Machine Learning-Assisted Multiplexed Fluorescence-Labeled miRNAs Imaging Decoding for Combined Mycotoxins Toxicity Assessment DOI

Lixin Kang,

Xianfeng Lin, Jiaqi Feng

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Mycotoxins, particularly deoxynivalenol (DON) and zearalenone (ZEN), are common food contaminants that frequently co-occur in grains, posing significant health risks. This study proposed a multiplexed detection platform for simultaneous quantification imaging of three microRNAs (miRNAs) integrated with machine learning to evaluate the combined toxicity DON ZEN. Based on Exonuclease III-assisted signal amplification, highly sensitive fluorescent molecular beacon probes (MBs) targeting miR-21, miR-221, miR-27a were developed, achieving remarkable limits 0.18 pM, 0.22 0.21 respectively. The MBs efficiently delivered into cells via liposome-mediated endocytosis, enabling intracellular miRNAs. By integrating algorithms, including linear discriminant analysis principal component analysis, RGB values extracted from cellular fluorescence images, robust analytical was established classifying miRNA expression patterns induced by various DON/ZEN concentrations. A highest single agent model subsequently constructed toxicity, revealing ZEN exhibited antagonistic effects at low doses but synergistic high doses. method demonstrates strong correlation between profiles providing an innovative tool multicomponent assessment.

Language: Английский

Aggregation-induced emission-based aptasensors for the detection of various targets: recent progress DOI

Masoomeh Esmaelpourfarkhani,

Mohammad Ramezani, Mona Alibolandi

et al.

Talanta, Journal Year: 2025, Volume and Issue: 292, P. 127995 - 127995

Published: March 20, 2025

Language: Английский

Citations

0
Machine Learning-Assisted Multiplexed Fluorescence-Labeled miRNAs Imaging Decoding for Combined Mycotoxins Toxicity Assessment DOI

Lixin Kang,

Xianfeng Lin, Jiaqi Feng

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Mycotoxins, particularly deoxynivalenol (DON) and zearalenone (ZEN), are common food contaminants that frequently co-occur in grains, posing significant health risks. This study proposed a multiplexed detection platform for simultaneous quantification imaging of three microRNAs (miRNAs) integrated with machine learning to evaluate the combined toxicity DON ZEN. Based on Exonuclease III-assisted signal amplification, highly sensitive fluorescent molecular beacon probes (MBs) targeting miR-21, miR-221, miR-27a were developed, achieving remarkable limits 0.18 pM, 0.22 0.21 respectively. The MBs efficiently delivered into cells via liposome-mediated endocytosis, enabling intracellular miRNAs. By integrating algorithms, including linear discriminant analysis principal component analysis, RGB values extracted from cellular fluorescence images, robust analytical was established classifying miRNA expression patterns induced by various DON/ZEN concentrations. A highest single agent model subsequently constructed toxicity, revealing ZEN exhibited antagonistic effects at low doses but synergistic high doses. method demonstrates strong correlation between profiles providing an innovative tool multicomponent assessment.

Language: Английский

Citations

0