Selection of reference genes for gene expression studies in human neutrophils by real-time PCR
Bibliographic record
Abstract
BACKGROUND: Reference genes, which are often referred to housekeeping genes, are frequently used to normalize mRNA levels between different samples. However the expression level of these genes may vary among tissues or cells, and may change under certain circumstances. Thus the selection of reference gene(s) is critical for gene expression studies. For this purpose, 10 commonly used housekeeping genes were investigated in isolated human neutrophils. RESULTS: Initial screening of the expression pattern demonstrated that 3 of the 10 genes were expressed at very low levels in neutrophils and were excluded from further analysis. The range of expression stability of the other 7 genes was (from most stable to least stable): GNB2L1 (Guanine nucleotide binding protein, beta polypeptide 2-like 1), HPRT1 (Hypoxanthine phosphoribosyl transferase 1), RPL32 (ribosomal protein L32), ACTB (beta-actin), B2M (beta-2-microglobulin), GAPD (glyceraldehyde-3-phosphate dehydrogenase) and TBP (TATA-binding protein). Relative expression levels of the genes (from high to low) were: B2M, ACTB, GAPD, RPL32, GNB2L1, TBP, and HPRT1. CONCLUSION: Our data suggest that GNB2L1, HPRT1, RPL32, ACTB, and B2M may be suitable reference genes in gene expression studies of neutrophils.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".