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Record W2793452045 · doi:10.1002/lsm.22810

Role of cell stress signaling networks in cancer cell death and antitumor immune response following proteotoxic injury inflicted by photodynamic therapy

2018· review· en· W2793452045 on OpenAlexaff
Mladen Korbelik

Bibliographic record

VenueLasers in Surgery and Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsProteostasisImmune systemPhotodynamic therapyProgrammed cell deathOxidative stressCancer researchCancer cellCellular stress responseCellCancerCell signalingImmunologyCell biologySignal transductionBiologyApoptosisMedicineFight-or-flight responseChemistryInternal medicine

Abstract

fetched live from OpenAlex

The insult delivered by photodynamic therapy (PDT) in treated cells is oxidative stress. The main burden threatening survival of PDT-treated cells is proteotoxic damage that jeopardizes proteostasis in these cells. For dealing with this type of proteostasis impairment, cells have developed protection mechanisms operating by signaling networks. This review will outline various components of signaling networks that can be engaged in stressed cells with highlighting the emerging aspects relevant to response to PDT. It will be also shown how the well known inflammatory/immune response associated with PDT is also based on the activity of these stress signaling networks. Lasers Surg. Med. 50:491-498, 2018. © 2018 Wiley Periodicals, Inc.

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)0.003
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.573
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.343
Teacher spread0.317 · 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 designSystematic review
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

Citations31
Published2018
Admission routes1
Has abstractyes

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