Effect of Temperature and Storage Time on DNA Quality and Quantity from Normal and Diseased Tissues
Bibliographic record
Abstract
DNA extraction and purification is an initial step for authentic results in advance molecular biology, therefore DNA degradation is unavoidable. The aim of present study is to investigate the DNA quality and quantity in terms of shorter time preservation with normal and diseased tissue, therefore tissues of normal (n = 18) and diseased (n = 18) liver, lung and heart was collected from goat after slaughtered. For DNA extraction Gene JET Genomic DNA Purification Kit protocol was followed, then stored at -20 oC and -04 oC temperatures for 24hrs and 48hrs period of time. The concentration and purity of the extracted DNA were measured with Spectrophotometer and purity confirmed at an absorbance ratio of 260 or 280. It was observed that at a -20 oC temperature for 24hours the concentration of DNA yield was numerically higher than at -04 oC temperature for tissue stored at 48hrs, whereas absorbance was higher, however in normal tissues in contrast with diseased the concentration and absorbance of DNA was somehow same at -20 and -04 oC but different in storage time. On the basis of these findings, it was concluded that time elapsed between sampling with the storage condition and with normal or diseased samples for DNA extraction will largely depend on the experiment. If tissue preservative conditions and sampling are appropriate, storage time will not be a factor at least for short storage periods.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".