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Record W2765923777 · doi:10.1155/2017/7989020

Comparison of Myeloid Cells in Circulation and in the Tumor Microenvironment of Patients with Colorectal and Breast Cancers

2017· article· en· W2765923777 on OpenAlexfundno aff
Salman M. Toor, Eyad Elkord

Bibliographic record

VenueJournal of Immunology Research · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsnot available
FundersTerry Fox Foundation
KeywordsMyeloidMyeloid cellsMedicineColorectal cancerTumor microenvironmentBreast cancerCancer researchImmune systemImmunologyCancerInternal medicineOncology

Abstract

fetched live from OpenAlex

We have previously reported levels of myeloid cells in the periphery and in the tumor microenvironment (TME) of patients with primary breast cancer (PBC) and colorectal cancer (CRC). We found that both PBC and CRC patients have significantly higher levels of granulocytic and immature myeloid cells in the TME. Additionally, we reported an expansion of circulating granulocytic myeloid cells in CRC patients, but not in PBC patients. In this report, we compared levels of myeloid cells between these two common cancers and have added data from more cancer patients. We also investigated associations between clinical stage/histological grade of tumors and levels of myeloid cells in cancer patients. We found that although granulocytic myeloid cells were expanded in the TME of both PBC and CRC patients, the levels of these cells were significantly higher in the TME of CRC patients. Moreover, our results indicate that increased levels of circulating granulocytic myeloid cells are associated with poorly differentiated tumors in CRC patients. Taken together, this work suggests that CRC patients may benefit more from the development of therapeutic agents to promote myeloid cell differentiation or inhibition for the reversal of immune suppression.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.646

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.315
Teacher spread0.292 · 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 designObservational
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

Citations8
Published2017
Admission routes1
Has abstractyes

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