[Salivary carcinoma showing thymus-like differentiation: clinicopathological analysis of 7 cases]

Objective: To analyze the clinicopathological features of salivary carcinoma with thymus-like differentiation(CASTLE). Methods: Cases diagnosed with salivary CASTLE from January 2020 to December 2023 were collected and selected from the Department of Oral Pathology, Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine. A total of 7 cases of salivary CASTLE were identified. All the cases originated from parotid. There were 3 males and 4 females. The patients' age range was 11-70 years.The clinical, microscopic, immunohistochemical and prognostic features of these cases were analyzed. Results: The duration of disease ranged from 1 month to 1 year, and 1 patient had facial numbness and 1 with swelling sensation occasionally. Radiographically, 4 cases showed malignant signs. Microscopically, 4 cases involved in parotid gland, and all the tumors had different degrees of lymphoid tissue background. The tumor cells arranged in nests, 5 cases with lymphoepithelial carcinoma-like and 2 cases with squamous cell carcinoma morphology. The tumor cells expressed CD5 and CD117 proteins diffusely in lymphoepithelial carcinoma-like cases. However, the tumor cells expressed CD5 diffusely and CD117 focally in cases with squamous cell carcinoma morphology. All the cases had no Epstein-Barr virus infection. Among the 6 patients with follow-up information, all of them underwent postoperative radiotherapy, and none of them had local recurrence and lymph node metastasis. Conclusions: Salivary CASTLE is a rare tumor, it should be distinguished from lymphoepithelial carcinoma and squamous cell carcinoma. The patients often have better prognosis and CD5 protein expression has a valuable role in the differential diagnosis.

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.

Diagnostic value of dynamic contrast enhancement combined with conventional MRI in differentiating benign and malignant lacrimal gland epithelial tumours

AIM

To establish the diagnostic value of the quantitative parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) combined with conventional MRI in differentiating of benign and malignant lacrimal gland epithelial tumours.

MATERIALS AND METHODS

A retrospective analysis of primary lacrimal gland epithelial tumours confirmed by histopathology was conducted. Conventional MRI features and DCE-MRI quantitative parameters were collected and subjected to analysis. The diagnostic value was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 53 patients were enrolled of which 29 had malignant, whereas 24 had benign tumours. Conventional MRI revealed statistically significant differences between benign and malignant tumours regarding maximum tumour diameter, posterior margin characteristic, bone destruction, and erosion. The Ktrans and Kep values obtained by DCE-MRI were higher in malignant than in benign tumours, with a statistically significant (p<0.001 and p=0.022). A type I time-signal intensity (TIC) curve was more frequent in benign tumours, whereas a type II TIC curve was prevalent in malignant tumours (p=0.001). ROC analysis showed that Ktrans had the best diagnostic value of the DCE-MRI parameters (area under the ROC curve [AUC] of 0.822, 75.9% sensitivity, and 83.3% specificity, p<0.001). The combination of conventional MRI and DCE-MRI factors had the best diagnostic value and balanced sensitivity and specificity (AUC of 0.948, 93.1% sensitivity, and 91.7% specificity, p<0.001).

CONCLUSIONS

The present findings indicate that the combination of quantitative parameters of DCE-MRI and image characteristics of conventional MRI have a high diagnostic value for the diagnosis of benign and malignant lacrimal gland epithelial tumours.

Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial

BACKGROUND

Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001).

INTERPRETATION

Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor.

FUNDING

Boehringer Ingelheim and Eli Lilly.

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

BACKGROUND

The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1).

INTERPRETATION

In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease.

FUNDING

Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.

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.

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.

[Preliminary study on the mechanism underlying the ecological isolation of Oncomelania hupensis populations in Changde City]

OBJECTIVE

To investigate ecological isolation between Oncomelania hupensis snail populations in hilly regions and marshland and lake regions in Yuanjiang valley, Changde City, Hunan Province, and to unravel its underlying mechanisms.

METHODS

Taoyuan County, Shimen County, Linli County and Lixian County in Changde City were selected as snail sampling sites in hilly regions, and Lixian County, Jinshi City, West Lake Administration District, Hanshou County and Dingcheng District were selected as snail sampling sites in marshland and lake areas. Cytochrome C oxidase 1 (cox 1) gene was amplified in snail samples and sequenced. The genetic sequences of O. hupensis snails were aligned using the software MEGA 11, and the haplotypes of O. hupensis snails were determined using the software DNASP 5.10.01. The phylogenetic tree was generated using Bayesian inference with the software MrBayes 3.2, and analysis of molecular variance (AMOVA) was performed to analyze the source of genetic divergence and estimate the genetic divergence index (F_ST) among snail populations with the software Arlequin 3.5.2.2. The genetic barrier among 11 O. hupensis snail populations was estimated using the Monmonier algorithm of adegenet toolkit in R package. The settings with "land in winter and water in summer" in the Yuanjian River section were divided into two categories according to the upstream and downstream, and the areas with "land in winter and water in summer" in the upstream and downstream were transformed into raster data, and then loaded into the software Fragstats 4 for analysis of landscape indicators. The trends in changes of digital elevation were extracted from the Yuanjiang River section based on the digital elevation model, and made three-dimensional visualization using the R package.

