Optimization of RNA isolation from human ocular tissues and analysis of prostanoid receptor mRNA expression using RT-PCR.
Bibliographic record
Abstract
PURPOSE: The isolation and analysis of human ocular RNA is problematic due to variables such as rapid degradation, tissue composition, and melanin contamination. The purpose of this work was to optimize an extraction protocol for the isolation of intact total RNA from a variety of diverse human ocular tissues and to employ RT-PCR to assess the expression of mRNA coding for all eight prostanoid receptors. METHODS: Total RNA was extracted from human iris, ciliary body, choroid, and retina using an RNeasy(R) Midi Kit. Total RNA was extracted from human cornea, sclera, and optic nerve using Tri-Pure(R) Isolation Reagent. 1.0 microgram of total RNA was reverse transcribed into cDNA and subsequently amplified by PCR (35 cycles) using primers designed against each of the human prostanoid receptor cDNAs. PCR products were analyzed by gel electrophoresis and endonuclease digestion. RESULTS: The total yield and quality of RNA derived from each tissue varied according to tissue composition and the isolation method employed. RT-PCR analysis revealed that each tissue expressed all prostanoid receptor mRNAs, however, 50 cycles of PCR was required to visualize FP receptor expression in scleral tissue. In all cases, prostanoid receptor mRNA expression was significantly lower than in human nonpregnant myometrium, which was used as the positive control. CONCLUSIONS: The different cellular composition of each ocular tissue ultimately dictated the methodology to be employed for the isolation of total RNA. Thus, two extraction protocols were optimized for the isolation of intact high quality RNA from a variety of human ocular tissues. The identification of all prostanoid receptor mRNAs in a diverse set of human ocular tissues suggests potential mechanisms for prostanoid-based therapeutics aimed at IOP reduction and stimulates speculation as to additional physiological and or pathophysiological roles mediated by prostanoids.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".