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The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal sequence of the protein defines the more than 200 M types of Strep A and also contains epitopes that elicit opsonic antibodies, some of which cross-react with heterologous M types. Current efforts to develop broadly protective M protein-based vaccines are directed at identifying potential cross-protective epitopes located in the N-terminal regions of cluster-related M proteins for use as vaccine antigens. In this study, we have used a comprehensive approach using the recurrent neural network ABCpred and IEDB epitope conservancy analysis tools to predict 16 residue linear B-cell epitopes from 117 clinically relevant M types of Strep A (~88% of global Strep A infections). To examine the immunogenicity of these epitope-based vaccines, nine peptides that together shared ≥60% sequence identity with 37 heterologous M proteins were incorporated into two recombinant hybrid protein vaccines, in which the epitopes were repeated 2 or 3 times, respectively. The combined immune responses of immunized rabbits showed that the vaccines elicited significant levels of antibodies against all nine vaccine epitopes present in homologous N-terminal 1-50 amino acid synthetic M peptides, as well as cross-reactive antibodies against 16 of 37 heterologous M peptides predicted to contain similar epitopes. The epitope-specificity of the cross-reactive antibodies was confirmed by ELISA inhibition assays and functional opsonic activity was assayed in HL-60-based bactericidal assays. The results provide important information for the future design of broadly protective M protein-based Strep A vaccines.
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http://dx.doi.org/10.1016/j.vaccine.2021.01.075 | DOI Listing |
Front Microbiol
August 2025
BIOASTER, Lyon, France.
We propose an innovative technology to classify the Mechanism of Action (MoA) of antimicrobials and predict their novelty, called HoloMoA. Our rapid, robust, affordable and versatile tool is based on the combination of time-lapse Digital Inline Holographic Microscopy (DIHM) and Deep Learning (DL). In combination with hologram reconstruction.
View Article and Find Full Text PDFCureus
August 2025
Dental and Oral Medical Center, Kurume University School of Medicine, Kurume, JPN.
Functional reconstruction of large mandibular defects, especially in young patients, presents a significant clinical challenge. The ideal approach should not only restore skeletal contour but also address nerve deficits and facilitate final occlusal rehabilitation, all while minimizing morbidity. This report describes a comprehensive, multi-staged strategy for such a case.
View Article and Find Full Text PDFFront Neurol
August 2025
Rehabilitation Department of Medicine, Xingtai People's Hospital, Xingtai, China.
Background: Stroke is a common acute cerebrovascular disease, and rehabilitation therapy plays a crucial role in the recovery of stroke patients.
Methods: In this retrospective study, we first enrolled 80 stroke patients. These participants were then randomly divided into two groups: the treatment group underwent finger acupressure combined with lower limb rehabilitation training machine, and the control group received basic rehabilitation therapy.
Spectrochim Acta A Mol Biomol Spectrosc
August 2025
State Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection, Anhui University of Science and Technology, Huainan 232001, China; School of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China. Electronic address: c
Conventional methods for soil sampling and soil water content (SWC) measurement are often labor-intensive and time-consuming. The Pedo-transfer function (PTF) integrating soil spectroscopy with soil physicochemical properties provides a more efficient approach for SWC estimation. However, existing studies highlight regional limitations in the accuracy of PTFs across diverse geographical regions.
View Article and Find Full Text PDFAlzheimer's disease shows significantly variable progressions between patients, making early diagnosis, disease monitoring, and care planning difficult. Existing data-driven Disease Progression Models try to tackle this issue, but they usually require sufficiently large datasets of specific diagnostic modalities, which are rarely available in clinical practice. Here, we introduce a new modeling framework capable of predicting individual disease trajectories from sparse, irregularly sampled, multi-modal clinical data.
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