RESULTS

The mitochondrial cox 1 gene were amplified in 165 O. hupensis snais from 11 sampling sites and sequenced, and a total of 152 valid gene sequences were obtained, with 46 haplotypes or 9 populations determined. No haplotype was shared in snails between Taoyuan County and Dingcheng District and Hanshou County along the downstream of the Yuanjiang River. The total area of settings with "land in winter and water in summer" was 617.66 hm² in the upsteram of the Yuanjiang River, which consisted of 473 patches, with each patch measuring 1.31 hm², the largest area index of 0.735 2, the landscape division index of 0.999 9, and the landscape shape index of 45.293 7. The total area of settings with "land in winter and water in summer" was 9 956.92 hm² in the downstream of the Yuanjiang River, which consisted of 771 patches, with each patch measuring 12.91 hm², the largest area index of 97.839 9, the landscape division index of 0.042 7, and the landscape shape index of 7.249 6. The area of settings with "land in winter and water in summer" was much larger in the downstream than that in the upstream of the Yuanjiang River, and the stronger landscape connectivity and non-remarkable alteration of riverbed elevation provided suitable habitats for snail breeding.

CONCLUSIONS

The hydrological and environmental characteristics of the upstream of the Yuanjiang River restrain the breeding and spread of O. hupensis, resulting in ecological isolation between Oncomelania hupensis in Taoyuan County and those in the downstream of Yuanjiang River.

[Comparison of the disease burden of schistosomiasis globally and in China and Zimbabwe]

OBJECTIVE

To investigate the trends in the disease burden of schistosomiasis worldwide and in China, and Zimbabwe from 1990 to 2019, so as to provide insights into the formulation of the schistosomiasis control strategy in Zimbabwe.

METHODS

Based on Global Burden of Disease Study 2019 (GBD 2019) data sources, the age-standardized prevalence, mortality, disability-adjusted life year (DALY) rate of schistosomiasis were compared in the world, China, and Zimbabwe and the trends in the disease burden of schistosomiasis from 1990 to 2019 were investigated using Joinpoint regression analysis. In addition, the associations between the burden of schistosomiasis worldwide and in China and Zimbabwe from 1990 to 2019 and socio-demographic index (SDI) were examined using Pearson correlation analysis.

RESULTS

The age-standardized prevalence, mortality, and DALY rate of schistosomiasis were 1 804.95/10⁵, 0.14/10⁵ and 20.92/10⁵ in the world, 707.09/10⁵, 0.02/10⁵ and 5.06/10⁵ in China, and 2 218.90/10⁵, 2.39/10⁵ and 90.09/10⁵ in Zimbabwe in 2019, respectively. The global prevalence, mortality, and DALY rate of schistosomiasis appeared a tendency towards a rise followed by a decline with age in 2019, while the prevalence and DALY rate of schistosomiasis appeared a tendency towards a sharp rise followed by a fluctuating decline in both China and Zimbabwe, and the mortality of schistosomiasis appeared a tendency towards a rise. The age-standardized prevalence [average annual percent change (AAPC) = -1.31%, -2.22% and -6.12%; t = -20.07, -83.38 and -53.06; all P values < 0.05)] and DALY rate of schistosomiasis (AAPC = -1.91%,-4.17% and -2.08%; t = -31.89, -138.70 and -16.45; all P values < 0.05) appeared a tendency towards a decline in the world, China and Zimbabwe from 1990 to 2019, and the age-standardized mortality of schistosomiasis appeared a tendency towards a decline in the world and China (AAPC = -3.46% and -8.10%, t = -41.03 and -61.74; both P values < 0.05), and towards a rise followed by a decline in Zimbabwe (AAPC = 1.35%, t = 4.88, P < 0.05). In addition, Pearson correlation analysis showed that the age-standardized prevalence (r = -0.75, P < 0.05), mortality (r = -0.73, P < 0.05), and DALY rate of schistosomiasis (r = -0.77, P < 0.05) correlated negatively with SDI in the world, China and Zimbabwe from 1990 to 2019.

