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.

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.

Long-distance spread of Tembusu virus, and its dispersal in local mosquitoes and domestic poultry in Chongming Island, China.

BACKGROUND: Chongming Island in China serves as a breeding and shelter point on the East Asian-Australasian Flyway. The resting frequency of migratory birds, abundance of mosquito populations, and the popular domestic poultry industry pose a potential risk of mosquito-borne zoonotic diseases. The aim of this study is to explore the role of migratory birds in the spread of mosquito-borne pathogens and their prevalent status on the island. METHODS: We conducted a mosquito-borne pathogen surveillance in 2021, in Chongming, Shanghai, China. Approximately 67,800 adult mosquitoes belonging to ten species were collected to investigate the presence of flaviviruses, alphaviruses, and orthobunyaviruses by RT-PCR. Genetic and phylogenetic analyses were conducted to explore the virus genotype and potential nature source. Serological survey was performed by ELISA to characterize Tembusu virus (TMUV) infection among domestic poultry. RESULTS: Two strains of TMUV and Chaoyang virus (CHAOV) and 47 strains of Quang Binh virus (QBV) were detected in 412 mosquito pools, with the infection rate of 0.16, 0.16, and 3.92 per 1000 Culex tritaeniorhynchus, respectively. Furthermore, TMUVs viral RNA was found in serum samples of domestic chickens and faecal samples of migratory birds. Antibodies against TMUV were detected in domestic avian serum samples, generally ranging from 44.07% in pigeons to 55.71% in ducks. Phylogenetic analyses indicated that the TMUV detected in Chongming belonged to Cluster 3, Southeast Asia origin, and most closely related to the CTLN strain, which caused a TMUV outbreak in chickens in Guangdong Province in 2020, but distant from strains obtained previously in Shanghai, which were involved in the 2010 TMUV outbreak in China. CONCLUSIONS: We speculate that the TMUV was imported to Chongming Island through long-distance spreading by migratory birds from Southeast Asia, followed by spill over and transmission in mosquitoes and domestic avian species, threatening the local domestic poultry. In addition, the expansion and prevalence of insect-specific flaviviruses and its simultaneous circulation with mosquito-borne virus are worthy of close attention and further study.

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.

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.

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.

Epidemiology of Mosquito-Borne Viruses in Egypt: A Systematic Review.

There are at least five common mosquito-borne viruses (MBVs) recorded in Egypt, including dengue virus (DENV), Rift Valley fever virus (RVFV), West Nile virus (WNV), Chikungunya virus, and Sindbis virus. Unexpected outbreaks caused by MBVs reflect the deficiencies of the MBV surveillance system in Egypt. This systematic review characterized the epidemiology of MBV prevalence in Egypt. Human, animal, and vector prevalence studies on MBVs in Egypt were retrieved from Web of Science, PubMed, and Bing Scholar, and 33 eligible studies were included for further analyses. The monophyletic characterization of the RVFV and WNV strains found in Egypt, which spans about half a century, suggests that both RVFV and WNV are widely transmitted in this nation. Moreover, the seropositive rates of DENV and WNV in hosts were on the rise in recent years, and spillover events of DENV and WNV to other countries from Egypt have been recorded. The common drawback for surveillance of MBVs in Egypt is the lack of seroprevalence studies on MBVs, especially in this century. It is necessary to evaluate endemic transmission risk, establish an early warning system for MBVs, and develop a sound joint system for medical care and public health for managing MBVs in Egypt.

Towards a global One Health index: a potential assessment tool for One Health performance.

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

IL-17 in intervertebral disc degeneration: Mechanistic insights and therapeutic implications.

Intervertebral disc degeneration (IDD) serves as an independent risk factor for lower back pain and is closely associated with spinal musculoskeletal disorders, including lumbar disc herniation, radiculopathy, and myelopathy. Interleukin-17 (IL-17), also named IL-17A, is a critical signature cytokine of T-helper 17 cells. Upon binding to the IL-17 receptor A/C heterodimeric complex, IL-17 can trigger multiple signal transduction pathways to stimulate gene transcription and increase messenger RNA stability. IL-17 expression is significantly increased in degenerative disc tissue and shows a positive correlation with disease severity. IL-17 has been shown to accelerate the development of IDD by promoting extracellular matrix degradation, enhancing inflammatory response, inducing neoangiogenesis, and inhibiting nucleus pulposus cell autophagy and proliferation. Targeting IL-17 represents a novel and promising approach for the therapeutic intervention of IDD. In this review, we summarized the recent progression about the role of IL-17 in IDD and highlighted its therapeutic implications.

Quantitative Microbial Risk Assessment of Cryptosporidium and Giardia in Public Drinking Water in China.

