Cloning of differentially expressed brain cDNAs
Bibliographic record
Abstract
Abstract The ability of cells to alter gene expression in response to stimuli or during development and ageing underlies the plasticity of the central nervous system. Although there are multiple levels of control of gene expression, induction or repression of specific genes is an important first step in the regulation of the function of the CNS. Therefore, molecular screening and selection techniques based on comparing mRNA populations can reveal important differences in gene expression between and among brain tissues. Differential display is a PCR-based screening method (for an overview see 1) that is especially useful to study changes in steady-state levels of mRNA in heterogeneous tissue such as brain for several reasons (1). First, this screening technique can be used to identify novel brain mRNAs or previously described mRNAs whose relative expression levels are altered as a result of a physiological change, a disorder, a disease state, or the administration of pharmacological agents. Secondly, it is possible to simultaneously compare multiple tissues, treatments, and time points (up to 60 comparisons per experiment and primer set) using small amounts of tissue from a number of individual animals for the original isolation of RNA. These features distinguish differential display from methods that require prior information about specific gene expression and from methods that are used to screen or select cDNAs from only two states. Moreover, differential display can be easily established in any laboratory that has basic molecular biology equipment and supplies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".