CONCLUSIONS

The disease burden of schistosomiasis appeared a remarkable decline in China from 1990 to 2019, and the prevalence of schistosomiasis showed a tendency towards a decline in Zimbabwe from 1990 to 2019; however, the mortality and DALY rate of schistosomiasis in Zimbabwe topped in the world. A schistosomiasis control strategy with adaptations to local epidemiology and control needs of schistosomiasis is needed to facilitate the elimination of schistosomiasis in Zimbabwe.

[Intelligent identification of livestock, a source of Schistosoma japonicum infection, based on deep learning of unmanned aerial vehicle images]

OBJECTIVE

To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection.

METHODS

Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision.

RESULTS

A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle.

CONCLUSIONS

The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.

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.

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.

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^-, SO₄^2-, 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.

[Clinical pathologic analysis and review of literature on 11 cases of calcifying epithelial odontogenic tumor]

Objective: To improve the understanding of histological variants of calcifying epithelial odontogenic tumor (CEOT). Methods: In this retrospective study, 11 cases of CEOT diagnosed from January 2008 to March 2022 were enrolled in the Department of Oral Pathology of Nanjing Stomatological Hospital, Medical School of Nanjing University. Among them, 10 were male and 1 was female. The patients were 19 to 58 years old [(43.0±11.9) years] and the course of disease was 2 weeks to 5 years. The clinicopathological characteristics were analyzed and the follow-up of patients ranged from 1 to 8 years, including 8 cases with follow-up data and 3 cases lost to follow-up. Furthermore, the related domestic and international literature was reviewed. Results: Eleven cases of CEOT included 6 cases of classic CEOT, 2 cases of clear cell CEOT, 2 cases of Langerhans cell-rich variant of CEOT and 1 case of non-calcified CEOT. In 6 cases of classic CEOT, the ratio of occurrence in mandible to maxilla was 2∶1, the ratio in central parts to peripheral parts was 5∶1, 2 cases were associated with unerupted teeth and 3 cases showed local aggressiveness. Histopathologically, classic CEOT showed eosinophilic epithelial cells, amyloid and calcification with Ki-67 value<5%. Among 4 cases with follow-up information, 1 case recurred after 1 year and 3 cases did not recur for 3 to 8 years. In 2 cases of clear cell CEOT, they both occurred in the periphery of mandible, pathologically showing a mix of lamellar balloon-like clear cells and typical CEOT, positive for CK5/6 and p63 in the area where the epithelial cells and clear cells were located, scattered positive for periodic acid-Schiff (PAS) in clear cells, which indicated the presence of glycogen. The maximum Ki-67 value was 5% in this type. One case lost to follow-up and the other case did not recur for 1 year follow-up after surgery. In 2 cases of Langerhans cell-rich variant of CEOT, they were cystic solid lesions and both occurred in the anterior maxilla. Langerhans cells were scattered in the epithelium and non-calcified amyloid glomeruli were present. Two cases were followed up for 1 year and 2 years without recurrence after surgery. One case of non-calcified CEOT that occurs within the jan showed invasion of surrounding soft tissues and the highest of Ki-67 value at 8% in all 11 cases without recurrence at 1 year follow-up. Conclusions: The histological pattern of classic CEOT is unique, and it is necessary to prompt the understanding of several histological variants derived from it.

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.

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.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-01024-5.

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.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05496-0.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08748-6.

[Predictive value of plasma TMAO combined with NT-proBNP on the prognosis and length of hospitalization of patients with ischemic heart failure]