We aimed to assess the risks of Cryptosporidium and Giardia infections associated with drinking water for local residents, based on a quantitative microbial risk assessment, in three densely populated regions of China. In total, 45 source water samples and 45 treated water samples were collected from June to December 2014. Five Cryptosporidium-positive samples and 5 Giardia-positive samples were found. The annual probability of infection for individuals in Jintan (6.27 × 10 -4-2.05 × 10 -3 for Cryptosporidium and 7.18 × 10 -4-2.32 × 10 -3 for Giardia), Ezhou (6.27 × 10 -4-1.10 × 10 -2 for Cryptosporidium and 3.65 × 10 -4-1.20 × 10 -3 for Giardia), and Binyang (3.79 × 10 -4-1.25 × 10 -3 for Cryptosporidium) exceeded the tolerable risk of infection of 10 -4 set by the United States Environmental Protection Agency. Moreover, the corresponding disease burdens of cryptosporidiosis and giardiasis, due to direct drinking and residual water in these regions, exceeded the threshold of 10 -6 disability-adjusted life years per person per year set by the World Health Organization. These results provide insights into strategies to improve the safety of drinking water.

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt.

BACKGROUND: Schistosomiasis control is striving forward to transmission interruption and even elimination, evidence-lead control is of vital importance to eliminate the hidden dangers of schistosomiasis. This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning. METHODS: The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis. RESULTS: There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5 km from the waterway, annual average temperature of 11.5-19.0 °C, annual average rainfall of 1000-1550 mm. IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920) compared with the other six models. The results of IV + GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province. CONCLUSIONS: The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control.

Molecular epidemiology of mosquito-borne viruses at the China-Myanmar border: discovery of a potential epidemic focus of Japanese encephalitis.

BACKGROUND: Mosquito-based arbovirus surveillance can serve as an early warning in evaluating the status of mosquito-borne virus prevalence and thus prevent local outbreaks. Although Tengchong County in Yunnan Province-which borders Myanmar-is abundant and diverse in mosquitoes, very few mosquito-based arbovirus investigations have been conducted in the recent decade. Herein, this study aims to evaluate the presence and the diffusion of mosquito-borne pathogens, currently prevalent in this region. METHODS: We collected 9486 mosquitoes, representing eight species, with Culex tritaeniorhynchus and Anopheles sinensis as the dominant species, during high mosquito activity seasons (July-October) in Tengchong, in 2018. Samples collected from 342 pools were tested using reverse-transcription PCR to determine the species, distribution, and infection rates of virus and parasite, and further analyze their genotypes, phylogenetic relationships, infection rate, and potential pathogenicity. RESULTS: Fifteen Japanese encephalitis virus (JEV) strains from Cx. tritaeniorhynchus pools were detected. Seven strains of insect-specific flaviviruses (ISFVs), including two Aedes flavivirus (AeFV) and Yunnan Culex flavivirus strains each, one Culex theileri flavivirus, Yamadai flavivirus (YDFV) and Anopheles-associated flavivirus (AAFV) strains each were detected in Aedes albopictus, Cx. tritaeniorhynchus, Cx. vagans, Cx. pseudovihnui, and An. sinensis pools, respectively. The whole-genome was successfully amplified in one strain of JEV and AeFV each. Phylogenetic analysis using the E gene placed all the newly detected JEV strains into the GI-b genotype. They showed highly nucleotide identities, and were most closely related to the strain detected in Tengchong in 2010. The comparison of the E protein of JEV strains and vaccine-derived strain, showed six amino residue differences. The bias-corrected maximum likelihood estimation values (and 95% confidence interval) for JEV in Cx. tritaeniorhynchus collected in Tengchong in 2018 were 2.4 (1.4-3.9). CONCLUSIONS: A potential Japanese encephalitis epidemic focus with the abundance of host mosquitoes and high JEV infection rate was observed in Tengchong. In addition, at least five species of ISFVs co-circulate in this area. This study highlights the importance of widespread and sustained mosquito-based arbovirus surveillance in local areas to prevent the transmission of JEV, and other emerging/re-emerging mosquito-borne pathogens.

Molecular Analysis of Targeted Insecticide Resistance Gene Mutations in Field-Caught Mosquitos of Medical Importance From Saudi Arabia.

Gene mutations on target sites can be a valuable indicator of the status of insecticide resistance. Jeddah, a global commercial and major port-of-entry city, is bearing the brunt of dengue disease burden in Saudi Arabia. In the current study, six genotypes of three codon combinations (989, 1016, and 1534) were observed on voltage-gated sodium channel (VGSC) gene in Jeddah's Aedes aegypti population, with PGF/PGC as the dominant one. Two types of introns between exon 20 and 21 on VGSC have been identified for the first time in Ae. aegypti in Saudi Arabia. Statistical and phylogenetic analyses showed that the intron type was significantly associated with the 1016 allele and may reflect the history of insecticide treatment in different continents. In addition, fixation of the L1014F allele on VGSC and G119S on acetylcholinesterase 1 gene was detected in local Culex quinquefasciatus populations, with frequencies of 95.24 and 100%, respectively. To the best of our knowledge, this is the first report of resistant-associated mutations in field-caught Cx. quinquefasciatus in Saudi Arabia. The high prevalence of insecticide resistance gene mutations in local primary mosquito vector species highlights the urgent need to carry out comprehensive insecticide resistance surveillance in Saudi Arabia.

