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Record W3197133949 · doi:10.3390/cancers13174444

Normalizing Tumor Vasculature to Reduce Hypoxia, Enhance Perfusion, and Optimize Therapy Uptake

2021· review· en· W3197133949 on OpenAlexaff
Kathy Matuszewska, Madison Pereira, Duncan Petrik, Jack Lawler, Jim Petrik

Bibliographic record

VenueCancers · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAngiogenesisHypoxia (environmental)Cancer researchTumor microenvironmentTumor hypoxiaMedicineMetastasisLymphatic systemImmunotherapyCarcinogenesisPathologyImmunologyImmune systemCancerRadiation therapyTumor cellsInternal medicineChemistry

Abstract

fetched live from OpenAlex

A basic requirement of tumorigenesis is the development of a vascular network to support the metabolic requirements of tumor growth and metastasis. Tumor vascular formation is regulated by a balance between promoters and inhibitors of angiogenesis. Typically, the pro-angiogenic environment created by the tumor is extremely aggressive, resulting in the rapid vessel formation with abnormal, dysfunctional morphology. The altered morphology and function of tumor blood and lymphatic vessels has numerous implications including poor perfusion, tissue hypoxia, and reduced therapy uptake. Targeting tumor angiogenesis as a therapeutic approach has been pursued in a host of different cancers. Although some preclinical success was seen, there has been a general lack of clinical success with traditional anti-angiogenic therapeutics as single agents. Typically, following anti-angiogenic therapy, there is remodeling of the tumor microenvironment and widespread tumor hypoxia, which is associated with development of therapy resistance. A more comprehensive understanding of the biology of tumor angiogenesis and insights into new clinical approaches, including combinations with immunotherapy, are needed to advance vascular targeting as a therapeutic area.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.323
Teacher spread0.295 · 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.

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

Citations104
Published2021
Admission routes1
Has abstractyes

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