Circulating HDL and Non-HDL Associated Apolipoproteins and Breast Cancer Severity
Bibliographic record
Abstract
Plasma lipids are carried within lipoproteins with various apolipoprotein content. This study evaluates the interest of measuring the apolipoproteins of circulating lipoproteins in breast cancer. Patients with early-stage breast cancer (n = 140) were included. Tumors differed by the expression of estrogen and progesterone receptor (HR− and HR+ for negative and positive expression) and the proliferation marker Ki-67 (≤20% or ≥30%). Apolipoprotein concentrations were determined in plasma, HDL and non-HDL fractions, and results are given in mg/dL, median (25th–75th). Patients did not differ in their plasma and lipoprotein lipid concentrations. HDL apoC-I and non-HDL apoC-II were reduced (1.34 (1.02–1.80) vs. 1.61 (1.32–2.04), p = 0.04; 0.31 (0.18–0.65) vs. 0.63 (0.39–1.02), p = 0.01; respectively), in RH-/high Ki-67 patients in comparison to RH-/low Ki-67 patients, while plasma apoD and HDL apoD were higher (3.24 (2.99–4.16) vs. 3.07 (2.39–3.51), p = 0.04; 2.74 (2.36–3.35) vs. 2.45 (2.01–2.99), p = 0.04; respectively). When RH+/high Ki-67 patients were compared with RH+/low Ki-67 patients, HDL apoC-I and HDL apoC-III were higher (1.56 (1.20–1.95) vs. 1.35 (1.10–1.62), p = 0.02; 2.80 (2.42–3.64) vs. 2.38 (1.69–2.96), p = 0.02; respectively). The distribution of exchangeable apolipoproteins, such as apoC-I, apoC-II, apoC-III, apoD, between lipoproteins is linked to the severity of breast cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".