AbstractThe circadian clock, a fundamental biological regulator, governs essential cellular processes in health and disease. Circadian-based therapeutic strategies are increasingly gaining recognition as promising avenues. Aligning drug administration with the circadian rhythm can enhance treatment efficacy and minimize side effects.

摘要生物钟是一种基本的生物调节剂，控制着健康和疾病的基本细胞过程。基于昼夜节律的治疗策略越来越被认为是有前途的途径。根据昼夜节律调整药物管理可以提高治疗效果并最大程度地减少副作用。

Yet, uncovering the optimal treatment timings remains challenging, limiting their widespread adoption. In this work, we introduce a high-throughput approach integrating live-imaging and data analysis techniques to deep-phenotype cancer cell models, evaluating their circadian rhythms, growth, and drug responses.

然而，揭示最佳治疗时机仍然具有挑战性，限制了它们的广泛采用。在这项工作中，我们引入了一种高通量方法，将实时成像和数据分析技术整合到深度表型癌细胞模型中，评估其昼夜节律，生长和药物反应。

We devise a streamlined process for profiling drug sensitivities across different times of the day, identifying optimal treatment windows and responsive cell types and drug combinations. Finally, we implement multiple computational tools to uncover cellular and genetic factors shaping time-of-day drug sensitivity.

我们设计了一个简化的过程，用于分析一天中不同时间的药物敏感性，确定最佳治疗窗口和反应性细胞类型以及药物组合。最后，我们实施了多种计算工具来揭示影响一天中药物敏感性的细胞和遗传因素。

Our versatile approach is adaptable to various biological models, facilitating its broad application and relevance. Ultimately, this research leverages circadian rhythms to optimize anti-cancer drug treatments, promising improved outcomes and transformative treatment strategies..

我们的多功能方法适用于各种生物模型，促进了其广泛的应用和相关性。最终，这项研究利用昼夜节律来优化抗癌药物治疗，有望改善结果和变革性治疗策略。。

IntroductionThe circadian clock is a central regulator of multiple physiological and behavioral processes found in cyanobacteria, plants, fungi, and animals. In mammals, the hierarchical organization of the circadian system ensures coordinated biological rhythms from the level of the individual cell to the whole organism level1.

简介生物钟是蓝藻，植物，真菌和动物中发现的多种生理和行为过程的中心调节剂。在哺乳动物中，昼夜节律系统的等级组织确保了从单个细胞水平到整个生物体水平的协调生物节律1。

Primate and mouse studies showed that protein-coding genes are rhythmically expressed in a tissue-specific by up to 80% and 40%, respectively2,3. These clock-controlled genes regulate key biological processes such as metabolism4,5, cell proliferation6, immune response7, DNA repair, and apoptosis8.Disruption of the circadian system is classified as a carcinogen and is associated with multiple cancer subtypes9,10,11,12.

灵长类动物和小鼠研究表明，蛋白质编码基因在组织特异性中的节律性表达分别高达80%和40%2,3。这些时钟控制的基因调节关键的生物过程，如代谢4,5，细胞增殖6，免疫反应7，DNA修复和凋亡8。昼夜节律系统的破坏被归类为致癌物，与多种癌症亚型有关9,10,11,12。

Cancer hallmarks such as sustained proliferation and metastasis13,14 have been linked to the circadian clock15,16 and patients with mutations in circadian clock genes exhibit lower survival rates17,18,19. Beyond its role in cancer development, the circadian clock directly interacts with therapeutic targets that affect drug responses19,20,21.

持续增殖和转移等癌症标志[13,14]与昼夜节律有关[15,16]，昼夜节律基因突变的患者生存率较低[17,18,19]。除了在癌症发展中的作用外，生物钟还直接与影响药物反应的治疗靶点相互作用19,20,21。

Consistent with these observations, recent works have shown that administration of chemotherapeutic agents aligned with the circadian rhythm changes their degree of efficacy throughout the day20,22,23,24. Despite the broad recognition of the benefits of circadian-based drug treatments22,25,26, an efficient strategy to identify optimal treatment times remains elusive, creating a bottleneck in the implementation.

