thailand, Laos, Cambodia, and Vietnam. Critical environmental determinants were quantitatively analyzed, revealing annual mean temperatures ranging between 14 and 20 °C in clonorchiasis-endemic areas and 24-30 °C in opisthorchiasis regions (P < 0.05). The machine learning model effectively mapped a distinct demarcation zone, demonstrating a clear separation between the endemic areas of these two liver flukes with AUC from 0.9 to1. The study in Vietnam delineates the coexistence and geographical boundaries of C. sinensis and O. viverrini, revealing distinct endemic zones and a transitional area where both liver fluke species overlap. CONCLUSIONS: Our findings highlight the critical role of specific climatic and environmental factors in influencing the geographical distribution of C. sinensis and O. viverrini. This spatial delineation offers valuable insights for integrated surveillance and control strategies, particularly in regions with sympatric transmission. The results underscore the need for tailored interventions, considering regional epidemiological variations. Future collaborations integrating eco-epidemiology, molecular epidemiology, and parasitology are essential to further elucidate the complex interplay of liver fluke distributions in Asia.

Risk assessment of imported malaria in China: a machine learning perspective.

BACKGROUND: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. METHODS: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. RESULTS: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. CONCLUSIONS: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning.

Background: Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA. Methods: Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments. Results: The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells. Conclusion: This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Tackling global health security by building an academic community for One Health action.

BACKGROUND: One Health approach is crucial to tackling complex global public health threats at the interface of humans, animals, and the environment. As outlined in the One Health Joint Plan of Action, the international One Health community includes stakeholders from different sectors. Supported by the Bill & Melinda Gates Foundation, an academic community for One Health action has been proposed with the aim of promoting the understanding and real-world implementation of One Health approach and contribution towards the Sustainable Development Goals for a healthy planet. MAIN TEXT: The proposed academic community would contribute to generating high-quality scientific evidence, distilling local experiences as well as fostering an interconnected One Health culture and mindset, among various stakeholders on different levels and in all sectors. The major scope of the community covers One Health governance, zoonotic diseases, food security, antimicrobial resistance, and climate change along with the research agenda to be developed. The academic community will be supported by two committees, including a strategic consultancy committee and a scientific steering committee, composed of influential scientists selected from the One Health information database. A workplan containing activities under six objectives is proposed to provide research support, strengthen local capacity, and enhance global participation. CONCLUSIONS: The proposed academic community for One Health action is a crucial step towards enhancing communication, coordination, collaboration, and capacity building for the implementation of One Health. By bringing eminent global experts together, the academic community possesses the potential to generate scientific evidence and provide advice to local governments and international organizations, enabling the pursuit of common goals, collaborative policies, and solutions to misaligned interests.

China's application of the One Health approach in addressing public health threats at the human-animal-environment interface: Advances and challenges.

Background: Due to emerging issues such as global climate change and zoonotic disease pandemics, the One Health approach has gained more attention since the turn of the 21st century. Although One Health thinking has deep roots and early applications in Chinese history, significant gaps exist in China's real-world implementation at the complex interface of the human-animal-environment. Methods: We abstracted the data from the global One Health index study and analysed China's performance in selected fields based on Structure-Process-Outcome model. By comparing China to the Belt & Road and G20 countries, the advances and gaps in China's One Health performance were determined and analysed. Findings: For the selected scientific fields, China generally performs better in ensuring food security and controlling antimicrobial resistance and worse in addressing climate change. Based on the SPO model, the "structure" indicators have the highest proportion (80.00%) of high ranking and the "outcome" indicators have the highest proportion (20.00%) of low ranking. When compared with Belt and Road countries, China scores above the median in almost all indicators (16 out of 18) under the selected scientific fields. When compared with G20 countries, China ranks highest in food security (scores 72.56 and ranks 6th), and lowest in climate change (48.74, 11th). Conclusion: Our results indicate that while China has made significant efforts to enhance the application of the One Health approach in national policies, it still faces challenges in translating policies into practical measures. It is recommended that a holistic One Health action framework be established for China in accordance with diverse social and cultural contexts, with a particular emphasis on overcoming data barriers and mobilizing stakeholders both domestically and globally. Implementation mechanisms, with clarified stakeholder responsibilities and incentives, should be improved along with top-level design.

