Sanming University

In this meta-analysis, we systematically compared the efficacy of the Allgöwer-Donati suture technique versus the interrupted vertical mattress suture technique in preventing postoperative incision complications following calcaneal fracture surgery. A systematic search of PubMed, Embase, the Cochrane Library, and Web of Science was conducted from their inceptions up to May 2025. This study included randomized-controlled trials (RCTs) involving adults (18-80 years) with closed calcaneal fractures who underwent open reduction and internal fixation (ORIF) via a lateral incision. The primary outcomes were surgical suture time, wound suture time, drainage tube removal time, and the incidence of postoperative complications. Standardized mean differences (SMDs) and odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using fixed-effect or random-effects models based on heterogeneity (I2 statistic). Eight RCTs comprising 640 patients were included. The Allgöwer-Donati technique significantly reduced surgical suture time (SMD = 0.81, 95% CI 0.22 to 1.39, p = 0.007; I2 = 47%), drainage tube removal time (SMD = 4.94, 95% CI 4.42-5.47, p < 0.00001; I2 = 65%), and wound suture time (SMD = -3.36, 95% CI -3.90 to -2.81, p < 0.00001; I2 = 34%) compared to the interrupted vertical mattress technique. However, there was no significant difference in the overall rate of postoperative complications between the two techniques (OR = 1.32, 95% CI 0.95-1.82, p = 0.10; I2 = 0%). The Allgöwer-Donati suture technique offers significant advantages in operative efficiency by reducing suture times and drainage duration without increasing the risk of postoperative complications compared to the interrupted vertical mattress technique. It represents a promising suturing option for calcaneal fracture surgery, particularly in settings valuing procedural efficiency.

Fetal hypoxia is a leading cause of neonatal morbidity and mortality. Cardiotocography (CTG) is widely used to predict fetal hypoxia during labor, but its interpretation remains suboptimal. Artificial intelligence (AI) models have been developed for CTG interpretation, but their clinical utility is limited by two major challenges: demonstrating superiority over human experts and ensuring explainability in real-world settings. A large dataset containing CTG traces from three tertiary hospitals between January 2014 and May 2022 was built for model development. Deep learning architectures, named Cardiotocography Artificial-intelligence Predictors (CAPs), were trained to predict fetal hypoxia from CTG traces based on CNN (CAP-C), Transformer (CAP-T), LSTM (CAP-L), and CfC (CAP-CfC) algorithms. The outcome was fetal hypoxia, determined by either low Apgar score (≤ 7 at 1 or 5 min) or umbilical artery acidemia (grade 1: pH of umbilical artery (pHa) < 7.20; grade 2: pHa < 7.15; grade 3: pHa < 7.10). Model performance was determined by area under the receiver operating characteristic curve (AUROC), evaluated through nationwide AI-human comparison and validated on the CTU-UHB dataset. Gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the CTG regions that contributed most to the model's predictions. A total of 20,780 CTG traces were obtained for model development, and 467 cases were held out for the nationwide AI-human comparison. Among all models, CAP-L achieved highest AUROC in predicting fetal hypoxia (grade 1: 0.758, 95% CI: 0.754-0.761; grade 2: 0.770, 95% CI: 0.764-0.776; grade 3: 0.716, 95% CI: 0.700-0.732). In comparison with 10,571 expert responses, all CAP models achieved higher AUROC (0.757-0.789 vs. 0.715, P values in Delong test < 0.05). On the public CTU-UHB dataset, CAP-L achieved AUROC of 0.709, 0.727, and 0.730 in predicting fetal hypoxia with grade 1, 2, and 3 acidemia. Grad-CAM analysis showed that the CAP models leveraged variable and prolonged decelerations to predict fetal hypoxia, verified by perturbation-based faithfulness test. The CAP algorithms developed in this study showed superior performance in detecting fetal hypoxia from CTG traces compared to human experts, and demonstrated promising explainability, supporting clinical CTG interpretation. Clinical trial registration number: ChiCTR2100045316, ChiCTR2100052695, ChiCTR2400085338.

