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Record W4308875506 · doi:10.3389/fmicb.2022.1024827

A simple and rapid technique of template preparation for PCR

2022· article· en· W4308875506 on OpenAlexaff
Yunyun Liu, Jia Chen, Yi Cheng, Yi Li, Xinwen Li, Zhengbing Zhang, Xiumei Xu, Yufeng Lin, Jianping Xu, Zhimin Li

Bibliographic record

VenueFrontiers in Microbiology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsMcMaster University
FundersAgricultural Science and Technology Innovation Program
KeywordsTemplateDNA extractionDNACentrifugationChromatographyMultiple displacement amplificationMolecular biologyPolymerase chain reactionBiologyNanotechnologyComputational biologyBiological systemChemistryMaterials scienceGeneBiochemistry

Abstract

fetched live from OpenAlex

Many techniques have been developed for extracting DNA, but most are often complex, time-consuming, and/or expensive. In this study, we describe a simple, rapid and cost-effective technique for preparing DNA template for PCR. This technique involves 0.1 M potassium hydroxide treatment at 100°C for 10 min followed by centrifugation. The suspended centrifuged sediments were shown as excellent templates for PCR. Templates prepared using this technique worked for diverse microorganisms belonging to bacteria, fungi and oomycetes and their amplification efficiencies were comparable to/better than those prepared using common but relatively more complex, time-consuming, and/or expensive methods, including commercial DNA extraction kits. Furthermore, this technology is suitable for high-throughput batch processing and for amplifications of long DNA fragments. Flow cytometry and scanning electronic microscopy analyzes showed that this technique generated primarily damaged cells and cell-bound DNA, not free naked DNA. This technique provides a great convenience for simple PCR template preparation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.253
Teacher spread0.248 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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