Evidence map›Paper›PMID 19480716›Full record

ArticleBMC biotechnology2009

Integrated application of uniform design and least-squares support vector machines to transfection optimization.

Jin-Shui Pan, Mei-Zhu Hong, Qi-Feng Zhou, Jia-Yan Cai, Hua-Zhen Wang, Lin-Kai Luo, De-Qiang Yang, Jing Dong, Hua-Xiu Shi, Jian-Lin Ren

Abstract read
In one paragraph

Article in BMC biotechnology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jin-Shui PanDivision of Gastroenterology, Zhongshan Hospital, Gastroenterology Institute of Xiamen University, Gastroenterology Center of Xiamen, Fujian Province, PR China. panjinshui@yahoo.com.cn
Mei-Zhu Hong
Qi-Feng Zhou
Jia-Yan Cai
Hua-Zhen Wang
Lin-Kai Luo
De-Qiang Yang
Jing Dong
Hua-Xiu Shi
Jian-Lin Ren

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTransfection in mammalian cells based on liposome presents great challenge for biological professionals. To protect themselves from exogenous insults, mammalian cells tend to manifest poor transfection efficiency. In order to gain high efficiency, we have to optimize several conditions of transfection, such as amount of liposome, amount of plasmid, and cell density at transfection. However, this process may be time-consuming and energy-consuming. Fortunately, several mathematical methods, developed in the past decades, may facilitate the resolution of this issue. This study investigates the possibility of optimizing transfection efficiency by using a method referred to as least-squares support vector machine, which requires only a few experiments and maintains fairly high accuracy.

resultsA protocol consists of 15 experiments was performed according to the principle of uniform design. In this protocol, amount of liposome, amount of plasmid, and the number of seeded cells 24 h before transfection were set as independent variables and transfection efficiency was set as dependent variable. A model was deduced from independent variables and their respective dependent variable. Another protocol made up by 10 experiments was performed to test the accuracy of the model. The model manifested a high accuracy. Compared to traditional method, the integrated application of uniform design and least-squares support vector machine greatly reduced the number of required experiments. What's more, higher transfection efficiency was achieved.

conclusionThe integrated application of uniform design and least-squares support vector machine is a simple technique for obtaining high transfection efficiency. Using this novel method, the number of required experiments would be greatly cut down while higher efficiency would be gained. Least-squares support vector machine may be applicable to many other problems that need to be optimized.

Indexed as

LiposomesSoftwareAlgorithmsCell Line, TransformedGenetic VectorsHumansLeast-Squares AnalysisModels, BiologicalTransfectionLiposomes

Identifiers

PMID19480716
PMCPMC2701423

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.