Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning.

AIM: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning. METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated. RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18. CONCLUSION: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

The impact of Thiopeptide antibiotics on inflammatory responses in periodontal tissues through the regulation of the MAPK pathway.

Periodontitis is a bacteria-induced inflammatory disease that damages the tissues supporting the teeth, gums, periodontal ligaments, and alveolar bone. Conventional treatments such as surgical procedures, anti-inflammatory drugs, and antibiotics, are somewhat effective; however, these may lead to discomfort and adverse events, thereby affecting patient outcomes. Therefore, this study aimed to find an effective method to prevent the onset of periodontal disease and explore the specific mechanisms of their action.The impact of thiostrepton on Porphyromonas gingivalis and periodontal ligament stem cells was evaluated in an inflammatory microenvironment. In vivo experiments were performed using a mouse periodontitis model to assess the effectiveness of locally applied thiostrepton combined with a silk fibroin hydrogel in impeding periodontitis progression. Thiostrepton exhibited significant antimicrobial effects against Porphyromonas gingivalis and anti-inflammatory properties by regulating the MAPK pathway through DUSP2. Locally applied thiostrepton effectively impeded the progression of periodontitis and reduced tissue damage. Thiostrepton treatment is a promising and tolerable preventive strategy for periodontitis, offering antimicrobial and anti-inflammatory benefits. These findings suggest the potential of thiostrepton as a valuable addition to periodontitis management, warranting further research and clinical exploration to improve patient outcomes.

Hsa_circ_0036872 has an important promotional effect in enhancing osteogenesis of dental pulp stem cells by regulating the miR-143-3p/IGF2 axis.

BACKGROUND: Circular RNAs (circRNAs), an extremely stable group of RNAs, possess a covalent closed-loop configuration. Numerous studies have highlighted the involvement of circRNAs in physiological processes and the development of various diseases. The present study aimed to investigate how circRNA regulates the osteogenic differentiation of human dental pulp stem cells (hDPSCs). METHODS: We isolated hDPSCs from dental pulp and used next-generation sequencing analysis to determine the differentially-expressed circRNAs during osteogenic differentiation. Bioinformatics and dual-luciferase reporter assays identified the downstream targets. The role of circRNAs in osteogenic differentiation was further confirmed through the use of heterotopic bone models. RESULTS: We found that hsa_circ_0036872 expression was increased during osteogenic differentiation of hDPSCs, and downregulation of hsa_circ_0036872 inhibited their osteogenic differentiation. Dual-luciferase reporter assays showed that both miR-143-3p and IGF2 were downstream targets of hsa_circ_0036872. Overexpression of IGF2 or inhibition of miR-143-3p restored the osteogenic differentiation ability of hDPSCs after silencing hsa_circ_0036872. Overexpression of IGF2 reversed the inhibitory effect of miR-143-3p on osteogenic differentiation. CONCLUSION: Taken together, our results show that hsa_circ_0036872 exerts an important promotional effect in enhancing the osteogenesis of dental pulp stem cells by regulating the miR-143-3p/IGF2 axis. These data suggest a novel therapeutic strategy for osteoporosis treatment and periodontal tissue regeneration.

Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar.

BACKGROUND: Having a reliable and feasible method to estimate whether an individual has reached 16 years of age would greatly benefit forensic analysis. The study of age using dental information has matured recently. In addition, machine learning (ML) is gradually being applied for dental age estimation. AIM: The purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian3M), measure the development index of the third molar (I3M) using the method by Cameriere, and assess the periodontal ligament development of the second molar (PL2M). This study aimed to predict whether Chinese adolescents have reached the age of criminal responsibility (16 years) by combining the above measurements with ML techniques. SUBJECTS & METHODS: A total of 665 Chinese adolescents aged between 12 and 20 years were recruited for this study. The development of the second and third molars was evaluated by taking orthopantomographs. ML algorithms, including random forests (RF), decision trees (DT), support vector machines (SVM), K-nearest neighbours (KNN), Bernoulli Naive Bayes (BNB), and logistic regression (LR), were used for training and testing to determine the dental age. This is the first study to combine ML with an evaluation of periodontal ligament and tooth development to predict whether individuals are over 16 years of age. RESULTS AND CONCLUSIONS: The study showed that SVM had the highest Bayesian posterior probability at 0.917 and a Youden index of 0.752. This finding provides an important reference for forensic identification, and the combination of traditional methods and ML is expected to improve the accuracy of age determination for this population, which is of substantial significance for criminal litigation.