Objective: To explore the value of the assessment of plasma trimethylamine N-oxide (TMAO) combined with N-terminal pro-B-type natriuretic peptide (NT-proBNP) on predicting the all-cause mortality, length of hospitalization, and hospital cost in ischemic heart failure (IHF) patients. Methods: This prospective cohort study included 189 patients (157 males, mean age (64.0±10.5) years) with a left ventricular ejection fraction<45% caused by coronary artery disease, who hospitalized in our department from March 2016 to December 2020. Baseline data, including demographics, comorbid conditions and laboratory examination, were analyzed. The cumulative rate of all-cause mortality was evaluated using the Kaplan-Meier method and compared between the groups according to the log-rank test. Relative risks were reported as hazard ratios (HR) and 95% confidence interval (95%CI) calculated using the Cox proportional-hazards analysis, with stepwise adjustment for covariables. Spearman correlation analysis was then performed to determine the relationship between TMAO combined with NT-proBNP and length of hospitalization and hospital cost. Results: There were 50 patients in the low TMAO+low NT-proBNP group, 89 patients in high TMAO or high NT-proBNP group, 50 patients in high TMAO+high NT-proBNP group. The mean follow-up period was 3.0 years. Death occurred in 70 patients (37.0%), 27 patients (54.0%) in high TMAO+high NT-proBNP group, 29 patients (32.6%) in high TMAO or high NT-proBNP group and 14 patients (28.0%) in low TMAO+low NT-proBNP group. TMAO, in combination with NT-proBNP, improved all-cause mortality prediction in IHF patients when stratified as none, one or both biomarker(s) elevation, with the highest risk of all-cause mortality in high TMAO+high NT-proBNP group (HR=3.62, 95%CI 1.89-6.96, P<0.001). ROC curve analysis further confirmed that TMAO combined with NT-proBNP strengthened the prediction performance on the risk of all-cause death (AUC=0.727(95%CI 0.640-0.813), sensitivity 55.0%, characteristic 83.1%). Spearman correlation analysis showed that IHF patients with high TMAO and high NT-proBNP were positively associated with longer duration of hospitalization (r=0.191,P=0.009), but not associated with higher hospital cost (r=0.030, P=0.686). Conclusions: TMAO combined with NT-proBNP are valuable prediction tool on risk stratification of patients with IHF, and those with two biomarkers elevation face the highest risk of mortality during follow-up period, and are associated with the longer hospital stay.

[Prediction of trends for fine-scale spread of Oncomelania hupensis in Shanghai Municipality based on supervised machine learning models]

OBJECTIVE

To predict the trends for fine-scale spread of Oncomelania hupensis based on supervised machine learning models in Shanghai Municipality, so as to provide insights into precision O. hupensis snail control.

METHODS

Based on 2016 O. hupensis snail survey data in Shanghai Municipality and climatic, geographical, vegetation and socioeconomic data relating to O. hupensis snail distribution, seven supervised machine learning models were created to predict the risk of snail spread in Shanghai, including decision tree, random forest, generalized boosted model, support vector machine, naive Bayes, k-nearest neighbor and C5.0. The performance of seven models for predicting snail spread was evaluated with the area under the receiver operating characteristic curve (AUC), F1-score and accuracy, and optimal models were selected to identify the environmental variables affecting snail spread and predict the areas at risk of snail spread in Shanghai Municipality.

RESULTS

Seven supervised machine learning models were successfully created to predict the risk of snail spread in Shanghai Municipality, and random forest (AUC = 0.901, F1-score = 0.840, ACC = 0.797) and generalized boosted model (AUC= 0.889, F1-score = 0.869, ACC = 0.835) showed higher predictive performance than other models. Random forest analysis showed that the three most important climatic variables contributing to snail spread in Shanghai included aridity (11.87%), ≥ 0 °C annual accumulated temperature (10.19%), moisture index (10.18%) and average annual precipitation (9.86%), the two most important vegetation variables included the vegetation index of the first quarter (8.30%) and vegetation index of the second quarter (7.69%). Snails were more likely to spread at aridity of < 0.87, ≥ 0 °C annual accumulated temperature of 5 550 to 5 675 °C, moisture index of > 39% and average annual precipitation of > 1 180 mm, and with the vegetation index of the first quarter of > 0.4 and the vegetation index of the first quarter of > 0.6. According to the water resource developments and township administrative maps, the areas at risk of snail spread were mainly predicted in 10 townships/subdistricts, covering the Xipian, Dongpian and Tainan sections of southern Shanghai.

CONCLUSIONS

Supervised machine learning models are effective to predict the risk of fine-scale O. hupensis snail spread and identify the environmental determinants relating to snail spread. The areas at risk of O. hupensis snail spread are mainly located in southwestern Songjiang District, northwestern Jinshan District and southeastern Qingpu District of Shanghai Municipality.

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.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00979-9.

Could China's journey of malaria elimination extend to Africa?

World Health Organization (WHO) certified China malaria-free on June 30, 2021, which brightens the goal of global malaria elimination efforts. China contributed its unique innovations to the global community: Artemisinin, discovered by Tu Youyou, has saved millions of lives globally; the "1-3-7" norm developed in 2012, has been adapted in the local contexts of countries in the Southeast Asia and Africa. How to the targets of Global Technical Strategy for Malaria (GTS) 2016-2030. By looking into the malaria control phase, towards elimination phase from 1960 to 2011 in sub-Saharan Africa and China, we found that the gap in malaria burden will widen unless the interventions in Africa are enhanced. It is imperative to identify the key China-Africa cooperation areas on malaria control and elimination, so that synergized efforts could be pooled together to help African countries achieve the elimination goal. The practices from China malaria control and elimination efforts could be leveraged to fast-track malaria elimination efforts in Africa, which makes it possible that the China's journey of malaria elimination extends to Africa.

