Sclerostin promotes human dental pulp cells senescence

Background Senescence-related impairment of proliferation and differentiation limits the use of dental pulp cells for tissue regeneration. Deletion of sclerostin improves the dentinogenesis regeneration, while its role in dental pulp senescence is unclear. We investigated the role of sclerostin in subculture-induced senescence of human dental pulp cells (HDPCs) and in the senescence-related decline of proliferation and odontoblastic differentiation. Methods Immunohistochemical staining and qRT-PCR analyses were performed to examine the expression pattern of sclerostin in young (20–30-year-old) and senescent (45–80-year-old) dental pulps. HDPCs were serially subcultured until senescence, and the expression of sclerostin was examined by qRT-PCR analysis. HDPCs with sclerostin overexpression and knockdown were constructed to investigate the role of sclerostin in HDPCs senescence and senescence-related impairment of odontoblastic differentiation potential. Results By immunohistochemistry and qRT-PCR, we found a significantly increased expression level of sclerostin in senescent human dental pulp compared with that of young human dental pulp. Additionally, elevated sclerostin expression was found in subculture-induced senescent HDPCs in vitro. By sclerostin overexpression and knockdown, we found that sclerostin promoted HDPCs senescence-related decline of proliferation and odontoblastic differentiation potential with increased expression of p16, p53 and p21 and downregulation of the Wnt signaling pathway. Discussion The increased expression of sclerostin is responsible for the decline of proliferation and odontoblastic differentiation potential of HDPCs during cellular senescence. Anti-sclerostin treatment may be beneficial for the maintenance of the proliferation and odontoblastic differentiation potentials of HDPCs

Development and evaluation of the control charts for variables

In quality control (QC), control chart is one of the most effective tools to monitor the process and ensure the product quality. It is a powerful Statistical Process Control (SPC) method to make quick response when quality problems occur in order to avoid serious economic loss. The first objective of this PhD project is to develop new SPC techniques, for which several highly effective control charts for variables have been proposed and carefully investigated. The second task is to provide a systematic performance comparison among all typical control charts for variables including the Shewhart chart, Cumulative Sum (CUSUM) chart and Sequential Probability Ratio Test (SPRT) chart. These studies are conducted in two phases. The first phase presents the development of the control charts for monitoring process mean. The Syn- , optimal SPRT and VSI (Variable Sampling Interval) SPRT charts have been developed in Chapter 3. A systematic study has also been conducted to compare the effectiveness and robustness of nine typical control charts for monitoring the mean of variables in Chapter 4. The nine charts are categorized into three types (the type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and fully-adaptive (FA) version). A design table has been made to facilitate the users to select a chart by taking into considerations of both performance and simplicity of the charts, as well as the probability distribution of the mean shift δµ of the process. The second phase presents the development of the control charts for monitoring both process mean and variance. The VSSI (Variable Sample Size and Sampling Interval) CUSUM and ABS (Absolute) SPRT charts have been developed in Chapter 5. This phase also studies the overall performance of nine typical control charts for monitoring process mean and variance in a quantitative and analytical manner in Chapter 6. A general model for the optimal designs of the control charts is adopted throughout this thesis. In this model, Average Extra Quadratic Loss (AEQL) is used as the objective function, in-control Average Time to Signal (ATS0) and inspection rate (R) are the constraints. It is expected that the development of the new charts and the systematic comparative studies carried out in this thesis will make useful contribution to the literature and practice of quality engineering.Doctor of Philosophy (MAE

A comparison study of effectiveness and robustness of control charts for monitoring process mean

This article compares the effectiveness and robustness of nine typical control charts for monitoring the mean of a variable, including the most effective optimal and adaptive Sequential Probability Ratio Test (SPRT) charts. The nine charts are categorized into three types (the X¯ type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and fully adaptive (FA) version). While the charting parameters of the basic charts are determined by common wisdoms, the parameters of the optimal and fully adaptive charts are designed optimally in order to minimize an index, Average Extra Quadratic Loss (AEQL), for the best overall performance. A Performance Comparison Index, PCI, is also proposed as the measure of the relative overall performance of the charts. This comparison study does not only compare the detection effectiveness of the charts, but also investigate their robustness in performance. Moreover, the probability distribution of the mean shift δ is studied explicitly as an influential factor in a factorial experiment. Apart from many other findings, the results of this study reveal that the SPRT chart is more effective than the CUSUM chart and chart by 58% and 126%, respectively, from an overall viewpoint. Moreover, it is found that the optimization design of charting parameters can increase the detection effectiveness by 29% on average, and the adaptive features can further enhance the detection power by 35%. Finally, a set of design tables are provided to facilitate the users to select a chart for their applications

