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Record W1542328710 · doi:10.3906/sag-0712-23

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

2009· article· en· W1542328710 on OpenAlexaboutno aff
Gülnur Tarhan, İsmail Ceyhan, Hülya Şimşek, Serdar Tunçer

Bibliographic record

VenueTURKISH JOURNAL OF MEDICAL SCIENCES · 2009
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsnot available
Fundersnot available
KeywordsDNA extractionSputumProteinase KDNAMedicineExtraction (chemistry)Mycobacterium tuberculosisLysisPolymerase chain reactionChromatographyPrecipitationTuberculosisMolecular biologyBiologyChemistryImmunologyPathologyGeneticsGene

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.439
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueTURKISH JOURNAL OF MEDICAL SCIENCESSame topicMycobacterium research and diagnosisFrench-language works237,207