与这些观察结果一致，最近的研究表明，与昼夜节律一致的化疗药物的给药会改变其在一天中的疗效程度20,22,23,24。。

In addition, the mechanisms shaping time-of-day (ToD) sensitivity profiles remain widely unknown.Here, we introduce a method for the thorough characterization of time-of-day responses in tumor and healthy tissue cell models (Fig. 1). Using an array of experimental and data analysis metho.

此外，形成一天中的时间（ToD）敏感性曲线的机制仍然是未知的。在这里，我们介绍了一种方法，用于彻底表征肿瘤和健康组织细胞模型中的时间响应（图1）。采用一系列实验和数据分析方法。

Detrending

删除趋势

Raw time-series data were detrended by applying a sinc filter with a 48-h cut-off period using the open-source software package pyBOAT27 (v0.9.1) within the Anaconda Navigator (v1.10.0).

原始时间序列数据通过使用Anaconda Navigator（v1.10.0）中的开源软件包pyBOAT27（v0.9.1）应用截止时间为48小时的sinc滤波器进行去趋势处理。

Amplitude envelope and normalization

振幅包络和归一化

Continuous amplitude envelope calculation was obtained using continuous wavelet transform implemented in pyBOAT with a time window of 48 h. The amplitude normalization was done by taking the inverse of the envelope of the detrended signal as described in Mönke G, et al.27.

使用在pyBOAT中实现的连续小波变换获得连续幅度包络计算，时间窗口为48小时。如Mönke G等人27所述，通过取去趋势信号包络的逆来进行幅度归一化。

Autocorrelation analysis

自相关分析

Periodicity data was assessed by calculating its autocorrelation and abscissa at the second peak using the ‘autocorr’ and ‘findpeaks’ MATLAB functions51 from the detrended time series.

通过使用去趋势时间序列中的“autocorr”和“findpeaks”MATLAB函数51计算其在第二个峰值处的自相关和横坐标来评估周期性数据。

Continuous wavelet transform

连续小波变换

The main oscillatory component, known as the ridge component, was obtained using a wavelet-based spectral analysis from amplitude-normalized and detrended signals. For the ridge detection, we used both an adaptable threshold and a fixed threshold. The adaptable threshold was based on each signal’s half-maximal spectral power and was implemented for the “period” and “phase difference” metrics shown in Fig. 2 and Fig.

使用基于小波的频谱分析从振幅归一化和去趋势信号中获得主要的振荡分量，称为脊分量。对于脊线检测，我们使用了自适应阈值和固定阈值。自适应阈值基于每个信号的最大光谱功率的一半，并针对图2和图2所示的“周期”和“相位差”度量实施。

S1. The instantaneous phase difference between the Bmal1 and Per2 signals was calculated using the MATLAB ‘atan2’ function (Supplementary Fig. 1a) and for the polar histogram representation, we deployed the ‘polarhistogram’ function and the ‘Circular Statistics Toolbox’ (v1.21.9.0. by Philipp Behrens) from MATLAB (Supplementary Fig. 1b).

S1。使用MATLAB“atan2”函数（补充图1a）计算Bmal1和Per2信号之间的瞬时相位差，对于极性直方图表示，我们部署了“极性直方图”函数和“循环统计工具箱”（v1.21.9.0）。来自MATLAB的Philipp Behrens）（补充图1b）。

For the analysis of “amplitudes” and “ridge lengths” shown in Fig. 2 and the determinants for time-of-day sensitivity shown in Fig. 5, the metrics of the main oscillatory component were derived using a fixed-ridge threshold of 40. This ridge threshold value was chosen as it offered a well-balanced threshold suitable for comparing signals of varying strengths..

为了分析图2所示的“振幅”和“脊长”以及图5所示的一天中时间敏感性的决定因素，使用40的固定脊阈值导出了主要振荡分量的度量。选择此脊阈值是因为它提供了一个平衡良好的阈值，适用于比较不同强度的信号。。

Multiresolution analysis

多分辨率分析

Detrended time-series data were decomposed into a set of different wavelet details Dj representing distinct disjoint frequency bands and a final smooth using a discrete wavelet transform-based multiresolution analysis52. The algorithm has been implemented using the ‘PyWavelets’ python package53, with a db20 wavelet of the Daubechies wavelet family as previously described in Myung, Schmal and Hong et al.54.

