Yunnan Institute of Endemic Diseases Control and Prevention

ABSTRACTWestern Yunnan Province is a scrub typhus endemic region, providing optimal environments for small mammal hosts and mite vectors. This study explored the association between mite occurrence on small mammals and landscape and host characteristics in this region. Two landscapes, farmland and forest edge, in four locations in Tengchong City were sampled for capturing small mammals and collecting mites. Associations among host, vector, and landscape were analyzed using hurdle negative binomial mixed models. Among 279 small mammals captured, ninety‐five individuals were infested with a total of 8308 mites. Infestation prevalence was higher in farmland than in the forest edge. Mean mite intensity was 87.5/infested mammal. The highest mite intensity was seen in Neotetracus sinensis. Rattus tanezumi, Suncus murinus, Rattus rattus, and Mus pahari were dominant species. Differences in infestation prevalence and mite intensity were inconsistent across species and landscape. In farmland, Rattus tanezumi and Rattus rattus had a significantly higher infestation prevalence than non‐dominant species. Adult mammals had significantly higher infestation prevalence independent of species and landscape. Larger body size, longer tail, hind leg, and ear were each related to significantly higher infestation prevalence, but the effects were reduced by half after adjusting for species and landscape. Mite intensity was not related to hosts. In conclusion, infestation prevalence and mite intensity varied across species. Infestation prevalence was higher in farmland than in forest edge and was positively related to adult mammals and larger body dimensions.

Serratia marcescens is an established pathogen implicated in hospital-acquired infections and is notorious for its propensity to form biofilms on medical devices, leading to persistent environmental contamination and increased infection risks. Addressing this challenge, our study introduces a novel bacteriophage, Weishan phage (WSPA), isolated from the gut of Periplaneta americana L., exhibiting potent activity against multidrug-resistant strains of S. marcescens. Through comprehensive genomic and proteomic characterization, we classified phage WSPA as a member of the genus Muldoonvirus within the class Caudoviricetes. The WSPA phage has a double-stranded DNA genome of 173,655 base pairs and a GC content of 40.09%. Of the 273 open reading frames identified, 124 encode for proteins with recognized functions in the National Center for Biotechnology Information (NCBI) database, while the remaining 149 are of unknown function. Additionally, we identified six tRNA genes and did not identify any virulence or antibiotic resistance genes. However, given the presence of numerous hypothetical genes with unknown functions, this phage may possess certain therapeutic safety potential, though additional research validation is still required. Comparative genomic analysis revealed that WSPA shares 86.92% sequence identity with the known Serratia phage 4S (MW082584.1). We further assessed the phage's one-step growth characteristics, thermal and pH stability, and determined its host range, which are critical for its application in environmental and clinical settings. Our findings suggest that bacteriophage WSPA could serve as an eco-friendly and effective agent in controlling S. marcescens infections, with promising implications for phage therapy and biocontrol in healthcare environments.IMPORTANCEThis study isolated a novel phage, WSPA, from Periplaneta americana L. gut that specifically targets multidrug-resistant Serratia marcescens. Genomic analysis identified WSPA as a new Muldoonvirus member lacking virulence/resistance genes. With excellent stability and lytic activity, WSPA shows potential for hospital infection control. As the first phage isolated from the cockroach gut, this work expands phage resources and supports medicinal insect phage library development, advancing phage therapy and biocontrol applications.

The natural and anthropogenic environment have contributed to the dynamic risk of plague and their threats to human health. Although evidence has indicated the environmental suitability for disease dynamics, the alteration of the risk by anthropogenic factors have not been fully investigated. We conducted a multicenter cross-sectional survey among 2998 residences across 54 villages in 13 counties in Southwest China. With the survey data, we developed composite anthropogenic indices to make systematic assessment of people's living environment, household sanitation levels, and risk perception regarding plague. We identified disparities of these anthropogenic indices among ethnic groups. By featuring plague, we further established statistical model to assess how environmental and anthropogenic factors associate with the occurrence of the typical zoonotic disease. Our results show that the improved living environment is significantly linked to the lower risk of plague occurrence. In contrast, we have no evidence for the significant association between household sanitation, protective behaviors, environmental conditions and plague risk. These findings pointed at the improved living environment as the most likely anthropogenic driver that is associated with the reduced risk of plague. Integrating anthropogenic modulators of disease dynamics in public health strategies would be the key for the effective management of disease risks.

