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Record W4414875942 · doi:10.33540/3124

Context matters

2025· dissertation· en· W4414875942 on OpenAlexaff
Laura Marjolein Timmerman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsImmune systemContext (archaeology)ImmunotherapyCancer immunotherapyCancerInflammationAutoimmune diseaseAutoimmunityReceptor

Abstract

fetched live from OpenAlex

The immune system protects the body against pathogens such as bacteria, viruses, and parasites, and removes malignant cells such as cancer cells. It recognizes harmful cells and initiates responses to eliminate them. The immune system consists of different immune cell types, proteins, and organs that together form a highly advanced and complex network. To prevent damage to the body, the immune system must be carefully regulated. Overactivation can cause inflammatory or autoimmune diseases, while insufficient activation increases the risk of infections and cancer. Immune inhibitory receptors (IIRs) help maintain this balance by controlling the start, duration, and strength of immune responses. IIRs are also important targets for therapy. Some tumor cells escape immune attack by producing ligands that activate IIRs, suppressing the immune system. Cancer immunotherapy seeks to block IIRs so that tumor ligands can no longer inhibit immune cells. This strategy is very effective in some cancers but less so in others. Conversely, therapies that activate IIRs are being developed to suppress excessive immune activity in autoimmune and inflammatory diseases. A detailed understanding of how IIRs function is essential for these therapeutic approaches. IIRs are transmembrane proteins with three parts: an extracellular domain that binds ligands, a transmembrane region, and an intracellular domain that transmits signals. These signals often rely on specific motifs, such as ITIM or ITSM, although some IIRs use other motifs. This research investigated three IIRs: CD200R, PD-1, and LAIR-1. • CD200R inhibits the immune system without ITIM or ITSM motifs. We wanted to know how CD200R then transmits signals. By comparing amino acid sequences of CD200R across species we found several overlapping amino acids. By changing these in human cells, we found that some of them are required for the inhibitory function of CD200R. Ultimately, we discovered a new and more extensive signaling motif: EEDExxPYxxYxxKxNxxY. • PD-1, a major target in cancer immunotherapy, suppresses T cells when activated by tumor ligands. Blocking PD-1 can restore T-cell activity. We showed that inflammatory factors, especially interferon-alpha, reduced the effectiveness of PD-1 blockade in vitro. However, in a study of 22 melanoma patients, we found no link between inflammation levels and therapy outcomes. • The outer parts of LAIR-1 and LILRB4 can bind to each other. We wanted to know what that means, so we studied their interaction. We found that LAIR-1 and LILRB4 do not act as ligands for each other when present on different cells. However, when expressed on the same cell, LILRB4 inhibits less effectively in response to the LILRB4 ligand in the presence of LAIR-1. This indicates that LAIR-1 disrupts the function of LILRB4 when they are present on the same cell. Conclusion: The context in which an IIR functions is important for its effect. Their signaling motifs, interactions with inflammation, and influence on each other all shape their effects on the immune system. This knowledge is important for developing improved therapies for both cancer and autoimmune diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.694
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0080.003
Open science0.0020.004
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.6940.450

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.241
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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