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Three-dimensional (3D) printing technologies are advanced manufacturing technologies based on computer-aided design digital models to create personalized 3D objects automatically. They have been widely used in the industry, design, engineering, and manufacturing fields for nearly 30 years. Three-dimensional printing has many advantages in process engineering, with applications in dentistry ranging from the field of prosthodontics, oral and maxillofacial surgery, and oral implantology to orthodontics, endodontics, and periodontology. This review provides a practical and scientific overview of 3D printing technologies. First, it introduces current 3D printing technologies, including powder bed fusion, photopolymerization molding, and fused deposition modeling. Additionally, it introduces various factors affecting 3D printing metrics, such as mechanical properties and accuracy. The final section presents a summary of the clinical applications of 3D printing in dentistry, including manufacturing working models and main applications in the fields of prosthodontics, oral and maxillofacial surgery, and oral implantology. The 3D printing technologies have the advantages of high material utilization and the ability to manufacture a single complex geometry; nevertheless, they have the disadvantages of high cost and time-consuming postprocessing. The development of new materials and technologies will be the future trend of 3D printing in dentistry, and there is no denying that 3D printing will have a bright future.

In this paper, we consider the optimal resource allocation problem for multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems, which consists of beam-forming, user clustering and power allocation, respectively. Users can be divided into different clusters, and the users in the same cluster are served by the same beam vector. Inter-cluster orthogonality can be guaranteed based on multi-user detection (MUD). In this paper, we propose a three-step framework to solve the multi-dimensional resource allocation problem. In step 1, we propose a beam-forming algorithm for a given user cluster. Specifically, fractional transmitting power control (FTPC) is applied for intra-cluster power allocation. The considered beam-forming problem can be transformed into a non-constrained one and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method is applied to obtain the optimal solution. In step 2, optimal user clustering is further considered. Channel differences and correlations are both involved in the design of user clustering. By assigning different weights to the two factors, we can produce multiple candidate clustering schemes. Based on the proposed beam-forming algorithm, beam-forming can be done for each candidate clustering scheme to compare their performances. Moreover, based on the optimal user clustering and beam-forming schemes, in step 3, power allocation can be further optimized. Specifically, it can be formalized as a difference of convex (DC) programming problem, which is solved by successive convex approximation (SCA) with strong robustness. Simulations results show that the proposed scheme can effectively improve spectral efficiency (SE) and edge users’ data rates.

The Metabolic Theory of Ecology (MTE) proposes universal constants for the scaling of metabolic rate (BMR) with body mass and temperature. However, the validity of these constants across different biological hierarchies, specifically within versus between species, remains debated. Using a comprehensive dataset of 3767 metabolic measurements across 174 species, we tested for systematic differences in scaling relationships. We employed a unified linear mixed-effects model to estimate intraspecific parameters and phylogenetic generalized least squares (PGLS) regression for interspecific comparisons. Our results reveal a decoupling of scaling parameters across hierarchical levels. The overall intraspecific mass-scaling exponent (b = 0.760 ± 0.012, fixed effect ± SE) was not significantly different from the phylogenetically corrected interspecific exponent (0.768 ± 0.023). In contrast, the overall intraspecific activation energy (E = 0.601 ± 0.016 eV) was significantly higher than the attenuated interspecific value (0.403 ± 0.073 eV). Taxonomic variation was prominent for mass-scaling, with fish exhibiting a significantly higher exponent than most other groups, whereas activation energy did not differ significantly among groups. We conclude that while the mass-scaling relationship converges through interspecific averaging, the sensitivity of metabolism to temperature is robust within species but becomes diluted in broad-scale comparisons. This demonstrates that metabolic scaling is inherently hierarchical, necessitating scale-explicit models rather than the pursuit of universal constants.