Background: Anaplasmosis, caused by Anaplasma species, poses significant threats to public health and livestock productivity. Understanding the distribution and genetic diversity of these pathogens in tick vectors across China is critical for risk assessment and disease control. Materials and Methods: From April to June 2023, 875 ticks were collected across three Chinese provinces: Jiangxi, Yunnan, and Shaanxi. The collected ticks comprised Rhipicephalus microplus (from Jiangxi and Yunnan) and Haemaphysalis longicornis (from Shaanxi). Pathogen detection was performed using PCR, followed by phylogenetic analysis of the obtained sequences to determine genetic relationships. Results: Anaplasma capra was detected predominantly in R. microplus from Yudu County, Jiangxi (4.95%), and in H. longicornis from Meixian County (3.16%), Long County (5.99%), and Zhenba County (0.83%) in Shaanxi, exhibiting regional genetic variations. The detection rate of Anaplasma marginale was 6.59% in Yudu County, Jiangxi; significantly higher rates were found in Yunnan province: 41.75% in Nanjian County, 40.38% in Weishan County, and 52.04% in Yongsheng County. Phylogenetic analysis revealed that A. marginale isolates from Lijiang (Yongsheng), Yunnan, were highly homologous (99.63%-100%) to those from Yudu County, Jiangxi, while isolates from Dali (Nanjian and Weishan), Yunnan, formed a distinct clade. Conclusion: The findings demonstrate the widespread distribution of A. capra and A. marginale in ticks across the surveyed regions of China, with notable variations in prevalence and genetic characteristics. These pathogens represent potential threats to local residents and livestock. Future research should expand the geographic sampling range to fully understand their distribution patterns and explore effective prevention and control strategies to safeguard public health and safety.

This study aims to assess the applicability of the serum iodine (SI) standards proposed by the WHO and other renowned laboratories for the Chinese population. Additionally, it seeks to establish reference intervals (RIs) for SI in Chinese adults and conduct a risk assessment between serum iodine concentration (SIC) and the development of thyroid diseases. This study enrolled 3138 adults aged 18-60 years from six provinces in China. A total of 1794 healthy adults with normal thyroid function were selected to assess the applicability of the international standards and establish China's RI, utilizing five external datasets for validation. Multiple logistic regression analysis was applied to explore the relationship between SIC and thyroid disease. The ratios of adults with normal thyroid function from five provinces whose SI levels fall outside the limits of the international standards are partially greater than 10%. China's RI was 40.11-97.59 µg/L, and the ratios of individuals in the validation datasets outside this RI were all less than 10%. Adults with low SIC levels (< 40.11 µg/L) were at higher risk of hypothyroxinemia (adjusted OR = 4.715, 95% CI = 1.459-15.235), overt hypothyroidism (adjusted OR = 4.719, 95% CI = 1.583-14.067), and autoimmune thyroiditis (adjusted OR = 2.394, 95% CI = 1.410-4.064). Adults with high SIC levels (> 97.59 µg/L) were at a higher risk of overt hyperthyroidism (adjusted OR = 18.170, 95% CI = 5.940-55.577). These international standards are not fully applicable to the Chinese population. We established RIs for SI in Chinese adults to better accommodate the country's specific conditions.