Natural bone is an intrinsically piezoelectric and mechanosensitive tissue that converts mechanical stimuli into bioelectrical signals regulating bone development and regeneration. However, most existing in vitro bone models fail to recapitulate this electromechanical behavior, as current studies have largely focused on piezoelectric materials for bone repair rather than mechanical stress transmission and its piezoelectric regulation during bone development. Bone organoids have emerged as advanced three-dimensional models provides an opportunity to address this limitation, yet the electromechanical dimension of bone organoid engineering has not been systematically examined. This review advances the central argument that piezoelectric mechanosensing represents a missing but essential biophysical axis in bone organoid development. From this perspective, the piezoelectric characteristics of native bone and their roles in osteogenesis are first summarized, followed by an integrated analysis of signaling pathways activated by piezoelectric stimulation. Representative piezoelectric materials are then evaluated based on their capacity to support force-electric transduction within bone organoid systems. Finally, piezoelectric behavior-guided strategies and remaining challenges in engineering electromechanically active bone organoids are discussed. By reframing bone organoids as electromechanically regulated systems rather than purely biochemical constructs, this review provides a conceptual foundation for studying bone development under piezoelectric conditions.

ABSTRACT Rising carbon emissions from tourism’s rapid growth have placed its environmental sustainability under mounting scrutiny, underscoring the urgency of improving tourism carbon productivity (TCP). Meanwhile, the digital economy (DE) has increasingly emerged as a transformative driver of sustainable tourism. This study constructs a model to measure TCP and, using Chinese city-level data with econometric methods, analyzes the impact of DE on TCP. Results show that DE significantly enhances TCP, with stronger effects in eastern regions and excellent tourism cities. Mechanism analysis indicates that DE improves TCP mainly by stimulating green technological innovation and alleviating resource misallocation. Spatial econometric analysis further identifies positive spillover effects of DE on neighboring cities’ TCP. These findings reveal the impact and spatial spillover pathways of DE on TCP, extend the theoretical framework of how DE affects tourism’s low-carbon development, and provide valuable reference for collaborative low-carbon governance in the tourism industry in the digitalization context.

Fiber Bragg gratings (FBGs) have evolved from passive sensing elements into actively programmable photonic components, enabling dynamic wavelength control across diverse applications. This review provides a comprehensive and systematic overview of active wavelength control technologies for FBGs, deliberately excluding passive sensing applications. We systematically categorize the fundamental tuning mechanisms-including mechanical, thermal, optothermal, electro-optic, nonlinear optical, and hybrid approaches-and compare their performance characteristics in terms of tuning range, speed, precision, and trade-offs. Key enhancement techniques, such as mechanical amplification, thermal packaging, femtosecond laser fabrication, and FPGA-based interrogation, are examined. The transformative impact of actively controlled FBGs is elucidated across three major application domains: tunable and narrow-linewidth fiber lasers, reconfigurable microwave photonic systems, and emerging fields including quantum information processing and biomedical imaging. A consolidated technology map visualizes the connections between enabling techniques and applications. Finally, we critically analyze core challenges-performance trade-offs, control complexity, and integration bottlenecks-and outline future research directions driven by novel materials, artificial intelligence, and quantum technologies. This review offers a structured framework for understanding active FBGs as programmable photonic primitives, providing actionable insights for researchers and engineers in academia and industry.

Slumber constitutes an indispensable facet of our daily existence, with the duration of rest for each individual comprising approximately one-third of their entire lifespan. The objective of this study was to elucidate the relationship between sleep duration on weekdays and rheumatoid arthritis (RA) within the United States (US) population. In this cross-sectional study, a cohort of 9,385 participants, aged 20 years and older, was meticulously selected from the National Health and Nutrition Examination Survey (NHANES). Sleep duration on weekdays data were collected through personal interviews, while RA status was determined via self-reported questionnaires. Sleep duration on weekdays was the main exposure variable, and RA was the outcome variable. The association between sleep duration on weekdays and RA was estimated by using the multivariable logistic regression models and restricted cubic spline (RCS) regression. A total of 2,595 participants with RA and 6,790 non-RA participants were included in the study. A linear trend between sleep duration on weekdays and the presence of RA (p < 0.0001, p = 0.01, respectively) was observed in both logistic regression models 1 and 2. However, this linear relationship was no longer evident in model 3 (p = 0.96). The results of RCS analysis in Model 1 identified a U-shaped relationship between sleep duration on weekdays and the presence of RA (p < 0.001). This U-shaped relationship persisted in the adjusted models 2 and 3 (p < 0.001, p = 0.0055, respectively). They revealed a significant increase in the odds of RA for sleep durations both below and above 7h (P < 0.05). Subgroup analysis indicated a significant interaction between age stratification (40-79 years old) and the association between obesity and RA (P for interaction < 0.05). Our research indicates that both insufficient and prolonged sleep durations are significantly associated with RA, particularly in individuals aged 40 to 79 and those with obesity.