Large Culprit Plaque and More Intracranial Plaques Are Associated with Recurrent Stroke: A Case-Control Study Using Vessel Wall Imaging

BACKGROUND AND PURPOSE

Intracranial atherosclerotic plaque features are potential factors associated with recurrent stroke, but previous studies only focused on a single lesion, and few studies investigated them with perfusion impairment. This study aimed to investigate the association among whole-brain plaque features, perfusion deficit, and stroke recurrence.

MATERIALS AND METHODS

Patients with ischemic stroke due to intracranial atherosclerosis were retrospectively collected and categorized into first-time and recurrent-stroke groups. Patients underwent high-resolution vessel wall imaging and DSC-PWI. Intracranial plaque number, culprit plaque features (such as plaque volume/burden, degree of stenosis, enhancement ratio), and perfusion deficit variables were recorded. Logistic regression analyses were performed to determine the independent factors associated with recurrent stroke.

RESULTS

One hundred seventy-five patients (mean age, 59 [SD, 12] years; 115 men) were included. Compared with the first-time stroke group (n = 100), the recurrent-stroke group (n = 75) had a larger culprit volume (P = .006) and showed more intracranial plaques (P < .001) and more enhanced plaques (P = .003). After we adjusted for other factors, culprit plaque volume (OR, 1.16 per 10-mm³ increase; 95% CI, 1.03-1.30; P = .015) and total plaque number (OR, 1.31; 95% CI, 1.13-1.52; P < .001) were independently associated with recurrent stroke. Combining these factors increased the area under the curve to 0.71.

CONCLUSIONS

Large culprit plaque and more intracranial plaques were independently associated with recurrent stroke. Performing whole-brain vessel wall imaging may help identify patients with a higher risk of recurrent stroke.

Transmission Risks of Mountain-Type Zoonotic Visceral Leishmaniasis — Six Endemic Provincial-Level Administrative Divisions, China, 2015–2020

What is already known about this topic?

Mountain-type zoonotic visceral leishmaniasis (MT-ZVL) cases in China have increased significantly between 2015 and 2020. A total of 25 regions had re-emerged yielding 88 MT-ZVL indigenous cases, while the total number of visceral leishmaniasis cases declined.

What is added by this report?

The transmission risk of MT-ZVL showed a trend of patchy dissemination centered on major endemic areas and medium-high risk occurrence areas of Phlebotomus chinensis with discrete foci. Multi-point re-emergence and local outbreaks of MT-ZVL were trending in historically endemic areas.

What are the implications for public health practice?

Risk identification and early warnings of MT-ZVL are essential in formulating precise prevention and control strategies in China. More frequent monitoring, establishing a mechanism of joint prevention and control, and highlighting health education are recommended.

Nucleic acid amplification techniques for the detection of Schistosoma mansoni infection in humans and the intermediate snail host: a structured review and meta-analysis of diagnostic accuracy

BACKGROUND

Schistosomiasis is a parasitic disease caused by hematodes of genus Schistosoma. This review evaluated the available nucleic acid amplification techniques for diagnosing S. mansoni infections in humans, intermediate host snails, and presumed rodent reservoirs.

METHODS

Sensitivity, specificity, diagnostic odds ratio (DOR), and 95% CI were calculated based on available literature. The potential of PCR, nPCR, PCR-ELISA, qPCR, and LAMP was compared for diagnosing S. mansoni infections.

RESULTS

A total of 546 published records were identified. Quality assessment by QUADAS-2 revealed an uncertain risk in most studies, and 21 references were included in the final. For human samples, the four nucleic acid amplification techniques showed an overall sensitivity of 89.79% (95% CI: 83.92%-93.67%), specificity of 87.70% (95% CI: 72.60%-95.05%), and DOR of 37.73 (95% CI: 21.79-65.33). LAMP showed the highest sensitivity, followed by PCR-ELISA, PCR, and qPCR, while this order was almost reversed for specificity; qPCR had the highest AUC. For rodent samples, qPCR showed modest sensitivity (68.75%, 95% CI: 43.32%-86.36%) and high specificity (92.45%, 95% CI: 19.94%-99.83%). For snail samples, PCR and nPCR assays showed high sensitivity of 90.06% (95% CI: 84.39%-93.82%) and specificity of 85.51% (95% CI: 54.39%-96.69%).

CONCLUSION

Nucleic acid amplification techniques had high diagnostic potential for identifying S. mansoni infections in humans.

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.