使用基于离散小波变换的多分辨率分析52，将去趋势的时间序列数据分解为一组不同的小波细节Dj，代表不同的不相交频带和最终平滑。该算法已使用“PyWavelets”python软件包53实现，其中Daubechies小波家族的db20小波如先前在Myung，Schmal和Hong等人54中所述。

Since each wavelet detail Dj represents a period range between 2jΔt and 2j+1Δt (for j = 1, 2, 3, …) we down-sampled the time series from a Δt = 10 min to a Δt = 30 min sampling frequency to obtain a circadian period band between 16–32 h for further analysis. Since the MRA decomposes the variance of the detrended signal with respect to the different disjoint period bands, it can be used to determine the rhythmicity of the signal in the circadian period range55..

由于每个小波细节Dj表示2jΔt和2j+1Δt之间的周期范围（对于j=1,2,3，…），因此我们对时间序列从Δt=10分钟到Δt=30分钟的采样频率进行了下采样，以获得16-32小时之间的昼夜节律周期带用于进一步分析。由于MRA分解了去趋势信号相对于不同不相交周期带的方差，因此它可用于确定昼夜节律范围内信号的节律性55。。

Global circadian strength

全球昼夜节律强度

To calculate the global circadian strength (GCS) of a cell line model i, the autocorrelation peak value (peak), the wavelet-based continuous ridge length (ridge), and the discrete circadianicity component (circadianicity) were normalized to the respective maximum (max) value measured among all tested cell line models and averaged according to the following equation:$${{GCS}}_{i}={{\rm{mean}}}\left(\frac{{{{\rm{peak}}}}_{i}}{{{{\rm{peak}}}}_{\max }},\frac{{{{\rm{ridge}}}}_{i}}{{{{\rm{ridge}}}}_{\max }},\frac{{{{\rm{circadianicity}}}}_{i}}{{{{\rm{circadianicity}}}}_{\max }}\right)$$.

为了计算细胞系模型i的整体昼夜节律强度（GCS），将自相关峰值（peak）、基于小波的连续脊长（ridge）和离散昼夜节律分量（昼夜节律）归一化为所有测试细胞系模型中测得的各自最大值（max），并根据以下公式取平均值：$${{GCS}}UI}={\ rm{mean}}}\ left（\ frac{{{{{\ rm peak}}}}}}UI}}{{{{{\rm{peak}}}}}}{\max}}、\frac{{{{{\rm{ridge}}}}}}}{{{\rm{ridge}}}}}}}}}}}{\max}}、\frac{{{\rm{昼夜节律}}}}}}}}}}}}{{i}}}}}{{\rm{昼夜节律}}}}{\max}}\右）$$。

(1)

(1)

Statistical analysis

统计分析

Linear regression model fitting of Bmal1 and Per2 circadianicity components obtained by MRA was done with the ‘fitlm’ MATLAB function. Significant variances in circadian parameters between wild-type U-2 OS cells and both Cry1-sKO and Cry1/2-dKO cells were calculated with a one-way ANOVA and Tukey’s post-hoc test using the ‘anova1’ and ‘multcompare’ MATLAB functions..

使用“fitlm”MATLAB函数对通过MRA获得的Bmal1和Per2昼夜节律分量进行线性回归模型拟合。使用“anova1”和“multcompare”MATLAB函数，通过单向方差分析和Tukey事后检验，计算了野生型U-2 OS细胞与Cry1 sKO和Cry1/2-dKO细胞之间昼夜节律参数的显着差异。。

Multi-parametric analysis of growth dynamicsGrowth data obtained from long-term live-cell imaging was smoothed using a robust local regression approach of weighted linear least squares and a 2nd degree polynomial model (‘rloess’ MATLAB function). Doubling times of smoothed cell numbers or confluency were calculated according to the following equation:$${{\rm{Doubling\; time}}}\left(t\right)=t \, * \, \frac{\log \left(2\right)}{\log \left({y}_{0}/{y}_{t}\right)}$$.

