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Anaplasma species are globally distributed and pose significant public health threats as zoonotic pathogens. However, the epidemiological characteristics and genetic diversity of Anaplasma in rodents from Fujian Province, southeastern China, remain poorly understood. From 2015 to 2024, we systematically collected 966 rodents across 22 counties and cities in Fujian. Screening was performed using real-time PCR and nested PCR targeting the groEL and 16S rRNA genes. Positive samples were subjected to sequencing, followed by phylogenetic analysis (MEGA11) and haplotype network construction (PopART). The overall infection rate of Anaplasma was 4.35% (42/966), and all positive samples were identified as Anaplasma phagocytophilum . Infection rates showed significant differences among rodent species, cities, habitat types, and sampling years ( P  < 0.05). Haplotype network analysis indicated that H1 was the dominant haplotype. This study confirms the circulation of A. phagocytophilum in rodents in Fujian, reveals its association with rodent age, seasonal and habitat factors, and underscores the necessity for enhanced ongoing surveillance and control of rodent-borne Anaplasma in this region.

Accurate differentiation and identification of flea species are essential for both basic and applied research on fleas, as well as for the diagnosis of flea-borne diseases. However, distinguishing between flea species can be challenging, especially among those with minimal morphological differences. Therefore, some scholars have suggested the necessity of comprehensive revisions to the classification of fleas, incorporating morphological, molecular, and phylogenetic data. In this study, we focused on classifying the rodents’ parasitic fleas in southeastern China and provided molecular and phylogenetic data. We also described a new subspecies Ctenophthalmus breviprojiciens fujiansis n. ssp. A total of 392 fleas were collected from 8 species of rodents in 10 counties. Morphologically, they belonged to 10 species, 9 genera and 5 families. Barcode identification based on COI gene and phylogenetic analysis based on five genetic markers ( 18S rDNA , 28S rDNA , EF-1a , COI , COII ) revealed that the molecular and morphological identification of Xenopsylla cheopis , Aviostivalius klossi bispiniformis , Leptopsylla segnis , Monopsyllus anisus and Ctenocephalides felis felis were consistent. The taxonomic status of Neopsylla specialis minpiensis and Peromyscopsylla himalaica sinica as subspecies is questionable due to significant intraspecific genetic distance, and further morphological and molecular data are required to determine if they should be elevated to species level. The molecular identification of C. breviprojiciens n. ssp., N. dispar fukienensis , and Nosopsyllus nicanus could not be completed at this time due to a lack of sequences for related species in existing GenBank databases. Additionally, phylogenetic relationships of 31 species from 9 genera and 5 families of Siphonaptera were inferred based on five molecular markers ( 18S rDNA , 28S rDNA , EF-1a , COI and COII ) using Maximum Likelihood analyses. The analyses revealed that various taxa of Siphonaptera are monophyletic at the subspecies, species, and genus levels. However, at the family level, Leptopsyllidae, Ceratophyllidae, Pulicidae, and Pygiopsyllidae are all monophyletic, while Ctenophthalmidae is paraphyletic. we support the view of some authors that revising the catchall group Ctenophthalmidae and elevating each of its constituent subfamilies to family status.

Background Fleas, considered to be the main transmission vectors of Bartonella , are highly prevalent and show great diversity. To date, no investigations have focused on Bartonella vectors in Southeast China. The aim of this study was to investigate the epidemiological and molecular characteristics of Bartonella in fleas in Southeast China. Methods From 2016 to 2022, flea samples ( n  = 1119) were collected from 863 rodent individuals in seven inland and coastal cities in Southeast China. Flea species, region, gender, host species and habitat were recorded. The DNA samples from each individual flea were screened by real-time PCR for the Bartonella ssrA gene. All positive samples were confirmed by PCR based on the presence of the gltA gene and sequenced. The factors associated with Bartonella infection were analyzed by the Chi-square test and Fisher's exact test. ANOVA and the t-test were used to compare Bartonella DNA load. Results Bartonella DNA was detected in 26.2% (293/1119) of the flea samples, including in 27.1% (284/1047) of Xenopsylla cheopis samples, 13.2% (5/38) of Monopsyllus anisus samples, 8.3% (2/24) of Leptopsylla segnis samples and 20.0% (2/10) of other fleas ( Nosopsyllus nicanus , Ctenocephalides felis , Stivalius klossi bispiniformis and Neopsylla dispar fukienensis ). There was a significant difference in the prevalence of Bartonella among flea species, sex, hosts, regions and habitats. Five species of Bartonella fleas were identified based on sequencing and phylogenetic analyses targeting the gltA gene: B. tribocorum , B. queenslandensis , B. elizabethae , B. rochalimae and B. coopersplainsensis . Conclusions There is a high prevalence and diversity of Bartonella infection in the seven species of fleas collected in Southeast China. The detection of zoonotic Bartonella species in this study, including B. tribocorum, B. elizabethae and B. rochalimae, raises public health concerns. Graphical Abstract

