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SENSORS AND ACTUATORS B-CHEMICAL · 2023 · IF 8.4

An easy-to-open multi-chamber device to study the molecular changes behind the plant root microscale phenotypic variations

Huihui Chai,Feng Chen,Ke Ning,Wei Sun,Ling Yu

Plant chips provide non-destructive, real-time, continuous high-resolution observations of plant development. However, the observations are limited to phenotypic changes such as morphology, number, growth rate, etc. Herein, we developed an easy-to-open multi-chamber device, namely a Lid open-seal Switchable Plant Chip (LSPC), to achieve high spatial observation and periodically collect plant samples for biochemical analysis. The proposed multi-chamber device was applied to study the morphological and molecular changes of the fibrous root system of O.Sativa . The result showed that switching the lid of the growth chambers did not affect the seedling growth. The physical damages on the root cap and tip were modeled on-chip to study the root regeneration capacity. The results showed that physical damage localized at the root cap did not ramp the renewal of the root tip. Periodically collecting root structures, such as border-like cells and root tips, was demonstrated to measure reactive oxygen species and soluble sugar. Last, the root tip swelling phenomenon was observed from the seedlings growing in 10 % and 20 % PEG 6000 mimicked drought environment. The swelling roots were collected for biochemical assays and found that the histology swelling was accompanied by an accumulation of soluble sugar within the root tip. The transcript levels of OsjDHN1 , C 4 -PEPC , NAC1 , PCD , and P450 genes in drought-induced swelling tips were significantly different from normal seedlings. The swelling tip phenomenon, corresponding soluble sugar level, and gene transcript changes will help underlie the plant adaptation response mechanisms to drought stress.

CAS / MDL: 25322-68-3
研究领域: 植物学 · 分子生物学
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ACS Sensors · 2024 · IF 8.2

Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy

Jiajia Sun,Wei Lai,Jiayan Zhao,Jinhong Xue,Tong Zhu,Mingshu Xiao,Tiantian Man,Ying Wan,Hao Pei,Li Li

Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process.

CAS / MDL: 919-30-2 5470-11-1
研究领域: 药学 · 分析化学 · 生物医学工程 · 机器学习
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