生长动态的多参数分析使用加权线性最小二乘的稳健局部回归方法和二次多项式模型（“rloess”MATLAB函数）平滑从长期活细胞成像获得的生长数据。平滑细胞数或融合的倍增时间根据以下公式计算：$${{rm{Doubling；time}}\ left（t \ right）=t \，*\，\ frac{\ log \ left（2 \ right）}{\ log \ left({y}_{0}/{y}_{t} \右）}$$。

(2)

(2)

where \(t\) refers to the time of assessment, in our case 96 h, and \({y}_{0}\) refers to the cell number at timepoint 0. To calculate the exponential growth rate \(k\) per unit of time \(t\), we normalized cell numbers to the initial timepoint 0 (\({y}_{0}\)) and fitted an exponential function to the growth curves:$${{\rm{Growth}}}\left(t\right)={y}_{0} \, * \, {e}^{(k * t)}$$.

其中（t）是指评估时间，在我们的例子中是96小时，并且\({y}_{0}\）是指时间点0处的单元格编号。为了计算单位时间（t）的指数增长率（k），我们将细胞数归一化为初始时间点0(\({y}_{0}\），并将指数函数拟合到生长曲线：$${{\ rm{growth}}\ left（t \ right）={y}_。

(3)

(3)

Exponential function fitting was done with the MATLAB ‘fit’ function51. Linear regression model fitting to different combinations of growth parameters was done with the ‘fitlm’ MATLAB function.Estimation of drug sensitivity parametersDrug response data obtained from long-term live-cell imaging was smoothed using a robust local regression approach of weighted linear least squares and a 2nd degree polynomial model (‘rloess’ MATLAB function).

使用MATLAB“拟合”函数51进行指数函数拟合。使用“fitlm”MATLAB函数对生长参数的不同组合进行线性回归模型拟合。药物敏感性参数的估计使用加权线性最小二乘的稳健局部回归方法和二次多项式模型（“rloess”MATLAB函数）平滑从长期活细胞成像获得的药物反应数据。

Following the method established by Hafner et al.30, we computed the growth rate inhibition (GR) at time t and for each dose c as follows:$${GR}\left(c,\, t\right)=\,{2}^{k(c,t)/k(0)}-1$$.

按照Hafner等人建立的方法，我们计算了时间t和每个剂量c的生长速率抑制（GR），如下所示：$${GR}\ left（c，\，t \ right）=\，{2}^{k（c，t）/k（0）}-1$$。

(4)

(4)

where \(k\left(c,\, t\right)\) is the growth rate under drug treatment and \(k\left(0\right)\) is the growth rate of untreated cells. Drug response parameters were retrieved by fitting the dose-dependent GR-values to a sigmoid curve using the following equation:$${GR}\left(c\right)=\,{{GR}}_{\inf }+\frac{1-{{GR}}_{\inf }}{1+{(c/{{GEC}}_{50})}^{{h}_{{{\rm{GR}}}}}}$$.

其中\（k \ left（c，\，t \ right）\）是药物处理下的生长速率，\（k \ left（0 \ right）\）是未处理细胞的生长速率。通过使用以下等式将剂量依赖性GR值拟合到乙状结肠曲线来检索药物反应参数：$${GR}\ left（c \ right）=\，{{GR}}{inf}+\frac{1-{{GR}}{\ inf}}{1+{（c/{GEC}}}{50}}^{{h}_{{{\rm{GR}}}}$$。

(5)

(5)

where the fitted parameters are as described in Hafner et al.30. Standard EC50-values were calculated by fitting final nucleus counts, normalized to the respective count of the control, to the following sigmoidal function:$$f\left(c\right) \,={E}_{\min }+\frac{1-{E}_{\min }}{1+({c/{{EC}}_{50}})^{h}}$$.

其中拟合参数如Hafner等人30所述。标准EC50值是通过将归一化为对照组各自计数的最终核计数拟合为以下S形函数来计算的：$$f \左（c \右）\={E}_{\min}+\frac{1-{E}_{\ min}}{1+（{c/{{EC}}}{50}}）^{h}}$$。

(6)

(6)

where Emin corresponds to the minimum response, restricted to values between 0 and 1, and h is the hill slope of the response curve, constrained to 0.5–10. Sigmoidal function fitting steps were done with the MATLAB functions ‘fit’ (GR metrics) or ‘lsqnonlin’ (EC50 value)51. Hierarchical clustering analysis was implemented with the MATLAB ‘clustergram’ function using an Euclidian distance and average linkage method51.