BackgroundAsian schistosomiasis is projected to be eliminated by 2030 according to World Health Organization road map for neglected tropical diseases 2021–2030. Snail control is an important measure but has not yet been systematically evaluated at a country scale. Here, we report the findings from a nationwide survey to demonstrate the dynamics of Oncomelania and its potential role in transmission interruption of schistisomiasis in the People’s Republic of China (P.R. China).MethodsBetween March 2016 and December 2017, we conducted a nationwide census on Oncomelania snail habitats in P.R. China. All historically recorded snail habitats were identified and reviewed. Information on habitat attributes, including the infestation of snails, was collected. The shape of habitats was determined using global positioning system and geographical information system technologies. The relationship between snail control and schistosomiasis elimination was established in 378 endemic counties. The comparison of accumulated snail-infested range (ASR) and the median ratio of eliminated ASR between the transmission-interrupted and endemic counties was tested by a non-parametric test (Mann–Whitney) with a significance level of 0.05.ResultsOverall, 15,377.7 million m2 of potential snail habitats with a total of 356,550 snail habitats were identified in P.R. China. The ASR amounted to 86.0% of the total area. Most of the ASR (94.9%) and habitats (68.5%) were distributed in the middle and lower reaches of the Yangtze River. Snail habitats were found up to an altitude of 2859 m above the mean sea level. By 2017, 85.1% of habitats (73.0% of the ASR) had been eliminated with almost half of them eliminated between 1965 and 1982. The elimination of snail habitats promoted transmission interruption of schistosomiasis, but showed variable patterns in different landscapes. The ratio of eliminated ASR was 99.6 and 91.4% in water network and hilly areas, respectively, while it was only 64.8% in marshland areas, particularly in Hunan and Jiangxi where the two largest freshwater lakes of P.R. China are located. Marshland habitats were seen as the most difficult for transmission interruption, which calls for additional control measures in these settings.ConclusionsOur results support recent recommendations by the World Health Organization to implement snail control and demonstrate that schistosomiasis elimination can be achieved. The nationwide, high-resolution map of Oncomelania snail habitats in P.R. China will support further efforts to eliminate schistosomiasis.Graphical

To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden's index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach's coefficient alpha. A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden's index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach's coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach's coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ2 = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ2 = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ2 = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ2 = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ2 = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ2 = 5.604, Padjusted < 0.025). The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Brucellosis is a globally prevalent zoonotic disease caused by Brucella spp. posing significant threats to animal and human health. In this study, a novel lytic brucellaphage designated Y17 was isolated from sheep fecal samples collected in Ludian County, Yunnan Province, China. Transmission electron microscopy revealed that Y17 was composed of an icosahedral head (48.1 ± 2 nm) and a short tail (10.8 ± 1 nm), making it the smallest brucellaphage described so far. The optimal multiplicity of infection (MOI) for phage Y17 is 0.001, with a burst size of ~187 PFU/cell, the largest value reported for any brucellaphage, and it has a relatively short latent period. It exhibits broad pH and temperature stability, retaining activity even after 1 h of exposure to ultraviolet radiation and various ethanol concentrations. Y17 shows strong lytic activity against Brucella abortus and can also infect some Brucella melitensis strains. The Y17 genome spans 38,025 bp with a GC content of 48.2%, making it the smallest genome among brucellaphages to date. It lacks virulence, antibiotic resistance, or lysogenic genes, indicating its potential as a safe biocontrol agent. Whole-genome average nucleotide identity (ANI) analysis reveals high homology across all lytic brucellaphages, but Y17 exhibits relatively lower genome coverage compared to other lytic brucellaphages. Genomic collinearity comparison revealed that Y17 lacks some terminal fragments present in the genomes of other lytic brucellaphages. Furthermore, compared to brucellaphages with genomes larger than 40 kb, Y17 also lacks segments corresponding to ORF21 (amidase), ORF28 (hypothetical protein), and ORF29 (carbohydrate-binding protein). Phylogenetic analysis indicates that Y17 is closely related to phages Iz, Bk2, S708, Wb, R/C, Pr, and Bk. Moreover, the capsid gene shows significantly higher conservation in comparison with the tail collar and amidase genes. This study significantly enriches the brucellaphage database and highlights the potential of Y17 as a biocontrol agent for managing brucellosis in endemic regions.

To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden's index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden's index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ2 = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ2 = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ2 = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden's index of 0.82 and AUC values of 0.969 in external test set, respectively. The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

Metagenomic sequencing has transformed the understanding of viral diversity in wildlife and the potential threats these viruses pose to human health. Despite this progress, such sequencing studies often have lacked systematic and ecologically informed sampling, thereby likely missing many potential human pathogens and the drivers behind their ecology, evolution and emergence. We conducted an extensive search for viruses in the lungs, spleens and guts of 1688 mammals from 38 species across 428 sites in Yunnan Province, China-a hotspot for zoonoses emergence. We identified 162 mammalian viruses, including 102 new ones and 24 posing potential risks to humans due to their relationships with known human pathogens associated with serious diseases or their ability to cross major host species barriers. Our findings offer an in-depth view of virus organotropism, cross-host associations, host sharing patterns, and the ecological factors influencing viral evolution, all of which are critical for anticipating and mitigating future zoonotic outbreaks.