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Record W3014352023 · doi:10.1002/cam4.3014

Chemokines and chemokine receptors: A new strategy for breast cancer therapy

2020· review· en· W3014352023 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCancer Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsCCR10Chemokine receptorChemokineCCL7CCL13CCR1CXC chemokine receptorsCCL21AngiogenesisCancer researchImmunologyCCL18BiologyMedicineInflammation

Abstract

fetched live from OpenAlex

Chemokines and chemokine receptors not only participate in the development of tissue differentiation, hematopoiesis, inflammation, and immune regulation but also play an important role in the process of tumor development. The role of chemokines and chemokine receptors in tumors has been emphasized in recent years. More and more studies have shown that chemokines and chemokine receptors are closely related to the occurrence, angiogenesis, metastasis, drug resistance, and immunity of breast cancer. Here, we review recent progression on the roles of chemokines and chemokine receptors in breast cancer, and discuss the possible mechanism in breast cancer that might facilitate the development of new therapies by targeting chemokines as well as chemokine receptors. Chemokines and chemokine receptors play an important role in the occurrence and development of breast cancer. In-depth study of chemokines and chemokine receptors can provide intervention targets for breast cancer biotherapy. The regulation of chemokines and chemokine receptors may become a new strategy for breast cancer therapy.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.126
GPT teacher head0.414
Teacher spread0.287 · 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