Definition of a Novel Immunogenic Cell Death-Relevant Gene Signature Associated with Immune Landscape in Gastric Cancer.

Immunogenic cell death (ICD) induces anti-tumor immunity and aids in dismantling the immunosuppressive immune microenvironment (TME), which belongs to a type of regulated cell death. The differentiation of gastric cancer (GC) subtypes and the discovery of prognostic biomarkers are crucial for its treatment because GC is a disease that is both highly heterogeneous and aggressive. However, although the induction of ICD in tumor cells is associated with a favorable prognosis, the exact mechanism of its role in GC remains unclear. Transcriptome profiling data and clinical data of GC patients were retrieved from The Cancer Genome Atlas (TCGA) database. Herein, patients were classified with the consensus clustering algorithm, and the associated biological functions and immune microenvironment infiltration were explored based on the expression of ICD-associated genes. A risk score signature consisting of 11 ICD-related genes was established via the least absolute shrinkage and selection operator regression (LASSO) method. We have retrieved similar studies in recent years and compared them with our study using the time-dependent receiver operating characteristic (ROC) curves. Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore the association between the signature and tumor microenvironment (TME). Two distinct subtypes associated with ICD in GC were identified, each with a different prognosis. The ICD-high expression subtype was associated with higher immune cell infiltration and a better prognosis. The ICD-related gene signature containing 11 genes (CGB5, Z84468.1, APOA5, EPHA8, CLEC18C, TLR7, MUC7, MUC15, CTLA4, CALB2, and UGT2B28), could independently and accurately predict the prognosis of GC. In this study, an ICD-based classification was conducted to assist in the diagnosis and personalized therapy for GC. The ICD-related genes risk score model was established to predict prognosis.

Assessment of integrated patterns of human-animal-environment health: a holistic and stratified analysis.

BACKGROUND: Data-driven research is a very important component of One Health. As the core part of the global One Health index (GOHI), the global One Health Intrinsic Drivers index (IDI) is a framework for evaluating the baseline conditions of human-animal-environment health. This study aims to assess the global performance in terms of GOH-IDI, compare it across different World Bank regions, and analyze the relationships between GOH-IDI and national economic levels. METHODS: The raw data among 146 countries were collected from authoritative databases and official reports in November 2021. Descriptive statistical analysis, data visualization and manipulation, Shapiro normality test and ridge maps were used to evaluate and identify the spatial and classificatory distribution of GOH-IDI. This paper uses the World Bank regional classification and the World Bank income groups to analyse the relationship between GOH-IDI and regional economic levels, and completes the case studies of representative countries. RESULTS: The performance of One Health Intrinsic Driver in 146 countries was evaluated. The mean (standard deviation, SD) score of GOH-IDI is 54.05 (4.95). The values (mean SD) of different regions are North America (60.44, 2.36), Europe and Central Asia (57.73, 3.29), Middle East and North Africa (57.02, 2.56), East Asia and Pacific (53.87, 5.22), Latin America and the Caribbean (53.75, 2.20), South Asia (52.45, 2.61) and sub-Saharan Africa (48.27, 2.48). Gross national income per capita was moderately correlated with GOH-IDI (R2 = 0.651, Deviance explained = 66.6%, P < 0.005). Low income countries have the best performance in some secondary indicators, including Non-communicable Diseases and Mental Health and Health risks. Five indicators are not statistically different at each economic level, including Animal Epidemic Disease, Animal Biodiversity, Air Quality and Climate Change, Land Resources and Environmental Biodiversity. CONCLUSIONS: The GOH-IDI is a crucial tool to evaluate the situation of One Health. There are inter-regional differences in GOH-IDI significantly at the worldwide level. The best performing region for GOH-IDI was North America and the worst was sub-Saharan Africa. There is a positive correlation between the GOH-IDI and country economic status, with high-income countries performing well in most indicators. GOH-IDI facilitates researchers' understanding of the multidimensional situation in each country and invests more attention in scientific questions that need to be addressed urgently.

Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors: A modeling study in Cambodia.

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.

Recognizing and monitoring infectious sources of schistosomiasis by developing deep learning models with high-resolution remote sensing images.