其中Emin对应于最小响应，限制在0到1之间，h是响应曲线的斜率，限制在0.5-10之间。。使用欧几里德距离和平均链接方法，使用MATLAB“clustergram”函数实现层次聚类分析51。

To account for the different tested dose ranges across drugs for clustering, GEC50-values were normalized to the dose at GRinf. Pearson’s linear correlation coefficients across drug sensitivity metrics were computed using the MATLAB ‘corr’ function. For the comparison of cellular growth dynamics to drug doses evoking the maximum tested effect, we chose growth curves corresponding to the doses closest to the determined GRinf-values.Time-of-day sensitivity evaluationDrug response data of time-of-day treatment experiments was smoothed using a moving average (‘smoothdata’ MATLAB function).

为了解释用于聚类的药物的不同测试剂量范围，将GEC50值标准化为GRinf的剂量。使用MATLAB“corr”函数计算药物敏感性指标之间的Pearson线性相关系数。为了比较细胞生长动力学与引起最大测试效果的药物剂量，我们选择了与最接近确定的GRinf值的剂量相对应的生长曲线。时间敏感性评估使用移动平均（“smoothdata”MATLAB函数）平滑时间治疗实验的药物反应数据。

Final nucleus counts were normalized to the nucleus count at the respective time of treatment. ToD response data from U-2 OS WT and Cry1/2-dKO cell lines were obtained from confluency readouts in the brightfield channel. Time-of-day response curves were generated from the relative final responses of each treatment timepoint to the final response at timepoint 0 and interpolated using the ‘smoothing spline’ function in MATLAB.

。来自U-2 OS WT和Cry1/2-dKO细胞系的ToD响应数据是从明场通道中的汇合读数获得的。从每个治疗时间点的相对最终响应到时间点0的最终响应生成一天中的时间响应曲线，并使用MATLAB中的“平滑样条”函数进行插值。

The smoothing parameter was set to 0.7. The maximum range across smoothed time-of-day responses (ToDMR) was calculated by subtraction of the minimum from the maximum relative response. To assess whether the smoothing of response data introduces artifacts into ToDMR estimates, we performed linear regression analysis between ToDMR-values fro.

平滑参数设置为0.7。平滑时间响应（ToDMR）的最大范围是通过从最大相对响应中减去最小值来计算的。为了评估响应数据的平滑是否会将伪影引入ToDMR估计中，我们在ToDMR值之间进行了线性回归分析。

Data availability

数据可用性

The experimental raw data and data tables generated in this study have been deposited in the Figshare database under the identifier https://figshare.com/projects/Time-of-Day-Drug-Response/180916. Source data are provided in this paper.

本研究中生成的实验原始数据和数据表已保存在Figshare数据库中的标识符下https://figshare.com/projects/Time-of-Day-Drug-Response/180916.。

Code availability

代码可用性

All code used for the data analysis in this work (in MATLAB and Python) is publicly available through the dataset repository Zenodo under the identifier https://zenodo.org/doi/10.5281/zenodo.11656060.

这项工作中用于数据分析的所有代码（在MATLAB和Python中）都可以通过数据集存储库Zenodo在标识符下公开获得https://zenodo.org/doi/10.5281/zenodo.11656060.

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Download referencesAcknowledgementsWe thank Malthe Skytte Nordentoft Nielsen, as well as the laboratories of Michela Di Virgilio and Ingeborg Tinhofer-Keilholz, for their valuable feedback on our project. We are grateful to Annika Winkler and Marie Möser for their assistance with sample preparation, as well as to Sofía Peso-García and Franziska Reiher for their experimental support.

。我们感谢Annika Winkler和Marie Möser在样品制备方面的帮助，以及Sofía Peso García和Franziska Reiher的实验支持。

Lastly, we thank Christian Gabriel and Valentina Alejandra Balde Araya for valuable feedback during the review of this manuscript. The results are part of a project funded by the German Federal Ministry of Education and Research (BMBF) through the e:Med Juniorverbund DeepLTNBC TP3-01ZX1917C. C.E. was partially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–RTG2424/CompCancer – project number: 377984878 and is enrolled in the doctoral program of the Berlin School of Integrative Oncology (BSIO).

