Integrated Eco-Health approach significantly reduces helminth infections in endemic Khong islands with emphasis on Schistosoma mekongi.

BACKGROUND: Helminth infections, including Opisthorchis viverrini, hookworm, and Trichuris trichiura, are prevalent in Khong district, Champasack province, southern Lao People's Democratic Republic (PDR). Schistosomiasis caused by Schistosoma mekongi is of public health concern on the islands of the Khong district. This study aimed to assess the impact of an Eco-Health/One-Health approach in combination with mass drug administration (MDA) to reduce these helminth infections. METHODS: We conducted a community intervention using a stepped-wedge trial approach on two endemic islands (Donsom and Donkhone) of the Khong district, Champasack province, Lao PDR, between April 2012 and March 2013. In each study village, 30-40 households were randomly selected. All members of selected households, who were at home during the study period were invited to participate in the study. A baseline study was conducted to assess helminth infections, knowledge attitudes and practices toward Schistosoma mekongi infection, behavior of open defecation and availability of latrine at home. After the baseline (T0), the Eco-Health/One-Health approach was implemented on Donsom (intervention) and Donkhone island (control). An assessment was conducted in 2014 (T1), one year after the completion of intervention implementation, to assess the short-term impact of the Eco-Health/One-Health approach on helminth infections and compare intervention and control islands. Later in 2015, the Eco-Health/One-Health approach was implemented on control island (Donkhone). After the implementation of intervention, the parasitological assessments were conducted annually in humans in 2015 (T2), in 2016 (T3) and in 2017 (T4), and in dogs in 2017 (T4) to evaluate the long-term impact of the intervention on helminth infections. Frequency was used to describe the prevalence of helminth infections. Logistic regression was applied to associate the KAP (knowledge, attitudes, and practices and open defecation behavior) and the reduction of helminth infections between intervention and control islands. The reduction in prevalence pre- and post-intervention was associated using a McNemar test. A two-independent sample t-test was applied to compare the mean eggs per gram (EPG) of helminth infections between control and intervention islands. A paired t-test test was used to compare the mean EPG of stool samples before (baseline) and after (follow-up) interventions for the two islands separately. A P-value lower than 0.05 was considered statistically significant. RESULTS: Eco-Health/One-Health approach appears to be associated with reduction in prevalence of S. mekongi by 9.0% [odds ratio (OR) = 0.49, P = 0.003] compared to the use of mass drug administration alone (control island). Additionally, this intervention package significantly reduced O. viverrini infection by 20.3% (OR = 1.92, P < 0.001) and hookworm by 17.9% (OR = 0.71, P = 0.045), respectively. Annual parasitological assessments between 2012 and 2017 showed that the Eco-Health/One-Health approach, coupled with MDA, steadily reduced the prevalence of S. mekongi on the intervention island from 29.1% to 1.8% and on the control island from 28.4% to 3.1%, respectively. CONCLUSIONS: The study findings suggest that the Eco-Health/One-Health approach appears to be associated with a significant reduction in prevalence of S. mekongi and helminth co-infections, particularly hookworm and T. trichiura. Therefore, implementing the Eco-Health/One-Health approach in schistosomiasis-endemic areas could accelerate the achievement of national goals for transmission interruption by 2025 and elimination by 2030.

Profile and Determinants for Complications of Imported Malaria in 5 Chinese Provinces From 2014 to 2021: Retrospective Analysis.

Background: In 2021, the World Health Organization officially declared the People's Republic of China as malaria-free. However, despite this milestone achievement, the continued occurrence of severe and fatal cases of imported malaria in China, due to globalization and increased international communication, remains a significant public health concern. Objective: The aim of this study was to elucidate the epidemiological characteristics of imported malaria in 5 Chinese provinces from 2014 to 2021 and to identify the factors that influence complications in imported malaria cases. The findings will provide a basis for enhancing prevention and control measures, thereby consolidating China's achievements in malaria elimination. Methods: A case-based retrospective study was performed, using surveillance data collected from the representative provinces of China from 2014 to 2021. Epidemiological characteristics were analyzed using descriptive statistics. Logistic regression was used to identify the factors influencing the occurrence of complications. Results: A total of 5559 malaria cases were included during the study period. The predominant species was Plasmodium falciparum (3940/5559, 70.9%), followed by Plasmodium ovale (1054/5559, 19%), Plasmodium vivax (407/5559, 7.3%), Plasmodium malariae (157/5559, 2.8%), and 1 case of Plasmodium knowlesi. Most of the cases were male (5343/5559, 96.1%). The complication rates for P falciparum and P ovale were 11.4% and 3.3%, respectively. Multivariate logistic regression analysis of the relevant factors of malaria complications revealed potential protective factors, including a previous infection by Plasmodium (P<.001; odds ratio [OR] 0.512, 95% CI 0.422-0.621), and risk factors, including increased age (P=.004; OR 1.014, 95% CI 1.004-1.024), misdiagnosis at the first clinical visit (P<.001; OR 3.553, 95% CI 2.886-4.375), and the time interval from onset to treatment (P=.001; OR 1.026, 95% CI 1.011-1.042). Subgroup analyses identified risk factors associated with P falciparum, which include advanced age (P=.004; OR 1.015, 95% CI 1.005-1.026), initial misdiagnosis during the first clinical visit (P<.001; OR 3.549, 95% CI 2.827-4.455), the time interval from onset to treatment (P<.001; OR 1.043, 95% CI 1.022-1.063), and a delay of more than 3 days from the first treatment to diagnosis (P<.001; OR 2.403, 95% CI 1.823-3.164). Additionally, the risk factors pertaining to P ovale involve misdiagnosis at the initial clinical visit (P=.01; OR 2.901, 95% CI 1.336-6.298), the time interval from onset to treatment (P=.002; OR 1.095, 95% CI 1.033-1.160), and the duration from the initial treatment to diagnosis (P=.43; OR 1.032, 95% CI 0.953-1.118). Previous infections can prevent the progression of both P falciparum and P ovale. Conclusions: This study showed that the increasing proportion of P ovale in recent years should not be ignored. Furthermore, there is a need to improve diagnostic awareness, enhance the capacity of medical institutions, and provide health education for high-risk groups.