BACKGROUND: China is progressing towards the goal of schistosomiasis elimination, but there are still some problems, such as difficult management of infection source and snail control. This study aimed to develop deep learning models with high-resolution remote sensing images for recognizing and monitoring livestock bovine, which is an intermediate source of Schistosoma japonicum infection, and to evaluate the effectiveness of the models for real-world application. METHODS: The dataset of livestock bovine's spatial distribution was collected from the Chinese National Platform for Common Geospatial Information Services. The high-resolution remote sensing images were further divided into training data, test data, and validation data for model development. Two recognition models based on deep learning methods (ENVINet5 and Mask R-CNN) were developed with reference to the training datasets. The performance of the developed models was evaluated by the performance metrics of precision, recall, and F1-score. RESULTS: A total of 50 typical image areas were selected, 1125 bovine objectives were labeled by the ENVINet5 model and 1277 bovine objectives were labeled by the Mask R-CNN model. For the ENVINet5 model, a total of 1598 records of bovine distribution were recognized. The model precision and recall were 81.9% and 80.2%, respectively. The F1 score was 0.81. For the Mask R-CNN mode, 1679 records of bovine objectives were identified. The model precision and recall were 87.3% and 85.2%, respectively. The F1 score was 0.87. When applying the developed models to real-world schistosomiasis-endemic regions, there were 63 bovine objectives in the original image, 53 records were extracted using the ENVINet5 model, and 57 records were extracted using the Mask R-CNN model. The successful recognition ratios were 84.1% and 90.5% for the respectively developed models. CONCLUSION: The ENVINet5 model is very feasible when the bovine distribution is low in structure with few samples. The Mask R-CNN model has a good framework design and runs highly efficiently. The livestock recognition models developed using deep learning methods with high-resolution remote sensing images accurately recognize the spatial distribution of livestock, which could enable precise control of schistosomiasis.

Efficient phosphate elimination from aqueous media by La/Fe bimetallic modified bentonite: Adsorption behavior and inner mechanism.

Nowadays, eutrophication problem in surface waterbodies has attracted specific attention. Herein, we reported facile synthesis and application of La/Fe engineered bentonite (LFB) for efficient phosphate elimination. Results indicated that bimetallic modified LFB composite could achieve efficient phosphate removal at pH 2-6, and satisfactory selectivity was implied by stable phosphate capturing within the interference of competing species (Cl-, NO3-, HCO3-, SO42-, F- and HA). Pseudo-second-order model could satisfactorily depict the kinetic behavior at different initial concentrations, indicating chemisorption of phosphate on LFB surface. Isotherm study suggested that phosphate adsorption behavior could be fitted well with Sips isotherm equation, indicating that both homogeneous monolayer adsorption and heterogeneous multilayer coverage of phosphate on LFB surface occurred within the investigated conditions. Adsorption thermodynamics implied the spontaneous and endothermic feature of phosphate loading on LFB composite. Characterization analysis confirmed successful La and Fe loading on bentonite, and electrostatic attraction and ligand exchange were the main adsorption mechanism. The high adsorption capacity, cost-effective feature and strong affinity towards phosphate demonstrated certain potential of as-prepared LFB composite for phosphate separation from eutrophic water.

Establishment of an indicator framework for global One Health Intrinsic Drivers index based on the grounded theory and fuzzy analytical hierarchy-entropy weight method.

BACKGROUND: One Health has become a global consensus to deal with complex health problems. However, the progress of One Health implementation in many countries is still relatively slow, and there is a lack of systematic evaluation index. The purpose of this study was to establish an indicator framework for global One Health Intrinsic Drivers index (GOH-IDI) to evaluate human, animal and environmental health development process globally. METHOD: First, 82 studies were deeply analyzed by a grounded theory (GT) method, including open coding, axial coding, and selective coding, to establish a three-level indicator framework, which was composed of three selective codes, 19 axial codes, and 79 open codes. Then, through semi-structured interviews with 28 health-related experts, the indicators were further integrated and simplified according to the inclusion criteria of the indicators. Finally, the fuzzy analytical hierarchy process combined with the entropy weight method was used to assign weights to the indicators, thus, forming the evaluation indicator framework of human, animal and environmental health development process. RESULTS: An indicator framework for GOH-IDI was formed consisting of three selective codes, 15 axial codes and 61 open codes. There were six axial codes for "Human Health", of which "Infectious Diseases" had the highest weight (19.76%) and "Injuries and Violence" had the lowest weight (11.72%). There were four axial codes for "Animal Health", of which "Animal Epidemic Disease" had the highest weight (39.28%) and "Animal Nutritional Status" had the lowest weight (11.59%). Five axial codes were set under "Environmental Health", among which, "Air Quality and Climate Change" had the highest weight (22.63%) and "Hazardous Chemicals" had the lowest weight (17.82%). CONCLUSIONS: An indicator framework for GOH-IDI was established in this study. The framework were universal, balanced, and scientific, which hopefully to be a tool for evaluation of the joint development of human, animal and environmental health in different regions globally.

Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models.

BACKGROUND: There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. METHODS: Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. RESULTS: Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81-303.84) and the actual number of the spillovers (0.94-74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. CONCLUSIONS: Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus.

A unified global genotyping framework of dengue virus serotype-1 for a stratified coordinated surveillance strategy of dengue epidemics.

BACKGROUND: Dengue is the fastest spreading arboviral disease, posing great challenges on global public health. A reproduceable and comparable global genotyping framework for contextualizing spatiotemporal epidemiological data of dengue virus (DENV) is essential for research studies and collaborative surveillance. METHODS: Targeting DENV-1 spreading prominently in recent decades, by reconciling all qualified complete E gene sequences of 5003 DENV-1 strains with epidemiological information from 78 epidemic countries/areas ranging from 1944 to 2018, we established and characterized a unified global high-resolution genotyping framework using phylogenetics, population genetics, phylogeography, and phylodynamics. RESULTS: The defined framework was discriminated with three hierarchical layers of genotype, subgenotype and clade with respective mean pairwise distances 2-6%, 0.8-2%, and ≤ 0.8%. The global epidemic patterns of DENV-1 showed strong geographic constraints representing stratified spatial-genetic epidemic pairs of Continent-Genotype, Region-Subgenotype and Nation-Clade, thereby identifying 12 epidemic regions which prospectively facilitates the region-based coordination. The increasing cross-transmission trends were also demonstrated. The traditional endemic countries such as Thailand, Vietnam and Indonesia displayed as persisting dominant source centers, while the emerging epidemic countries such as China, Australia, and the USA, where dengue outbreaks were frequently triggered by importation, showed a growing trend of DENV-1 diffusion. The probably hidden epidemics were found especially in Africa and India. Then, our framework can be utilized in an accurate stratified coordinated surveillance based on the defined viral population compositions. Thereby it is prospectively valuable for further hampering the ongoing transition process of epidemic to endemic, addressing the issue of inadequate monitoring, and warning us to be concerned about the cross-national, cross-regional, and cross-continental diffusions of dengue, which can potentially trigger large epidemics. CONCLUSIONS: The framework and its utilization in quantitatively assessing DENV-1 epidemics has laid a foundation and re-unveiled the urgency for establishing a stratified coordinated surveillance platform for blocking global spreading of dengue. This framework is also expected to bridge classical DENV-1 genotyping with genomic epidemiology and risk modeling. We will promote it to the public and update it periodically.

Cross-watershed distribution pattern challenging the elimination of Oncomelania hupensis, the intermediate host of Schistosoma japonica, in Sichuan province, China.

BACKGROUND: Snail control is critical to schistosomiasis control efforts in China. However, re-emergence of Oncomelania hupensis is challenging the achievements of schistosomiasis control. The present study aimed to test whether the amphibious snails can spread across watersheds using a combination of population genetics and geographic statistics. METHODS: The digital maps and attributes of snail habitats were obtained from the national survey on O. hupensis. Snail sampling was performed in 45 counties of Sichuan Province. The cox1 gene of specimens was characterized by sequencing. Unique haplotypes were found for phylogenetic inference and mapped in a geographical information system (GIS). Barriers of gene flow were identified by Monmonier's maximum difference algorithm. The watercourses and watersheds in the study area were determined based on a digital elevation model (DEM). Plain areas were defined by a threshold of slope. The slope of snail habitats was characterized and the nearest distance to watercourses was calculated using a GIS platform. Spatial dynamics of high-density distributions were observed by density analysis of snail habitats. RESULTS: A total of 422 cox1 sequences of O. hupensis specimens from 45 sampling sites were obtained and collapsed into 128 unique haplotypes or 10 clades. Higher haplotype diversity in the north of the study area was observed. Four barriers to gene flow, leading to five sub-regions, were found across the study area. Four sub-regions ran across major watersheds, while high-density distributions were confined within watersheds. The result indicated that snails were able to disperse across low-density areas. A total of 63.48% habitats or 43.29% accumulated infested areas were distributed in the plain areas where the overall slope was < 0.94°. Approximately 90% of snail habitats were closer to smaller watercourses. Historically, high-density areas were mainly located in the plains, but now more were distributed in hilly region. CONCLUSIONS: Our study showed the cross-watershed distribution of Oncomelania snails at a large scale. Natural cross-watershed spread in plains and long-distance dispersal by humans and animals might be the main driver of the observed patterns. We recommend cross-watershed joint control strategies for snail and schistosomiasis control.

