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Background: Liver cancer (LC) is a major global health issue, with China accounting for nearly half of global cases. Key contributors include chronic hepatitis B virus (HBV) infection, aflatoxin exposure, and modifiable lifestyle risk factors. With significant global implications, China's high LC incidence and mortality underscore the need for focused research. Methods: This study analyses age- and sex-specific LC trends and risk factors across China's 34 provincial-level administrative regions using GBD 2021 data. LC metrics examined include incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost(YLLs). We also examined the relationship between these metrics and gross-domestic-product-per-capita (GDPPC). An attentive deep-learning pipeline was developed to provide long-term scenario forecasts with scenarios simulation. Findings: Between 1990 and 2021, LC incidence and prevalence in China escalated (except in Fujian, Shanghai, and Zhejiang), reaching 196,637 new cases and 265,539 total cases in 2021, predominantly affecting individuals aged 45+ years. In 2021, LC attributed to HBV recorded the highest age-standardised incidence rate (ASIR), whereas increases in ASIR were most pronounced for cases due to alcohol use and non-alcoholic steatohepatitis (NASH) from 1990-to-2021. The national age-standardised death rate (ASDR) declined from 10.8 to 8.4 per 100,000 from 1990 to 2021. Notably, hepatoblastoma showed the most significant drop in ASDR, plummeting by 79.7%, with subsequent declines in LC due to HBV, HCV, and alcohol use. Conversely, LC due to NASH saw a marginal increase of 0.8%. Trends in age-standardised DALYs, YLDs, and YLLs moderately mirrored those of the ASDR. Men, particularly those over 20 years old, faced higher LC burdens across all provincial regions compared to women. Provincially, the lowest LC burdens were observed in high-GDPPC regions, such as Beijing and Shanghai. In 2021, smoking emerged as the leading risk factor for LC-related mortality and disability in men, especially in HBV-associated cases. Men under 20 years displayed distinct patterns, with high alcohol use being the predominant fatal risk factor. For women, inappropriate drug use was the foremost risk factor for LC-related mortality and disability, especially in HCV-related cases. Without targeted interventions, forecasts from 2021 to 2040 predict declines in age-standardized LC rates: incidence by 9.5%, mortality by 4.7%, DALYs by 6.5%, YLLs by 4.3%, and YLDs by 13%. However, the incidence of LC due to alcohol use is expected to significantly increase among those under 20 years old in all provinces except Tibet, indicating a worrying trend towards younger onset. Targeted interventions, including tobacco control, appropriate medication use, alcohol use restriction, and regular physical activity, are projected to significantly reduce LC-attributed health losses in both sexes. Notably, reductions in mortality, DALYs, YLDs, and YLLs rates for LC in men are primarily expected through tobacco control; while in women, appropriate medication use will play the most critical role. Interpretation: Our findings indicate a rapidly increasing LC burden, particularly for cases attributable to HBV, in China. The scenario-based forecasting model provides compelling evidence that can encourage national and provincial stakeholders to develop effective interventions tailored to their specific populations.

ObjectiveIn order to address the issues of inconvenience, high medical costs, and lack of universality associated with traditional knee rehabilitation equipment, a portable intelligent wheelchair for knee rehabilitation was designed in this study. MethodsBased on the analysis of the knee joint’s structure and rehabilitation mechanisms, an electric pushrod-driven rehabilitation institution was developed. A multi-functional module was designed with a modular approach, and the control of the wheelchair body and each functional module was implemented using an STM32 single-chip microcomputer. A three-dimensional model was established using SolidWorks software. In conjunction with Adams and Ansys simulation software, kinematic and static analyses were conducted on the knee joint rehabilitation institution and its core components. A prototype was constructed to verify the equipment’s actual performance. ResultsAccording to the prototype testing, the actual range of motion for the knee joint swing rod is 15.1°~88.9°, the angular speed of the swing rod ranges from −7.9 to 8.1°/s, the angular acceleration of the swing rod varies from −4.2 to 1.6°/s², the thrust range of the electric pushrod is −82.6 to 153.1 N, and the maximum displacement of the load pedal is approximately 1.7 mm, with the leg support exhibiting a maximum deformation of about 1.5 mm. ConclusionThe intelligent knee joint rehabilitation wheelchair meets the designed functions and its actual performance aligns with the design criteria, thus validating the rationality and feasibility of the structural design.

