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Road extraction from high-resolution remote sensing images has been an important research problem for decades. Despite the breakthrough progress of road extraction studies in recent years due to the rapid advancement of deep learning techniques in a remote sensing domain, the vast majority of methods pay little attention to the basic structure of roads. Especially in complex scenes such as a tree or shadow occlusion and stacking of multiple roads, existing road extraction methods still suffer from generating broken road surfaces, inaccurate topology, and connections. In this work, we propose a novel structure-aware road extraction method, named RoadCorrector, which solves the above limitations of existing methods by adding structure-related assistance branches and two correction modules. Specifically, RoadCorrector encompasses three main stages: road segmentation, connectivity refinement, and topology correction. First, we design a multibranch road extraction network (MBRE-Net) that combines intersection and frequency domain information to efficiently extract road masks. Subsequently, a connectivity refinement strategy based on energy function is introduced to deal with the discontinuity of roads in the occluded and intersection regions. Finally, the topology correction module aims at constructing vectorized road networks with more accurate connection relations. Experimental results on several public datasets show that our RoadCorrector achieves remarkable improvements compared with state-of-the-art methods, with the $F1$ -score and intersection over union (IoU) improved by 3.3%–4.5% and 2.0%–5.1%, respectively. Moreover, the road network extraction results of RoadCorrector have more accurate topological properties, demonstrating its great potential in actual application scenes. The code and dataset of RoadCorrector will be released at https://github.com/Lijp411/RoadCorrector.

Purpose: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. Methods: OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. Results: Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. Conclusion: OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well.

Chemical cleaning and disinfection are crucial steps for eliminating infection in root canal treatment. However, irrigant selection or irrigation procedures are far from clear. The vapor lock effect in the apical region has yet to be solved, impeding irrigation efficacy and resulting in residual infections and compromised treatment outcomes. Additionally, ambiguous clinical indications for root canal medication and non-standardized dressing protocols must be clarified. Inappropriate intracanal medication may present side effects and jeopardize the therapeutic outcomes. Indeed, clinicians have been aware of these concerns for years. Based on the current evidence of studies, this article reviews the properties of various irrigants and intracanal medicaments and elucidates their effectiveness and interactions. The evolution of different kinetic irrigation methods, their effects, limitations, the paradigm shift, current indications, and effective operational procedures regarding intracanal medication are also discussed. This expert consensus aims to establish the clinical operation guidelines for root canal irrigation and a position statement on intracanal medication, thus facilitating a better understanding of infection control, standardizing clinical practice, and ultimately improving the success of endodontic therapy.

Endodontic diseases are a kind of chronic infectious oral disease. Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha. However, it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy (RCT). Recent research, encompassing bacterial etiology and advanced imaging techniques, contributes to our understanding of the root canal system’s anatomy intricacies and the technique sensitivity of RCT. Success in RCT hinges on factors like patients, infection severity, root canal anatomy, and treatment techniques. Therefore, improving disease management is a key issue to combat endodontic diseases and cure periapical lesions. The clinical difficulty assessment system of RCT is established based on patient conditions, tooth conditions, root canal configuration, and root canal needing retreatment, and emphasizes pre-treatment risk assessment for optimal outcomes. The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT. These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics.

Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratification of gliomas. Herein, we established three separate case cohorts, consisting of 655 glioma patients, and carried out a retrospective study. Image and clinical data of three cohorts were used for training (N= 188), cross-validation (N= 411), and independent testing (N= 56) of the IS model. All tumors were segmented from magnetic resonance (MR) images by the 3D U-net, followed by extraction of high-throughput network features, which were referred to as IS. IS was then used to perform noninvasive histopathological diagnosis and molecular subtyping. Moreover, a new IS-based clustering method was applied for prognostic stratification in IDH-wild-type lower-grade glioma (IDHwt LGG) and triple-negative glioblastoma (1p19q retain/IDH wild-type/TERTp-wild-type GBM). The average accuracies of histological diagnosis and molecular subtyping were 89.8 and 86.1% in the cross-validation cohort, while these numbers reached 83.9 and 80.4% in the independent testing cohort. IS-based clustering method was demonstrated to successfully divide IDHwt LGG into two subgroups with distinct median overall survival time (48.63 vs 38.27 months respectively, P= 0.023), and two subgroups in triple-negative GBM with different median OS time (36.8 vs 18.2 months respectively, P= 0.013). Our findings demonstrate that our novel IS-based radiomics model is an effective tool to achieve noninvasive histo-molecular pathological diagnosis and prognostic stratification of gliomas. This IS model shows potential for future routine use in clinical practice.