Monitoring Mosquito-Borne Arbovirus in Various Insect Regions in China in 2018.

Background: Increases in global travel and trade are changing arbovirus distributions worldwide. Arboviruses can be introduced by travelers, migratory birds, or vectors transported via international trade. Arbovirus surveillance in field-collected mosquitoes may provide early evidence for mosquito-borne disease transmission. Methods: During the seasons of high mosquito activity of 2018, 29,285 mosquitoes were sampled from seven sentinel sites in various insect regions. The mosquitoes were analyzed by RT-PCR for alphaviruses, flaviviruses, and orthobunyaviruses. Results: We detected three strains of Japanese encephalitis virus (JEV), five strains of Getah virus (GETV), and 45 strains of insect-specific flaviviruses including Aedes flavivirus (AeFV, 1), Chaoyang virus (CHAOV, 1), Culex flavivirus (CxFV, 17), Hanko virus (HANKV, 2), QuangBinh virus (QBV, 22), and Yunnan Culex flavivirus (YNCxFV, 2). Whole genomes of one strain each of GETV, CxFV, CHAOV, and AeFV were successfully amplified. Phylogenetic analysis revealed that the new JEV strains detected in the Shanghai and Hubei Provinces belong to the GI-b strain and are phylogenetically close to the NX1889 strain (MT134112) isolated from a patient during a JE outbreak in Ningxia in 2018. GETVs were found in Inner Mongolia, Hubei, and Hainan and belonged to Group III. They were closely related to strains isolated from swine. HANKV was recorded for the first time in China and other ISFVs were newly detected at several sentinel sites. The bias-corrected maximum likelihood estimation value for JEV in Jinshan, Shanghai was 4.52/1,000 (range 0.80-14.64). Hence, there is a potential risk of a JEV epidemic in that region. Conclusion: GI-b is the dominant circulating JEV genotype in nature and poses a health risk to animals and humans. The potential threat of widespread GETV distribution as a zoonosis is gradually increasing. The present study also disclosed the dispersion and host range of ISFVs. These findings highlight the importance of tracing the movements of the vectors and hosts of mosquito-borne pathogens in order to prevent and control arbovirus outbreaks in China.

Detection of DENV-2 and Insect-Specific Flaviviruses in Mosquitoes Collected From Jeddah, Saudi Arabia.

Background: Mosquito-borne diseases are rapidly spreading due to increasing international travel and trade. Routine mosquito surveillance and screening for mosquito-borne pathogens can be early indicators for local disease transmission and outbreaks. However, arbovirus detection in mosquito vectors has rarely been reported in Saudi Arabia. Methods: A total of 769,541 Aedes and Culex mosquitoes were collected by Black Hole traps during routine mosquito surveillance in the first half of 2016. Culex. quinquefasciatus and Ae. aegypti were the most prevalent species observed. Twenty-five and 24 randomly selected pools of Ae. aegypti and Cx. quinquefasciatus, respectively, were screened for arboviruses by RT-PCR. Results: Dengue 2 (DENV-2) and four strains of insect-specific flaviviruses, including one of cell-fusing agent virus (CFAV) and three of Phlebotomus-associated flavivirus (PAFV) were detected in pools of Ae. aegypti. We also detected 10 strains of Culex flavivirus (CxFV) in pools of Cx. quinquefasciatus. Phylogenetic analysis using whole genome sequences placed the DENV strain into the cosmopolitan 1 sub-DENV-2 genotype, and the CxFVs into the African/Caribbean/Latin American genotype. These analyses also showed that the DENV-2 strain detected in the present study was closely related to strains detected in China in 2014 and in Japan in 2018, which suggests frequent movement of DENV-2 strains among these countries. Furthermore, the phylogenetic analysis suggested at least five introductions of DENV-2 into Saudi Arabia from 2014 through 2018, most probably from India. Conclusions: To our knowledge, this study reports the first detection of four arboviruses DENV, CFAV, PAFV, and CxFV in mosquitoes in Saudi Arabia, which shows that they are co-circulating in Jeddah. Our findings show a need for widespread mosquito-based arbovirus surveillance programs in Saudi Arabia, which will improve our understanding of the transmission dynamics of the mosquito-borne arboviruses within the country and help early predict and mitigate the risk of human infections and outbreaks.

