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Record W4200285656 · doi:10.1097/md.0000000000028419

Increased expression of interleukin-22 and its receptor is relevant to poor prognosis in laryngeal squamous cell carcinoma

2021· article· en· W4200285656 on OpenAlexaff
Wenjun Ji, Jing Li, Xin Wang, Xiangdong Gao, Tiantian Zhang

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsMedicineCarcinomaOncologyBasal cellCancer researchLaryngeal NeoplasmReceptorInternal medicineInterleukinCytokine

Abstract

fetched live from OpenAlex

ABSTRACT: To detect the expression of interlerukin-22 (IL-22) and associated genes and to evaluate their relationship with clinicopathological features and prognosis in laryngeal squamous cell carcinoma (LSCC).The expression of IL-22 and associated genes were evaluated by immunohistochemistry and real time polymerase chain reaction in LSCC tissues from 30 patients and adjacent non-tumor tissues. A statistical analysis was implemented to assess the relationship among levels of expression, clinicopathological factors, and overall survival.The expression of IL-22 and interleukin 22 receptor 1 (IL-22R1) was mainly located in the cytoplasm, and the expression of LSCC was significantly higher than in controls. The expression of aryl hydrocarbon receptor and signal transducer and activator of transcription 3 distributed in the cell nucleus, which was significantly higher in LSCC than in controls. The expression of IL-22 and IL-22R1 was associated with metastasis of lymph node and clinical stage of LSCC. Overall survival of LSCC was significantly poorer with higher expression of IL-22 and IL-22R1 than in those with lower expression.The present research indicated that the increased level of IL-22 and IL-22R1 may be related to the pathogenesis and prognosis of LSCC. IL-22 may be the important biomarker, which need further research.

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 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 score0.012
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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