Histologic and histomorphometric evaluation of minicono abutment on implant surrounding tissue healing and bone resorption on implants placed in healed bone. An experimental study in dogs

José Luis Calvo-Guirado; Marta Belén Cabo-Pastor; Francisco Martínez-Martínez; Miguel Ángel Garcés-Villalá; Félix de Carlos-Villafranca; Nuria García-Carrillo; Manuel Fernández-Domínguez; José Luis Calvo-Guirado; Marta Belén Cabo-Pastor; Francisco Martínez-Martínez; Miguel Ángel Garcés-Villalá; Félix de Carlos-Villafranca; Nuria García-Carrillo; Manuel Fernández-Domínguez

doi:10.3934/bioeng.2023013

AIMS Bioengineering

2023, Volume 10, Issue 3: 183-201. doi: 10.3934/bioeng.2023013

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Research article Special Issues

Histologic and histomorphometric evaluation of minicono abutment on implant surrounding tissue healing and bone resorption on implants placed in healed bone. An experimental study in dogs

1.
Department of Research, Health Sciences Faculty, Autonomous. University of Chile, 7500912, Chile; Private Practice Murcia, Murcian Biomedical Research Institute, IMIB, Spain
2.
Department of Advanced Prosthodontics, Cardenal Herrera University, C.E.U, 46001 Valencia, Spain
3.
Department of Traumatology, University Hospital Virgen of Arrixaca, 30120, El Palmar, Murcia, Murcian Biomedical Research Institute, IMIB, Spain
4.
Department of Implant and Biomaterial Research, Jesus Heart Foundation, San Juan, 5400, Argentina
5.
Department of Orthodontics, Faculty of Medicine, Oviedo University, 33001 Asturias, Spain
6.
Department of Veterinary, University of Murcia, 30007 Murcia, Spain, Murcian Biomedical Research Institute, IMIB, Spain
7.
Department of Dentistry, Camilo José Cela University, 28001 Madrid, Spain

Received: 03 June 2023 Revised: 28 July 2023 Accepted: 28 July 2023 Published: 14 August 2023

The objective of this evaluation was to measure the width and length of connective tissue (CT) and crestal bone resorption (CBR) related to minicono® abutment inserted in conical connection dental implants, which were placed crestal and subcrestally in a dog's mandible.

Materials and Methods

Forty-eight Top DM implants with the same coronal diameter were placed at the crestal level, 1 mm (test 1 group) and 2 mm (test 2 group) positions underneath buccal-lingual bone crests. Dental implants used in the study were separated into three groups of 16 implants each. The implants were randomly inserted into healed bone after two months post-extraction sockets of three lower premolars, and first molar, bilaterally in six male fox hound dogs. One 3 mm minicono height abutment was connected to conical connection implants placed at the crestal level (control), 1 mm (test 1) and 2 mm (test 2) positions under buccal-lingual crests.

Results

All abutments and implants used were clinically and histologically integrated into the bone-soft tissue. Soft tissue behavior was observed at eight and 12 weeks in all test groups, displaying similar quantitative findings with significant differences (p > 0.05). However, crestal bone loss was significantly greater at the buccal side around that control group compared to the test 1 and 2 groups. The difference values between groups at the implant shoulder to the top of the lingual bone crest (IS-LBC) and the implant shoulder to the top of the buccal bone crest (IS-BBC) were significantly greater for the test 2 group in comparison with the other two groups (p < 0.05) at eight weeks. In addition, crestal bone resorption (CBR) increased in the crestal group at twelve weeks, but it was reduced for the test 1 and test 2 groups in implants placed sub-crestally (p < 0.05).

Conclusions

Crestal bone loss could be reduced using a 3 mm high abutment on implants submerged below the bone crest from 1 to 2 mm positions.

Keywords:

collar design,
implant placement,
crestal placement,
sub-crestal placement soft tissue height,
minicono abutment,
transmucosal abutment

Citation: José Luis Calvo-Guirado, Marta Belén Cabo-Pastor, Francisco Martínez-Martínez, Miguel Ángel Garcés-Villalá, Félix de Carlos-Villafranca, Nuria García-Carrillo, Manuel Fernández-Domínguez. Histologic and histomorphometric evaluation of minicono abutment on implant surrounding tissue healing and bone resorption on implants placed in healed bone. An experimental study in dogs[J]. AIMS Bioengineering, 2023, 10(3): 183-201. doi: 10.3934/bioeng.2023013

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Abstract

Materials and Methods

Results

Conclusions

Crestal bone loss could be reduced using a 3 mm high abutment on implants submerged below the bone crest from 1 to 2 mm positions.