Side Sensitive Group Runs Xbar Chart with Estimated Process Parameters

International audienc

An optimal design algorithm of the SPRT chart for minimizing weighted ATS

The Sequential Probability Ratio Test (SPRT) chart is a very effective control chart. It is much faster to detect out-of-control cases compared with the and CUSUM charts, even their VSSI (Variable Sample Sizes and Sampling Intervals) versions. The new contribution of this paper is the development of an algorithm for the optimal design of the SPRT chart based on the Average Extra Quadratic Loss (AEQL). More importantly, the results of the systematic comparative studies show that this design algorithm is indeed effective. Using the design algorithm proposed here yields charts that significantly outperform other designs in terms of the proposed AEQL performance criteria for detecting process mean shifts. Moreover, this paper conducts a designed experiment to study the effect of the Average Sample Number (ASN) (i.e., the average sample size) on the chart's performance. It is found that, in order to achieve higher overall detection effectiveness, the ASN of the SPRT chart should be set substantially smaller compared with that recommended by the existing guidelines

An adaptive CUSUM chart with single sample size for monitoring process mean and variance

This article proposes an adaptive absolute cumulative sum chart (called the adaptive ACUSUM chart) for statistical process control. The new development includes the variable sampling interval (VSI), variable sample size (VSS) and VSS and interval (VSSI) versions, all of which are highly effective for monitoring the mean and variance of a variable x by inspecting the absolute sample shift inline image (where μ0 is the in-control mean or target value of x). While the adaptive ACUSUM chart is a straightforward extension of the ABS CUSUM chart developed by Wu, et al., it is much more effective than all other adaptive CUSUM charts. Noteworthily, the superiority of VSI ACUSUM chart over the best adaptive CUSUM chart in literature is about 35% from an overall viewpoint. Moreover, the design and implementation of the adaptive ACUSUM chart are much simpler than that of all other adaptive CUSUM schemes. All these desirable features of the adaptive ACUSUM chart may be attributable to the use of a single sample size (n = 1). Another quite interesting finding is that the simpler VSI ACUSUM chart works equally well as the more complicated VSSI ACUSUM chart

Economic-Statistical Design of the Synthetic Xbar Chart with Estimated Process Parameters

International audienc

A comparison study on effectiveness and robustness of control charts for monitoring process mean and variance

This article compares the effectiveness and robustness of nine typical control charts for monitoring both process mean and variance, including the most effective optimal and adaptive sequential probability ratio test (SPRT) charts. The nine charts are categorized into three types (the X type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and adaptive version). While the charting parameters of the basic charts are determined by common wisdoms, the parameters of the optimal and adaptive charts are designed optimally in order to minimize an index average extra quadratic loss for the best overall performance. Moreover, the probability distributions of the mean shift δµ and standard deviation shift δσ are studied explicitly as the influential factors in a factorial experiment. The main findings obtained in this study include: (1) From an overall viewpoint, the SPRT-type chart is more effective than the CUSUM-type chart and X type chart by 15 and 73%, respectively; (2) in general, the adaptive chart outperforms the optimal chart and basic chart by 16 and 97%, respectively; (3) the optimal CUSUM chart is the most effective fixed sample size and sampling interval chart and the optimal SPRT chart is the best choice among the adaptive charts; and (4) the optimal sample sizes of both the X charts and the CUSUM charts are always equal to one. Furthermore, this article provides several design tables which contain the optimal parameter values and performance indices of 54 charts under different specifications

Correlation between peri-implant bone mineral density and primary implant stability based on artificial intelligence classification

Abstract Currently, the classification of bone mineral density (BMD) in many research studies remains rather broad, often neglecting localized changes in BMD. This study aims to explore the correlation between peri-implant BMD and primary implant stability using a new artificial intelligence (AI)-based BMD grading system. 49 patients who received dental implant treatment at the Affiliated Hospital of Stomatology of Fujian Medical University were included. Recorded the implant stability quotient (ISQ) after implantation and the insertion torque value (ITV). A new AI-based BMD grading system was used to obtain the distribution of BMD in implant site, and the bone mineral density coefficients (BMDC) of the coronal, middle, apical, and total of the 1 mm site outside the implant were calculated by model overlap and image overlap technology. Our objective was to investigate the relationship between primary implant stability and BMDC values obtained from the new AI-based BMD grading system. There was a significant positive correlation between BMDC and ISQ value in the coronal, middle, and total of the implant (P  0.05). Furthermore, BMDC was notably higher at implant sites with greater ITV (P < 0.05). BMDC calculated from the new AI-based BMD grading system could more accurately present the BMD distribution in the intended implant site, thereby providing a dependable benchmark for predicting primary implant stability