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Background: Current biological evidence suggests that purine involvement in purine metabolism may contribute to the development and progression of ovarian cancer (OC), but the epidemiological association is currently unknown. Methods: A total of 703 newly diagnosed patients with OC aged 18–79 years were included in this prospective cohort study. Utilizing a verified food-frequency questionnaire, the participants' dietary consumption was gathered. Using medical records and ongoing follow-up, the deaths up until 31 March 2021 were determined. To assess the hazard ratios (HRs) and 95% confidence intervals (CIs) of purine intake with OC mortality, Cox proportional-hazard models were utilized. Results: During the median follow-up of 31 months (interquartile: 20–47 months), 130 deaths occurred. We observed an improved survival for the highest tercile of total purine intake compared with the lowest tercile (HR = 0.39, 95% CI = 0.19–0.80; p trend < 0.05), and this protective association was mainly attributed to xanthine intake (HR = 0.52, 95% CI = 0.29–0.94, p trend < 0.05). Additionally, we observed a curving relationship in which OC mortality decreased with total purine intake, and the magnitude of the decrease was negatively correlated with intake (p non-linear < 0.05). Significant inverse associations were also observed in subgroup analyses and sensitivity analyses according to demographic and clinical characteristics. Moreover, we observed that xanthine intake and hypoxanthine intake had a multiplicative interaction with ER and PR expression (p < 0.05), respectively. Conclusion: A high total purine and xanthine intake was linked to a lower risk of OC mortality. Further clarification of these findings is warranted. [ABSTRACT FROM AUTHOR]

Stimulus-responsive hydrogels are an important member of smart materials owing to their reversibility, soft/wet properties, and biocompatibility, which have a wide range of applications in the field of intelligent actuations. However, poor mechanical property and complicated fabrication process limit their further applications. Herein, we report a light-responsive tissue paper/hydrogel composite actuator which was developed by combining inkjet-printed tissue paper with poly(N-isopropylacrylamide) (PNIPAM) hydrogel through simple in situ polymerization. Due to the high strength of natural tissue paper and the strong interaction within the interface of the bilayer structure, the mechanical property of the composite actuator was highly enhanced, reaching 1.2 MPa of tensile strength. Furthermore, the light-responsive actuation of remote manipulation can be achieved because of the stamping graphite with high efficiency of photothermal conversion. Most importantly, we also made a few remotely controlled biomimetic actuating devices based on the near-infrared (NIR) light response of this composite actuator. This work provides a simple strategy for the construction of biomimetic anisotropic actuators and will inspire the exploration of new intelligent materials. [ABSTRACT FROM AUTHOR]

Background: Sleep is closely related to various diseases. Several meta-analyses have provided evidence of sleep and cancer, and yet the credibility of this evidence has not been comprehensively quantified. Thus, we conducted an umbrella review to quantify the evidence for systematic reviews and meta-analyses of observational studies on sleep characteristics (sleep duration, sleep quality, napping, bedtime, and wake-up time) and cancer-related outcomes. Methods: PubMed, Web of Science (Core Collection), and Embase databases were searched from inception until 29 July 2022. Assessment of Multiple Systematic Reviews, version 1, was used to evaluate the methodological quality of each eligible systematic review or meta-analysis. For each association, the summary effect with a 95% confidence interval was evaluated by fixed and random effects models. The 95% prediction interval, heterogeneity, small-study effects, and excess significance bias were also evaluated. Evidence of the associations from systematic reviews and meta-analyses was ranked based on the established criteria of published literature as convincing, highly suggestive, suggestive, weak, or non-significant. Results: The umbrella review identified thirty meta-analyses on the aforementioned associations from six articles. The methodological quality of five articles was high or moderate. Suggestive evidence was found for associations between long sleep duration and a 21% increased risk of colorectal cancer, a 9% increased all-cancer mortality and a 65% increased mortality of lung cancer, and associations between short sleep duration and a 21% increased mortality of lung cancer. Additionally, the evidence of associations between short sleep duration and lung cancer mortality was upgraded to convincing, and between long sleep duration and lung cancer mortality was upgraded to highly suggestive, among the population reporting 24 h sleep duration. Conclusion: Abnormal sleep duration might be linked to several adverse cancer-related outcomes. [ABSTRACT FROM AUTHOR]

Objective: To explore if pre-diagnosis sleep status is associated with overall survival (OS) of ovarian cancer (OC). Methods: This is a prospective cohort study of 853 OC patients newly diagnosed between 2015 and 2020. Sleep status was measured by the Pittsburgh Sleep Quality Index (PSQI). Vital status of patients was obtained through active follow-up and linkage to medical records and cancer registry. The Cox proportional hazards regression model was utilized to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for aforementioned associations. Results: During the follow-up period (median: 37.57 months, interquartile: 25.00 to 50.17 months), 123 (18.39%) OC patients died. The HR (95%CI) for OS of OC was 2.13 (1.42–3.18) for sleeping after 22:00, compared with sleeping before 22:00; 2.43 (1.64–3.62) for poor sleep quality, compared to good sleep quality; 2.26 (1.37–3.72) for late bed-early rise and 1.93 (1.09–3.42) for late bed-late rise, compared with early bed-early rise; 0.40 (0.24–0.67) for night sleep duration of ≥7.5 h/day, compared with 7–7.5 h/day; 0.53 (0.29–0.98) for total sleep duration of ≥8 h/day, compared with 7.5–8 h/day. Further, the interaction effects were significant between residual lesions and wake-up time, night bedtime, sleep pattern, and between total sleep duration and menopausal status, parity. Additionally, there was a significant curvilinear association between PSQI score and OS (p nonlinear <0.05). Conclusions: Pre-diagnosis longer total and night sleep duration were associated with better OS, whereas later sleeping time, poor sleep quality, and bad sleep patterns were associated with poor OS among OC survivors. [ABSTRACT FROM AUTHOR]

Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing machanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model. [ABSTRACT FROM AUTHOR]