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.Coatings are playing an important role in corrosion mitigation of magnesium alloys, and in this study, a facile and eco-friendly chemical deposition process is proposed to improve the corrosion resistance of magnesium-neodymium alloys. The mixture of 1.5 mol/L KH2PO4 solution and 1.2 mol/L CaCl2 solution is used for reaction solution, and ultrasound is introduced into the process for assisting the chemical deposition. After 40 minutes of the surface treatment, the surface and cross-sectional morphologies are observed by scanning electron microscope (SEM), which reveals that a layer of dense coating is formed on Mg alloy. Energy-dispersive X-ray spectroscopy (EDS) and X-ray Diffraction (XRD) are further combined to analyze the coating, and it is thereby confirmed that this coating mainly consists of CaHPO4·2H2O. Electrochemical tests and soaking experiments are conducted to evaluate the corrosion resistance of the treated samples in simulated concrete pore solutions. Both the untreated and treated samples have a good corrosion resistance in the Cl- free simulated concrete pore solution, but their corrosion behavior is influenced by the introduction of Cl- in this study. Fortunately, the coating can protect the substrate effectively in the Cl- containing simulated concrete pore solution. In summary, it provides a possible way for magnesium alloys to improve their corrosion resistance when they are used in building engineering.Coupling between plasmonic nanoparticles (NPs) in nanoparticle assemblies has been investigated extensively via far-field properties, such as absorption and scattering, but very rarely via near-field properties, and a quantitative investigation of near-field properties should provide great insight into the nature of the coupling. We report a numerical procedure to obtain reliable near-field spectra (Q NF) around spherical gold nanoparticles (Au NPs) using Discrete Dipole Approximation (DDA). The reliability of the method was tested by comparing Q NF from DDA calculations with exact results from the Mie theory. We then applied the method to examine Au NPs assembled in dimer, trimer, and up to pentamer in a linear arrangement. For the well-studied dimer system, we show that the Q NF enhancement, due to coupling in longitudinal mode, is much greater than the enhancement in Q ext. There is a linear correlation between the Q NF and Q ext peak positions, with the Q NF peak redshifted from the Q ext peak by an averatract from the incident E-field. These results provide new insight into the coupling properties of Au NPs.Induced hyperthermia has been demonstrated as an effective oncological treatment due to the reduced heat tolerance of most malignant tissues; however, most techniques for heat generation within a target volume are insufficiently selective, inducing heating and unintended damage to surrounding healthy tissues. Plasmonic photothermal therapy (PPTT) utilizes light in the near-infrared (NIR) region to induce highly localized heating in gold nanoparticles, acting as exogenous chromophores, while minimizing heat generation in nearby tissues. However, optimization of treatment parameters requires extensive in vitro and in vivo studies for each new type of pathology and tissue targeted for treatment, a process that can be substantially reduced by implementing computational modeling. Herein, we describe the development of an innovative model based on the finite element method (FEM) that unites photothermal heating physics at the nanoscale with the micron scale to predict the heat generation of both single and arrays of gold nanoparticles. Plasmonic heating from laser illumination is computed for gold nanoparticles with three different morphologies nanobipyramids, nanorods, and nanospheres. Model predictions based on laser illumination of nanorods at a visible wavelength (655 nm) are validated through experiments, which demonstrate a temperature increase of 5 °C in the viscinity of the nanorod array when illuminated by a 150 mW red laser. We also present a predictive model of the heating effect induced at 810 nm, wherein the heating efficiencies of the various morphologies sharing this excitation peak are compared. Our model shows that the nanorod is the most effective at heat generation in the isolated scenario, and arrays of 91 nm long nanorods reached hyperthermic levels (an increase of at least 5 °C) within a volume of over 20 μm3.Graphical model is a powerful and popular approach to study high-dimensional omic data, such as genome-wide gene expression data. Nonlinear relations between genes are widely documented. However, partly due to sparsity of data points in high dimensional space (i.e., curse of dimensionality) and computational challenges, most available methods construct graphical models by testing linear relations. We propose to address this challenge by a two-step approach first use a model-free approach to prioritize the neighborhood of each gene, then apply a non-parametric conditional independence testing method to refine such neighborhood estimation. Our method, named as „mofreds” (MOdel FRee Estimation of DAG Skeletons), seeks to estimate the skeleton of a directed acyclic graph (DAG) by this