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Record W4387100162 · doi:10.3390/curroncol30100634

Autoantibody Diversity Is Augmented in Women with Breast Cancer and Is Related to the Stage of the Disease

2023· article· en· W4387100162 on OpenAlexvenueno aff
Jesús Pérez‐Hernández, Rosalba León-Díaz, Alejandro Zentella‐Dehesa, Edmundo Lamoyi, Marcela Esquivel‐Velázquez, Antonia Barranca-Enríquez, Tania Romo‐González

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y TecnologíaSistema Nacional de InvestigadoresInstituto de Ciencia y Tecnología del Distrito Federal
KeywordsAutoantibodyBreast cancerMedicineAntigenStage (stratigraphy)CancerDiseaseImmunologyAntibodyDiversity (politics)OncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Breast cancer (BC) is the most frequent malignant neoplasia and leading cause of cancer mortality for women. A timely diagnosis of BC is crucial to ensure the best chances of survival. Among the various screening tools for BC, antibodies directed towards self-antigens or tumor-associated antigens (autoantibodies) have emerged as an alternative to image-based screening modalities. However, little attention has been paid to the global diversity of autoantibodies. This work aimed to analyze the diversity of autoantibodies reactive to antigens expressed by the BC cell line T47D in the sera of Mexican women with BC, benign breast pathology (BBP), or without breast pathology (WBP). We found that the diversity of antibodies in the sera was higher in the BC and BBP groups than in the WBP group. Likewise, the diversity changed with the progression of BC. Our results show and measure the complexity of the antibody response in breast health and disease.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.403
Teacher spread0.340 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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