[Clinical features of primary isolated chylopericardium: a retrospective review study]

Objective: To examine the clinical characteristics and abnormal reflux branches of primary isolated chylopericardium. Methods: Totally 43 patients with primary isolated chylopericardium at Department of Lymphatic Surgery, Affiliated Beijing Shijitan Hospital,Capital Medical University from June 2007 to January 2018 were recruited in this study. There were 21 males and 22 females, aging (23.0±15.9) years (range: 2 to 57 years). The levels of triglyceride, total cholesterol, total protein and albumin in pericardial effusion and blood were compared by paired-t test, and the characteristics of lymphatic system in direct lymphangiography and postoperative CT were analyzed. Results: Pericardial effusion was mainly milky white and monocytes, and 95.3%(41/43) were positive for Rivalta test. The level of triglyceride in pericardial effusion was significantly higher than that of blood ((9.67±5.11) mmol/L vs. (1.28±0.89) mmol/L, t=10.557, P<0.01), and the levels of total cholesterol ((2.19±0.52) mmol/L vs. (4.12±1.06) mmol/L, t=-3.732, P<0.01), total protein ((61.25±16.17) g/L vs. (68.26±8.30) g/L, t=-2.958, P=0.005) and albumin ((36.63±7.06) g/L vs. (42.32±4.73) g/L, t=-5.747, P<0.01) were significantly lower than that of blood. In the direct lymphangiography, the imaging of iliac and retroperitoneal lymphatics showed dilated or tortuous in 90.7% (39/43), the thoracoabdominal segment of thoracic duct showed dilation in 46.5% (20/43), and cervical thoracic duct imaging showed dilation in 44.2% (19/43) and stenosis in 55.8% (24/43). The image of lipiodol flowing into the vein showed obstruction at the venous angle. There were 60.5%(26/43) of the patients with lipiodol reflux through the bronchomediastinal trunk (type Ⅰ), 11.6%(5/43) with lipiodol diffusion to the pericardium through the abnormal pathway from the thoracic segment of the thoracic duct (type Ⅱ), while no communication pathway between the thoracic duct and the pericardial cavity (type Ⅲ) found in 27.9%(12/43). CT images obtained after the direct lymphangiography showed 34.9%(15/43) had abnormal distribution of lipiodol in pericardium, mediastinal lymph nodes and lung hilar lymph nodes, 46.5%(20/43) in mediastinal lymph nodes and lung hilar lymph nodes, 14.0%(6/43) only mediastinal lymph nodes, 4.6%(2/43) had no lipiodol in the above areas. Conclusions: Pericardial effusion compared with same period blood, has higher triglyceride, lower total cholesterol, total protein and albumin. The obstruction of the cervical segment of the thoracic duct and the formation of abnormal reflux branches would be corelative to primary isolated chylopericardium.

Prognostic value of late gadolinium enhancement in arrhythmogenic right ventricular cardiomyopathy: a meta-analysis

AIM

To assess systematically the prognostic value of cardiac magnetic resonance imaging (CMRI) in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC).

MATERIALS AND METHODS

The full text of studies of the clinical efficacy of late gadolinium enhancement (LGE) in ARVC was retrieved in multiple databases. Stata 14 was adopted for meta-analysis and bias analysis. Heterogeneity was assessed with the I² statistic.

RESULTS

After exclusions, 561 patients were included in five studies, and the eligibility criteria were met. The meta-analysis suggested that there was a significant difference between LGE positive and negative patients with ARVC in all-cause mortality (relative risk [RR] = 4.78, 95% confidence interval [CI] = 1.41, 16.23, p=0.012; p for heterogeneity = 0.692, I² = 0%); major adverse cardiovascular events (MACE) (RR=2.48, 95% CI = 1.24, 4.96, p=0.010; p for heterogeneity = 0.596, I² = 0%); ventricular tachycardia (RR=3.13, 95% CI = 1.69, 5.78, p<0.001; p for heterogeneity = 0.825, I² = 0%); implanted cardiac defibrillators (RR=3.15, 95% CI = 1.69, 5.87], p<0.001; p for heterogeneity = 0.353, I² = 9.4%).

CONCLUSION

LGE in ARVC patients is a predictor of all-cause mortality and MACE.

Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach

BACKGROUND

Oncomelania hupensis is only intermediate snail host of Schistosoma japonicum, and distribution of O. hupensis is an important indicator for the surveillance of schistosomiasis. This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China, with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy.

METHODS

The snail presence and absence records were collected from Anhui, Hunan, Hubei, Jiangxi and Jiangsu provinces in 2018. A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the O. hupensis intermediated snail host of S. japonicum. Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites. The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve (AUC).