two-step approach. We studied the theoretical properties of mofreds, and evaluated its performance in extensive simulation settings. We found mofreds has substantially better performance than the state-of-the art method which is designed to detect linear relations of Gaussian graphical models. We applied mofreds to analyze gene expression data of breast cancer patients from The Cancer Genome Atlas (TCGA). We found that it discovers non-linear relationships among gene pairs that are missed by the Gaussian graphical model methods.A patient’s medical problem list describes his or her current health status and aids in the coordination and transfer of care between providers. Because a problem list is generated once and then subsequently modified or updated, what is not usually observable is the provider-effect. That is, to what extent does a patient’s problem in the electronic medical record actually reflect a consensus communication of that patient’s current health status? To that end, we report on and analyze a unique interview-based design in which multiple medical providers independently generate problem lists for each of three patient case abstracts of varying clinical difficulty. Due to the uniqueness of both our data and the scientific objectives of our analysis, we apply and extend so-called multistage models for ordered lists and equip the models with variable selection penalties to induce sparsity. Each problem has a corresponding non-negative parameter estimate, interpreted as a relative log-odds ratio, with larger values suggesting greater importance and zero values suggesting unimportant problems. We use these fitted penalized models to quantify and report the extent of consensus. We conduct a simulation study to evaluate the performance of our methodology and then analyze the motivating problem list data. For the three case abstracts, the proportions of problems with model-estimated non-zero log-odds ratios were 10/28, 16/47, and 13/30. Physicians exhibited consensus on the highest ranked problems in the first and last case abstracts but agreement quickly deteriorated; in contrast, physicians broadly disagreed on the relevant problems for the middle – and most difficult – case abstract.Intracellular drug delivery by rapid squeezing is one of the most recent and simple cell membrane disruption-mediated drug encapsulation approaches. In this method, cell membranes are perforated in a microfluidic setup due to rapid cell deformation during squeezing through constricted channels. While squeezing-based drug loading has been successful in loading drug molecules into various cell types, such as immune cells, cancer cells, and other primary cells, there is so far no comprehensive understanding of the pore opening mechanism on the cell membrane and the systematic analysis on how different channel geometries and squeezing speed influence drug loading. This article aims to develop a three-dimensional computational model to study the intracellular delivery for compound cells squeezing through microfluidic channels. The Lattice Boltzmann method, as the flow solver, integrated with a spring-connected network via frictional coupling, is employed to capture compound capsule dynamics over fast squeezing. Thocity, variations of axial and vertical cell dimensions, cell porosity, and normalized drug concentration to shed light on the underlying physics in fast squeezing-based drug delivery. Consistent with experimental findings in the literature, the numerical results confirm that constriction width reduction, constriction length enlargement, and average cell velocity promote intracellular drug delivery. The results show that the existence of the nucleus increases cell porosity and loaded drug concentration after squeezing. Given geometrical parameters and cell average velocity, the maximum porosity is achieved at three different locations constriction entrance, constriction middle part, and outside the constriction. Our numerical results provide reasonable justifications for experimental findings on the influences of constriction geometry and cell velocity on the performance of cell-squeezing delivery. We expect this model can help design and optimize squeezing-based cargo delivery.Microfluidics-enhanced bioprinting holds great promise in the field of biofabrication as it enables the fabrication of complex constructs with high shape fidelity and utilization of a broad range of bioinks with varying viscosities. Microfluidic systems contain channels on the micrometer-scale, causing a change in fluid behaviors, enabling unconventional bioprinting applications such as facilitating the precise spatial positioning and switching between bioinks with higher accuracy compared to traditional approaches. These systems can roughly be divided into three groups microfluidic chips, co- and triaxial printheads, and printheads combining both. Although several aspects and parameters remain to be improved, this technology is promising as it is a step toward recapitulating the complex native histoarchitecture of human tissues more precisely. In this Perspective, key research on these different systems will be discussed before moving onto the limitations and outlook of microfluidics-enhanced bioprinting as a whole.