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Pathogenic spirochetes bind and interact with various host structures and molecules throughout the course of infection. By utilizing their outer surface molecules, spirochetes can effectively modulate their dissemination, interact with immune system regulators, and select specific destination niches within the host. The three-dimensional structures of multiple spirochetal surface proteins have been elucidated, providing insight into their modus operandi. This review focuses on the structural characteristics of these sticky molecules and their functional implications, highlighting how these features contribute to the pathogenicity of spirochetes and their ability to persist in the host and vector environments. Recognizing the structural motifs and ligands to which these important virulence determinants bind could open new avenues for developing strategies to block colonization by spirochetal pathogens.
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http://dx.doi.org/10.1002/pro.70185 | DOI Listing |
Can Vet J
September 2025
Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, Saskatchewan S7N 5B4 (Pollock, Campbell, Waldner); Faculty of Veterinary Medicine, University of Calgary, 11877 85 Street NW, Calgary, Alberta T3R 1J3 (Windey
Objective: Our objective was to estimate the seroprevalences of 6 serovars in beef calves at or near fall weaning and assess how concentrations of serovar antibody titers in weaning-age calves varied with herd vaccination programs.
Animals: Serum was collected from 1922 beef calves from 106 herds in the Canadian Cow-Calf Surveillance Network (C3SN).
Procedure: A microscopic agglutination test was used to measure antibody titers for serovars Bratislava, Canicola, Grippotyphosa, Hardjo, Icterohaemorrhagiae, and Pomona.
J Med Entomol
September 2025
Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Fort Collins, CO, USA.
The United States Centers for Disease Control and Prevention introduced the National Tick Surveillance Program in 2018 to better define areas of acarologic risk in response to the increasing burden of blacklegged tick (Ixodes scapularis, Acari: Ixodidae)-associated infections. The program coordinates surveillance efforts conducted by state and local public health programs and collates acarological data in the ArboNET Tick Module national database. Among the metrics collected, the density of infected host-seeking nymphs (DIN) is believed to be most closely correlated with the reported occurrence of tick-borne diseases.
View Article and Find Full Text PDFPathogens
August 2025
Diagnostics and Laboratory Research Task Force, Balkan Association for Vector-Borne Diseases, 21000 Novi Sad, Serbia.
Several diseases caused by tick-borne pathogens, including Lyme borreliosis (LB) and spotted fever group rickettsioses, are endemic in the Balkan Peninsula, positioned between Central Europe and the Middle East. This cross-sectional study aimed to assess serological exposure to spp. and spotted fever group (SFGR) among individuals with recent tick bites and healthy controls in two Balkan countries-Serbia and North Macedonia.
View Article and Find Full Text PDFPathogens
August 2025
Yunnan Province Key Laboratory of Children's Major Diseases Research, Department of Pathogens Biology and Immunology, Faculty of Basic Medicine Sciences, Kunming Medical University, Kunming 650500, China.
As direct detection methods of are limited, serology plays an important role in diagnosing Lyme disease (LD). There are various types of Lyme serological tests with varying diagnostic accuracy, so it is necessary to compare and rank them. The aim of this study is to compare the accuracy of various serological diagnostic methods for LD using network meta-analysis (NMA).
View Article and Find Full Text PDFSensors (Basel)
August 2025
Department of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
This article presents a deep learning approach for classifying the developmental stages (larvae, nymphs, adult females, and adult males) of ticks, the most common tick species in Europe and a major vector of tick-borne pathogens, including , , and tick-borne encephalitis virus (TBEV). Each developmental stage plays a different role in disease transmission, with nymphs considered the most epidemiologically relevant stage due to their small size and high prevalence. We developed a convolutional neural network (CNN) model trained on a dataset of microscopic tick images collected in the area of Upper Silesia, Poland.
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