Adipose Tissue Transcriptome by Serial Analysis of Gene Expression
Bibliographic record
Abstract
OBJECTIVE: To describe the genomic expression profile or transcriptome of adipose tissue using the serial analysis of gene expression method. RESEARCH METHODS AND PROCEDURES: The serial analysis of gene expression strategy is based on isolation of short sequences (tags), which usually correspond to unique transcripts, and on their concatenation into long DNA molecules, which are then cloned and sequenced. Experiments were performed with mRNA from retroperitoneal adipose tissue of male C57BL6 mice. RESULTS: We isolated 45,996 tags corresponding to more than 17,000 different genes. Eighty-eight genes were expressed at more than 0.1% of the total population and represented 26% of the mRNA population identified. The most expressed genes were: carbonic anhydrase 3 (1.97%), cytochrome c oxidase (COX) 1 (1.47%), COX2 (1.25%), diazepam binding inhibitor (1.04%), a novel transcript (0.87%), COX3 (0.55%), fatty acid-binding protein 4 (0.55%), and NADH dehydrogenase 4 (0.52%). Other genes known to be expressed in adipose tissue, such as uncoupling protein 2, angiotensinogen, adipsin, and insulin-like growth factor 1, were found at a lower level. Several tags corresponding to novel transcripts were also found. DISCUSSION: To our knowledge, the present results provide for the first time a quantitative description of the transcriptome in adipose tissue.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".