Glioma, a type of brain tumor, is one of the most challenging cancers for human beings due to its genetic heterogeneity, high recurrence rate and difficulty of effective treatment. To explore a novel treatment method for glioma, a scientific hypothesis was proposed that a chemical substance or biological agent may generate a characteristic magnetic signal, and this signal is the fundamental reason that the chemical substance or biological agent works for cancer treatment. Based on this hypothesis, a superconducting quantum interference device (SQUID) using the stochastic resonance (SR) method was established, and the characteristic magnetic signal of paclitaxel was detected and clearly visualized for the first time. Then, cell experimental results demonstrated that the detected characteristic magnetic signal of paclitaxel was as effective as paclitaxel for the inhibition of glioma cells. Our study suggested that the application of paclitaxel's characteristic magnetic signal might provide an ideal treatment direction for glioma patients due to its lack of side effects, high focusable penetration and effectiveness.

Based on the reforecast data (1999–2010) of three operational models [the China Meteorological Administration (CMA), the National Centers for Environmental Prediction of the U.S. (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF)] that participated in the Subseasonal to Seasonal Prediction (S2S) project, we identified the major sources of subseasonal prediction skill for heatwaves over the Yangtze River basin (YRB). The three models show limited prediction skills in terms of the fraction of correct predictions for heatwave days in summer; the Heidke Skill Score drops quickly after a 5-day forecast lead and falls down close to zero beyond the lead time of 15 days. The superior skill of the ECMWF model in predicting the intensity and duration of the YRB heatwave is attributable to its fidelity in capturing the phase evolution and amplitude of high-pressure anomalies associated with the intraseasonal oscillation and the dryness of soil moisture induced by less precipitation via the land-atmosphere coupling. The effects of 10–30-day and 30–90-day circulation prediction skills on heatwave predictions are comparable at shorter forecast leads (10 days), while the biases in 30–90-day circulation amplitude prediction show close connection with the degradation of heatwave prediction skill at longer forecast leads (> 15–20 days). The biases of intraseasonal circulation anomalies further affect precipitation anomalies and thus land conditions, causing difficulty in capturing extremely hot days and their persistence in the S2S models.

Purpose: The evaluation of fetal lung maturity is critical for clinical practice since the lung immaturity is an important cause of neonatal morbidity and mortality. For the evaluation of the development of fetal lung maturation degree, our study established a deep model from ultrasound images of four-cardiac-chamber view plane. Methods: A two-stage transfer learning approach is proposed for the purpose of the study. A specific U-net structure is designed for the applied deep model. In the first stage, the model is to first learn the recognition of fetal lung region in the ultrasound images. It is hypothesized in our study that the development of fetal lung maturation degree is generally proportional to the gestational age. Then, in the second stage, the pretrained deep model is trained to accurately estimate the gestational age from the fetal lung region of ultrasound images. Results: Totally 332 patients were included in our study, while the first 206 patients were used for training and the subsequent 126 patients were used for the independent testing. The testing results of the established deep model have the imprecision as 1.56 ± 2.17 weeks on the gestational age estimation. Its correlation coefficient with the ground truth of gestational age achieves 0.7624 (95% CI 0.6779 to 0.8270, Pvalue < 0.00001). Conclusion: The hypothesis that the development of fetal lung maturation degree can be represented by the texture information from ultrasound images has been preliminarily validated. The fetal lung maturation degree can be considered as being represented by the deep model’s output denoted by the estimated gestational age.

ABSTRACTAs one of the most widely used medical imaging methods, magnetic resonance imaging (MRI) and its corresponding automatic image analysis are research hotspots in computer-aided medical diagnosis. However, due to the disparities in imaging parameters and scanner characteristics, the variation in intensity distribution between scanners results in performance reduction in automatic image analysis and diagnosis. This paper aims at obtaining sub-region intensity distribution and forming a robust non-rigid intensity transforming function in order to standardise the intensities of MR images. The proposed method includes multi-modality image registration, sub-region standard intensity estimation, weighted -dimensional point cloud generation and global intensity transformation. Between the target images and reference images, this novel method could not only avoid the intensity distortion due to the inconsistency of inter-tissue brightness relationship, but also reduce the dependence on the accuracy of multi-modality MR image registration. Experiments show performance enhancement in peak signal-to-noise ratio, average disparity, histogram correlation, mean square error and structural similarity over the existing methods. Therefore, the intensities of MR images from different scanners could be standardised by the proposed method, so that multi-centre/multi-machine correlation could be promoted.