The carbon catabolite repression 4-negative on TATA-less transcription complex subunit 3 gene ( CONT3 ) plays a key role in regulating the mRNA transcription and protein translation of other genes. Mutations in CONT3 have also recently been implicated as a causative factor of intellectual developmental disorder with speech delay, autism, and dysmorphic facies (IDDSADF). However, to date, only a few CONT3 mutations have been reported to be associated with IDDSADF-related diseases. In the present case, we report a Chinese patient with developmental delay, verbal regression, and facial dysmorphism, in whom cerebral magnetic resonance imaging showed an expansion of the lateral ventricle. The patient was diagnosed with an IDDSADF-related disease caused by a de novo c.1616_1623del mutation in exon 14 of CONT3 , which was confirmed by whole-exome sequencing and direct Sanger sequencing. This case report is the first known documentation of a pathogenic mutation at the c.1616_1623del locus of CONT3 in the worldwide population. It provides a critical theoretical basis for the specific gene-based diagnosis of IDDSADF-related diseases and expands the mutation profile of CONT3 .

The density analysis of woven fabrics is a critical part of quality control in textile production. Traditional image-processing-based methods for density analysis of woven fabrics require complicated manual feature design and lack adaptability to different weaving patterns. To address these problems, we propose a woven fabric density analysis method based on small object detection and rule-based post-processing. First, we capture high-resolution images of woven fabrics using macro-microscopic camera equipment, and then construct a woven fabric microscopic image dataset for our study through pre-processing and data augmentation. Next, we propose a multitask feature fusion network (MTF-Net), a small object detection network, to detect the float-points of warp and weft yarns. The detection ability of the model is improved by the cooperation of a reconstruction branch network, a pixel-level branch network, and an object-level branch network. Additionally, we introduce a feature rotation selection module (FRSM) to solve the problem of yarns with small angle rotations. We finally propose a rule-based post-processing method to complete the density analysis of woven fabrics. The experimental results demonstrate that the proposed method is effective and achieves higher accuracy than the popular object detection methods for density analysis on the constructed woven fabric dataset.

PURPOSETo investigate the feasibility and usefulness of 2-deoxy-2-(18F)-fluoro-D-glucose positron emission tomography/computed tomography [(18F)-FDG PET/CT] as a novel examination in the surveillance of abnormal myocardial energy metabolism and cardiac dysfunction after cardiopulmonary resuscitation (CPR).METHODSThirteen male Sprague–Dawley rats were randomly divided into a sham group (n = 4), CPR group (n = 4), and trimetazidine (TMZ) + CPR group (n = 5). The expression levels of the myocardial injury marker cardiac troponin I (CTNI) in serum were tested at 6 hours after CPR or TMZ + CPR. The ejection fraction and fraction shortening were evaluated by echocardiography. (18F)-FDG PET/CT was used to measure the FDG uptake and the standardized uptake value (SUV) after CPR or TMZ + CPR for 6 hours. The intermediary carbohydrate metabolites of glycolysis including phosphoenolpyruvate, 3-phospho-D-glycerate, and the lactate/pyruvate ratio were detected through the multiple reaction monitoring approach. Simultaneously, the authors also tested the expression levels of the total adenosine triphosphate (ATP) and the key intermediate products of glucose ovidation as alpha ketoglutarate, citrate, and succinate in the myocardium.RESULTSThe authors found that the aerobic oxidation of glucose was reduced, and the anaerobic glycolysis was significantly enhanced in the myocardium in the early stage of CPR. Meanwhile, the myocardial injury marker CTNI was upregulated considerably (P = 0.014, P = 0.021), and the left ventricular function of the animal heart also markedly deteriorated with the downregulation of ATP after CPR. In contrast, myocardial injury and cardiac function were greatly improved with the increase of ATP in the CPR + TMZ group. In addition, aerobic glucose oxidation metabolites were significantly increased (P < 0.05) and anaerobic glycolysis metabolites were significantly decreased (P < 0.05) after CPR in the myocardium. Surprisingly, (18F)-FDG PET/CT could track the above changes by detecting the FDG uptake value and the SUV.CONCLUSIONGlucose metabolism is an essential factor for myocardial self-repair after CPR. (18F) FDG PET/CT, as a non-invasive technology, can monitor myocardial energy metabolism and cardiac function by tracking changes in glucose metabolism after CPR.

Brain–computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.

Real-world systems, ranging from social to infrastructural, can be abstracted into complex networks. Promoting the spreading of some typical information (for instance, the commercial message, vaccination guidance, innovation, and political movement) on these networked systems can bring benefits to all aspects of society. In this study, we propose an effective edge-based approach for promoting the spreading of information on complex networks. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the information spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way to promote the spreading of information by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading dynamics.

