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Record W2067127991 · doi:10.1139/o04-003

Multiple variants and a differential splicing pattern of kinectin in human hepatocellular carcinoma

2004· article· en· W2067127991 on OpenAlexvenueno aff
Hongcheng Wang, Yan-Rong Su, Ke-Jun Han, Xuewen Pang, Jirun Peng, Bin Liang, Shan Wang, Weifeng Chen

Bibliographic record

VenueBiochemistry and Cell Biology · 2004
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyHepatocellular carcinomaRNA splicingImmunogenicityAlternative splicingcDNA libraryComplementary DNARecombinant DNAAntigenMolecular biologyGeneImmunologyGeneticsMessenger RNARNA

Abstract

fetched live from OpenAlex

To extend the search for hepatocellular carcinoma (HCC) associated antigens with immunogenicity for clinical applications, we constructed a cDNA expression library using resected human HCC tissue sample and screened it by serological analysis of recombinant cDNA expression library (SEREX) with autologous and allogeneic sera. A total of 24 distinct antigens were isolated and kinectin was the antigen most frequently identified. We found that kinectin was alternatively spliced at four sites and obtained all eight theoretical forms of variant, six by SEREX and two by RT-PCR, from the different splicing combinations of the last three sites. In addition, the splicing patterns of four sites were analyzed. Variant containing D2 was overexpressed in cancerous tissues and this alteration may be tumor associated. The four splicing sites, the variants generated by alternative splicing, and the humoral immune response in HCC patients, may help to analyze the role of kinectin in human HCC cell biology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2004
Admission routes1
Has abstractyes

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