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Record W2760807868 · doi:10.1016/j.apsb.2017.06.007

Regulation of immune-related diseases by multiple factors of chromatin, exosomes, microparticles, vaccines, oxidative stress, dormancy, protein quality control, inflammation and microenvironment: a meeting report of 2017 International Workshop of the Chinese Academy of Medical Sciences (CAMS) Initiative for Innovative Medicine on Tumor Immunology

2017· article· en· W2760807868 on OpenAlexafffund
Bing Cui, Xuetao Cao, Weiping Zou, Yonghong Wan, Ning Wang, Yaohe Wang, Pingping Li, Fang Hua, Yuying Liu, Xiaowei Zhang, Ke Li, Xiaoxi Lv, Bo Huang, Zhuowei Hu

Bibliographic record

VenueActa Pharmaceutica Sinica B · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsMcMaster University Medical Centre
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchChinese Academy of Medical SciencesMedical Research CouncilZhengzhou UniversityHuazhong University of Science and TechnologyNational Natural Science Foundation of ChinaNational Institutes of HealthInnovent BiologicsOntario Institute for Cancer Research
KeywordsImmune systemInflammationMicrovesiclesImmunologyTumor microenvironmentMedicineDiseaseImmunotherapyCancerCancer immunotherapyBiologymicroRNAPathologyInternal medicine

Abstract

fetched live from OpenAlex

Immune cells play key roles in cancer and chronic inflammatory disease. A better understanding of the mechanisms and risks will help develop novel target therapies. At the 2017 International Workshop of the Chinese Academy of Medical Sciences Initiative for Innovative Medicine on Tumor Immunology held in Beijing, China, on May 12, 2017, a number of speakers reported new findings and ongoing studies on immune-related diseases such as cancer, fibrotic disease, diabetes, and others. A considerably insightful overview was provided on cancer immunity, tumor microenvironments, and new immunotherapy for cancer. In addition, chronic inflammatory diseases were discussed. These findings may offer new insights into targeted immunotherapy.

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)0.005
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 score0.323
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.058
GPT teacher head0.390
Teacher spread0.333 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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