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Record W3042954004 · doi:10.1186/s13045-020-00930-1

Role of CD47 in Hematological Malignancies

2020· review· en· W3042954004 on OpenAlexafffund
Entsar Eladl, Rosemarie Tremblay‐LeMay, Nasrin Rastgoo, Rumina Musani, Wen-Ming Chen, Aijun Liu, Hong Chang

Bibliographic record

VenueJournal of Hematology & Oncology · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCancer Research Society
KeywordsCD47PhagocytosisHematologyCancer researchMedicineImmune systemImmunologyTumor microenvironmentCancerMetastasisInternal medicine

Abstract

fetched live from OpenAlex

CD47, or integrin-associated protein, is a cell surface ligand expressed in low levels by nearly all cells of the body. It plays an integral role in various immune responses as well as autoimmunity, by sending a potent "don't eat me" signal to prevent phagocytosis. A growing body of evidence demonstrates that CD47 is overexpressed in various hematological malignancies and its interaction with SIRPα on the phagocytic cells prevents phagocytosis of cancer cells. Additionally, it is expressed by different cell types in the tumor microenvironment and is required for establishing tumor metastasis. Overexpression of CD47 is thus often associated with poor clinical outcomes. CD47 has emerged as a potential therapeutic target and is being investigated in various preclinical studies as well as clinical trials to prove its safety and efficacy in treating hematological neoplasms. This review focuses on different therapeutic mechanisms to target CD47, either alone or in combination with other cell surface markers, and its pivotal role in impairing tumor growth and metastatic spread of various types of hematological malignancies.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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.

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

Citations164
Published2020
Admission routes2
Has abstractyes

Explore more

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