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Record W4366156329 · doi:10.4049/jimmunol.2200914

Here, There, and Everywhere: Myeloid-Derived Suppressor Cells in Immunology

2023· review· en· W4366156329 on OpenAlexaff
Suzanne Ostrand‐Rosenberg, Tracey J. Lamb, Graham Pawelec

Bibliographic record

VenueThe Journal of Immunology · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsHealth Sciences North
FundersNational Institute of General Medical SciencesNational Cancer Institute
KeywordsImmunologyMyeloid-derived Suppressor CellImmunityInflammationImmune systemDiseaseSuppressorAutoimmunityBiologyCancerVaccinationMedicinePathology

Abstract

fetched live from OpenAlex

Myeloid-derived suppressor cells (MDSCs) were initially identified in humans and mice with cancer where they profoundly suppress T cell- and NK cell-mediated antitumor immunity. Inflammation is a central feature of many pathologies and normal physiological conditions and is the dominant driving force for the accumulation and function of MDSCs. Therefore, MDSCs are present in conditions where inflammation is present. Although MDSCs are detrimental in cancer and conditions where cellular immunity is desirable, they are beneficial in settings where cellular immunity is hyperactive. Because MDSCs can be generated ex vivo, they are being exploited as therapeutic agents to reduce damaging cellular immunity. In this review, we discuss the detrimental and beneficial roles of MDSCs in disease settings such as bacterial, viral, and parasitic infections, sepsis, obesity, trauma, stress, autoimmunity, transplantation and graft-versus-host disease, and normal physiological settings, including pregnancy and neonates as well as aging. The impact of MDSCs on vaccination is also discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.289
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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

Citations54
Published2023
Admission routes1
Has abstractyes

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