1. Introduction

As environment issues have been increasingly prominent, low carbon development has gradually been a needed option for the economic development of all countries. China is striving to foster its ecological progress, seeking high-quality development, and it has been stated that China's economy has been transitioning from a phase of high-speed growth to a stage of high-quality development. Economic growth has gone from emphasizing quantity to stressing quality, which shows the intrinsic requirements of keeping a sustainable and healthy economic growth in China. Since China is at the pivotal moment of promoting ecological progress and achieving high-quality development, the exploration and grasp of the changing regularities of low carbon economy and quality of economic growth is an important guarantee for successfully achieving high-quality development. The construction of the Guangdong-Hong Kong-Macao Greater Bay Area is a key strategic plan of China. The goal is to build it into a first-class bay area and a world-class urban agglomeration and thus set it as an example of high-quality development. As a vital part of the Guangdong-Hong Kong-Macao Greater Bay Area, Guangdong Province is one of the most economically and socially developed provinces in China; therefore, it must be at the forefront of the provinces in China in exploring and achieving high-quality economic development.

There is limited research on measuring low-carbon economy and quality of economic growth from the perspective of new development concepts, and further research is needed on the regional imbalance and its changing patterns of low-carbon economy and quality of economic growth. The convergence of low-carbon economy and quality of economic growth has been a new exploration direction in recent years, with a very broad space for expansion. The major innovations of this paper include: On the one hand, we construct low-carbon economy and quality of economic growth measurement indicators from the perspective of new development concepts, and adopt the entropy method for measurement; on the other hand, we use the coefficient of variation and convergent model with fixed effects to analyze the convergences of low carbon economy and quality of economic growth.

This paper is conducive to better grasping the changing regularities of low carbon economy, quality of economic growth and their convergences, enriching related research on low carbon economy and quality of economic growth, and providing a reference for the formulation of urban and regional economic policies. This provides the realization of high-quality economic development and "double carbon goal" of China.

The Kyoto Protocol, aiming to address climate changes, promoted the formation of the concept of low carbon economy, which was first proposed in the UK's Energy White Paper in 2003 ^[15]. Chinese scholars have also begun to discuss low carbon economy in the context of China's economic development model ^[6,16,19], and since then, most studies have discussed low carbon and high-quality development at the macro level ^{[23,44,47,50]}. Some research has studied how firms can achieve low carbon development at the micro level ^[38].

Quality of economic growth has a definition in both narrow and broad sense. In a narrow sense, it can be analyzed from perspectives of total factor productivity ^[30,49], economic structure ^[26,45], and environment ^[17]. In a broad sense, which is widely recognized, scholars have begun to comprehensively analyze it from the aspects of the environment, income, welfare, education, law, and order ^[5,37]. The evaluation of quality of economic growth is developing in a multi-dimensional way. Also, different research needs to make the evaluation dimension subjective ^[27].

Some scholars put quality of economic growth into the theoretical framework of economic growth for analysis, and believed that it was an extension of the theoretical framework of economic growth and that quantitative growth and qualitative growth were two aspects of the same question in economic growth theory ^[33]. Ren's research team conducted in-depth discussions from this perspective ^[9,10,40], which had a broad impact on subsequent research. Therefore, most scholars regard in their research the quality of economic growth as a concept that is closely related to economic growth amount but has a broader connotation. In terms of measurement methods, by constructing a multi-dimensional indicator system, principal component analysis ^{[10,21,40,48]}, and entropy method ^[41,42] are adopted to measure the quality of economic growth. The low carbon economy in economic growth quality has been a hot topic of concern in the academic community in recent years ^[20], with more and more related research focusing on different countries ^[2,12,22,28], a series of related studies have been conducted from different perspectives ^{[51,52,53,54,55,56,57]}.