Assessment of the influence of levees along Yangtze River on Oncomelania hupensis, the intermediate host of Schistosoma japonicum.

BACKGROUND: Oncomelania hupensis is the exclusive intermediate host of Schistosoma japonicum in China. Snail control is an essential component of schistosomiasis elimination programme. With 70 years of continuous efforts, the range of O. hupensis had reduced significantly, but slowed down in last decades. A large number of levees against flooding were constructed along Yangtze River and its affiliated lakes in the middle and lower reaches, which influenced the hydrology and ecology in the alluvial plains. The purpose of this study was to assess the impact of levees on the distribution of O. hupensis in the middle and lower reaches of the Yangtze River. METHODS: The snail habitats were digitalised by hand-held GPS system. The years for discovery and elimination of snail habitats were extracted from historical records. The accumulated snail-infested range for each habitat was calculated on the basis of annual reports. The current distribution of O. hupensis was determined by systematic and environmental sampling. The geographical distribution of levees was obtained from satellite imagery. To assess the impact of levees, the data pertaining to O. hupensis were divided into two parts: inside and outside the Yangtze River. Joinpoint regression was utilised to divide the study time span and further characterise the regression in each period. The 5-year-period moving averages of eliminated area infested by snails were calculated for the habitats inside and outside Yangtze River. The moving routes of corresponding geographical median centres were simulated in ArcGIS. Hotspot analysis was used to determine the areas with statistical significance clustering of O. hupensis density. RESULTS: Three periods were identified according to Joinpoint regression both inside and outside Yangtze River. The area infested by O. hupensis increased in the first two periods. It decreased rapidly outside Yangtze River year over year after 1970, while that inside the Yangtze River did not change significantly. Furthermore, the latter was significantly higher than the former. It was observed that the present density of O. hupensis inside Yangtze River was lower than outside the Yangtze River. The median centre for eliminated ranges inside Yangtze River wavered between the east (lower reach) and the west (middle reach). In contrast, the median centre for eliminated ranges continuously moved from the east to the west. CONCLUSIONS: Our findings indicated that the levees had a considerable negative impact on the distribution of O. hupensis outside Yangtze River. Some hotspots observed in the irrigation areas need a sluice system at the inlet of branch for snail control. The major distribution of O. hupensis located in Hubei might be caused by severe waterlogging. The intensive surveillance should be implemented there. The biggest two freshwater lakes, the major endemic regions historically, were identified as cold spots. The long-term impact of Three Gorges Dam on the distribution of O. hupensis in the lakes should be monitored and evaluated.

Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.

Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R2). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S. mansoni infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher S. haematobium prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher S. mansoni prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R2 = 0.517 for S. haematobium, and with the corresponding figures for S. mansoni being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of S. haematobium at 13.8% and that of S. mansoni at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of S. haematobium 17.1% and that of S. mansoni prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to S. mansoni. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.

How far has the globe gone in achieving One Health? Current evidence and policy implications based on global One Health index.

Background: In the 21st century, as globalization accelerates and global public health crises occur, the One Health approach, guided by the holistic thinking of human-animal-environment and emphasizing interdisciplinary collaboration to address global health issues, has been strongly advocated by the international community. An immediate requirement exists for the creation of an assessment tool to foster One Health initiatives on both global and national scales. Methods: Built upon extensive expert consultations and dialogues, this follow-up study enhances the 2022 global One Health index (GOHI) indicator system. The GOHI framework is enriched by covering three indices, e.g. external drivers index (EDI), intrinsic drivers index (IDI), and core drivers index (CDI). The comprehensive indicator system incorporates 13 key indicators, 50 indicators, and 170 sub I-indicators, utilizing a fuzzy analytic hierarchy process to ascertain the weight for each indicator. Weighted and summed, the EDI, IDI, and CDI scores contribute to the computation of the overall GOHI 2022 score. By comparing the ranking and the overall scores among the seven regions and across 160 countries/territories, we have not only derived an overall profile of the GOHI 2022 scores, but also assessed the GOHI framework. We also compared rankings of indicators and sub I-indicators to provide greater clarity on the strengths and weaknesses of each region within the One Health domains. Results: The GOHI 2022 performance reveals significant disparities between countries/territories ranged from 39.03 to 70.61. The global average score of the GOHI 2022 is 54.82. The average score for EDI, IDI, and CDI are 46.57, 58.01, and 57.25, respectively. In terms of global rankings, countries from North America, Europe and Central Asia, East Asia and Pacific present higher scores. In terms of One Health domains of CDI, the lowest scores are observed in antimicrobial resistance (median: 43.09), followed by food security (median: 53.78), governance (median: 54.77), climate change (median: 64.12) and zoonotic diseases (median: 69.23). Globally, the scores of GOHI vary spatially, with the highest score in North America while lowest in sub-Saharan Africa. In addition, evidence shows associations between the socio-demographic profile of countries/territories and their GOHI performance in certain One Health scenarios. Conclusion: The objective of GOHI is to guide impactful strategies for enhancing capacity building in One Health. With advanced technology and an annually updated database, intensifying efforts to refine GOHI's data-mining methodologies become imperative. The goal is to offer profound insights into disparities and progressions in practical One Health implementation, particularly in anticipation of future pandemics.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.