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Receptors, Immunologic

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Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Patients with severe burns often show systemic coagulation changes in the early stage and even develop extensive coagulopathy. Previous studies have confirmed that soluble TREM-like transcript-1 (sTLT-1) mediates a novel mechanism of haem...

Construction and characterization of recombinant adenovirus carrying a mouse TIGIT-GFP gene.

Genetics and molecular research : GMR
Recombinant adenovirus vector systems have been used extensively in protein research and gene therapy. However, the construction and characterization of recombinant adenovirus is a tedious and time-consuming process. TIGIT is a recently discovered im...

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires.

Frontiers in immunology
The adaptive immune system recognizes antigens an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in...

Deep generative selection models of T and B cell receptor repertoires with soNNia.

Proceedings of the National Academy of Sciences of the United States of America
Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recogn...

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification.

GigaScience
BACKGROUND: Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of sc...

Predicting adaptive immune receptor specificities by machine learning is a data generation problem.

Cell systems
Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted form antibodies, and T cell receptors (TCRs)-is critical for understanding immune responses and advancing immunotherapy and drug discovery. Immune recept...

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning.

Cell systems
The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active in...

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Nucleic acids research
Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics disco...

Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.

Clinical and experimental medicine
Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms we...