Falling weight deflectometer dispersion curve method for pavement modulus calculation.

The falling weight deflectometer (FWD) test is a common non-destructive testing method for evaluating the structural capacity of pavements. At present, data processing of the FWD test mainly focuses on the deflection data, while paying less attention to the deflection-time history. Because a FWD is equipped with impulse loads and geophones, which allow for the generation and capture of surface wave signal propagation, it is hypothesized that Rayleigh wave dispersion theory can be applied to calculate the modulus profile along the pavement depth by analysing the dispersive properties of the deflection signal measured during FWD tests. To test this hypothesis, we develop a new methodology for the FWD test and data analysis, referred to as the FWD dispersion curve method. We first introduce the concept of the new method, followed by an illustration of the procedure and the experimental set-up. Case studies on three concrete pavement segments are then presented to evaluate the effectiveness of the FWD dispersion curve method. Modifications to the existing FWD device are further recommended for the impact loading sources and signal collection process so that the modulus of a much shallower layer, such as the concrete slab and upper asphalt layers, can be obtained. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Influence of Waste Toner on Asphalt Binder: Chemical and Rheological Characterization.

The growing amount of waste toner (WT) has posed a significant environmental challenge. Meanwhile, researchers are interested in the feasibility of utilizing waste toner as an asphalt binder modifier because its primary chemical components (Styrene-acrylic copolymer and carbon black) are known to improve asphalt properties. The objective of this study was to evaluate the chemical and rheological properties of the waste-toner-modified asphalt binder and hence determine the suitability of integrating waste toner for asphalt modification. The waste-toner-modified asphalt (TMA) binders were produced by blending base asphalt with two types of waste toners of different gradation sizes. Microscopic tests such as x-ray fluorescence (XRF), attenuated total reflectance transform infrared spectroscopy (ATR-FTIR), and scanning electron microscopy with energy dispersive X-ray (SEM-EDS) and fluorescence microscope, as well as rheology tests such as multiple stress creep recovery (MSCR) tests, oscillation tests, and bending beam rheometer tests were performed. The FTIR results showed that there was a chemical reaction between waste toners and base asphalt binder. A fluorescence effect was observed on the binders produced with different toners used in this research. The binder modified with an optimal content of 8%WTs revealed better high and low-temperature properties. Additionally, 8%WTs used in this research could change the PG70-22 binder to PG76-22 binder. The rutting properties of asphalt material were improved for its improved elasticity. In addition, the 200-mesh TMA binders were desirable with respect to waste toner particle size. Overall, there is a benefit to using waste toner in the asphalt industry.

Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods.

Background: Machine learning (ML) algorithms play a key role in estimating dental age. In this study, three ML models were used for dental age estimation, based on different preprocessing methods. Aim: The seven mandibular teeth on the digital panorama were measured and evaluated according to the Cameriere and the Demirjian method, respectively. Correlation data were used for decision tree (DT), Bayesian ridge regression (BRR), k-nearest neighbors (KNN) models for dental age estimation. An accuracy comparison was made among different methods. Subjects and methods: We analyzed 748 orthopantomographs (392 males and 356 females) from eastern China between the age of 5 and 13 years in this retrospective study. Three models, DT, BRR, and KNN, were used to estimate the dental age. The data in ML is obtained according to the Cameriere method and the Demirjian method. Coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE), the above five metrics were used to evaluate the accuracy of age estimation. Results: Our experimental results showed that the prediction accuracy of dental age was affected by ML algorithms. MD, MAD, MSE, RMSE of the dental age predicted by ML were significantly decreased. Among all the methods, the KNN model based on the Cameriere method had the highest accuracy (ME = 0.015, MAE = 0.473, MSE = 0.340, RMSE = 0.583, R2 = 0.94). Conclusion: The results show that the prediction accuracy of dental age is influenced by ML algorithms and preprocessing method. The KNN model based on the Cameriere method was able to infer dental age more accurately in a clinical setting.

