Employment of 4-(1,2,4-triazol-1-yl)phenol as a signal enhancer of the chemiluminescent luminol-H2O2-horseradish peroxidase reaction for detection of hepatitis C virus in real samples.

Highly sensitive detection of hepatitis C virus (HCV) in serum is a key method for diagnosing and classifying the extent of HCV infection. In this study, a p-phenol derivative, 4-(1,2,4-triazol-1-yl)phenol (4-TRP), was employed as an efficient enhancer of the luminol-hydrogen peroxide (H2O2)-horseradish peroxidase (HRP) chemiluminescence (CL) system for detection of HCV. Compared with a traditional enhancer, 4-TRP strongly enhanced CL intensity with the effect of prolonging and stabilizing light emission. The developed CL system was applied to detecting HCV core antigen (HCV-cAg) using a sandwich structure inside microwells. Our experimental results showed that there was good linear relationship between CL intensity and HCV-cAg concentration in the 0.6-3.6 pg/mL range (R = 0.99). The intra- and inter-assay coefficients of variation were 4.5-5.8% and 5.0-7.3%, respectively. In addition, sensitive determination of HCV-cAg in serum samples using the luminol-H2O2-HRP-4-TRP CL system was also feasible in clinical settings.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI.

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Inhibition of protein disulfide isomerase mitigates steroid-induced osteonecrosis of the femoral head by suppressing osteoclast activity through the reduction of cellular oxidative stress.

Osteonecrosis of the femoral head (ONFH) is a devastating and irreversible hip disease usually associated with increased oxidative stress due to the clinical application of high-dose or long-term glucocorticoids (GCs). Previous publications have demonstrated protein disulfide isomerase (PDI) plays a critical role in regulating cellular production of reactive oxygen species (ROS). We therefore ask whether interfering PDI could affect GCs-stimulated osteoclastogenesis. To test the hypothesis, we conducted bioinformatics and network analysis based on potential gene targets of steroid-induced osteonecrosis of the femoral head (SIONFH) in light of multiple databases and concomitantly verified the associated biological effect via the in vitro model of dexamethasone (DEX)-stimulated osteoclastogenesis. The results revealed 70 potential gene targets for SIONFH intervention, including the P4HB gene that encodes PDI. Further analysis based on network topology-based analysis techniques (NTA), protein-protein interaction (PPI) networks, and mouse cell atlas database identified the importance of PDI in regulating the cellular redox state of osteoclast during ONFH. Western blotting (WB) validations also indicated that PDI may be a positive regulator in the process of DEX-stimulated osteoclastogenesis. Hence, various PDI inhibitors were subjected to molecular docking with PDI and their performances were analyzed, including 3-Methyltoxoflavin (3M) which inhibits PDI expression, and ribostamycin sulfate (RS) which represses PDI chaperone activity. The binding energies of DEX, 3M, and RS to PDI were -5.3547, -4.2324, and -5.9917 kcal/mol, respectively. The Protein-Ligand Interaction Profiler (PLIP) analysis demonstrated that both hydrogen bonds and hydrophobic interactions were the key contributions to the DEX-PDI and 3M-PDI complexes, while only hydrogen bonds were identified as the predominant driving forces in the RS-PDI complex. Subsequent experiments showed that both 3M and RS reduced osteoclast differentiation and bone resorption activity by stifling the expression of osteoclastic markers. This reduction was primarily due to the PDI inhibitors boosting the antioxidant system, thereby reducing the production of intracellular ROS. In conclusion, our results supported PDI's involvement in SIONFH progression by regulating ROS in osteoclasts and highlighted PDI inhibitors may serve as potential options for SIONFH treatment.

An integrated approach of machine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction.

