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Record W2347100538 · doi:10.1038/srep25602

miRNA let-7b modulates macrophage polarization and enhances tumor-associated macrophages to promote angiogenesis and mobility in prostate cancer

2016· article· en· W2347100538 on OpenAlexaff
Zhigang Wang, Lu Xu, Yinying Hu, Yanqin Huang, Yujuan Zhang, Xiufen Zheng, Shanshan Wang, Yifan Wang, Yanrong Yu, Meng Zhang, Keng Yuan, Wei‐Ping Min

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsMacrophage polarizationAngiogenesisProstate cancermicroRNACancer researchMacrophageM2 MacrophagePhenotypeProstateTumor necrosis factor alphaTumor microenvironmentBiologyMedicineImmunologyCancerInternal medicineIn vitroGeneGeneticsTumor cells

Abstract

fetched live from OpenAlex

Macrophage polarization is a highly plastic physiological process that responds to a variety of environmental factors by changing macrophage phenotype and function. Tumor-associated macrophages (TAMs) are generally recognized as promoting tumor progression. As universal regulators, microRNAs (miRNAs) are functionally involved in numerous critical cellular processes including macrophage polarization. Let-7b, a miRNA, has differential expression patterns in inflamed tissues compared with healthy controls. However, whether and how miRNA let-7b regulates macrophage phenotype and function is unclear. In this report, we find that up-regulation of let-7b is characteristic of prostatic TAMs, and down-regulation of let-7b in TAMs leads to changes in expression profiles of inflammatory cytokines, such as IL-12, IL-23, IL-10 and TNF-α. As a result, TAMs treated with let-7b inhibitors reduce angiogenesis and prostate carcinoma (PCa) cell mobility. Let-7b may play a vital role in regulating macrophage polarization, thus modulating the prognosis of prostate cancer.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations99
Published2016
Admission routes1
Has abstractyes

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