Research Progress on Osteogenic Differentiation of Apical Papilla Stem Cells
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摘要: 根尖牙乳头干细胞(stem cells from apical papilla,SCAP)具有很强的多系分化潜能,其中成骨分化可以应用于骨组织再生,为口腔颌骨缺损治疗提供新思路。成骨分化是个复杂的网络调控过程,诸如各种细胞因子、表观遗传物质、各种信号分子和信号通路等内源性物质均可产生不同程度的影响。这些因素相互作用可以促进SCAP的增殖、迁移和成骨分化,但其在SCAP成骨分化的不同进程中的具体机制和内在联系各不相同。对近年来有关促进SCAP成骨分化的各种因素及其可能的调控机制研究文献进行综述,以期为其进一步的应用研究提供新信息。Abstract: Stem cells from apical papilla (SCAP) have a strong multi-line differentiation potential, in which osteogenic differentiation can be applied to bone tissue regeneration, providing a new idea for the treatment of oral jaw defects. Osteogenic differentiation is a complex network regulation process, and endogenous substances such as various cytokines, epigenetic material, various signaling molecules and signaling pathways can have different degrees of influence. The interaction of these factors can promote the proliferation, migration and osteogenic differentiation of SCAP, but the specific mechanisms and internal links in different processes of osteogenic differentiation of SCAP are different. In this paper, the factors that promote osteogenic differentiation of SCAP and their possible regulatory mechanisms were reviewed to provide new information for further application research.
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孕产妇死亡率(maternal mortality rate,MMR)是反映国家或地区经济、教育、卫生等社会因素的敏感指标,也是衡量母婴安全、妇女健康状况和生存质量的重要尺度[1],也被列入联合国千年发展目标(Millennium Development Goals,MDGs)和可持续发展目标(Sustainable Development Goals,SDGs)的具体指标之一[2]。我国政府高度重视孕产妇死亡控制工作,先后颁布的《“健康中国2030”规划纲要》《中国妇女发展纲要(2021—2030年)》对孕产妇健康提出高要求[3]。云南省政府颁布《云南妇女发展规划(2021—2030年)》提出“2030年全省MMR下降至9/10万”的目标[4]。
通过构建统计模型来预测事物的发生发展趋势是认识事物发展规律及制定决策的一个重要手段和措施[5]。国内学者使用不同预测模型对全国或部分省份的孕产妇死亡率进行了预测及模型评估,其中,差分自回归移动平均模型(auto regressive integrated moving average model,ARIMA)和灰色预测模型GM(1,1)适用于短、中长或长期时间序列数据预测。ARIMA模型作为一个经典的时间序列预测模型,经验证对MMR的预测精度较好[6-7]。GM(1,1)模型是较为常见、且比较成熟的时间序列预测模型,因模型构建过程简单,只需要历史数据即可进行建模预测,可行性较高。两类模型均被广泛应用于疾病发病率和死亡率预测、妇幼保健指标预测、卫生人力资源和卫生费用预测、药品价格预测、门诊量预测等医疗卫生领域,为医疗决策的有效制定提供了科学依据[8]。
孕产妇死亡率影响因素多、地区差异大,不同预测模型对不同地区的预测效果可能不同。尚无学者对云南省孕产妇死亡率预测模型构建和模型预测效果比较的相关研究报道,本研究基于1994—2023年云南省MMR,构建ARIMA和GM(1,1)模型,并对模型拟合效果进行比较,选择最优模型对2024—2030年云南省MMR进行预测,为我省卫生行政部门制定年度MMR控制目标和实现2030年MMR目标保障政策提供参考依据。
1. 资料与方法
1.1 资料来源
本研究数据来源于云南省妇幼卫生年报(1994—2023年),收集1994—2023年共30年云南省MMR数据。