Microvascularity is highly correlated with the grading and subtyping of gliomas, making this one of its most important histological features. Accurate quantitative analysis of microvessels is helpful for the development of a targeted therapy for antiangiogenesis. The deep-learning algorithm is by far the most effective segmentation and detection model and enables location and recognition of complex microvascular networks in large images obtained from hematoxylin and eosin (H&E) stained specimens. We proposed an automated deep-learning-based method to detect and quantify the microvascularity in glioma and applied it to comprehensive clinical analyses. A total of 350 glioma patients were enrolled in our study, for which digitalized imaging of H&E stained slides were reviewed, molecular diagnosis was performed and follow-up was investigated. The microvascular features were compared according to their histologic types, molecular types, and patients’ prognosis. The results show that the proposed method can quantify microvascular characteristics automatically and effectively. Significant increases of microvascular density and microvascular area were observed in glioblastomas (95% p< 0.001 in density, 170% p< 0.001 in area) in comparison with other histologic types; increases were also observed in cases with TERT-mut only (68% p< 0.001 in density, 54% p< 0.001 in area) compared with other molecular types. Survival analysis showed that microvascular features can be used to cluster cases into two groups with different survival periods (hazard ratio [HR] 2.843, log-rank <0.001), which indicates the quantified microvascular features may potentially be alternative signatures for revealing patients’ prognosis. This deep-learning-based method may be a useful tool in routine clinical practice for precise diagnosis and antiangiogenic treatment.

In the presence of an MMC-HVDC system, current differential protection (CDP) has the risk of failure in operation under an internal fault. In addition, CDP may also incur security issues in the presence of current transformer (CT) saturation and outliers. In this paper, a current trajectory image-based protection algorithm is proposed for AC lines connected to MMC-HVDC stations using a convolution neural network improved by a channel attention mechanism (CA-CNN). Taking the dual differential currents as two-dimensional coordinates of the moving point, the moving-point trajectories formed by differential currents have significant differences under internal and external faults. Therefore, internal faults can be identified using image recognition based on CA-CNN. This is improved by a channel attention mechanism, data augmentation, and adaptive learning rate. In comparison with other machine learning algorithms, the feature extraction ability and accuracy of CA-CNN are greatly improved. Various fault conditions like different network structures, operation modes, fault resistances, outliers, and current transformer saturation, are fully considered to verify the superiority of the proposed protection algorithm. The results confirm that the proposed current trajectory image-based protection algorithm has strong learning and generalizability, and can identify internal faults reliably.

Macrophage performs multiple functions such as pathogen phagocytosis, antigen presentation, and tissue remodeling by polarizing toward a spectrum of phenotypes. Dynamic imaging of macrophage phenotypes is critical for evaluating disease progression and the therapeutic response of drug candidates. However, current technologies cannot identify macrophage phenotypes in vivo. Herein, a surface‐enhanced Raman scattering nanoprobe, AH1, which enables the accurate determination of physiological pH with high sensitivity and tissue penetration depth through ratiometric Raman signals is developed. Due to the phenotype‐dependent metabolic reprogramming, AH1can effectively identify macrophage subpopulations by measuring the acidity levels in phagosomes. After intravenous administration, AH1not only visualizes the spatial distribution of macrophage phenotypes in brain tumors and epileptic regions of mouse models, but also reveals the repolarization of macrophages in brain lesions after drug intervention. This work provides a new tool for dynamically monitoring the disease‐associated immune microenvironment and evaluating the efficacy of immune‐therapeutics in vivo. A pH ratiometric surface‐enhanced Raman scattering (SERS) probe is developed for identifying macrophage phenotypes in vivo by determining the phagosomal acidities. This SERS probe holds the promise to assess immune microenvironment of diseases and the immunotherapeutic response of drug candidates by monitoring macrophage polarization.