Early discussions on the phenomenon of economic growth convergence include the analysis of changes in per capita income within a neoclassical framework ^[32], and the "Yerkes-Dodson Law" in regional income convergence ^[43]. Since then, scholars have begun to carry out corresponding empirical research in line with local conditions ^[1,7]. Although some studies have also been questioned ^[14], they have played a great role in promoting relevant research. In the 1990s, scholars questioned the traditional empirical process and began to try new methods to empirically analyze the phenomenon of economic growth convergence ^[8,29,31]. Furthermore, scholars have also begun to explore the phenomenon of economic growth convergence in China ^[11,18,34], and influential representative results have emerged one after another, such as economic growth convergence mechanism based on the neoclassical analysis framework ^[3,4] and its empirical analysis ^[24,39], "club convergence" of China's economic growth ^[35]. In contrast, there are relatively few studies on the convergence of low carbon economy and quality of economic growth. In recent years, scholars have begun to try to analyze the convergence of quality of economic growth from different perspectives and with various methods ^{[13,25,36,46]}, but it is at the early stage of exploration.

Most literatures have focused on the convergence of economic growth and income. Recently, scholars have begun to pay attention to the convergence of low-carbon economy and quality of economic growth, yet it is at the early stage of exploration, and there is a very broad space for expansion. As cities pay more and more attention to the quality of economic growth, how does their convergences of low carbon economy and quality of economic growth change? Based on the research status quo and the reality of urban economic development, we construct measurement indicators of low carbon economy and quality of economic growth in combination with the new development philosophy, and analyze the convergences in multiple dimensions.

2. Measurement of low carbon economy and quality of economic growth

2.1. Construction of the indicator system

The essence of low carbon economy is to realize the green development of economy. Thus, green development indicators of quality of economic growth can be adopted to construct measurement indicators of low carbon economy (see Table 1). Therefore, the green dimension of quality of economic growth has reflected the low carbon development of economy. The secondary indicators are composed of coordination of energy efficiency and environmental pollution. Energy efficiency is shown by growth of energy consumption per unit of GRP, which is a negative indicator. Environmental pollution consists of four basic indicators, namely waste water discharge, industrial waste gas emission, industrial fume (dust) emission, and industrial solid waste production. In order to eliminate the biased error in the measurement of indicators, the indicators used here are relative indicators, which are all negative indicators. These indicators all have made energy efficiency and environmental pollution highly relative to GRP, which better shows the low carbon development of economy.

Table 1. Indicator system of low carbon economy.

Primary indicators	Secondary indicators	Basic indicators	Calculation methods	Unit	Indicator attribute
Green development dimension (Low carbon development)	energy efficiency	energy efficiency (x11)	growth of energy consumption per unit of GRP	%	negative
	environmental pollution	waste water discharge (x12)	total amount of waste water discharge/GRP	ton/ten thousand yuan	negative
		industrial waste gas emission (x13)	total amount of industrial waste gas emission/GRP	M³/yuan	negative
		industrial fume (dust) emission (x14)	total amount of industrial smoke (dust) emission/GRP	ton/one hundred million yuan	negative
		industrial solid waste production (x15)	industrial solid waste production amount/GRP	ton/ten thousand yuan	negative

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Many Chinese representative studies ^[10,33,40] have constructed indicator measurement systems of quality of economic growth, which involve some dimensions related to the new development philosophy, but few have constructed such system from the very five dimensions of the new development philosophy. Based on existing studies, we measured the quality of economic growth from the five dimensions of the new development philosophy, namely innovative, coordinated, green, open, and shared development dimensions. Quality of economic growth is first manifested in the transformation from traditional-factor-driven economic growth to innovation-driven economic growth and in the coordination within the economic system and among economic systems of different regions. Then, the external manifestation of the economic system is the coordinated development between humans and nature, as well as the linkage between domestic and international economies, whose ultimate goal is to widely share the final results. Table 2 is the indicator system of quality of economic growth with a total of 19 basic indicators.

Table 2. Indicator system of quality of economic growth.

