Which suggestions for behavioral modifications, based on mathematical models, are most likely to be followed in the real world? We address this question in the context of human circadian rhythms. Jet lag is a consequence of the misalignment of the body’s internal circadian (~24-hour) clock during an adjustment to a new schedule. Light is the clock’s primary synchronizer. Previous research has used mathematical models to compute light schedules that shift the circadian clock to a new time zone as quickly as possible. How users adjust their behavior when provided with these optimal schedules remains an open question. Here, we report data collected by wearables from more than 100 travelers as they cross time zones using a smartphone app, Entrain. We find that people rarely follow the optimal schedules generated through mathematical modeling entirely, but travelers who better followed the optimal schedules reported more positive moods after their trips. Using the data collected, we improve the optimal schedule predictions to accommodate real-world constraints. We also develop a scheduling algorithm that allows for the computation of approximately optimal schedules "on-the-fly" in response to disruptions. User burnout may not be critically important as long as the first parts of a schedule are followed. These results represent a crucial improvement in making the theoretical results of past work viable for practical use and show how theoretical predictions based on known human physiology can be efficiently used in real-world settings.

Abundant epidemiological evidence links circadian rhythms to human health, from heart disease to neurodegeneration. Accurate determination of an individual’s circadian phase is critical for precision diagnostics and personalized timing of therapeutic interventions. To date, however, we still lack an assay for physiological time that is accurate, minimally burdensome to the patient, and readily generalizable to new data. Here, we present TimeMachine, an algorithm to predict the human circadian phase using gene expression in peripheral blood mononuclear cells from a single blood draw. Once trained on data from a single study, we validated the trained predictor against four independent datasets with distinct experimental protocols and assay platforms, demonstrating that it can be applied generalizably. Importantly, TimeMachine predicted circadian time with a median absolute error ranging from 1.65 to 2.7 h, regardless of systematic differences in experimental protocol and assay platform, without renormalizing the data or retraining the predictor. This feature enables it to be flexibly applied to both new samples and existing data without limitations on the transcriptomic profiling technology (microarray, RNAseq). We benchmark TimeMachine against competing approaches and identify the algorithmic features that contribute to its performance.

Purpose: Recent studies show that glioblastoma (GBM) is more sensitive to temozolomide (TMZ) in the morning. In cells, inhibiting O6-Methylguanine-DNA-Methyltransferase ( MGMT ) abolished time-dependent TMZ efficacy, suggesting that circadian regulation of this DNA repair enzyme underlies daily TMZ sensitivity. Here, we tested the hypotheses that MGMT promoter methylation and protein abundance vary with time-of-day in GBM, resulting in daily rhythms in TMZ efficacy. Methods: We assessed daily rhythms in MGMT promoter methylation in GBM in vitro and retrospectively analyzed MGMT methylation status in human GBM biopsies collected at different times of day. Next, we measured MGMT and BMAL1 protein abundances in GBM cells collected at four-hour intervals. To understand the therapeutic implications of circadian variations in MGMT, we incorporated its daily rhythms into an in vitro mathematical model capturing interactions between MGMT, TMZ, and GBM DNA. Results: We found daily rhythms in MGMT promoter methylation and protein levels in GBM in vitro, and in patient biopsies peaking at midday. Further, MGMT protein levels peaked at CT4, corresponding to the time of maximal TMZ efficacy in vitro. When we incorporated cell-intrinsic circadian rhythms in MGMT protein into a mathematical model for GBM chemotherapy, we found that dosing when daily MGMT levels peaked and began to decline produced maximum DNA damage. Conclusion: Our findings suggest that the likelihood of diagnosis of MGMT promoter methylation may vary with time of biopsy in GBM. Furthermore, theoretical modeling predicts that efforts to deliver TMZ after the daily peak of MGMT activity, with exact time being dose-dependent, may significantly enhance its therapeutic efficacy. Graphical