BACKGROUND: The pathogen of Q fever, Coxiella burnetii, can persist in the environment for yearsand it can form infectious aerosols that facilitate long-distance airborne transmission of Q fever under certain conditions. Ticks are obligate hematophagous ectoparasites with a broad host range. Accumulating evidence suggests that, within natural ecosystems, ticks may contribute to the maintenance and circulation of Coxiella burnetii among wildlife and livestock, thereby representing a potential component of its transmission cycle. In recent years, cases of Q fever have been reported in Fujian Province, southeastern China. This study aimed to investigate the prevalence and genetic characteristics of Coxiella burnetii carried by parasitic ticks in coastal and inland regions of Fujian Province. METHODS: Our research team conducted tick collection work in coastal and inland areas of Fujian Province from 2011 to 2023.Ticks were collected from different hosts, and species, developmental stage, and sampling location were recorded. DNA was extracted and screened for the IS1111 gene by real-time PCR. Positive samples were further tested by nested PCR targeting the groEL gene, followed by sequencing and phylogenetic analysis (neighbor-joining). Factors associated with infection were analyzed using Chi-square tests, Fisher-Freeman-Halton exact tests, and logistic regression analysis. RESULTS: A total of 746 tick samples were examined, representing 5 genera and 13 identified species, with additional specimens identified only to the genus level and recorded as Haemaphysalis sp. Forty-seven samples were positive for Coxiella burnetii, with an overall positivity rate of 6.30%. The infection rate in inland areas was significantly higher than that in coastal areas (10.22% and 2.60%). The dominant tick species in inland regions was Dermacentor taiwanensis, and higher infection rates were observed in Haemaphysalis Yeni (25.00%), Ixodes sinensis (25.00%), and Amblyomma testudinarium (19.35%). In coastal areas, Haemaphysalis formosensis was the dominant tick species, and Haemaphysalis hystricis showed a higher infection rate (9.84%, 6/61). Significant differences in infection rates were observed among tick genera, hosts, and regions (P < 0.05). Logistic regression showed that regional difference was the main influencing factor; ticks distributed in inland areas were 4.258 times more likely to be infected with Coxiella burnetii than those in coastal areas (P < 0.001). Phylogenetic analysis showed that the groEL sequences from both coastal and inland samples clustered with Coxiella burnetii, and sequence variation was minimally affected by regional and host differences. Three haplotypes exist in the Fujian region, with H1 being the predominant haplotype. CONCLUSION: The positivity rate of Coxiella burnetii in ticks from inland areas of Fujian Province was higher than that in coastal areas. Ticks parasitizing wild animals exhibit significantly higher infection rates, suggesting that ticks may participate in natural cycles in the wild. The groEL gene sequences of tick-derived Coxiella burnetii in Fujian Province were highly homologous to those from other regions.

Corrosion and scaling critically threaten the safety and efficiency of oil–gas gathering stations. Through field inspections, water chemistry analysis, scale characterization, and corrosion simulation in Yanchang oilfield, this study identifies severe localized damage in key components—such as valves, bends, and injection pipelines—with service lives of only 1–2 years. Analysis of over 200 scale samples revealed that CaCO3 (42 wt%) and CaSO4 (23 wt%) were the predominant scale types. High salinity >56,000 mg/L, Cl− >31,000 mg/L, and Ca2+ promote under-deposit pitting, galvanic corrosion (e.g., Cu–steel couples), and erosion-corrosion at high-velocity zones. Simulations based on OLI Analyzer Studio (a professional thermodynamic simulation software for electrolyte solution and high-salinity brine systems) reveal that the carbon steel (the primary material for the process pipelines and water injection pipelines in the studied oil–gas gathering and transportation stations) has a corrosion rate rising from 0.078 mm/year at 25 °C to 1.94 mm/year at 90 °C. Despite common use of coatings and cathodic protection, these measures often fail to address site-specific failure mechanisms. The study advocates a tailored mitigation strategy combining material compatibility, real-time water monitoring, optimized filtration, and component-level design. This integrated approach enhances asset reliability and operational safety in onshore oilfields.

Predicting enzyme-substrate interactions has long been a fundamental problem in biochemistry and metabolic engineering. While existing methods could leverage databases of expert-curated enzyme-substrate pairs for models to learn from known pair interactions, the databases are often sparse, i.e., there are only limited and incomplete examples of such pairs, and also labor-intensive to maintain. This lack of sufficient training data significantly hinders the ability of traditional enzyme prediction models to generalize to unseen interactions. In this work, we try to exploit chemical reaction equations from domain-specific databases, given their easier accessibility and denser, more abundant data. However, interactions of multiple compounds, e.g., educts and products, with the same enzymes create complex relational data patterns that traditional models cannot easily capture. To tackle that, we represent chemical reaction equations as triples of (educt, enzyme, product) within a knowledge graph, such that we can take advantage of knowledge graph embedding (KGE) to infer missing enzyme-substrate pairs for graph completion. Particularly, in order to capture intricate relationships among compounds, we propose our knowledge-enhanced hypergraph model for enzyme prediction, i.e., Hyper-Enz, which integrates a hypergraph transformer with a KGE model to learn representations of the hyper-edges that involve multiple educts and products. Also, a multi-expert paradigm is introduced to guide the learning of enzyme-substrate interactions with both the proposed model and chemical reaction equations. Experimental results show a significant improvement, with up to a 88% relative improvement in average enzyme retrieval accuracy and 30% improvement in pair-level prediction compared to traditional models, demonstrating the effectiveness of our approach.