Type 2 diabetes (T2D) is a major public health problem, and gut microbiota dysbiosis has been implicated in the emergence of T2D in humans. Dietary interventions can indirectly influence the health status of patients with type 2 diabetes through their modulatory effects on the intestinal microbiota. In recent years, fecal microbiota transplantation is becoming familiar as a new medical treatment that can rapidly improve intestinal health. We conducted a 90-day controlled open-label trial to evaluate the health improvement ability of a specially designed diet, and the diet combined with fecal microbiota transplantation (FMT). According to our study, both diet and diet plus FMT treatments showed great potential in controlling blood glucose and blood pressure levels. Sequencing the V4 region of 16S rRNA gene on the Illumina MiniSeq platform revealed a shift of intestinal microbial community in T2D patients, and the changes were also observed in response to the treatments. FMT changed the gut microbiota more quickly than diet. Beneficial bacterium, such as Bifidobacterium , increased along the study and was negatively correlated with blood glucose, blood pressure, blood lipid and BMI. Sulfate-reducing bacteria (SRB), Bilophila and Desulfovibrio , decreased significantly after treatment, showed a positive correlation with blood glucose indices. Thus, the specially designed diet is beneficial to improve blood glucose control in diabetic patients, it also showed the potential to reverse dyslipidemia and dysarteriotony.

Spontaneous intracerebral hemorrhage (sICH) is associated with high mortality and disability, with early hematoma expansion (EHE) being a key factor in poor prognosis. The most effective strategies for predicting EHE and improving patient outcomes remain unclear. This study systematically reviewed clinical studies on EHE in sICH from the PubMed database (2013–2023) to identify key predictors for a prediction model. Key factors included: Hematoma hypodensity, Island sign, Glasgow Coma Scale, Hematoma in ventricular, and Taking anticoagulant(H-I-G-H-T). The HIGHT model was developed based on these predictors and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. A dynamic nomogram (DN) was created for clinical use. Results: The model was tested in a retrospective cohort of 532 patients, yielding a good fit (AUC = 0.706, P  < 0.0001). In a prospective cohort of 83 patients, the AUC was 0.642 ( P  = 0.048), with sensitivity of 93.75% and specificity of 32.84%.Conclusion: The HIGHT prediction model demonstrated It has shown a certain extent predictive effectiveness in both retrospective and prospective evaluations. And the online DN is now available for clinical use.

The objectives of this study were to investigate the associations of single and mixed exposure to the environmental pollutants per- and polyfluoroalkyl substances (PFAS) with renal function and mortality in non-dialysis chronic kidney disease (CKD) patients. Non-dialysis CKD1-4 stage patients in the 2003-2018 US National Health and Nutrition Examination Survey (NHANES) who were ≥20 years old were included. Five PFAS were measured and all patients were followed up till 31 December 2019. Multivariate linear, logistic, and Cox regressions were used to evaluate the associations between PFAS exposure and renal function, mortality. Stratified subgroups were analyzed based on baseline characteristics. Bayesian kernel machine regression (BKMR) was used in sensitivity analysis. Among 1503 CKD patients included, baseline renal function declined in 701 patients (44.4%) and 462 patients (24.9%) died during the follow-up. Single exposure to perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonic acid (PFHxS) was positively associated with renal function decline (  < .05). Mixed exposure to five kinds of PFAS was found to be associated with renal function decline. Restricted cubic spline (RCS) showed only PFOS had an inverted U-shaped association with renal function decline ( non-linear < .05). There was no statistically significant association between PFAS exposure and mortality. Urinary protein and drug use might interact with the associations between PFAS and renal function. PFAS single and mixed exposure were closely related to renal function and renal progression in adult CKD patients. There was no statistically significant association between PFAS exposure and mortality.

We present the first example of visible-light-mediated multicomponent annulation of 1,7-diynes by taking advantage of quadruple cleavage olf carbon-halogen bonds of BrCCl3 to generate a C1 synthon, which was adeptly applied to the preparation of skeletally diverse 3-benzoyl-quinolin-2(1H)-one acetates in moderate to good yields. Controlled experiments demonstrated that H2O acted as both oxygen and hydrogen sources, and gem-dichlorovinyl carbonyl compound exhibited as a critical intermediate in this process. The mechanistic pathway involves Kharasch-type addition/6-exo-dig cyclization/1,5-(SN”)-substitution/elimination/binucleophilic 1,6-addition/proton transfer/tautomerization sequence.

