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Background Cardiovascular disease (CVD) remains the leading cause of death worldwide. While traditional risk factors are well-established, emerging evidence suggests shift work causing circadian rhythm disruption significantly contributes to CVD risk. This systematic review investigated molecular mechanisms linking circadian disruption with cardiovascular pathophysiology through in vivo models. Methods We systematically searched Medline, Embase, and Web of Science through February 2025. Studies employing genetic (clock gene knockouts/mutations) or environmental (light phase shift, sleep deprivation) models of circadian disruption in vivo were included. Meta-analyses were performed for key cardiovascular indicators, and certainty of evidence was evaluated using a modified GRADE approach. Results Among 9012 references, 34 studies met inclusion criteria. Following quality assessment for study design and reporting, 32 studies with low or moderate risk of bias were included in the synthesis. Meta-analyses revealed cardiac hypertrophy as the most robust finding, with high-certainty evidence for increased left ventricular mass-to-body weight ratio (LV/BW; SMD: 0.89, 95% CI: 0.38 to 1.39) and moderate-certainty evidence for increased cardiomyocyte size. These convergent organ and cellular-level findings, supported by elevated natriuretic peptides and pro-fibrotic markers, indicate circadian disruption contributes to pathological cardiac remodeling. Sensitivity analyses revealed low-certainty evidence for impaired systolic function, with significant reductions in ejection fraction (SMD: − 1.70, 95% CI: − 3.22 to − 0.17) and fractional shortening (SMD: − 1.60, 95% CI: − 2.71 to − 0.49). Low-certainty evidence was found for impaired endothelium-dependent vasorelaxation (SMD: − 2.72, 95% CI: − 4.90 to − 0.53) based on three genetic model studies with high heterogeneity and elevated triglyceride levels (SMD: 1.64, 95% CI: 0.07 to 3.21). Other markers showed very low-certainty evidence. Conclusions This systematic review improves mechanistic understanding of CVD development following circadian misalignment by demonstrating cardiac hypertrophy as a major pathophysiological consequence in animal models. Cardiac structural changes at organ and cellular levels, supported by biomarkers of pathological remodeling, indicate circadian disruption contributes to adverse cardiac remodeling. Future animal research should prioritize standardized protocols, sex-balanced designs, and environmental models replicating human shiftwork patterns. Substantial epidemiological gaps remain, warranting further investigation in shift workers. Graphical Abstract

Chronic sleep disruption and shift work elevate the risk of neurodegeneration and Alzheimer's disease (AD). While disrupted sleep affects canonical AD biomarkers, its impact on other mechanisms, such as circulating microRNAs (miRNAs), remains less understood. Therefore, we here examined the effects of overnight wakefulness on plasma levels of several miRNAs implicated in neurodegeneration and AD, as well as in sleep and circadian regulation—namely miR‐127‐3p, miR‐132‐3p, and miR‐142‐3p. Following a baseline period in each highly controlled in‐lab session, in total 15 healthy normal‐weight young men underwent two conditions on separate occasions, in randomised order: a night of normal sleep, and a night of sustained wakefulness. After overnight wakefulness, morning plasma levels of miR‐127‐3p and miR‐142‐3p were significantly elevated compared with post‐sleep levels. These changes were not associated with the significant increase in self‐reported morning stress levels observed after wakefulness compared with sleep. This study is the first to demonstrate that a single night of wakefulness—mimicking overnight shift work—significantly elevates circulating levels of miR‐127‐3p and miR‐142‐3p in humans. These findings, though based on a limited sample size, suggest a potential molecular link between sleep loss and neurodegeneration, warranting further investigation. Trial Registration: Clinical Trial number: NCT01800253; www.clinicaltrials.gov

Neurobehavioral task performance is modulated by the circadian and homeostatic processes of sleep/wake regulation. Biomathematical modeling of the temporal dynamics of these processes and their interaction allows for prospective prediction of performance impairment in shift-workers and provides a basis for fatigue risk management in 24/7 operations. It has been reported, however, that the impact of the circadian rhythm—and in particular its timing—is inherently task-dependent, which would have profound implications for our understanding of the temporal dynamics of neurobehavioral functioning and the accuracy of biomathematical model predictions. We investigated this issue in a laboratory study designed to unambiguously dissociate the influences of the circadian and homeostatic processes on neurobehavioral performance, as measured during a constant routine protocol preceded by three days on either a simulated night shift or a simulated day shift schedule. Neurobehavioral functions were measured every 2 h using three functionally distinct assays: a digit symbol substitution test, a psychomotor vigilance test, and the Karolinska Sleepiness Scale. After dissociating the circadian and homeostatic influences and accounting for inter-individual variability, peak circadian performance occurred in the late biological afternoon (in the “wake maintenance zone”) for all three neurobehavioral assays. Our results are incongruent with the idea of inherent task-dependent differences in the endogenous circadian impact on performance. Rather, our results suggest that neurobehavioral functions are under top-down circadian control, consistent with the way they are accounted for in extant biomathematical models.

Objectives Shift workers are prone to obesity and associated co-morbidities such as diabetes and cardiovascular disease. Sleep restriction associated with shift work results in dramatic endocrine and metabolic effects that predispose shift workers to these adverse health consequences. While sleep restriction has been associated with increased caloric intake, food preference may also play a key role in weight gain associated with shift work. This study examined the impact of an overnight simulated night shift on food preference. Methods Sixteen participants [mean 20.1, standard deviation (SD) 1.4 years; 8 women] underwent a simulated night shift and control condition in a counterbalanced order. On the following morning, participants were provided an opportunity for breakfast that included high- and low-fat food options (mean 64.8% and 6.4% fat, respectively). Results Participants ate significantly more high-fat breakfast items after the simulated night shift than after the control condition [167.3, standard error of the mean (SEM) 28.7) g versus 211.4 (SEM 35.6) g; P=0.012]. The preference for high-fat food was apparent among the majority of individuals following the simulated night shift (81%), but not for the control condition (43%). Shift work and control conditions did not differ, however, in the total amount of food or calories consumed. Conclusions A simulated night shift leads to preference for high-fat food during a subsequent breakfast opportunity. These results suggest that food choice may contribute to weight-related chronic health problems commonly seen among night shift workers.