RESULTS

The highest accuracy (AUC = 0.889 and Kappa = 0.618) was achieved at the 5 km distance weight. The five factors with the strongest correlation to O. hupensis infestation probability were: (1) distance to lake (48.9%), (2) distance to river (36.6%), (3) isothermality (29.5%), (4) mean daily difference in temperature (28.1%), and (5) altitude (26.0%). The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River, with the highest probability in the dividing, slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui, followed by areas near the shores of China's two main lakes, the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi.

CONCLUSIONS

Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability, an approach that could improve the sensitivity of the Chinese schistosome surveillance system. Redesign of the snail surveillance system by spatial bias correction of O. hupensis infestation in the Yangtze River Basin to reduce the number of sites required to investigate from 2369 to 1747.

[Construction of a forecast system for prediction of schistosomiasis risk in China based on the flood information]

OBJECTIVE

To create a model based on meteorological data to predict the regions at risk of schistosomiasis during the flood season, so as to provide insights into the surveillance and forecast of schistosomiasis.

METHODS

An interactive schistosomiasis forecast system was created using the open-access R software. The schistosomiasis risk index was used as a basic parameter, and the species distribution model of Oncomelania hupensis snails was generated according to the cumulative rainfall and temperature to predict the probability of O. hupensis snail distribution, so as to identify the regions at risk of schistosomiasis transmission during the flood season.

RESULTS

The framework of the web page was built using the Shiny package in the R program, and an interactive and visualization system was successfully created to predict the distribution of O. hupensis snails, containing O. hupensis snail surveillance site database, meteorological and environmental data. In this system, the snail distribution area may be displayed and the regions at risk of schistosomiasis transmission may be predicted using the species distribution model. This predictive system may rapidly generate the schistosomiasis transmission risk map, which is simple and easy to perform. In addition, the regions at risk of schistosomiasis transmission were predicted to be concentrated in the middle and lower reaches of the Yangtze River during the flood period.

CONCLUSIONS

A schistosomiasis forecast system is successfully created, which is accurate and rapid to utilize meteorological data to predict the regions at risk of schistosomiasis transmission during the flood period.

Machine Learning-Based Prediction of Small Intracranial Aneurysm Rupture Status Using CTA-Derived Hemodynamics: A Multicenter Study

BACKGROUND AND PURPOSE

Small intracranial aneurysms are being increasingly detected while the rupture risk is not well-understood. We aimed to develop rupture-risk models of small aneurysms by combining clinical, morphologic, and hemodynamic information based on machine learning techniques and to test the models in external validation datasets.

MATERIALS AND METHODS

From January 2010 to December 2016, five hundred four consecutive patients with only small aneurysms (<5 mm) detected by CTA and invasive cerebral angiography (or surgery) were retrospectively enrolled and randomly split into training (81%) and internal validation (19%) sets to derive and validate the proposed machine learning models (support vector machine, random forest, logistic regression, and multilayer perceptron). Hemodynamic parameters were obtained using computational fluid dynamics simulation. External validation was performed in other hospitals to test the models.

RESULTS

The support vector machine performed the best with areas under the curve of 0.88 (95% CI, 0.85-0.92) and 0.91 (95% CI, 0.74-0.98) in the training and internal validation datasets, respectively. Feature ranks suggested hemodynamic parameters, including stable flow pattern, concentrated inflow streams, and a small (<50%) flow-impingement zone, and the oscillatory shear index coefficient of variation, were the best predictors of aneurysm rupture. The support vector machine showed an area under the curve of 0.82 (95% CI, 0.69-0.94) in the external validation dataset, and no significant difference was found for the areas under the curve between internal and external validation datasets (P = .21).

CONCLUSIONS

This study revealed that machine learning had a good performance in predicting the rupture status of small aneurysms in both internal and external datasets. Aneurysm hemodynamic parameters were regarded as the most important predictors.

Spatial patterns of <em>Plasmodium vivax</em> transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China

The transition from the control phase to elimination of malaria in China through the national malaria elimination programme has focussed attention on the need for improvement of the surveillance- response systems. It is now understood that routine passive surveillance is inadequate in the parasite elimination phase that requires supplementation by active surveillance in foci where cluster cases have occurred. This study aims to explore the spatial clusters and temporal trends of malaria cases by the multivariate auto-regressive state-space model (MARSS) along the border to Myanmar in southern China. Data for indigenous cases spanning the period from 2007 to 2010 were extracted from the China's Infectious Diseases Information Reporting Management System (IDIRMS). The best MARSS model indicated that malaria transmission in the study area during 36 months could be grouped into three clusters. The estimation of malaria transmission patterns showed a downward trend across all clusters. The proposed methodology used in this study offers a simple and rapid, yet effective way to categorize patterns of foci which provide assistance for active monitoring of malaria in the elimination phase.