最后，我们感谢Christian Gabriel和Valentina Alejandra Balde Araya在审阅本手稿期间提供的宝贵反馈。这些结果是德国联邦教育与研究部（BMBF）通过e:Med Juniorverbund DeepLTNBC TP3-01ZX1917C资助的项目的一部分。C、 E.得到了德国科学基金会（DFG，德国研究基金会）RTG2424/CompCancer的部分支持，项目编号：377984878，并参加了柏林综合肿瘤学院（BSIO）的博士课程。

C.S. acknowledges support from the DFG–SCHM 3362/4–1 project number: 511886499.FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthor notesAnna-Marie FingerPresent address: Department of Anatomy, University of California, San Francisco, San Francisco, CA, USAFrancesca Müller-MarquardtPresent address: Institute of Research for Development, University of Montpellier, Montpellier, FranceJohannes H.

C、 美国感谢DFG-SCHM 3362/4-1项目编号：511886499的支持。资金开放获取资金由Projekt交易启用和组织。作者信息作者注释Sanna Marie Fingers目前的地址：美国加利福尼亚大学旧金山分校解剖学系Francesca Müller Marquardt目前的地址：蒙彼利埃大学蒙彼利埃分校发展研究所，FranceJohannes H。

SchultePresent address: Clinic for Pediatrics and Adolescent Medicine, Universitätsklinikum Tübingen, Tübingen, GermanyAuthors and AffiliationsCharité Comprehensive Cancer Center, Charité – Universitätsmedizin Berlin, Berlin, GermanyCarolin Ector, Francesca Müller-Marquardt, Ulrich Keilholz & Adrián E.

舒尔特目前的地址：特宾根大学儿科和青少年医学诊所，德国作者和附属机构柏林Charité–Universitätsmedizin综合癌症中心，柏林，德国Carolin Ector，Francesca Müller Marquardt，Ulrich Keilholz＆Adrián E。

GranadaFaculty of Life Sciences, Humboldt-Universität zu Berlin, Berlin, GermanyCarolin EctorInstitute for Theoretical Biolog.

德国柏林洪堡大学格拉纳达生命科学学院卡罗林-埃克托理论生物学研究所。

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PubMed Google ScholarContributionsC.E., A.K., and A.E.G. conceived and planned the experiments. C.E. performed the experiments. F.M.M. performed bioluminescence recordings of three cell lines. C.E., C.S., J.D., and S.D.L. analyzed the data. A.M.F. and A.K. supported the planning and implementation of the bioluminescence recordings.

PubMed谷歌学术贡献中心。E、 ，A.K.和A.E.G.构思并计划了实验。C、 。F、 M.M.对三种细胞系进行了生物发光记录。C、 E.，C.S.，J.D。和S.D.L.分析了数据。A、 M.F.和A.K.支持生物发光记录的规划和实施。

C.E., C.S., A.M.F., J.D., S.D.L., J.S., T.S., U.K, H.H., A.K., and A.E.G. contributed to the interpretation of the results. C.E. and A.E.G. wrote the manuscript. All authors provided critical feedback and helped shape the research and manuscript.Corresponding authorCorrespondence to.

C、 E.，C.S.，A.M.F.，J.D.，S.D.L.，J.S.，T.S.，英国，H.H.，A.K。和A.E.G.对结果的解释做出了贡献。C、 。所有作者都提供了批判性的反馈，并帮助塑造了研究和手稿。对应作者对应。

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阿德里安·E·格拉纳达。道德宣言

Competing interests

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Reprints and permissionsAbout this articleCite this articleEctor, C., Schmal, C., Didier, J. et al. Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping.

转载和许可本文引用本文Ector，C.，Schmal，C.，Didier，J。等人。高通量深度表型揭示的癌症药物的时间效应。

Nat Commun 15, 7205 (2024). https://doi.org/10.1038/s41467-024-51611-3Download citationReceived: 07 November 2023Accepted: 13 August 2024Published: 22 August 2024DOI: https://doi.org/10.1038/s41467-024-51611-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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ChemotherapyCircadian rhythmsHigh-throughput screening

化疗昼夜节律高通量筛选

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