Primary indicators	Secondary indicators	Basic indicators	Calculation methods	Unit	Indicator attribute
Innovative development dimension	R&D input	R&D intensity (x1)	total R&D spending/GRP	%	positive
	R&D input	public budget on science and technology expenditure (x2)	public budget on science and technology expenditure/total public budget expenditure	%	positive
	R&D result	number of licensed patent (x3)		piece	positive
	Education input	public budget on education expenditure (x4)	public budget on education expenditure/total public budget expenditure	%	positive
Coordinated development dimension	coordination of industrial structure	proportion of the secondary industry (x5)		%	positive
	coordination of industrial structure	proportion of the tertiary industry (x6)		%	positive
	coordination of investment and consumption structure	investment rate(x7)	gross capital formation/expenditure-based GRP	%	positive
		consumption rate (x8)	final consumption expenditure/expenditure-based GRP	%	positive
		investment-to-consumption ratio(x9)	gross capital formation/final consumption expenditure		moderate
	coordination of urban and rural areas	urban-rural income ratio (x10)	per capita disposable income of urban residents/per capita disposable income of rural residents		negative
Green development dimension	energy efficiency	energy efficiency (x11)	growth of energy consumption per unit of GRP	%	negative
	environmental pollution	waste water discharge (x12)	total amount of waste water discharge/GRP	ton/ten thousand yuan	negative
		industrial waste gas emission (x13)	total amount of industrial waste gas emission/GRP	M³/yuan	negative
		industrial fume (dust) emission (x14)	total amount of industrial smoke (dust) emission/GRP	ton/one hundred million yuan	negative
		industrial solid waste production (x15)	industrial solid waste production amount/GRP	ton/ten thousand yuan	negative
Open development dimension	opening structure	use of foreign capital (x16)	actual use of foreign capital/GRP	%	positive
Open development dimension	opening structure	degree of dependence on foreign trade (x17)	total import and export volume/GRP	%	positive
Shared development dimension	regional sharing	regional income sharing level (x18)	per capita GRP at prefecture level/per capita GRP at provincial level	%	positive
Shared development dimension	welfare change	per capita GRP (x19)		yuan	positive

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The first dimension is the innovative development dimension. In this dimension, the secondary indicators are composed of R&D investment, R&D result, and education input. R&D investment has two basic indicators, R&D intensity and public budget on science and technology expenditure, which refer to the investment of R&D entities and the government's investment, respectively. The R&D result adopts the number of licensed patent as the basic indicator, which shows the output of R&D. Education input takes the proportion of education expenditure in the public budget as a basic indicator, which reflects the potential of R&D.

The second dimension is the coordinated development dimension. In this dimension, the secondary indicators are composed of coordination of industrial structure, coordination of investment and consumption structure, and coordination of urban and rural areas. Coordination of industrial structure consists of the proportion of the secondary industry and the proportion of the tertiary industry. Coordination of investment and consumption structure consists of investment rate and consumption rate. For the two rates, the higher is not the better. Xiang ^[45] believes that it is more appropriate that the investment rate is lower than 38% and the consumption rate is higher than or equal to 60%. Based on this standard, it can be inferred that investment-to-consumption ratio should be lower than 63.3%. Therefore, investment rate and consumption rate are regarded as positive indicators, and investment-to-consumption ratio, a moderate indicator whose standard is lower than 63.3%, is utilized for adjustment. Coordination of urban and rural areas takes urban-rural income ratio as its basic indicator, which is a negative indicator and shown as "per capita disposable income of urban residents/per capita disposable income of rural residents".

The third dimension is the green development dimension, whose indicators are consistent with the previous ones of low carbon economy.

The fourth dimension is the open development dimension. In this dimension, the secondary indicator is composed of an opening structure. An opening structure consists of two basic indicators, namely the use of foreign capital and the degree of dependence on foreign trade, which reflect the concept of economic openness. The use of foreign capital is shown as "actual use of foreign capital/GRP", and the degree of dependence on foreign trade is represented as "total import and export volume/GRP".

The fifth dimension is the shared development dimension. In this dimension, the secondary indicators are composed of regional sharing and welfare change. According to Wei and Li ^[41,42], the regional income sharing level refers to the ratio of per capita GRP at a prefecture level to per capita GRP at a provincial level, and welfare change takes per capita GRP as its basic indicator.

2.2. Methods and data

2.2.1. Measurement method

In this paper, the entropy value method and the multi-objective linear weighted function method, which are commonly used in academia, are utilized to measure the quality of economic growth of cities in the Pearl River Delta. Specific steps are as follows:

The first step is to standardize the variables. The measurement indicator x_ij is standardized through the range method, which can eliminate the influence of quantity variance and dimension difference on the measurement indicator.