1.2 研究方法
灰色预测模型(GM(1,1)) GM(1,1)的理论步骤[9−11],见图1。
ARIMA模型ARIMA(p,d,q)模型由3个主要参数决定,q为滑动平均系数,表示误差项滞后q阶。q参数由偏自相关函数(partial auto correlation function,PACF)决定,p参数由自相关函数(auto correlation function,ACF)决定。参数d指的是实现平稳性所需的差异,根据数据的性质来确定的。运用SPSS 26.0软件进行ARIMA模型构建,确定时间序列的性质是否平稳,如果时间序列不平稳,则进行差分处理,直到序列平稳;通过Ljung-BoxQ检验判断数据是否为白噪声序列。根据ACF和PACF的图形走向,确定自回归项和移动平均项的阶数;建立ARIMA模型,进行参数估计和模型检验。
1.3 模型对比评价指标
经过计算不同模型的平均绝对误差(mean absolute error,MAE)、均方误差(mean squared error,MSE)和均方根误差(root mean squared error,RMSE)来进行模型间的比较,误差越小拟合数据越好[9]。
$$\tag{8} {\mathrm{MAE}} = \frac{{\displaystyle\sum\nolimits_{i = 1}^n {\left| {{y_i} - {x_i}} \right|} }}{N} $$ (1) $$\tag{9} {\mathrm{MSE}} = \frac{{\displaystyle\sum\nolimits_{i = 1}^n {{{({y_i} - {x_i})}^2}} }}{N} $$ (2) $$ \tag{10} {\mathrm{RMSE}} = \sqrt {\frac{{\displaystyle\sum\nolimits_{i = 1}^n {{{({y_{^i}} - {x_i})}^2}} }}{N}} $$ (3) 其中,$ {y_i} $为真实数据,$ {x_i} $为预测数据,n为数据个数。
1.4 统计学分析
用趋势卡方检验分析近30年云南省MMR变化趋势,α取0.05。用R 4.3.1软件建立GM(1,1)模型并进行拟合精度检验。用SPSS 26.0软件进行ARIMA模型建立、预测和分析拟合效果。
2. 结果
2.1 MMR变化趋势描述
云南省MMR从1994年的149.19/10万下降到2023年的9.70/10万,下降了139.49/10万,降幅为93.5%,整体呈持续下降趋势(χ2 =
50170.0 ,P < 0.05),见图2。2.2 GM(1,1)的构建与评估
1994—2023年云南省MMR的GM(1,1)预测结果计算得ɑ = 0.094,u = 172.865。所以GM(1,1)为:
$$ x(1)(t + 1) = -1697.96e^{-0.094t}+1847.15 $$ 发展系数-a < 0.3,说明模型适用于中长期预测。经拟合优度检验,后验差比值C = 0.079,小概率误差P = 1,预测精确度为1级,模型可进行外推。据数据分布散点图判断,其拟合效果较为接近真实值,但1997、2001和2006年预测值与真实值之间偏差分别为8.19/10万、8.37/10万和9.84/10万,其他年份偏差则均小于5/10万,见图3。
2.3 ARIMA的构建与评估
2.3.1 平稳性检验
通过原始时序图4可知序列有长期递减趋势,进行ACF、PACF图检验,可判定原始时间序列为非平稳序列,见图5、图6。
2.3.2 ARIMA模型构建
模型识别因1994—2023年云南省MMR的时间序列是非平稳序列,需要对原始序列差分消除趋势性影响。经过一次差分后,时间序列没有达到平稳化进行两次差分,在两次差分后,时间序列达到了平稳化,见图7。
进行ACF和PACF图检验,观察截尾性,可以看到二阶差分后的ACF和PACF呈现不规则变化,回归系数p = 1,移动平均值q = 1,见图8、图9。
根据确定的p,d,q3个参数确定ARIMA(1,2,1)Ljung-BoxQ(LBQ) = 22.087,P = 0.14 > 0.05,差异无统计学意义,残差序列不存在自相关。构建的ARIMA(1,2,1)为最优模型,拟合后的残差项为白噪声序列,无须继续建模,见图10。最终拟合效果显示:ARIMA模型拟合值与实际值之间存在一定偏差,2005年前较为明显,见图11。
2.4 两种不同模型拟合和预测结果比较
从两个模型对比来看,GM(1,1)灰色预测模型的整体偏差率低于ARIMA(1,2,1)模型 ,模型预测效果更好,见表1。
表 1 两种模型预测结果Table 1. Prediction results of the two models年份 GM(1,1)
偏差率(%)平均偏
差值(%)ARIMA
(1,2,1)
偏差率(%)平均偏