Maize (Zea Mays) is the major food crop in Kenya. Its production variation has devastating consequences on people’s basic food availability. This study will investigate the relationships between climate variability and maize yield using observed weather data from Kenya Meteorological Department and national annual maize yield data from the Ministry of Agriculture for the period 1979–2012. Mann–Kendall test was used to detect a trend in precipitation, minimum, and maximum temperature. Location-wise correlation method was performed between each climate variable and maize yield in every station. Stations which had significant correlations were aggregated to form climate indices which were used to build multiple linear regression model. The results revealed that maize yield in Kenya was significantly decreasing at a rate of 0.07 tons/ha/decade at the 95% confidence level accompanied by high inter-annual variation, while world average was increasing at a rate of 0.6 tons/ha/decade. This reduction was accredited to a significant increasing temperature and reduction in seasonal rainfall. Empirical relationship derived from multiple regression models indicates that 67.53% of yield variance was attributed to varying seasonal climate indices. Precipitation is the dominant predictor accounting to 49.73% of yield variance. There is a significant correlation of 0.78 between the modeled and observed yield hence high credibility of the statistical model. A Continuous decrease of maize yield is expected under the influence of climate change which threatens national food security if effective measures to raise maize production are not endorsed. These findings form a framework for designing policies geared towards the reduction of climate-related vulnerability in many parts of the world.

B-mode ultrasound (B-US) and strain elastography ultrasound (SE-US) images have a potential to distinguish thyroid tumor with different lymph node (LN) status. The purpose of our study is to investigate whether the application of multi-modality images including B-US and SE-US can improve the discriminability of thyroid tumor with LN metastasis based on a radiomics approach. Ultrasound (US) images including B-US and SE-US images of 75 papillary thyroid carcinoma (PTC) cases were retrospectively collected. A radiomics approach was developed in this study to estimate LNs status of PTC patients. The approach included image segmentation, quantitative feature extraction, feature selection and classification. Three feature sets were extracted from B-US, SE-US, and multi-modality containing B-US and SE-US. They were used to evaluate the contribution of different modalities. A total of 684 radiomics features have been extracted in our study. We used sparse representation coefficient-based feature selection method with 10-bootstrap to reduce the dimension of feature sets. Support vector machine with leave-one-out cross-validation was used to build the model for estimating LN status. Using features extracted from both B-US and SE-US, the radiomics-based model produced an area under the receiver operating characteristic curve (AUC) $$=$$ = 0.90, accuracy (ACC) $$=$$ = 0.85, sensitivity (SENS) $$=$$ = 0.77 and specificity (SPEC) $$=$$ = 0.88, which was better than using features extracted from B-US or SE-US separately. Multi-modality images provided more information in radiomics study. Combining use of B-US and SE-US could improve the LN metastasis estimation accuracy for PTC patients.

Inhibiting β-amyloid (Aβ)-induced microglial activation is proposed as an effective strategy for the treatment of Alzheimer’s disease. Tetrahydroxystilbene glycoside (TSG) is the main active ingredient of Polygonum multiflorumand has a wide range of biological properties, including antiinflammation. Here, we focused on the function and regulatory mechanism of TSG in Aβ-induced N9 and BV2 cells. The results showed that Aβ treatment induced the activation of microglia cells and the production of inflammatory molecules, including inducible nitric oxide synthase, nitric oxide, cyclooxygenase 2, and prostaglandin E2, which were significantly inhibited by TSG pretreatment. Furthermore, we found Aβ exposure increased the levels of microglial M1 markers, interleukin (IL)-1β, IL-6, and tumor necrosis factor α, and the pretreatment of TSG suppressed the increase of M1 markers and enhanced the levels of M2 markers, including IL-10, brain-derived neurotrophic factor, glial cell-derived neurotrophic factor, and arginase-1. PU.1 overexpression was found to eradicate the anti-inflammatory effects of TSG in Aβ-induced microglial cells. Taken together, these findings indicate that TSG attenuates Aβ-induced microglial activation and polarizes microglia towards M2 phenotype, which may be closely associated with the regulation of PU.1.