${X}_{ij} = \left\{\begin{array}{c}\frac{{x}_{ij}-\mathrm{m}\mathrm{i}\mathrm{n}\left({x}_{ij}\right)}{\mathrm{max}\left({x}_{ij}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left({x}_{ij}\right)}\left(\mathrm{When}\ {x}_{ij}\text{ is a positive indicator}\right)\\ \frac{\mathrm{m}\mathrm{a}\mathrm{x}\left({x}_{ij}\right){-x}_{ij}}{\mathrm{max}\left({x}_{ij}\right)-\mathrm{min}\left({x}_{ij}\right)}\left(\text{When }{x}_{ij}\text{ is a negative indicator}\right)\end{array}\right. .$

(1)

In the above formulas, i represents city, j basic indicator, and x_ij and X_ij original basic indicator and standardized basic indicator respectively.

If the indicator is a moderate one, it should be standardized in line with the following reference standards:

${x}_{ij} = \left\{\begin{array}{c}{z}_{ij}-{z}_{0}(\text{when }{z}_{ij} > {z}_{0}, \text{ the more }{z}_{ij}\text{ is greater than }{z}_{0}, \text{ the better})\\ {z}_{0}-{z}_{ij}(\text{when }{z}_{ij} < {z}_{0}, \text{ the more }{z}_{ij}\text{ is less than }{z}_{0}, \text{ the better})\end{array}\right. .$

(2)

In these formulas, z_ij is a moderate indicator, and z₀ is the reference standard of a moderate indicator. The new transformed indicator should be standardized through the method of positive indicators as is shown in formula (1).

The second step is to calculate the information entropy E_j with standardized indicator X_ij.

${E}_{j} = -k\sum _{i = 1}^{n}\left(\frac{{X}_{ij}}{\sum _{i = 1}^{n}{\mathrm{X}}_{ij}}\mathrm{l}\mathrm{n}\frac{{X}_{ij}}{\sum _{i = 1}^{n}{X}_{ij}}\right) .$

(3)

In this formula, k = 1/ln(N). The data in this paper are all panel data, so N means the total number of samples.

The third step is to calculate the weight W_j of each basic indicator with the information entropy E_j.

${W}_{j} = \frac{(1-{E}_{j})}{\sum _{j = 1}^{m}(1-{E}_{j})} .$

(4)

In this formula, 1-E_j represents the information utility value of the jth indicator.

The fourth step is to calculate the quality of economic growth indicator QEG in the multi-objective linear weighted function.

$QEG = \sum _{j = 1}^{m}{W}_{j}{X}_{ij} .$

(5)

The value range of the quality of economic growth indicator QEG is (0, 1), and the larger the value, the higher the quality of economic growth of the city.

2.2.2. Data resources

According to the research needs and data availability, we selected the data of various prefecture-level cities in Guangdong Province from 2008 to 2019, which are mainly from China City Statistical Yearbook, Guangdong Statistical Yearbook, and the Statistical Yearbook of various prefecture-level cities in Guangdong. For some missing data, they were replaced by the average value (or the mean) of calculations with the series mean method, the proximal point mean method, the proximal point median method, the linear interpolation method, and the linear trend method at certain points.

(1) Urban-rural income ratio. As for the coordination of urban-rural areas, the secondary indicator in coordinated development dimension, when calculating the urban-rural income ratio, the per capita rural income was also the per capita net income of rural residents in some areas before 2013. Since there was no relevant statistics on rural residents in Shenzhen, the urban-rural income ratio in Shenzhen was replaced by the average value of Guangdong Province from 2003 to 2013. After 2014, the Pearl River Delta has collected relevant data, and then the urban-rural income ratio in Shenzhen is replaced by the mean of the data of the Pearl River Delta from 2014 to 2019.

(2) Environmental pollution. Environmental pollution, the second indicator in green development dimension, consists of four basic indicators, namely waste water discharge, industrial waste gas emission, industrial fume (dust) emission, and industrial solid waste production. When it is calculated, relative indicators are used in the measurement, that is, per unit (ten thousand yuan or yuan) GRP discharge or emission (ton or M³).

(3) The sum of actual use of foreign capital and total import and export volume. When the basic indicators in open development dimension are measured and calculated, the sum of actual use of foreign capital and total import and export volume should be converted to Chinese yuan based on the average exchange rate for that year in Guangdong Province.