The use of traffic conflicts in road safety evaluation is gaining considerable popularity as it plays a vital role in developing a proactive safety management strategy and allowing for real-time safety analysis. This study proposes an integrated approach that combines a machine learning (ML) algorithm and a Bayesian spatial Poisson (BSP) model to conduct large-scale real-time traffic conflict prediction by considering traffic states as the explanatory variables. Traffic conflicts are measured by two indicators, the Time to Collision (TTC) and the Post-Encroachment Time (PET). Based on both TTC and PET, traffic conflict severity is classified into five categories. For each conflict severity category, a binary variable (conflict occurrence) and a count variable (conflict frequency) are developed, respectively. In addition to conflict variables, traffic state parameters are extracted from a large-scale high-resolution trajectory dataset. The traffic parameters include volume, density, speed, and the corresponding space-based and space-time-based measures within a 30-second interval. Eight ML-based classifiers are applied to predict conflict occurrence, and the best classifier is selected. A binary logistic regression is developed to explore the potential linkages between traffic states and conflict occurrence. As well, a resampling technique Borderline-SMOTE is used to mitigate the sparsity caused by the predefined short interval. The BSP model is utilized to predict the specific number of conflicts. Further, the BSP model can also explain the relationship between traffic states and conflict frequency, and thus the significant influencing traffic states are identified. The results show that random forest outperforms the other MLs in terms of conflict occurrence prediction accuracy. Further, the proposed integrated approach achieves a high performance of conflict frequency prediction with RMSE values of 0.1384 âˆ¼ 0.1699, MAPE values of 9.25% ∼ 36.99%, and MAE values of 0.0087 âˆ¼ 0.6398. The finding emphasizes the need for separately predicting the occurrence and frequency of conflicts with different severities.

Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks.

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

Random parameters Bayesian hierarchical modeling of traffic conflict extremes for crash estimation.

The use of Extreme Value Theory (EVT) models for traffic conflict-based crash estimation is becoming increasingly popular with considerable recent advances achieved. The latest advances include developing EVT models that combine several conflict indicators and the use of data from several sites to increase the sample size of conflict extremes. Nevertheless, one important issue while developing EVT models is accounting for the unobserved heterogeneity across different conflict observation sites and road user behaviours which can lead to biased and inefficient parameter estimates and erroneous inferences. This study proposes a random parameters (RP) Bayesian hierarchical extreme value modeling approach to account for the unobserved heterogeneity. The proposed approach is applied to estimate rear-end crashes from traffic conflicts collected from four signalized intersections in the city of Surrey, British Columbia. Traffic conflicts were characterized by four indicators: time to collision (TTC), modified TTC (MTTC), post-encroachment time (PET), and deceleration rate to avoid a crash (DRAC). MTTC was used to fit the generalized extreme value distribution, while the other three conflict indicators were treated as covariates. Six covariates including TTC, PET, DRAC, traffic volume, shock wave area, and platoon ratio were considered to account for non-stationarity in conflict extremes. Several RP, random intercepts (RI), and fixed parameters (FP) Bayesian hierarchical univariate extreme value models were developed. The results indicate that the RP model outperforms both the RI model and the FP model in terms of crash estimation accuracy and precision. Such superiority may be due to the ability of the RP model to better account for the unobserved heterogeneity.

Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation.

There is an increased interest in the use of traffic conflicts as a surrogate safety measure and several traffic conflict indicators have been developed. One of these indicators is the deceleration rate to avoid a crash (DRAC). Generally, the greater the DRAC value, the higher the crash risk and a crash would occur when the DRAC exceeds the maximum available deceleration rate (MADR). It is noted that the MADR varies considerably for individual vehicles and depends on many factors such as the pavement conditions, vehicle weight, tire, and the braking system. Previous studies usually either set a specific value for the MADR or randomly sample values from a truncated normal distribution of MADR. However, little is known about which threshold determination approach is better. Therefore, this study aims to compare the threshold determination approaches for DRAC-based crash estimation applying Bayesian hierarchical extreme value modeling. Using traffic conflict and crash data collected from four signalized intersections in the city of Surrey, several Bayesian hierarchical models are developed for five specific values of MADR and values from two truncated normal distributions of MADR. The crash frequencies estimated from these models were compared with observed crashes. The results show that, in terms of DRAC-based crash estimation accuracy, the truncated normal distribution N(8.45, 1.42)I(4.23, 12.68) of MADR outperforms other determination methods of MADR. Moreover, in terms of DRAC-based crash estimation accuracy and precision, the use of multisite Bayesian hierarchical models outperforms the at-site models. The truncated normal distribution N(8.45, 1.42)I(4.23, 12.68) of MADR is therefore recommended for DRAC-based crash estimation.