Background: This study aims to compare the efficacy of the sinus tarsal approach (STA) with that of the conventional L-shaped lateral approach (CLSLA) in the treatment of calcaneal fractures by meta-analysis.Methods: PubMed, Embase, Web of Science, the Chinese National Knowledge Infrastructure, and China Wanfang database were searched to collect clinical randomized or non-randomized controlled trials of STA and CLSLA in the treatment of calcaneal fractures from January 2010 to May 2020. The data were analyzed by Stata 15.0 software.Results: A total of 12 clinical trials were included, all of which were retrospective studies, including 961 patients. The results showed that when STA was compared with CLSLA, there was no difference in operation time with mean difference (MD) = −5.51 [95% confidence interval (CI): −12.57 to 1.55, P > 0.05], less bleeding during operation with MD = −18.49 (95% CI:−23.79 to −13.18), no difference in Böhler angle after an operation with MD = 0.78 (95% CI: −0.09 to 1.65) and in Gissane angle with MD = −0.07 (95% CI: −1.90 to 1.77), no difference in American Orthopedic Foot and Ankle Society score with MD = 2.16 (95% CI: −1.07 to 5.38), higher-excellent and better rate of Maryland food function with relative ratio = 1.12 (95% CI: 1.04 to 1.20), and lower of incidence of postoperative complications with relative ratio = 0.23 (95% CI: 0.14–0.37).Conclusion: STA was more effective than CLSLA in the treatment of calcaneal fractures. Moreover, STA had advantages in less intraoperative bleeding, higher-excellent and better rate of Maryland foot function, lower incidence of postoperative complications, and higher safety.

Tumor cells develop a series of metabolic reprogramming mechanisms to meet the metabolic needs for tumor progression. As metabolic hubs in cells, mitochondria play a significant role in this process, including energy production, biosynthesis, and redox hemostasis. In this study, we show that 4-hydroxyphenylpyruvate dioxygenase-like protein (HPDL), a previously uncharacterized protein, is positively associated with the development of pancreatic ductal adenocarcinoma (PDAC) and disease prognosis. We found that overexpression of HPDL in PDAC cells promotes tumorigenesis in vitro, whereas knockdown of HPDL inhibits cell proliferation and colony formation. Mechanistically, we found that HPDL is a mitochondrial intermembrane space localized protein that positively regulates mitochondrial bioenergetic processes and adenosine triphosphate (ATP) generation in a glutamine dependent manner. Our results further reveal that HPDL protects cells from oxidative stress by reprogramming the metabolic profile of PDAC cells toward glutamine metabolism. In short, we conclude that HPDL promotes PDAC likely through its effects on glutamine metabolism and redox balance.

The network-based cooperative information spreading is a widely existing phenomenon in the real world. For instance, the spreading of disease outbreak news and disease prevention information often coexist and interact with each other on the Internet. Promoting the cooperative spreading of information in network-based systems is a subject of great importance in both theoretical and practical perspectives. However, very limited attention has been paid to this specific research area so far. In this study, we propose an effective approach for identifying the influential latent edges (that is, the edges that do not originally exist) which, if added to the original network, can promote the cooperative susceptible-infected-recovered (co-SIR) dynamics. To be specific, we first obtain the probabilities of each nodes being in different node states by the message-passing approach. Then, based on the state probabilities of nodes obtained, we come up with an indicator, which incorporates both the information of network topology and the co-SIR dynamics, to measure the influence of each latent edge in promoting the co-SIR dynamics. Thus, the most influential latent edges can be located after ranking all the latent edges according to their quantified influence. We verify the rationality and superiority of the proposed indicator in identifying the influential latent edges of both synthetic and real-world networks by extensive numerical simulations. This study provides an effective approach to identify the influential latent edges for promoting the network-based co-SIR information spreading model and offers inspirations for further research on intervening the cooperative spreading dynamics from the perspective of performing network structural perturbations.

Epidemic spreading processes in the real world can interact with each other in a cooperative, competitive, or asymmetric way, requiring a description based on coevolution dynamics. Rich phenomena such as discontinuous outbreak transitions and hystereses can arise, but a full picture of these behaviors in the parameter space is lacking. We develop a theory for interacting spreading dynamics on complex networks through spectral dimension reduction. In particular, we derive from the microscopic quenched mean-field equations a two-dimensional system in terms of the macroscopic variables, which enables a full phase diagram to be determined analytically. The diagram predicts critical phenomena that were known previously but only numerically, such as the interplay between discontinuous transition and hysteresis as well as the emergence and role of tricritical points.

