Transcriptome of mouse uterus by serial analysis of gene expression (SAGE): Comparison with skeletal muscle
Bibliographic record
Abstract
The aim of this study was to identify the transcriptome of the normal mouse uterus by Serial Analysis of Gene Expression method. mRNA was extracted from the uterus and also from the gastrocnemius muscle of mice. Short sequences (tags), each one usually corresponding to a distinct transcript, were isolated and concatemerized into long DNA molecules which were cloned and sequenced. We detected 44,484 tags for the uterus and 42,518 tags for the muscle, representing 14,543 and 14,958 potential transcript species, respectively. Seventy-five and sixty-nine genes were expressed at more than 0.1%, thus corresponding to 37 and 34% of the mRNA population detected in the respective tissues. In both cases, the most highly expressed genes are especially involved in muscle contraction, energy metabolism, and protein synthesis. Compared to skeletal muscle, some differentially expressed genes in the uterus are likely to correspond to its specific reproductive functions. The majority of these genes remain to be characterized. More than 70% of the different tags detected in the uterus did not match any sequence in the public databases and can represent novel or poorly identified genes. This study is the first quantitative description of the transcriptome of the uterus.
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 | 0.000 | 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) | 0.000 | 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".