The geometric characteristics of tropical cyclone (TC) eyes before landfall in South China are examined using ground-based radar reflectivity. It is found that the median and mean eye area decrease with TC intensity, except for the severe typhoon category, and the eye size increases with height. The increasing rate of eye size is relatively greater in upper layers. Moreover, the ratio of eye size change in the vertical direction does not correlate with TC intensity. No relationship is presented between the ratio of eye size change in the vertical direction and the vertical wind shear. No relationship between the vertical change in eye size and the eye size at a certain level is found, inconsistent with other studies. No relationship exists between the vertical change in eye size and the intensity tendency. The eye roundness values range mainly from 0.5 to 0.7, and more intense TCs generally have eyes that are more circular. 本研究利用地基雷达反射率研究登陆华南的热带气旋在登陆前眼的几何特征. 研究发现尽管眼的面积有随热带气旋的增强而减小的趋势, 但这种关系在台风和强台风强度之间不成立. 眼的面积随高度增加, 且在对流层高层增大较快. 眼的面积在垂直方向上的变化与强度、强度变化、垂直风切变及眼本身大小没有统计关系. 眼的圆度值大多集中于0.5-0.7之间, 通常热带气旋越强则眼越圆.

This work focused on extracting novel and validated digital high‐throughput features to present a detailed and comprehensive description of the American College of Radiology Breast Imaging Reporting and Data System (BI‐RADS) with the goal of improving the accuracy of ultrasound breast cancer diagnosis. First, the phase congruency approach was used to segment the tumors automatically. Second, high‐throughput features were designed and extracted on the basis of each BI‐RADS category. Then features were selected based on the basis of a Student ttest and genetic algorithm. Finally, the AdaBoost classifier was used to differentiate benign tumors from malignant ones. Experiments were conducted on a database of 138 pathologically proven breast tumors. The system was compared with 6 state‐of‐art BI‐RADS feature extraction methods. By using leave‐one‐out cross‐validation, our system achieved a highest overall accuracy of 93.48%, a sensitivity of 94.20%, a specificity of 92.75%, and an area under the receiver operating characteristic curve of 95.67%, respectively, which were superior to those of other methods. The experiments demonstrated that our computerized BI‐RADS feature system was capable of helping radiologists detect breast cancers more accurately and provided more guidance for final decisions.

As a common but critical dynamic crossover in glass-forming liquids (GFLs), the discovery of fragile-to-strong (F-S) transition promises a novel route for understanding supercooled liquid and glass transition. The present work, for the first time, successfully realizes the quantitative prediction of the F-S transition in nine metallic glass-forming liquids, by a counter-intuitive approach that focuses on local atomic activation events, rather than relaxation, upon cooling. The dynamic crossover originates from a disorder-to-order transition by self-regulating behavior of atomic position within a cage controlled by finite atomic activation events, due to the appearance of local cooperative motion of nearest neighborhood atoms. Moreover, the dominant role of entropy in this anomaly has been discovered, and the correspondence between the crossover of configuration entropy involved in activation events and the occurrence of F-S transition has been found. Our work implies that the feature of atomic energy fluctuations reflected by atomic activation events has a close linkage to complex dynamic behaviors of disordered systems.

The short-lag spatial coherence (SLSC) imaging has been demonstrated to be advantageous over the traditional B-mode ultrasound imaging. With focused scanning beams, the SLSC imaging has an excellent performance in clutter reduction and lesion detection, especially in the low signal-to-noise ratio (SNR) scenarios. The synthetic aperture (SA) imaging is an appropriate mode for the SLSC imaging as the dynamic transmit focusing could keep a good focusing quality at any depth. However, the SLSC image may still suffer a bad resolution performance when a low lag value is used in the coherence summation to ensure the contrast enhancement. In this paper, an adaptive synthetic transmit (Tx) aperture focusing strategy is proposed for the SLSC imaging with the SA mode. Based on the achievements of adaptive beamforming, a minimum variance beamformer is applied in the Tx aperture to realize adaptive focusing. Spatial coherence is then measured in the receive aperture to form the SLSC image. Simulation and experimental studies were conducted to evaluate the proposed method. Experiments showed that the proposed method not only improved the poor resolution of the original SLSC image but also enhanced the speckle performance, which led to increased contrast-to-noise ratio and speckle SNR values.