Potential impact of flooding on schistosomiasis in Poyang Lake regions based on multi-source remote sensing images.

BACKGROUND: Flooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images. METHODS: Normalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. RESULTS: The surface area of Poyang Lake was approximately 2207 km2 in May 2020 before the flood and 4403 km2 in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions. CONCLUSIONS: Data on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.

Molecular surveillance of pfcrt, pfmdr1 and pfk13-propeller mutations in Plasmodium falciparum isolates imported from Africa to China.

BACKGROUND: The emergence and spread of multidrug resistance poses a significant risk to malaria control and eradication goals in the world. There has been no indigenous malaria cases reported in China since 2017, and China is approaching national malaria elimination. Therefore, anti-malarial drug resistance surveillance and tracking the emergence and spread of imported drug-resistant malaria cases will be necessary in a post-elimination phase in China. METHODS: Dried blood spots were obtained from Plasmodium falciparum-infected cases returned from Africa to China between 2012 and 2015, prior to anti-malarial drug treatment. Whole DNA were extracted and known polymorphisms relating to drug resistance of pfcrt, pfmdr1 gene, and the propeller domain of pfk13 were evaluated by nested PCR and sequencing. The haplotypes and prevalence of these three genes were evaluated separately. Chi-squared test and Fisher's exact test were used to evaluate differences among the different sub-regions of Africa. A P value < 0.05 was used to evaluate differences with statistical significance. The maps were created using ArcGIS. RESULTS: A total of 731 P. falciparum isolates were sequenced at the pfcrt locus. The wild type CVMNK was the most prevalent haplotype with prevalence of 62.8% and 29.8% of the isolates showed the triple mutant haplotype CVIET. A total of 434 P. falciparum isolates were successfully sequenced and pfmdr1 allelic variants were observed in only codons 86, 184 and 1246. Twelve haplotypes were identified and the prevalence of the wild type pfmdr1 NYD was 44.1%. The single mutant pfmdr1 in codons 86 and 184 was predominant but the haplotype NYY with single mutation in codon 1246 was not observed. The double mutant haplotype YFD was common in Africa. About 1,357 isolates were successfully sequenced of pfk13-propeller domain, the wild type was found in 1,308 samples (96.4%) whereby 49 samples (3.6%) had mutation in pfk13. Of 49 samples with pfk13 mutations, 22 non-synonymous and 4 synonymous polymorphic sites were confirmed. The A578S was the most common mutation in pfk13-propeller domain and three mutations associated with artemisinin resistance (M476I, R539T, P553L) were identified in three isolates. CONCLUSION: This study provides evidence that could give insight into potential issues with anti-malarial drug resistance to inform national drug policy in China in order to treat imported cases.

Comprehensive analysis of N6-methyladenosine (m6A) modification during the degeneration of lumbar intervertebral disc in mice.

OBJECTIVE: To study the N6-methyladenosine (m6A) modification pattern of nucleus pulposus (NP) tissue during intervertebral disc degeneration (IDD). METHODS: A standing mouse model was generated, and staining and imaging methods were used to evaluate the IDD model. Methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-seq) was used to analyze m6A methylation-associated transcripts in the NP, and real-time quantitative polymerase chain reaction (qRT-PCR) was used to detect the expression of methylation-related enzymes and conduct bio-informatics analysis. RESULTS: The standing mouse model caused IDD. Continuous axial pressure changed the expression of related methylases in degenerated NP tissue. Relative to the control group, the expression levels of KIAA1429, METTL14, METTL3, METTL4, WTAP, DGCR8, EIF3A and YTHDC1 in the experimental group were higher, while those of FTO, ELAVL1, HNRNPC1 and SRSF2 were lower. We identified 985 differentially expressed genes through MeRIP-Seq, among which 363 genes were significantly up-regulated, and 622 genes were significantly down-regulated. In addition, among the 9648 genes counted, 1319 m6A peaks with significant differences in methylation were identified, among which 933 were significantly up-regulated, and 386 were significantly down-regulated. Genes and pathways that were enriched in IDD have been identified. CONCLUSION: The results of this study elucidated the m6A methylation pattern of NP tissue in degenerated lumbar intervertebral disc of mice and provided new perspectives and clues for research on and the treatment of lumbar disc degeneration. THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: As one of the important causes of low back and leg pain, intervertebral disc degeneration brings a huge economic burden to the society, family and medical system. Therefore, understanding the molecular and cellular mechanisms of intervertebral disc degeneration is of great significance for guiding clinical treatment. In this study, methylated RNA immunoprecipitation with next-generation sequencing on mice lumbar nucleus pulposus tissues found that differentially expressed genes and changes in the expression of related methylases, confirming that RNA methylation is involved in intervertebral disc degeneration. The process provides new vision and clues for future research on intervertebral disc degeneration.