Assessment of performance for a key indicator of One Health: evidence based on One Health index for zoonoses in Sub-Saharan Africa.

BACKGROUND: Zoonoses are public health threats that cause severe damage worldwide. Zoonoses constitute a key indicator of One Health (OH) and the OH approach is being applied for zoonosis control programmes of zoonotic diseases. In a very recent study, we developed an evaluation system for OH performance through the global OH index (GOHI). This study applied the GOHI to evaluate OH performance for zoonoses in sub-Saharan Africa. METHODS: The framework for the OH index on zoonoses (OHIZ) was constructed including five indicators, 15 subindicators and 28 datasets. Publicly available data were referenced to generate the OHIZ database which included both qualitative and quantitative indicators for all sub-Sahara African countries (n = 48). The GOHI algorithm was used to estimate scores for OHIZ. Indicator weights were calculated by adopting the fuzzy analytical hierarchy process. RESULTS: Overall, five indicators associated with weights were generated as follows: source of infection (23.70%), route of transmission (25.31%), targeted population (19.09%), capacity building (16.77%), and outcomes/case studies (15.13%). Following the indicators, a total of 37 sub-Sahara African countries aligned with OHIZ validation, while 11 territories were excluded for unfit or missing data. The OHIZ average score of sub-Saharan Africa was estimated at 53.67/100. The highest score was 71.99 from South Africa, while the lowest score was 40.51 from Benin. It is also worth mentioning that Sub-Sahara African countries had high performance in many subindicators associated with zoonoses, e.g., surveillance and response, vector and reservoir interventions, and natural protected areas, which suggests that this region had a certain capacity in control and prevention or responses to zoonotic events. CONCLUSIONS: This study reveals that it is possible to perform OH evaluation for zoonoses in sub-Saharan Africa by OHIZ. Findings from this study provide preliminary research information in advancing knowledge of the evidenced risks to strengthen strategies for effective control of zoonoses and to support the prevention of zoonotic events.

EyeScreen: Development and Potential of a Novel Machine Learning Application to Detect Leukocoria.

Purpose: Early diagnosis and treatment of retinoblastoma are of paramount importance for a positive clinical outcome. The most common sign of retinoblastoma is leukocoria, or white pupil. Effective, easy-to-perform, community-based screening is needed to improve outcomes in lower-income regions. The EyeScreen (developed by Joshua Meyer from the University of Michigan) Android (Google LLC) smartphone application is an important step toward addressing this need. The purpose of this study was to examine the potential of the novel use of low-cost technologies-a cell phone application and machine learning-to identify leukocoria. Design: A cell phone application was developed and refined with the feedback from on-site, single-population use in Ethiopia. Application performance was evaluated in this technology validation study. Participants: One thousand four hundred fifty-seven participants were recruited from ophthalmology and pediatric clinics in Addis Ababa, Ethiopia. Methods: Photographs obtained with inexpensive Android smartphones running the EyeScreen Application were used to train an ImageNet (ResNet) machine learning model and to measure the performance of the app. Eighty percent of the images were used in training the model, and 20% were reserved for testing. Main Outcome Measures: Performance of the model was measured in terms of sensitivity, specificity, receiver operating characteristic (ROC) curve, and precision-recall curve. Results: Analyses of the participant images resulted in the following at the participant level: sensitivity, 87%; specificity, 73%; area under the ROC curve, 0.93; and area under the precision-recall curve, 0.77. Conclusions: EyeScreen has the potential to serve as an effective screening tool in the areas of the world most affected by delayed retinoblastoma diagnosis. The relatively high initial performance of the machine learning model with small training datasets in this early-phase study can serve as a proof of concept for future use of machine learning and artificial intelligence in ophthalmic applications.

Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019.

BACKGROUND: Dengue has become one of the major vector-borne diseases, which has been an important public health concern. We aimed to estimate the disease burden of dengue in major endemic regions from 1990 to 2019, and explore the impact pattern of the socioeconomic factors on the burden of dengue based on the global burden of diseases, injuries, and risk factors study 2019 (GBD 2019). METHODS: Using the analytical strategies and data from the GBD 2019, we described the incidence and disability-adjusted life years (DALYs) of dengue in major endemic regions from 1990 to 2019. Furthermore, we estimated the correlation between dengue burden and socioeconomic factors, and then established an autoregressive integrated moving average (ARIMA) model to predict the epidemic trends of dengue in endemic regions. All estimates were proposed as numbers and age-standardized rates (ASR) per 100,000 population, with uncertainty intervals (UIs). The ASRs of dengue incidence were compared geographically and five regions were stratified by a sociodemographic index (SDI). RESULTS: A significant rise was observed on a global scale between 1990 and 2019, with the overall age-standardized rate (ASR) increasing from 557.15 (95% UI 243.32-1212.53) per 100,000 in 1990 to 740.4 (95% UI 478.2-1323.1) per 100,000 in 2019. In 2019, the Oceania region had the highest age-standardized incidence rates per 100,000 population (3173.48 (95% UI 762.33-6161.18)), followed by the South Asia region (1740.79 (95% UI 660.93-4287.12)), and then the Southeast Asia region (1153.57 (95% UI 1049.49-1281.59)). In Oceania, South Asia, and Southeast Asia, increase trends were found in the burden of dengue fever measured by ASRs of DALY which were consistent with ASRs of dengue incidence at the national level. Most of the countries with the heaviest burden of dengue fever occurred in areas with low and medium SDI regions. However, the burden in high-middle and high-SDI countries is relatively low, especially the Solomon Islands and Tonga in Oceania, the Maldives in South Asia and Indonesia in Southeast Asia. The age distribution results of the incidence rate and disease burden of dengue fever of major endemic regions showed that the higher risk and disease burden are mainly concentrated in people under 14 or over 70 years old. The prediction by ARIMA showed that the risk of dengue fever in South and Southeast Asia is on the rise, and further prevention and control is warranted. CONCLUSIONS: In view of the rapid population growth and urbanization in many dengue-endemic countries, our research results are of great significance for presenting the future trend in dengue fever. It is recommended to policy makers that specific attention needs to be paid to the negative impact of urbanization on dengue incidence and allocate more resources to the low-SDI areas and people under 14 or over 70 years old to reduce the burden of dengue fever.

Annexin A2 (ANXA2) regulates the transcription and alternative splicing of inflammatory genes in renal tubular epithelial cells.

BACKGROUND: Renal inflammation plays a crucial role during the progression of Chronic kidney disease (CKD), but there is limited research on hub genes involved in renal inflammation. Here, we aimed to explore the effects of Annexin A2 (ANXA2), a potential inflammatory regulator, on gene expression in human proximal tubular epithelial (HK2) cells. RNA-sequencing and bioinformatics analysis were performed on ANXA2-knockdown versus control HK2 cells to reveal the differentially expressed genes (DEGs) and regulated alternative splicing events (RASEs). Then the DEGs and RASEs were validated by qRT-PCR. RESULTS: A total of 220 upregulated and 171 downregulated genes related to ANXA2 knockdown were identified. Genes enriched in inflammatory response pathways, such as interferon-mediated signaling, cytokine-mediated signaling, and nuclear factor κB signaling, were under global transcriptional and alternative splicing regulation by ANXA2 knockdown. qRT-PCR confirmed ANXA2-regulated transcription of chemokine gene CCL5, as well as interferon-regulating genes ISG15, IFI6, IFI44, IFITM1, and IRF7, in addition to alternative splicing of inflammatory genes UBA52, RBCK1, and LITAF. CONCLUSIONS: The present study indicated that ANXA2 plays a role in inflammatory response in HK2 cells that may be mediated via the regulation of transcription and alternative splicing of inflammation-related genes.