3.1. Convergence of quality of economic growth

3.1. σ convergence

The σ convergence of quality of economic growth (low carbon economy) refers to the trend that the gap of quality of economic growth (low carbon economy) between various regions gradually narrows over time, and it is usually measured by the coefficient of variation as follows^①:

${\sigma }_{rt} = \frac{\sqrt{\left[\sum _{i = 1}^{{n}_{r}}{({QEG}_{it}-\stackrel{-}{{QEG}_{rt}})}^{2}\right]/{n}_{r}}}{\stackrel{-}{{QEG}_{rt}}} .$

(6)

^① The variable is QEG when measuring quality of economic growth and is LCE when measuring low carbon economy, the same below.

r refers to the region, and n_r represents the number of prefecture-level cities in r region. Coefficient of variation is the ratio of standard deviation to the mean of quality of economic growth (low carbon economy) indicator in the prefecture-level city of the region (r).

3.2. β convergence

The β convergence of quality of economic growth (low carbon economy) refers to the trend that the gap of growth rates of economic growth quality (low carbon economy) between various regions gradually narrows over time, and the β convergence model based on standard panel data model is as follows:

$\ln\left(\frac{{QEG}_{i, t+1}}{{QEG}_{i, t}}\right) = \alpha +\beta \ln{QEG}_{it}+\delta {X}_{it}+{\mu }_{i}+{\gamma }_{t}+{\epsilon }_{it} .$

(7)

When δ = 0, it is an absolute β convergence model; when δ≠0, it is a conditional β convergence model. The government is regarded as an import factor that affects quality of economic growth (low carbon economy); therefore, it is defined as a major control variable. When β is significantly less than 0, convergence exists, and convergence rate is $\frac{{ - \ln (1 + \beta)}}{T}$ . On the contrary, divergence exists.

3.3. Club convergence

The club convergence of quality of economic growth (low carbon economy) refers to the convergence of regions with similar characteristics. According to the division of economic regions in Guangdong Province in the Guangdong Statistical Yearbook, we divide economic regions in Guangdong Province into the Pearl River Delta, the East Wing, the West Wing and the mountain area. According to Opinions on Constructing the New Development Pattern of "One Core, One Belt, One Area" and Promoting Coordinated Regional Development of Guangdong Province (July, 2019) issued by Guangdong Provincial Party Committee and the People's Government of Guangdong Province, this paper divides economic regions in Guangdong Province into One Core (the Pearl River Delta), One Belt (the coastal economic belt), and One Area (the northern ecological development area). The convergences of these "clubs" are analyzed separately.

3.4. Relevant variables and data description

It is difficult to directly observe governmental influence. However, the amount of fiscal expenditure reflects the ability of the government to regulate and intervene in the economy, so fiscal expenditure is chosen as a substitute variable for governmental influence. The relevant variable data is the data of various prefecture-level cities in Guangdong Province from 2008 to 2019, mainly collected from the China City Statistical Yearbook, Guangdong Statistical Yearbook and the statistical yearbooks of various prefecture-level cities. The descriptive statistics of the main variables of the model are shown in Table 3.

Table 3. Descriptive statistics of the main variables.

Variables	Meaning	Measured value	Mean	Standard deviation	Median	Minimum	Maximum
LCE(D3)	low carbon economy indicator (score in green development dimension)	231	0.0182	0.0029	0.0188	0.0063	0.0230
lnLCE	logarithm of low carbon economy indicator	231	-4.0229	0.1960	-3.9734	-5.0654	-3.7714
ln( $\frac{{LCE}_{i, t+1}}{{LCE}_{i, t}}$ )	growth rate of low carbon economy	231	0.0257	0.0690	0.0157	-0.1870	0.4242
QEG	growth quality indicator	231	0.2242	0.1175	0.1650	0.0893	0.6832
lnQEG	logarithm of growth quality indicator	231	-1.6132	0.4716	-1.8015	-2.4155	-0.3810
$\ln\left(\frac{{QEG}_{i, t+1}}{{QEG}_{i, t}}\right)$	growth rate of economic quality	231	0.0153	0.0564	0.0189	-0.2408	0.2447
D1	score in innovative development dimension	231	0.0536	0.0503	0.0317	0.0122	0.3601
D2	score in coordinated development dimension	231	0.0633	0.0125	0.0618	0.0368	0.1077
D4	score in open development dimension	231	0.0415	0.0371	0.0294	0.0005	0.1968
D5	score in shared development dimension	231	0.0475	0.0479	0.0225	0.0010	0.1869
lnPFE	public fiscal expenditure	231	5.4648	0.9156	5.3735	3.6225	8.4325

