Evaluation of the Efficacy of Five DNA Extraction Methods for the Detection of Mycobacterium tuberculosis DNA in Direct and Processed Sputum by an In-House PCR Method
Bibliographic record
Abstract
Aim: DNA extraction is an important step from clinical samples for molecular diagnosis of tuberculosis by PCR. The aim of this study was to evaluate the efficacy of five DNA extraction methods [boiling, single step proteinase K, GuSCN lysis and izopropanol precipitation (Heliosis, METİS Company, TURKEY), DNA precipitation (Epicentre Technologies), and solid phase absorption (QIAamp DNA mini kit, QIAGEN, Valancia,CA)] in searching Mycobacterium tuberculosis DNA in smear positive sputum samples. Materials and Methods: A total of 50 sputum samples were extracted directly and after digested with 4% NaOH-NALC methods using 5 DNA extraction methods. All DNA extracts were studied by an in-house PCR method. Results: The rate of the positive detection for 5 extraction methods was 22% with boiling method, 38% with single step proteinase K, 38% with guanidium isothiocyanate lysis and isopropanol precipitation method (Heliosis, METIS ), 42% with DNA precipitation (Epicentre Technologies), and 58% with solid phase absorption (QIAamp). Conclusions: When the rate of positive detection is taken into consideration in smear positive patients, solid phase absorption method seems to be more proper to use routinely for DNA isolation from clinical samples.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".