Comparative assessment of the Willems dental age estimation methods: a Chinese population-based radiographic study.

BACKGROUND: The comparison of the two Willems dental age estimation methods (gender-specific (Willems I) and non-gender-specific (Willems II)) has not been fully investigated. Here we aimed to explore the applicability of the Willems dental age estimation in an Eastern Chinese population, which may cast light on the field of dental age estimation. METHODS: A total of 1211 oral panoramic radiographs (582 boys and 629 girls) of the Chinese Han population aged 11-16 years old were collected. Dental ages (DAs) were calculated using the Willems method. Statistical significance was set at a p-value < 0.05. Age differences between chronological age (CA) and dental age were analyzed by paired t-tests and mean absolute error (MAE). RESULTS: The differences between CA and DA determined by the Willems I method were + 0.44 and + 0.09 years for boys and girls, respectively. When using the Willems II method, these differences were + 0.57 and - 0.09. The MAEs of the Willems I method between DA and CA were 0.95 and 1.00 years in boys and girls, respectively. For Willems II, MAEs were 1.02 and 1.00 years in boys and girls. CONCLUSIONS: This study showed that the Willems I method was more accurate than the Willems II method in the boys' group for predicting age from a whole scale. In comparison, Willems II is more competitive in the girls' group. Neither method may be satisfactory for 11-to-16-year-old teenagers in Eastern China.

Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.

[Comparison of the accuracy for evaluating cervical vertebral bone age and dental age of children in Shanghai].

PURPOSE: To compare the applicability and validity of dental age (DA) estimated by Willems method and cervical vertebral bone age (CVBA) evaluated by regression formula in estimating the chronological age of children in Shanghai. METHODS: Panoramic radiographs and lateral cephalograms were retrospectively collected from 320 subjects (160 males, 160 females), totaling 640 images. Discrepancies between chronological and estimated ages were statistically calculated by paired samples t test or Wilcoxon signed rank test using SPSS 25.0 software package. The accuracy of the two methods was comprehensively evaluated by comparing their standard deviation, mean absolute error (MAE) and the correct rate of acceptable range of estimated age error. RESULTS: The mean DA underestimated CA by 0.75±1.03 years for males and by 1.05±1.18 years for females; whereas the mean CVBA underestimated CA by 0.78±1.40 years for males and 0.53±1.31 years for females. MAE of Willems method was 1.15 years and the MAE of regression formula of CVBA was 1.20 years. The correct rate of clinically acceptable error of 0.5 years was 26.25% for Willems method and 27.19% for regression formula of CVBA. CONCLUSIONS: Willems method is more accurate than regression formula in indicating cervical vertebral skeletal age of adolescents in Shanghai children. Because of significant differences between CA and estimated ages, further modifications are urged to improve the accuracy of these two methods.

AMTB, a TRPM8 antagonist, suppresses growth and metastasis of osteosarcoma through repressing the TGFß signaling pathway.