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4. Convergence analysis of economic growth quality and low carbon economy

4.1. σ convergence analysis

4.1.1. σ convergence analysis of economic regions

The σ convergence of low carbon economy divided by economic regions of Guangdong Province is shown in Figure 1. From Figure 1, we can see that the σ convergence coefficient of the low carbon economy in the province is generally declining, indicating that σ convergence appears in the data of low carbon economy in the province, and the regional imbalance of the low carbon economy in the province tends to decrease. The σ convergence coefficients of low carbon economy in the Pearl River Delta, the East Wing and the mountain areas show an overall downward trend, and the σ convergence coefficient of the low carbon economy in the West Wing increases before the year of 2016, but also tends to decline since then, indicating that σ convergence appears in the data of the regions, and the regional imbalance of low carbon economy tends to decrease.

Figure 1. σ convergences of low carbon economy divided by economic regions in Guangdong Province.

Variables	Guangdong Province	Pearl River Delta	East Wing	West Wing	Mountain area
β	-0.2329^***	-0.2511^***	-0.2138^***	-0.3382^***	-0.2304^***
	(-7.3841)	(-5.3896)	(-5.0191)	(-2.8374)	(-3.3772)
Constants	-0.9114^***	-0.9744^***	-0.8278^***	-1.3220^***	-0.9245^***
	(-7.1782)	(-5.3075)	(-4.8397)	(-2.8090)	(-3.2038)
Individual effects	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes
Hausman test	7.82^***
sigma_u	0.0195	0.0131	0.0053	0.0268	0.0207
sigma_e	0.0622	0.0245	0.0329	0.0430	0.1157
R²	0.2069	0.2461	0.3924	0.2173	0.1888
N	231	99	44	33	55
Note: Figures in parentheses are t statistics; Hausman test here refers to the χ² value of Hausman test; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

Variables	Guangdong Province	Innovative development	Coordinated development	Green development (LCE)	Open development	Shared development
β	-0.1745^***	-0.1539^***	-0.3196^***	-0.2329^***	-0.2114^***	-0.2170^***
	(-4.3876)	(-4.5696)	(-8.3474)	(-7.3841)	(-4.6537)	(-14.7338)
Constants	-0.2663^***	-0.4330^***	-0.8663^***	-0.9114^***	-0.8870^***	-0.7071^***
	(-4.1426)	(-3.9510)	(-8.1408)	(-7.1782)	(-5.3113)	(-13.3941)
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
sigma_u	0.0889	0.1399	0.0544	0.0195	0.2260	0.1868
sigma_e	0.0539	0.1318	0.0612	0.0622	0.3260	0.0673
R²	0.0843	0.0908	0.2500	0.2069	0.0939	0.5095
N	231	231	231	231	231	231
Note: Figures in parentheses are t statistics; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

Variables	Coastal economic belt	Innovative development	Coordinated development	Green development (LCE)	Open development	Shared development
β	-0.1202^**	-0.1447^***	-0.2278^***	-0.2316^***	-0.3411^***	-0.2286^***
	(-2.5908)	(-3.8652)	(-6.8822)	(-7.1835)	(-5.0351)	(-13.9911)
Constants	-0.1647^**	-0.3751^***	-0.6193^***	-0.8991^***	-1.3186^***	-0.6950^***
	(-2.3176)	(-3.2207)	(-6.6534)	(-7.0374)	(-5.4158)	(-12.8137)
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
sigma_u	0.0679	0.1406	0.0370	0.0120	0.4151	0.1869
sigma_e	0.0507	0.1293	0.0369	0.0307	0.3579	0.0565
R²	0.0461	0.0970	0.2541	0.2707	0.1543	0.5848
N	154	154	154	154	154	154
Note: Figures in parentheses are t statistics; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