This study aimed to investigate sleep and circadian phase in the relationships between neurobehavioral performance and the number of consecutive shifts worked. Thirty-four shift workers [20 men, mean age 31.8 (SD 10.9) years] worked 2-7 consecutive night shifts immediately prior to a laboratory-based, simulated night shift. For 7 days prior, participants worked their usual shift sequence, and sleep was assessed with logs and actigraphy. Participants completed a 10-minute auditory psychomotor vigilance task (PVT) at the start (~21:00 hours) and end (~07:00 hours) of the simulated night shift. Mean reaction times (RT), number of lapses and RT distribution was compared between those who worked 2-3 consecutive night shifts versus those who worked 4-7 shifts. Following 4-7 shifts, night shift workers had significantly longer mean RT at the start and end of shift, compared to those who worked 2-3 shifts. The slowest and fastest 10% RT were significantly slower at the start, but not end, of shift among participants who worked 4-7 nights. Those working 4-7 nights also demonstrated a broader RT distribution at the start and end of shift and had significantly slower RT based on cumulative distribution analysis (5 (th), 25 (th), 50 (th), 75 (th)percentiles at the start of shift; 75th percentile at the end of shift). No group differences in sleep parameters were found for 7 days and 24 hours prior to the simulated night shift. A greater number of consecutive night shifts has a negative impact on neurobehavioral performance, likely due to cognitive slowing.

In this work, we consider the shift design problem and we define a novel, complex formulation arising from practical cases. In addition, we propose a new search method based on a fast Simulated Annealing, that, differently from previous approaches, solves the overall problem as a single-stage procedure. The core of the method is a composite neighborhood that includes at the same time changes in the staffing of shifts, the shape of shifts, and the position of breaks. Finally, we present a statistically-principled experimental analysis on a set of instances obtained from real cases. Both instances and results are available on the web for future comparisons.

This paper focuses on the last stage of the aluminium production process in the context of Industry 4.0: schedule optimization in the casting process. Casting is one of the oldest manufacturing processes in which a liquid material is usually poured into a mold that contains a hollow cavity of the desired shape and then allowed to solidify. This is a complex scheduling problem in which several constraints, such as different maintenance processes, maximum stocks, machine breakdowns, work shifts, or the maximum number of mold changes per day, come into play. Four objective functions have to be taken into account simultaneously. We have to minimize both the unmet demand at the end of the schedule, and the delays in the injection process with regard to daily demands. Production costs, including the cost of electricity consumption in the injection process and gas consumption associated with melting furnaces, should be minimized. Finally, the total number of mold changes throughout the schedule must also be reduced to a minimum. The simulated annealing (SA) metaheuristic has been adapted to solve this complex optimization process and parameterized for application to a wide variety of aluminium making processes. SA efficiently solves the problem and provides an optimal solution in about three minutes.

Lot streaming means breaking a lot into sublots, where sublots may be transferred to a number of machines for the operations. Here, the multi-job lot streaming problem in a multistage hybrid flow shop having identical parallel machines at stages with work-in-process (WIP) jobs, work shifts constraint, and sequence-dependent setup times is studied. The aim is to minimize the sum of weighted completion times of jobs in each shift in order to furnish a better machine utilization for the following shifts. Our model in meeting the job demands appropriates job scheduling on machines for processing, the sequence of operations on allocated machines, the size of the sublots in the work shifts, the work completion times in all the shifts, and the jobs in each stage as the WIP jobs. To solve the problem, a genetic algorithm (GA) and simulated annealing (SA) are proposed to compute the best scheduling for the hybrid flow shop problem. Numerical illustrations demonstrate the applicability of the proposed model and the effectiveness of the GA.

This paper deals with the air traffic controller (ATCo) work shift scheduling problem. This is a multi-objective optimization problem, as it involves identifying the best possible distribution of ATCo work and rest periods and positions, ATCo workload and control center changes in order to cover an airspace sector configuration, while, at the same time, complying with ATCo working conditions. We propose a three-phase problem-solving methodology based on the variable neighborhood search (VNS) to tackle this problem. The solution structure should resemble the previous template-based solution. Initial infeasible solutions are built using a template-based heuristic in Phase 1. Then, VNS is conducted in Phase 2 in order to arrive at a feasible solution. This constitutes the starting point of a new search process carried out in Phase 3 to derive an optimal solution based on a weighted sum fitness function. We analyzed the performance in the proposed methodology of VNS against simulated annealing, as well as the use of regular expressions compared with the implementation in the code to verify the feasibility of the analyzed solutions, taking into account four representative and complex instances of the problem corresponding to different airspace sectorings.

The primary objective of the nurse scheduling problem is to ensure there are sufficient nurses on each shift. There are also a number of secondary objectives designed to make the schedule more pleasant. Neighbourhood search implementations use a weighted cost function with the weights dependent on the importance of each objective. Setting the weights on binding constraints so they are satisfied but still allow the search to find good solutions is difficult. This paper compares two methods for overcoming this problem, SAWing and Noising with simulated annealing and demonstrates that Noising produces better schedules.