Modeling duration of overtaking between non-motorized vehicles: A nonparametric survival analysis based approach.

The use of non-motorized vehicles in urban city has improved the convenience of short-distance travel and reduced traffic pollution. However, the overtaking behaviour of non-motorized vehicles impacts traffic safety and efficiency significantly. The objective of this study is to model the durations of overtaking behaviour in the non-motorized vehicle exclusive lane. A total of 3010 overtaking events of non-motorized vehicles were extracted from two locations in Chengdu, China. The nonparametric survival analysis was conducted to model the overtaking duration of non-motorized vehicles. The categorical variables that significantly influence the overtaking duration were examined by the Log-rank test. The results show that the overtaking durations of female riders is longer than that of male riders. It takes longer for electrical bikes to complete overtaking than conventional bikes. When the non-motorized vehicle is under the load state (i.e. passengers or goods on the non-motorized vehicle), the overtaking behaviour takes more time than the un-load state. Moreover, it takes less time to overtake the non-motorized vehicle with load than to overtake the one without load. When there is a wrong-way driving phenomenon or under higher traffic volume, the duration is longer compared to the normal traffic and lower traffic volume conditions. The findings of this study attempt to provide a more profound understanding of non-motorized vehicles overtaking behaviour under different traffic conditions and give insights to the safety research of non-motorized vehicles.

Multi-type Bayesian hierarchical modeling of traffic conflict extremes for crash estimation.

Most existing Extreme Value Theory (EVT) models were developed based on the total number of conflicts or a single type of traffic conflict to estimate the corresponding frequency of crashes. Using the total number of conflicts to estimate the total number of crashes may make it difficult to diagnose safety problems as countermeasures are usually related to specific conflict/crash types. Single-type EVT models may help to better explain the mechanism of crash occurrence of a certain type, but they only reflect the partial safety of a road entity. Therefore, developing EVT models for multiple types of traffic conflicts would be more representative. However, one important issue in modeling various types of traffic conflicts is that there will be considerable correlation among various conflict types. The modeled crash prediction results would be biased if the conflict type correlation is not accounted for. This study proposes a multi-type Bayesian hierarchical extreme value modeling approach, which has four advantages: 1) integrates multiple types of traffic conflicts; 2) incorporates the influence of several covariates; 3) combines traffic conflicts from different sites; 4) accounts for the unobserved heterogeneity in conflict extremes. The proposed multi-type approach was applied to estimate rear-end crashes and side-impact crashes of left-turning vehicles based on their corresponding traffic conflicts observed from two signalized intersections in the city of Surrey, British Columbia. Both conflict types of left-turning vehicles were characterized by the same indicator time-to-collision (TTC). Overall, the results show that the standard errors of the multi-type model parameters are smaller than those of single-type models. Moreover, the multi-type model produces more accurate crash estimates than its corresponding single-type models. The more accurate crash estimates are probably attributed to the two-type model accounting for the conflict type correlation.

Operational factor analysis of the aggressive taxi speeders using random parameters Bayesian LASSO modeling approach.