Discursive practice in political discourse translation is an underexplored research area from the perspective of critical discourse analysis. To fill this gap, this research proposes a framework including national consciousness, intersubjectivity, and social context to investigate the discursive production, and distribution of translated white papers on China-US economic and trade friction issued by China, and its discursive consumption on social media. This study applies dual narrative progression theory to analyze the discursive production of the translation, with an emphasis on how these translations construct and express national image and ideology. Furthermore, the approach considers foreign news reports and public stances within the target social context, which covers distribution and consumption in discursive practice. It is shown that the meaning and production of national images are predominantly influenced by national consciousness, and cultural and ideological negotiations exist among translation subjects. Although translated white papers describe China as a defender of national interests and a supporter of dialog, foreign news reports present China with a more unyielding and aggressive stance. This portrayal could influence public perceptions on the issue, and it could also be influenced by those perceptions. This paper reveals that translating political discourse is a form of social practice and highlights the social functions that discursive practice plays in this process.

Identifying locoregional gastric cancer patients who are at high risk for relapse after resection could facilitate early intervention. By detecting molecular residual disease (MRD), circulating tumor DNA (ctDNA) has been shown to predict post-operative relapse in several cancers. Here, we aim to evaluate MRD detection by ctDNA and its association with clinical outcome in resected gastric cancer. This prospective cohort study enrolled 46 patients with stage I–III gastric cancer that underwent resection with curative intent. Sixty resected tumor samples and 296 plasma samples were obtained for targeted deep sequencing and longitudinal ctDNA profiling. ctDNA detection was correlated with clinicopathologic features and post-operative disease-free (DFS) and overall survival (OS). ctDNA was detected in 45% of treatment-naïve plasma samples. Primary tumor extent (T stage) was independently associated with pre-operative ctDNA positivity ( p  = 0.006). All patients with detectable ctDNA in the immediate post-operative period eventually experienced recurrence. ctDNA positivity at any time during longitudinal post-operative follow-up was associated with worse DFS and OS (HR = 14.78, 95%CI, 7.991–61.29, p  < 0.0001 and HR = 7.664, 95% CI, 2.916–21.06, p  = 0.002, respectively), and preceded radiographic recurrence by a median of 6 months. In locoregional gastric cancer patients treated with curative intent, these results indicate that ctDNA-detected MRD identifies patients at high risk for recurrence and can facilitate novel treatment intensification studies in the adjuvant setting to improve survival.

Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessment model to predict the risk of lower extremity DVT during hospitalization in patients with spontaneous cerebral hemorrhage. The retrospective study began by randomly dividing the data into a training set and a test set in a 7:3 ratio. Feature selection was performed in the training set, and Boruta and LASSO algorithms were used to screen significant predictors. Five machine learning algorithms were used to construct the prediction model and the model accuracy was evaluated by ROC curves. To validate the model, we constructed calibration curves and compared the calibration of the model using the Brier score. Finally, the clinical value of the model was assessed by Decision Clinical Curve (DCA) and the “black box” model was interpreted by SHAP. The training and test sets did not show significant differences between the individual variables. Screening by the LASSO and Boruta algorithms yielded 15 and 7 potentially relevant variables, respectively, resulting in the identification of six significant predictors associated with DVT. Subsequently, the performance of five machine learning algorithms in DVT prediction was evaluated in the test set. These results suggest that the LGBM model has significant advantages in predicting DVT after cerebral hemorrhage. We developed a model to predict the risk of lower extremity deep vein thrombosis during hospitalization in patients with spontaneous cerebral hemorrhage, and this model can accurately identify high-risk patients.