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Record W4395697616 · doi:10.1016/j.mbm.2024.100068

Tug of war: Understanding the dynamic interplay of tumor biomechanical environment on dendritic cell function

2024· review· en· W4395697616 on OpenAlexfundno aff
Brian Chesney Quartey, Gabriella Torres, Mei ElGindi, Aseel Alatoom, Jiranuwat Sapudom, Jeremy Teo

Bibliographic record

VenueMechanobiology in Medicine · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsMechanotransductionMechanobiologyExtracellular matrixImmune systemCell biologyAntigen presentationCell mechanicsDendritic cellTumor microenvironmentNeuroscienceBiologyCellImmunologyT cellCytoskeleton

Abstract

fetched live from OpenAlex

Dendritic cells (DCs) play a pivotal role in bridging the innate and adaptive immune systems. From their immature state, scavenging tissue for foreign antigens to uptake, then maturation, to their trafficking to lymph nodes for antigen presentation, these cells are exposed to various forms of mechanical forces. Particularly, in the tumor microenvironment, it is widely known that microenvironmental biomechanical cues are heightened. The source of these forces arises from cell-to-extracellular matrix (ECM) and cell-to-cell interactions, as well as being exposed to increased microenvironmental pressures and fluid shear forces typical of tumors. DCs then integrate these forces, influencing their immune functions through mechanotransduction. This aspect of DC biology holds alternative, but important clues to understanding suppressed/altered DC responses in tumors, or allow the artificial enhancement of DCs for therapeutic purposes. This review discusses the current understanding of DC mechanobiology from the perspectives of DCs as sensors of mechanical forces and providers of mechanical forces.

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 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 score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.304
Teacher spread0.273 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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