Partial taxi speeders are observed with both high speeding frequency and severity (range). They thereby can be viewed as aggressive speeders whose behaviors may result in more hazards than others. Among the factors contributing to taxi speeding, the operational factors are proven to be deterministic. However, previous studies mainly investigate the operational factors of taxi speeding frequency, which fail to comprehensively unveil the impact of factors on speeders, especially for aggressive speeders. This study intends to disclose the operational factors affecting the aggressive taxi speeders with the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach. Taxi speeding behaviors and several operational factors are extracted from taxi GPS trajectory data in Chengdu, China. Based on the hourly speeding frequency and average speeding severity of each speeder, the fuzzy C-means clustering algorithm is employed to categorize taxi speeders into three cohorts: restrained speeder (RS), moderate speeder (MS), and belligerent speeder (BS). Compared to RS, MS and BS are treated as the aggressive taxi speeders. Several binary logistic models are developed with RS as the reference category. The random parameters Bayesian binary logistic LASSO model that captures the unobserved heterogeneity and tackles the multicollinearity is found to be the best fit model to identify the significant operational factors. The results indicate that aggressive taxi speeders are linked to longer daily driving distance and cruise distance, shorter delivery time, higher hourly income, driving at night, and driving on low-speed limit roads. However, intensive lane-changes and sufficient daily naps do not contribute to aggressive taxi speeders. Moreover, BS is more sensitive to the operational factors than MS. This study stresses the necessity of implementing speeder classification in taxi driver management and conceiving countermeasures considering the operational factors which are significantly associated with the aggressive taxi speeders.

Investigating influence factors of traffic violation using multinomial logit method.

Deaths and injuries resulted from road traffic crashes remain a serious problem globally, and current trends suggest that this will continue to be the case in the foreseeable future mainly in developing countries. Among diverse cause of traffic safety challenges, traffic violation has been considered as one of the noticeable contributing factors. The main aim of the study is to identify and evaluate the major traffic violation with related risk factors using multinomial logit model. Traffic violation data of Luzhou were collected from Sichuan Province Public Security Department, China. The study result revealed six major traffic violations, including traffic light violation, illegal parking, wrong-way driving, speeding, and NOT wearing a seat belt. Urban roads classified with congested driving and severe weather conditions were the major risk factors. Among different vehicle types and use, those small car/automobile categories with private purpose use exhibit statistically significant association (p-value < 0.05) with the aforementioned traffic violations. Taking into consideration these risky contributing factors during the development of traffic regulations and enforcement will help to reduce traffic violations and create a smooth/healthy driving condition with improved traffic safety and will also increase the performance of driving in general.

Therapeutic Applications of Nanozymes in Chronic Inflammatory Diseases.

Since the discovery of horseradish peroxidase-like activity of magnetite nanoparticles in 2007, many researchers have investigated different types of nanoparticles that show enzyme-like activities, namely, nanozymes. Nanozymes possess high efficiency, stability, and low production costs compared to natural enzymes. Thus, nanozymes have already been widely studied in various domains including medical science, food industry, chemical engineering, and agriculture. This review presents the utilization of nanozymes in medicine and focuses particularly on their therapeutic applications in chronic inflammatory diseases because of their antioxidant-like activity. Furthermore, the treatment of chronic inflammatory diseases with nanozymes of different materials was introduced emphatically.

Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera.

To effectively reduce traffic violations that often cause severe crashes at signalized intersections, exploring their contributing factors seems hugely urgent and essential. This study attempted to investigate the influence factors of wrong-way driving (WWD), red-light-running (RLR), violating traffic markings (VTM), and driving in the inaccurate oriented lane (DIOL) at signalized intersections by using data collected from traffic enforcement camera in Hohhot, China. To this end, an ordinary multinomial logit model was developed. By considering the unobserved heterogeneity between observations, a random effects multinomial logit model was proposed as well. After that, the marginal effects of explanatory variables were computed. The outcomes showed that non-local vehicles were more likely to commit WWD and VTM than local vehicles. WWD and RLR frequently occurred in the daytime and evening (6:00-23:59), and on most days within a week. RLR and DIOL mainly happened in June and July. The left-turn lane ratio significantly increased RLR and DIOL. The cloudy, partly cloudy, and rainy days obviously increased WWD and VTM. The temperature from 21 to 30 degrees centigrade was apparently associated with the higher likelihoods of RLR and DIOL. According to the findings of this study, some intervention measures, targeting different vehicle types and considering temporal factors, road, and weather conditions, were recommended to reduce WWD, RLR, VTM, and DIOL at signalized intersections.