差值(%)1994 / 4.15 / 6.01 1995 −2.14 / 1996 −0.84 −12.81 1997 8.19 −0.45 1998 1.93 −9.79 1999 −2.94 −9.39 2000 0.30 6.38 2001 −8.37 −3.99 2002 −5.18 4.73 2003 −2.47 8.62 2004 0.03 3.59 2005 3.82 3.18 2006 9.84 4.71 2007 1.05 −11.31 2008 4.47 0.13 2009 0.61 −0.26 2010 0.00 −0.53 2011 0.77 3.16 2012 −2.90 −2.29 2013 −1.41 1.97 2014 −3.49 0.11 2015 0.29 4.02 2016 2.01 2.71 2017 0.29 −3.5 2018 0.27 −1.57 2019 −1.58 −1.58 2020 −2.20 −0.45 2021 −1.41 1.8 2022 0.15 2.12 2023 −1.34 −1.93 经模型构建,GM(1,1)和ARIMA预测拟合效果不同,见图12。
经过构建预测效果比较,GM(1,1)的MAE、MSE、RMSE均比ARIMA小,可以说明GM(1,1)比ARIMA预测效果好,见表2。
表 2 两种模型的指标数据值比较Table 2. Comparison of indicator data values between the two models模型 指标数据值比较 MAE MSE RMSE GM(1,1) 2.4238 12.39 3.52 ARIMA 3.9659 27.65 5.25 MAE,平均绝对误差;MSE,均方误差;RMSE,均方根误差。 2.5 2024—2030年云南省MMR预测
选用GM(1,1)对云南省2024—2030年MMR进行预测,结果显示2024—2030年的孕产妇死亡率依然呈下降趋势,见图13。
3. 讨论
3.1 1994—2023年云南省MMR整体呈持续下降趋势
自2000年MDGs提出“2015年MMR较1990年降低3/4,实现普遍享有生殖保健”以来,全球孕产妇死亡率呈明显下降趋势。我国2014年MMR下降至21.7/10万,较1990年88.8/10万相比,下降了75.6%,提前1年实现MDGs[12]。2015年9月联合国可持续发展峰会上193个成员国正式通过的SDGs中,第3项提出“2030年将全球MMR降至70/10万,所有国家MMR均不超过全球平均水平的2倍(140/10万)。2010年420/10万及以下的国家2030年MMR较2010年下降2/3”[13]。但全球各地孕产妇死亡下降情况各异,不同发达地区MMR也各不相同,2010—2020年高收入国家MMR的平均水平为12.3/10万,中高收入国家的平均水平为44.1/10万。发达国家中42个国家孕产妇死亡率呈现不同程度下降,下降速度最快的是塞舌尔(年平均变化速度8.6%)。发展中国家中35个国家MMR呈现不同程度下降,下降最快的是白俄罗斯(年平均变化速度为8.0%),2个国家几乎没有变化,14个国家呈上升趋势[14]。2020年,我国MMR为16.9/10万,比2010年降低43.7%,指标水平居全球中高收入国家前列,被世界卫生组织评定为“全球十个妇幼健康高绩效国家之一”[15−17]。1991—2021年我国MMR呈现明显的下降趋势,年平均下降速度为5.00%,平均下降速度明显高于世界及中高等收入国家的平均水平[18−19]。
1994—2023年云南省MMR从149.19/10万下降至9.7/10万,年平均下降速率为8.86%,高于我国1991—2021年MMR年平均下降速率5.00%,也高于海南省2003—2022年MMR年平均下降速率4.13%[20]。2023年,云南省MMR低于全国平均水平,母婴安全核心指标创云南最优水平[21]。云南省在国家母婴安全工作总体部署下,结合本省实际,加强顶层设计,巩固完善制度,优化资源配置,出台有关制度规范持续推进孕产妇健康管理和危重救治服务网络建设,提升服务质量,促进了云南省MMR持续降低。同时,提示云南省MMR在经过快速下降后,当前已到达低位,随着生育政策调整,云南省MMR保持低位且稳中有降面临较大挑战,要进一步下降难度可能有所增加,仍需继续加强孕产妇健康管理工作。
3.2 GM(1,1)对云南省MMR的预测效果优于ARIMR
MMR受经济社会发展状况、居民健康意识、医疗资源分配、服务公平性、可及性和服务质量等因素影响,而不同地区的影响因素各不相同。因此,不同预测模型在不同地区的预测效果可能存在不同。本研究显示GM(1,1)对云南省MMR的预测效果较好,与张亚慧[22]对中国孕产死亡率预测和张彬等[23]对我国农村预测结果相同。可能由于GM(1,1)模型精度较高,运算简便,建模所需信息少,对原始数据资料的限制较少,运用比较灵活,可被广泛运用于短期预测[10]。用该模型预测云南省2030年MMR为5.73/10万,可达到《健康中国“2030”规划纲要》《中国妇女发展纲要(2021—2030年)》《健康云南“2030”规划纲要》中的MMR控制目标。
3.3 统计模型的应用分析
本研究发现,GM(1,1)对云南省MMR的预测拟合优度较好,可进一步将该统计模型应用于婴儿死亡率、5岁以下儿童死亡率等妇幼健康核心指标,为该省2030年实现可持续发展目标中的妇幼健康指标控制目标提供对策与建议。本次构建的GM(1,1)中仅有3个年份预测值与真实值之间差异略高,提示单一的时间趋势预测模型难以对波动较大的时间点进行更精准的预测,后续研究可加入更多孕产妇死亡率影响因素进行预测模型的构建。
综上所述,统计模型在MMR变化趋势和预测应用具有良好的效果和较强的现实意义,可为卫生健康行政部门判断妇幼健康政策效果,为未来妇幼健康指标发展趋势提供理论依据。
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