Since its first identification in prostate cancers and prostate tissues, transient receptor potential melastatin-subfamily member 8 (TRPM8) is subsequently found to be overexpressed in a wide range of cancers and is shown to be implicated in tumorigenesis and tumor progression. Here, we used N-(3-aminopropyl)-2-[(3-methylphenyl) methoxy] -N-(2-thienylmethyl) benzamide hydrochloride (AMTB), a specific TRPM8 antagonist, to explore its antitumoral effect on osteosarcoma. We find that AMTB suppresses osteosarcoma cell proliferation, metastasis and induces cellular apoptosis. Xenograft model in nude mice experiments also define that AMTB can increase the sensitivity of tumor cells to cisplatin, the cytotoxic chemotherapeutic regimens in treating osteosarcoma. Molecularly, AMTB specifically antagonizes TRPM8 which is upregulated in osteosarcoma and its expression level in osteosarcoma tissues is negatively related to patients' prognosis. Finally, RNA sequencing analysis was performed to explore the mechanism underlying the antitumoral effect of AMTB on osteosarcoma cells and the results prove that AMTB suppresses the Transforming Growth Factor ß (TGFß) signaling pathway. Our study provides evidence that TRPM8 could be a potential therapeutic target and AMTB can suppress growth and metastasis of osteosarcoma cells through repressing the TGFß signaling pathway and increase the sensitivity of tumor cells to cisplatin.

Effects of Field Aging on Material Properties and Rutting Performance of Asphalt Pavement.

This study evaluates field asphalt aging based on material property changes in pavement with time, and investigates if such changes could have an impact on field rutting performance. Four projects from three different climate zones were monitored as part of the NCHRP 9-49A project at two stages: during pavement construction and two to three years after opening it to traffic. Construction information were collected, and field cores were drilled at both stages to evaluate the material properties of recovered asphalt binder and asphalt mixture. Field rut depth was also measured. In addition, pavement structure, climate and base/subgrade modulus information were also obtained. Results indicate that the asphalt mixture stiffening is caused in major part by asphalt aging. However, the effect of asphalt aging on pavement mixture property may not follow a proportional liner trend. The parameters that are most sensitive to field ageing are MSCR R3.2 and dynamic modulus. It is also found that the variables which showed a good ranking trend with the field rut depth are climate condition (relative humidity, high temperature hour, solar radiation), material properties (Hamburg rut depth, rutting resistance index, high temperature performance grade, MSCR, and dynamic modulus, base and subgrade moduli), as well as air voids.

Machine learning assisted Cameriere method for dental age estimation.

BACKGROUND: Recently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful. AIM: The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation. SUBJECTS AND METHODS: This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5-13 years. Data were randomly divided into training and test datasets in an 80-20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). RESULTS: The research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (- 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively. CONCLUSIONS: Compared to the Cameriere formula, ML methods based on the Cameriere's maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula.

Loss of Smad4 promotes aggressive lung cancer metastasis by de-repression of PAK3 via miRNA regulation.

SMAD4 is mutated in human lung cancer, but the underlying mechanism by which Smad4 loss-of-function (LOF) accelerates lung cancer metastasis is yet to be elucidated. Here, we generate a highly aggressive lung cancer mouse model bearing conditional KrasG12D, p53fl/fl LOF and Smad4fl/fl LOF mutations (SPK), showing a much higher incidence of tumor metastases than the KrasG12D, p53fl/fl (PK) mice. Molecularly, PAK3 is identified as a downstream effector of Smad4, mediating metastatic signal transduction via the PAK3-JNK-Jun pathway. Upregulation of PAK3 by Smad4 LOF in SPK mice is achieved by attenuating Smad4-dependent transcription of miR-495 and miR-543. These microRNAs (miRNAs) directly bind to the PAK3 3'UTR for blockade of PAK3 production, ultimately regulating lung cancer metastasis. An inverse correlation between Smad4 and PAK3 pathway components is observed in human lung cancer. Our study highlights the Smad4-PAK3 regulation as a point of potential therapy in metastatic lung cancer.

A quick method of determining the age of 8 years old: Based on the first premolars on Eastern Chinese population.

