Role of circadian clock in the chronoefficacy and chronotoxicity of clopidogrel

BACKGROUND AND PURPOSE

The role of circadian locomotor output cycles kaput (CLOCK) in regulating drug chronoefficacy and chronotoxicity remains elusive. Here, we aimed to uncover the impact of CLOCK and dosing time on clopidogrel efficacy and toxicity.

EXPERIMENTAL APPROACH

The antiplatelet effect, toxicity and pharmacokinetics experiments were conducted with Clock-/- mice and wild-type mice, after gavage administration of clopidogrel at different circadian time points. The expression levels of drug-metabolizing enzymes were determined by quantitative polymerase chain reaction (qPCR) and western blotting. Transcriptional gene regulation was investigated using luciferase reporter and chromatin immunoprecipitation assays.

KEY RESULTS

The antiplatelet effect and toxicity of clopidogrel in wild-type mice showed a dosing time-dependent variation. Clock ablation reduced the antiplatelet effect of clopidogrel, but increased clopidogrel-induced hepatotoxicity, with attenuated rhythms of clopidogrel active metabolite (Clop-AM) and clopidogrel, respectively. We found that Clock regulated the diurnal variation of Clop-AM formation by modulating the rhythmic expression of CYP1A2 and CYP3A1, and altered clopidogrel chronopharmacokinetics by regulation of CES1D expression. Mechanistic studies revealed that CLOCK activated Cyp1a2 and Ces1d transcription by directly binding to the enhancer box (E-box) elements in their promoters, and promoted Cyp3a11 transcription through enhancing the transactivation activity of albumin D-site-binding protein (DBP) and thyrotroph embryonic factor (TEF).

CONCLUSIONS AND IMPLICATIONS

CLOCK regulates the diurnal rhythmicity in clopidogrel efficacy and toxicity through regulation of CYP1A2, CYP3A11 and CES1D expression. These findings may contribute to optimizing dosing schedules for clopidogrel and may deepen understanding of the circadian clock and chronopharmacology.

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

BACKGROUND

The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI.

RESULTS

In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively.

CONCLUSIONS

Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.