This study aimed to investigated the effect and mechanism of zinc-finger protein 418 (ZNF418) on cardiac hypertrophy caused by aortic banding (AB), phenylephrine (PE) or angiotensin II (Ang II) in vivo and in vitro.The expression of ZNF418 in hearts of patients with dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM) and AB-induced cardiac hypertrophy mice, as well as in Ang II- or PE-induced hypertrophic primary cardiomyocytes was detected by western blotting. Then, the expression of ZNF418 was up-regulated or down-regulated in AB-induced cardiac hypertrophy mice and Ang II -induced hypertrophic primary cardiomyocytes. The hypertrophic responses and fibrosis were evaluated by echocardiography and histological analysis. The mRNA levels of hypertrophy markers and fibrotic markers were detected by RT-qPCR. Furthermore, the phosphorylation and total levels of c-Jun were measured by western blotting.ZNF418 was markedly down-regulated in hearts of cardiac hypertrophy and hypertrophic primary cardiomyocytes. Down-regulated ZNF418 exacerbated the myocyte size and fibrosis, moreover increased the mRNA levels of ANP, BNP, β-MHC, MCIP1.4, collagen 1a, collagen III, MMP-2 and fibronection in hearts of AB-treated ZNF418 knockout mice or Ang II-treated cardiomyocytes with AdshZNF418. Conversely, these hypertrophic responses were reduced in the ZNF418 transgenic (TG) mice treated by AB and the AdZNF418-transfected primary cardiomyocytes treated by Ang II. Additionally, the deficiency of ZNF418 enhanced the phosphorylation level of c-jun, and overexpression of ZNF418 suppressed the phosphorylation level of c-jun in vivo and in vitro.ZNF418 maybe attenuate hypertrophic responses by inhibiting the activity of c-jun/AP-1.

Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understood. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of "transductive" double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets. [ABSTRACT FROM AUTHOR]

PURPOSE To investigate the feasibility and usefulness of 2-deoxy-2-(18F)-fluoro-D-glucose positron emission tomography/computed tomography [(18F)-FDG PET/CT] as a novel examination in the surveillance of abnormal myocardial energy metabolism and cardiac dysfunction after cardiopulmonary resuscitation (CPR). METHODS Thirteen male Sprague-Dawley rats were randomly divided into a sham group (n = 4), CPR group (n = 4), and trimetazidine (TMZ) + CPR group (n = 5). The expression levels of the myocardial injury marker cardiac troponin I (CTNI) in serum were tested at 6 hours after CPR or TMZ + CPR. The ejection fraction and fraction shortening were evaluated by echocardiography. (18F)-FDG PET/CT was used to measure the FDG uptake and the standardized uptake value (SUV) after CPR or TMZ + CPR for 6 hours. The intermediary carbohydrate metabolites of glycolysis including phosphoenolpyruvate, 3-phospho-D-glycerate, and the lactate/pyruvate ratio were detected through the multiple reaction monitoring approach. Simultaneously, the authors also tested the expression levels of the total adenosine triphosphate (ATP) and the key intermediate products of glucose ovidation as alpha ketoglutarate, citrate, and succinate in the myocardium. RESULTS The authors found that the aerobic oxidation of glucose was reduced, and the anaerobic glycolysis was significantly enhanced in the myocardium in the early stage of CPR. Meanwhile, the myocardial injury marker CTNI was upregulated considerably (P = 0.014, P = 0.021), and the left ventricular function of the animal heart also markedly deteriorated with the downregulation of ATP after CPR. In contrast, myocardial injury and cardiac function were greatly improved with the increase of ATP in the CPR + TMZ group. In addition, aerobic glucose oxidation metabolites were significantly increased (P < 0.05) and anaerobic glycolysis metabolites were significantly decreased (P < 0.05) after CPR in the myocardium. Surprisingly, (18F)-FDG PET/CT could track the above changes by detecting the FDG uptake value and the SUV. CONCLUSION Glucose metabolism is an essential factor for myocardial self-repair after CPR. (18F) FDG PET/CT, as a non-invasive technology, can monitor myocardial energy metabolism and cardiac function by tracking changes in glucose metabolism after CPR. [ABSTRACT FROM AUTHOR]