The mammalian circadian system comprises a network of endogenous oscillators, spanning from the central clock in the brain to peripheral clocks in other organs. These clocks are tightly coordinated to orchestrate rhythmic physiological and behavioral functions. Dysregulation of these rhythms is a hallmark of aging, yet it remains unclear how age-related changes lead to more easily disrupted circadian rhythms. Using a two-population model of coupled oscillators that integrates the central clock and the peripheral clocks, we derive simple mean-field equations that can capture many aspects of the rich behavior found in the mammalian circadian system. We focus on three age-associated effects that have been posited to contribute to circadian misalignment: attenuated input from the sympathetic pathway, reduced responsiveness to light, and a decline in the expression of neurotransmitters. We find that the first two factors can significantly impede re-entrainment of the clocks following perturbation, while a weaker coupling within the central clock does not affect the recovery rate. Moreover, using our minimal model, we demonstrate the potential of using the feed–fast cycle as an effective intervention to accelerate circadian re-entrainment. These results highlight the importance of peripheral clocks in regulating the circadian rhythm and provide fresh insights into the complex interplay between aging and the resilience of the circadian system.

Cancer therapies often fail when intolerable toxicity or drug-resistant cancer cells undermine otherwise effective treatment strategies. Over the past decade, adaptive therapy has emerged as a promising approach to postpone emergence of resistance by altering dose timing based on tumor burden thresholds. Despite encouraging results, these protocols often overlook the crucial role of toxicity-induced treatment breaks, which may permit tumor regrowth. Herein, we explore the following question: would incorporating toxicity feedback improve or hinder the efficacy of adaptive therapy? To address this question, we propose a mathematical framework for incorporating toxic feedback into treatment design. We find that the degree of competition between sensitive and resistant populations, along with the growth rate of resistant cells, critically modulates the impact of toxicity feedback on time to progression. Further, our model identifies circumstances where strategic treatment breaks, which may be based on either tumor size or toxicity, can mitigate overtreatment and extend time to progression, both at the baseline parameterization and across a heterogeneous virtual population. Taken together, these findings highlight the importance of integrating toxicity considerations into the design of adaptive therapy.

Background/Objectives: Chronic rhinosinusitis (CRS) is a persistent inflammatory condition of the sinonasal mucosa lasting for at least three months. For patients, CRS-related sleep disturbances can significantly disrupt circadian rhythms, leading to further health complications such as cognitive impairment. Despite the well-documented sleep disturbances associated with CRS, there is limited research on objective assessment methods. Additionally, the severity of these issues can vary among patients. This study aims to assess sleep quality and timing in CRS patients and investigate their impact on cognition, providing guidance for personalized and tailored assessment and management of CRS. Methods: Our case–control study compares sleep patterns and cognitive function between CRS patients and healthy controls utilizing actigraphy, a non-invasive device for measuring sleep–wake cycles and circadian rhythms. The actigraphy-derived sleep variables include inter-daily variability, intra-daily variability, highest 10 h activity (M10), lowest 5 h activity (L5), relative amplitude (RA), sleep onset latency, sleep efficiency, sleep and wake time, time spent in bed, total sleep time, and wakefulness after sleep onset. We also used a standard questionnaire assessing sleep quality, the Pittsburgh Sleep Quality Index (PSQI). Results: Our study enrolled 44 CRS and 43 control participants. Our findings indicate that the actigraphy-derived sleep variables were comparable between groups, all with a p-value > 0.05. However, CRS patients exhibited greater early morning activity and significantly lower PSQI-reported sleep quality compared to controls (8.78 ± 3.45, 4.71 ± 2.96, respectively; adjusted p < 0.001). Actigraphy-derived sleep variables showed trends towards significance in association with episodic memory (p = 0.051) and executive function (p = 0.15). Conclusions: Actigraphy-derived sleep outcomes revealed associations with episodic and executive function, underscoring the potential of actigraphy in understanding the individualized sleep-related cognitive impacts in CRS patients. This highlights the importance of personalized assessment and management strategies to address the unique sleep and cognitive challenges faced by each patient.