MétaCan
Menu
Back to cohort
Record W4401027491 · doi:10.3390/biomedicines12081667

NK Cells in the Lymph Nodes and Their Role in Anti-Tumour Immunity

2024· review· en· W4401027491 on OpenAlexfundno aff
Lara V. Graham, Salim I. Khakoo, Matthew D. Blunt

Bibliographic record

VenueBiomedicines · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsnot available
FundersMedical Research CouncilUniversity of SouthamptonMedical Research Council CanadaCancer Research UKLeukaemia UK
KeywordsLymphImmune systemLymph nodeImmunologyLymph node stromal cellCytotoxic T cellBiologyPriming (agriculture)Context (archaeology)NK-92Cancer researchImmunityCancer cellAcquired immune systemCancerMedicineT cellAntigen-presenting cellPathologyIn vitro

Abstract

fetched live from OpenAlex

The lymph nodes are vital to enable adaptive immune responses to infection. Natural killer (NK) cells are cytotoxic lymphocytes that directly kill cancer cells and modulate the activation of other immune cells during anti-tumour immune response. NK cells in the lymph nodes are involved in the regulation of T-cell and B-cell populations and the clearance of viral infections. In solid tumours, lymph nodes are a frequent site of metastasis and immune cell priming, whilst in haematological malignancies, tumour cells can proliferate in the lymph nodes. Thus, lymph nodes are an important site in anti-tumour immunity and therapy resistance. It is therefore crucial to identify strategies to increase recruitment and overcome suppression of NK cells in the lymph node microenvironment to improve tumour clearance. In this review, we summarise the literature interrogating NK cell phenotype and function in the lymph nodes in the context of infection and cancer and evaluate both current and potential strategies to mobilise and activate NK cells within the lymph nodes of cancer patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBiomedicinesSame topicImmune Cell Function and InteractionFrench-language works237,207