Bayesian hierarchical spatial count modeling of taxi speeding events based on GPS trajectory data.

Speeding behavior, especially serious speeding, is more common in taxi driver than other driving population due to their high exposure under traffic environment, which increases the risk of being involved in crashes. In order to prevent the taxi and other road users from speed-related crash, previous studies have revealed contributors of demographic and driving operation affecting taxi speeding frequency. However, researches regarding road factors, and spatial effect are typically rare. For this sake, the current study explores the contributions of 10 types of road characteristics and two kinds of spatial effects (spatial correlation and spatial heterogeneity) on taxi total speeding and serious speeding frequency. Taxi GPS trajectory data in a Chinese metropolis were used to identify speeding event. The study then established four kinds of Bayesian hierarchical count models base on Poisson and negative binominal distribution to estimate the contributor impacts, respectively. Results show that Bayesian hierarchical spatial Poisson log-linear model is optimum for fitting both total and serious speeding frequency. For the analysis, it is found that drivers are more likely to commit speeding on long multilane road with median strip, and road with non-motorized vehicle lane, bus-only lane and viaduct or road tunnel. Roads with low speed limit, and work zone are associated with increasing speeding as well. In terms of serious speeding, bus-only lane is not a contributor, while road speed camera number and one-way organization are significantly positive to the speeding frequency. Furthermore, it reveals that two spatial effects significantly increase the occurrence of speeding events; the impact of spatial heterogeneity is more critical.

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt.

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.

RGS12 Is a Novel Critical NF-κB Activator in Inflammatory Arthritis.

Rheumatoid arthritis (RA) is the most common inflammatory disease, which currently lacks effective treatment. Here, we discovered that the Regulator of G Protein Signaling 12 (RGS12) plays a key role in regulating inflammation. Transcriptional and protein analysis revealed that RGS12 was upregulated in human and mouse RA macrophages. Deletion of RGS12 in myeloid lineage or globally inhibits the development of collagen-induced arthritis including joint swelling and bone destruction. Mechanistically, RGS12 associates with NF-κB(p65) to activate its phosphorylation and nuclear translocation through PTB domain, and NF-κB(p65) regulates RGS12 expression in a transcriptional manner. The nuclear translocation ability of NF-κB(p65) and RGS12 can both be enhanced by cyclooxygenase-2 (COX2). Furthermore, ablation of RGS12 via RNA interference significantly blocks the inflammatory process in vivo and in vitro. These results demonstrate that RGS12 plays a critical role in the pathogenesis of inflammatory arthritis.

PTEN Inhibits Inflammatory Bone Loss in Ligature-Induced Periodontitis via IL1 and TNF-α.

Phosphatase and tensin homolog (PTEN) is a critical regulator of tumorigenesis and bone remodeling, which is also found expressed in the periodontal tissues. Periodontitis is one of the most common oral diseases and associated with alveolar bone resorption and tooth loosening in adults. However, the functional relevance of PTEN in periodontitis remains unclear. Here, we report that PTEN plays an essential role in periodontitis. The in vivo results of our study showed a significant decrease of PTEN in the ligature-induced mouse periodontitis model. The function of PTEN in the macrophages was shown to be associated with inflammatory factors interleukin 1 (IL1) and tumor necrosis factor (TNF-α) by using overexpression and silence methods. Further mechanistic studies indicated lack of PTEN-activated IL1 and TNF-α, which increased the number of osteoclasts and led to alveolar bone erosion and loss. Moreover, PTEN nanoparticles could directly inhibit the inflammatory process and bone erosion, suggesting a controlling role of PTEN during bone remodeling. All these data identified the novel function of PTEN as a key factor in periodontitis and bone remodeling.