BACKGROUND: Whether an individual is over 8 years old is of great significance in clinical medicine and law. The Demirjian, Willems and Cameriere methods are one of the most widely used methods of dental age estimation, but neither method was suitable in Eastern China. And all of them are time-consuming for clinical work. A new and time-saving method for judging whether an individual is 8 years old is yet to be proposed on Eastern Chinese population. AIM: The paper proposes a method for judging whether an individual is 8 years old based on the relationship between age and the first premolar maturity index (I1PM), which is related to the measurement of the open apices of the first premolar. SUBJECTS & METHODS: It is a retrospective study where orthopantomograms of 748 children (356 female and 392male) aged 5-13 years were collected and analyzed. Linear regression was used to infer the value of I1PM for eastern China. Furthermore, this method was compared to those based on Demirjian's stages F. RESULTS AND CONCLUSIONS: A cutoff value of I1PM = 0.35 was taken. The sensitivity of this test was 90.0% and specificity was 93.3%. Furthermore, the proportion of individuals with a correct classification was 90.9%. The results of the test showed that Demirjian's stage F performance was lightly better than I1pm < 0.35. However, the sensitivity, specificity, and overall accuracy of both are greater than or equal to 90.0%, and the differences are not very significant.

Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System.

Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.

Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors.

Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.

A modified dental age assessment method for 5- to 16-year-old eastern Chinese children.

OBJECTIVES: Age estimation is widely applied in the field of orthodontics, pediatric dentistry, and forensic science. Dental age estimation by the radiological method is frequently used because of its convenience and noninvasiveness. However, there are not enough suitable methods for eastern Chinese children. This study aimed to establish a modified formula for eastern Chinese children according to the Demirjian method and then compared the accuracy of the modified method with the Demirjian method and Willems method. MATERIALS AND METHODS: A total of 2367 dental panoramic radiographs from individuals aged 5-16 years of eastern China were collected as samples. Age estimation was conducted using the Demirjian and Willems methods. The polynomial curve fitting method was used to modify the Demirjian method to improve its application to the eastern Chinese children. The paired t test and accuracy ratio were used to compare the applicability of the modified methods with two commonly used methods. RESULTS: The mean chronological age (CA) of the subjects was 11.20 ± 3.29 years for boys and 10.99 ± 3.12 years for girls. The mean difference values between the CA and dental age (DA) (CA-DA) using the Demirjian and Willems methods were 0.73 and 0.7 for boys, respectively, and both 0.79 for girls. The modified method using the polynomial curve fitting presented a smaller underestimation compared with CA for both boys (0.04 years) and girls (0.09 years), which showed a high suitability to Chinese children to some extent. CONCLUSIONS: The Willems method was more accurate in estimating DA compared with the Demirjian method. However, the modified method was more accurate than the two methods; therefore, it can be used in eastern Chinese children. CLINICAL RELEVANCE: It was thought to be a non-invasive, convenient, and efficient method to connect DA and CA. By estimating dental age, pediatrist, and orthodontists can better understand the development of permanent teeth and provide a more accurate orthodontic treatment time and treatment plan to children patients.

Testing the nonlinear equations for dental age evaluation in a population of eastern China.

OBJECTIVE: The study evaluated the validity of the nonlinear equations (Qingdao model) for dental age assessment in an eastern Chinese population. MATERIALS AND METHODS: We studied 1073 digital panoramic radiographs of children aged 11-16 years from a Chinese Han population. Dental ages (DAs) were calculated using the Demirjian and the new model methods. Statistical significance was set at p < 0.05. For each method, differences between the chronological age (CA) and dental age were analyzed by paired t-tests and mean absolute error (MAE). RESULTS: The discrepancies between CA and DA determined by Qingdao model were 0.18 and 0.30 years for males and females, respectively. While using Demirjian method, these differences were and 0.46 and 0.30. The Qingdao model's MAEs between DA and CA were 1.23 and 0.90 years in males and females, respectively. As for the Demirjian method, MAEs were 1.43 and 0.86 years in males and females. CONCLUSIONS: This study showed that the new nonlinear equations were more accurate than the traditional Demirjian method. Especially, the new nonlinear Qingdao model is more competitive in 11-14-year male groups and 15-16-year female groups. We recommend a combined Qingdao model and Demirjian method may reasonably reflect the CAs among children in the eastern Chinese population.

Author Correction: The REGγ inhibitor NIP30 increases sensitivity to chemotherapy in p53-deficient tumor cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.