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.

[Epidemiological profiles of echinococcosis cases reported in the National Notifiable Disease Report System in non-endemic areas of China from 2004 to 2016]

OBJECTIVE

To investigate the epidemiological profiles of echinococcosis cases reported in non-endemic areas of China in the National Notifiable Disease Report System from 2004 to 2016, so as to provide insights into the development of effective surveillance and response measures.

METHODS

The data pertaining to the echinococcosis cases reported in the National Notifiable Disease Report System in 22 non-endemic provinces of China from 2004 to 2016 were collected, and the epidemiological profiles of the reported echinococcosis cases were descriptively analyzed.

RESULTS

A total of 462 echinococcosis cases were reported in the 22 non-endemic provinces of China from 2004 to 2016, and the number of reported cases increased with time (χ² = 4.516, P = 0.034). During the 13-year period from 2004 to 2016, the highest number of echinococcosis cases was reported in central and eastern China (56.49%), followed by in northern and northeastern China (30.30%), and the highest number of echinococcosis cases was reported in Henan Province (99 cases). Among the 462 echinococcosis cases reported, there were 234 men and 228 women, and the mean age was (41.42 ± 16.03) years (range, 4 to 86 years), with the highest number of echinococcosis cases reported at ages of 20 to 50 years (63.20%). The highest proportion of occupations was farmers and herdsmen (36.15%), and the greatest source was from echinococcosis-endemic provinces (50.43%); in addition, 97.40% of the echinococcosis cases were reported by hospitals.

CONCLUSIONS

Echinococcosis cases were reported in all 22 non-endemic provinces of China in the National Notifiable Disease Report System from 2004 to 2016, and the number of reported cases appeared an overall tendency for sporadicity and local increase with time. Screening of echinococcosis is recommended among famers and herdsmen at ages of 20 to 50 years from endemic regions by medical institutions in non-endemic regions for timely identification and treatment of echinococcosis cases.

Accessing the syndemic of COVID-19 and malaria intervention in Africa

BACKGROUND

The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa.

METHODS

We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (1) contact restriction and social distancing, and (2) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity.

RESULTS

We conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number [Formula: see text] and the duration of infection [Formula: see text]) of COVID-19 in each country are estimated as follows: Ethiopia ([Formula: see text], [Formula: see text]), Nigeria ([Formula: see text], [Formula: see text]), Tanzania ([Formula: see text], [Formula: see text]), and Zambia ([Formula: see text], [Formula: see text]). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020.

CONCLUSIONS

By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

Distribution of Suitable Environments for Phlebotomus chinensis as the Vector for Mountain-Type Zoonotic Visceral Leishmaniasis - Six Provinces, China

What is already known on this topic?

Phlebotomus chinensis(P. chinensis) is a sandfly and the main vector of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in China. However, the distribution of suitable environments for the vector has not been studied yet.

What is added by this report?

This study found that temperate hilly zones in midwestern China are suitable for P. chinensis survival with appropriate environmental factors such as moderate normalized difference vegetation value (NDVI), land use type, landform, temperature, and vegetation. Suitable living conditions for the high-density P. chinensis that caused the reemergence of MT-ZVL already existed.

What are the implications for public health practice?

Targeted strategies should be implemented to control the vector and the reemergence of MT-ZVL, such as by strengthening key environment monitoring and taking accurate measures for residents, mobile and migrant populations.

Inference and prediction of malaria transmission dynamics using time series data

BACKGROUND

Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data.

METHODS

A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China.

RESULTS

The results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections.

CONCLUSIONS

By involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

Epidemiological big data and analytical tools applied in the control programmes on parasitic diseases in China: NIPD's sustained contributions in 70 years

The analysis of epidemiological data has played an important role for the academic research carried out by the National Institute of Parasitic Diseases, China CDC, since its foundation in 1950s. Those researches, e.g., the temporal-spatial patterns of disease transmission and the identification of risk factors, have contributed significantly to the national parasitic disease control and elimination programmes in China. With the development and application of epidemiological data analysis in the last decade, all research results improve our understanding of parasitic diseases epidemiology and related health issues through the application platform of epidemiological big data and analytical tools. In particular, implementation research on analytical predictions on disease outbreak or epidemic risks have provided references to the scientific guidance on effective preventions and interventions in the parasitic disease elimination in China, such as fliariasis, malaria and schistosomiasis. This review has reflected the function of data accumulation and application of temporospatial tools in parasitic diseases control, and the ways of the NIPD's sustained contributions to the disease control programmes in China.