Regulator of G protein signaling 12 enhances osteoclastogenesis by suppressing Nrf2-dependent antioxidant proteins to promote the generation of reactive oxygen species.

Regulators of G-protein Signaling are a conserved family of proteins required in various biological processes including cell differentiation. We previously demonstrated that Rgs12 is essential for osteoclast differentiation and its deletion in vivo protected mice against pathological bone loss. To characterize its mechanism in osteoclastogenesis, we selectively deleted Rgs12 in C57BL/6J mice targeting osteoclast precursors using LyzM-driven Cre mice or overexpressed Rgs12 in RAW264.7 cells. Rgs12 deletion in vivo led to an osteopetrotic phenotype evidenced by increased trabecular bone, decreased osteoclast number and activity but no change in osteoblast number and bone formation. Rgs12 overexpression increased osteoclast number and size, and bone resorption activity. Proteomics analysis of Rgs12-depleted osteoclasts identified an upregulation of antioxidant enzymes under the transcriptional regulation of Nrf2, the master regulator of oxidative stress. We confirmed an increase of Nrf2 activity and impaired reactive oxygen species production in Rgs12-deficient cells. Conversely, Rgs12 overexpression suppressed Nrf2 through a mechanism dependent on the 26S proteasome, and promoted RANKL-induced phosphorylation of ERK1/2 and NFκB, which was abrogated by antioxidant treatment. Our study therefore identified a novel role of Rgs12 in regulating Nrf2, thereby controlling cellular redox state and osteoclast differentiation.

Regulator of G Protein Signaling Protein 12 (Rgs12) Controls Mouse Osteoblast Differentiation via Calcium Channel/Oscillation and Gαi-ERK Signaling.

Bone homeostasis intimately relies on the balance between osteoblasts (OBs) and osteoclasts (OCs). Our previous studies have revealed that regulator of G protein signaling protein 12 (Rgs12), the largest protein in the Rgs super family, is essential for osteoclastogenesis from hematopoietic cells and OC precursors. However, how Rgs12 regulates OB differentiation and function is still unknown. To understand that, we generated an OB-targeted Rgs12 conditional knockout (CKO) mice model by crossing Rgs12fl/fl mice with Osterix (Osx)-Cre transgenic mice. We found that Rgs12 was highly expressed in both OB precursor cells (OPCs) and OBs of wild-type (WT) mice, and gradually increased during OB differentiation, whereas Rgs12-CKO mice (OsxCre/+ ; Rgs12fl/fl ) exhibited a dramatic decrease in both trabecular and cortical bone mass, with reduced numbers of OBs and increased apoptotic cell population. Loss of Rgs12 in OPCs in vitro significantly inhibited OB differentiation and the expression of OB marker genes, resulting in suppression of OB maturation and mineralization. Further mechanism study showed that deletion of Rgs12 in OPCs significantly inhibited guanosine triphosphatase (GTPase) activity and cyclic adenosine monophosphate (cAMP) level, and impaired Calcium (Ca2+ ) oscillations via restraints of major Ca2+ entry sources (extracellular Ca2+ influx and intracellular Ca2+ release from endoplasmic reticulum), partially contributed by the blockage of L-type Ca2+ channel mediated Ca2+ influx. Downstream mediator extracellular signal-related protein kinase (ERK) was found inactive in OBs of OsxCre/+ ; Rgs12fl/fl mice and in OPCs after Rgs12 deletion, whereas application of pertussis toxin (PTX) or overexpression of Rgs12 could rescue the defective OB differentiation via restoration of ERK phosphorylation. Our findings reveal that Rgs12 is an important regulator during osteogenesis and highlight Rgs12 as a potential therapeutic target for bone disorders. © 2018 American Society for Bone and Mineral Research.