基于明场和暗场实验图像的线虫存活率自动识别方法。

An automatic recognition method of nematode survival rate based on bright field and dark field experimental images.

机构信息

Beijing Center for Disease Prevention and Control, Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing, China.

School of Cyber Science and Engineering, Qufu Normal University, Qufu, Shandong, China.

出版信息

Technol Health Care. 2023;31(S1):199-208. doi: 10.3233/THC-236017.

DOI:10.3233/THC-236017

PMID:37038792

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10200152/

Abstract

BACKGROUND

The survival rate of experimental animals is a very important index in chemical toxicity evaluation experiments. The calculation of nematode survival rate is used in many experiments.

OBJECTIVE

Traditional survival rate quantification methods require manual counting. This is a time-consuming and laborious work when using 384-well plate for high-throughput chemical toxicity assessment experiments. At present, there is a great need for an automatic method to identify the survival rate of nematodes in the experiment of chemical toxicity evaluation.

METHODS

We designed an automatic nematode survival rate recognition method by combining the bright field experimental image of nematodes and the dark field image of nematodes which is captured after adding Propidium Iodide dye, and used it to calculate the nematode survival rate in different chemical environments. Experiment results show that the survival rate obtained by our automatic counting method is very similar to the survival rate obtained by manual counting.

RESULTS

Through several different chemical experiments, we can see that chemicals with different toxicity have different effects on the survival rate of nematodes. And the survival rate of nematodes under different chemical concentrations has an obvious gradient trend from high concentration to low concentration. In addition, our method can quantify the motility of nematodes. There are also significant differences in the motility of nematodes cultured in different chemical environments. Moreover, the nematode motility under different chemical concentrations showed an obvious gradient change trend from high concentration to low concentration.

CONCLUSION

Our study provides an accurate and efficient nematode survival rate recognition method for chemical toxicology research.

摘要

背景

实验动物的存活率是化学毒性评价实验中非常重要的指标。线虫存活率的计算在许多实验中都有应用。

目的

传统的存活率量化方法需要手动计数。在进行高通量化学毒性评估实验时，使用 384 孔板时，这是一项耗时耗力的工作。目前，非常需要一种自动方法来识别化学毒性评价实验中线虫的存活率。

方法

我们设计了一种自动线虫存活率识别方法，该方法结合了线虫的明场实验图像和添加碘化丙啶染料后捕获的线虫暗场图像，并用于计算不同化学环境下线虫的存活率。实验结果表明，我们的自动计数方法得到的存活率与手动计数得到的存活率非常相似。

结果

通过几次不同的化学实验，我们可以看到，毒性不同的化学物质对线虫的存活率有不同的影响。而且，线虫在不同化学浓度下的存活率从高浓度到低浓度呈明显的梯度趋势。此外，我们的方法可以定量线虫的运动能力。在不同化学环境中培养的线虫的运动能力也存在显著差异。而且，线虫在不同化学浓度下的运动能力从高浓度到低浓度呈明显的梯度变化趋势。