In ultrasound (US), optical coherence tomography, synthetic aperture radar, and other coherent imaging systems, images are corrupted by multiplicative speckle noise that obscures image interpretation. An anisotropic diffusion (AD) method based on the Gabor transform, named Gabor-based anisotropic diffusion (GAD), is presented to suppress speckle in medical ultrasonography. First, an edge detector using the Gabor transform is proposed to capture directionality of tissue edges and discriminate edges from noise. Then the edge detector is embedded into the partial differential equation of AD to guide the diffusion process and iteratively denoise images. To enhance GAD’s adaptability, parameters controlling diffusion are determined from a fully formed speckle region that is automatically detected. We evaluate the GAD on synthetic US images simulated with three models and clinical images acquired in vivo. Compared with seven existing speckle reduction methods, the GAD is superior to other methods in terms of noise reduction and detail preservation.

An easy practical algebraic algorithm was proposed for partial expansion of rational functions with multiple high-order poles. The simple recursive implementation of the proposed method involves neither long division nor differentiation and requires only elementary arithmetic operations. It is suitable for computer or hand calculation of partial expansion of both proper and improper functions with multiple high-order poles with desirable accuracy. [ABSTRACT FROM AUTHOR]

Stem cell–mediated root regeneration offers opportunities to regenerate a bio-root and its associated periodontal tissues to restore tooth loss. Periodontal ligament (PDL) and cementum complex and dentin pulp complex have been tissue engineered using human dental pulp stem cells and PDL stem cells, respectively. The aim of this study was to explore whether dentin formation could be induced using an inductive substrate and whether bioengineered dentin could induce cementum and PDL formation. First, dentin was bioengineered from tooth papillae of Sprague-Dawley (SD) rats with an inductive substrate, and its phenotype was characterized; then primarily cultured human PDL cells were seeded on the surface of dentin and transplanted under the skin of immunocompromised mice. Histological, immunohistochemical, and scanning electronic microscopy examinations results showed that bioengineered dentin could induce cementogenesis and PDL formation, and condense PDL arranged perpendicularly on the dentin surface via a layer of cementum-like tissue. The results indicated that tissue-engineered dentin could be induced using an inductive substrate and could be used as a further substrate for cementum and PDL tissue engineering.

Tooth loss adversely affects not only mouth functions but also the esthetics of one's face. To repair these defects, current treatment methods mainly depend on nonbiological materials or artificial implants that also can, sometimes, reduce the quality of life because of their limited physiological function, or elicit an immunological rejection. Theoretically, a biological tooth (bio-tooth) that is made from the patient's own cells and grows in its intended location should be the best choice for treating tooth loss, although such bioengineered teeth have been nothing more than a dream for many centuries. Recently, significant advances in the fields of tissue engineering, stem cell biology, developmental biology, molecular genetics, and bionics have brought us close to the realization of a bio-tooth. However, issues involving in the reconstruction of a bio-tooth regarding the shape determination, size control, availability of dental epithelium, directional growth and eruption, and graft rejection in the jaws remain to be resolved. Here, this review outlines the current approaches toward the tooth regeneration, and focuses on several key challenges that must be met in the making of a bio-tooth.

Investigations of the odontoblast phenotype are hindered by obstacles such as the limited number of odontoblasts within the dental pulp and the difficulty in purification of these cells. Therefore, it is necessary to develop a cell culture system in which the local environment is inductive and can promote dental pulp stem cells (DPSCs) to differentiate into odontoblast lineage. In this study, we investigated the effect of conditioned medium from developing tooth germ cells (TGCs) on the differentiation and dentinogenesis of DPSCs both in vitro and in vivo. DPSCs were enzymatically isolated from the lower incisors of 4-week-old Sprague-Dawley rats and co-cultured with TGC conditioned medium (TGC-CM). The cell phenotype of induced DPSCs presents many features of odontoblasts, as assessed by the morphologic appearance, cell cycle modification, increased alkaline phosphatase level, synthesis of dentin sialoprotein, type I collagen and several other noncollagenous proteins, expression of the dentin sialophosphoprotein and dentin matrix protein 1 genes, and the formation of mineralized nodules in vitro. The induced DPSC pellets in vivo generated a regular-shaped dentin-pulp complex containing distinct dentinal tubules and predentin, while untreated pellets spontaneously differentiated into bone-like tissues. To our knowledge, this is the first study to mimic the dentinogenic microenvironment from TGCs in vitro, and our data suggest that TGC-CM creates the most odontogenic microenvironment, a feature essential and effective for the regular dentinogenesis mediated by DPSCs.

Background: Papillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images.Methods: Four hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model.Results: Among 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710.Conclusion: Papillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.