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[19]	Mangano F, Mangano C, Ricci M, et al. (2012) Single-tooth Morse taper connection implants placed in fresh extraction sockets of the anterior maxilla: an aesthetic evaluation. Clin Oral Implants Res 23: 1302-1307. https://doi.org/10.1111/j.1600-0501.2011.02307.x
[20]	Donovan R, Fetner A, Koutouzis T, et al. (2010) Crestal bone changes around implants with reduced abutment diameter placed non-submerged and at subcrestal positions: A 1-year radiographic evaluation. J Periodontol 81: 428-434. https://doi.org/10.1902/jop.2009.090317
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[24]	Nagarajan B, Murthy V, Livingstone D, et al. (2015) Evaluation of crestal bone loss around implants placed at equicrestal and subcrestal levels before loading: a prospective clinical study. J Clin Diagn Res 9: ZC47-ZC50. https://doi.org/10.7860/JCDR/2015/13911.7000
[25]	Calvo-Guirado JL, Boquete-Castro A, Negri B, et al. (2014) Crestal bone reactions to immediate implants placed at different levels in relation to crestal bone. A pilot study in Foxhound dogs. Clin Oral Impl Res 25: 344-351. https://doi.org/10.1111/clr.12110
[26]	Sotto-Maior BS, Lima CA, Senna PM, et al. (2014) Biomechanical evaluation of subcrestal dental implants with different bone anchorages. Braz Oral Res 28: 1-7. https://doi.org/10.1590/1807-3107BOR-2014.vol28.0023
[27]	Cochran DL, Mau LP, Higginbottom FL, et al. (2013) Soft and hard tissue histologic dimensions around dental implants in the canine restored with smaller-diameter abutments: a paradigm shift in peri-implant biology. Int J Oral Max Impl 28: 494-502. https://doi.org/10.11607/jomi.3081
[28]	de Val JEMS, Gómez-Moreno G, Ruiz-Linares M, et al. (2017) Effects of surface treatment modification and implant design in implants placed crestal and subcrestally applying delayed loading protocol. J Craniofac Surg 28: 552-558. https://doi.org/10.1097/SCS.0000000000003209
[29]	Calvo-Guirado JL, López-López PJ, Mate Sanchez JE, et al. (2014) Crestal bone loss related to immediate implants in crestal and subcrestal position: a pilot study in dogs. Clin Oral Implants Res 25: 1286-1294. https://doi.org/10.1111/clr.12267
[30]	Calvo-Guirado JL, Delgado Ruiz RA, Ramírez-Fernández MP, et al. (2016) Retracted: Histological and histomorphometric analyses of narrow implants, crestal and subcrestally placed in severe alveolar atrophy: a study in foxhound dogs. Clin Oral Implants Res 27: 497-504. https://doi.org/10.1111/clr.12569
[31]	Sánchez-Siles M, Muñoz-Cámara D, Salazar-Sánchez N, et al. (2018) Crestal bone loss around submerged and non-submerged implants during the osseointegration phase with different healing abutment designs: a randomized prospective clinical study. Clin Oral Implants Res 29: 808-812. https://doi.org/10.1111/clr.12981
[32]	De Siqueira RAC, Savaget Goncalves Junior R, Dos Santos PGF, et al. (2020) Effect of different implant placement depths on crestal bone levels and soft tissue behavior: a 5-year randomized clinical trial. Clin Oral Implants Res 31: 282-293. https://doi.org/10.1111/clr.13569
[33]	de Siqueira RAC, Fontão FNGK, Sartori IAM, et al. (2017) Effect of different implant placement depths on crestal bone levels and soft tissue behavior: a randomized clinical trial. Clin Oral Implants Res 28: 1227-1233. https://doi.org/10.1111/clr.12946
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[35]	Linkevicius T, Linkevicius R, Gineviciute E, et al. (2021) The influence of new immediate tissue level abutment on crestal bone stability of subcrestally placed implants: A 1-year randomized controlled clinical trial. Clin Implant Dent R 23: 259-269. https://doi.org/10.1111/cid.12979
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Variables	Guangdong Province	Innovative development	Coordinated development	Green development (LCE)	Open development	Shared development
β	-0.2436^***	-0.3384^***	-0.4740^***	-0.3302^***	-0.5023^***	-0.3051^***
	(-5.1577)	(-6.1530)	(-8.7327)	(-7.4553)	(-7.9999)	(-13.3076)
lnPFE	0.0219^***	0.1166^***	0.0440^***	0.0349^***	-0.3650^***	0.0663^***
	(2.6258)	(4.1540)	(3.8933)	(3.0686)	(-6.2114)	(4.8526)
Constants	-0.4975^***	-1.6683^***	-1.5361^***	-1.4937^***	0.0471	-1.3841^***
	(-4.5851)	(-5.2870)	(-7.6615)	(-6.5815)	(0.2191)	(-9.3360)
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
sigma_u	0.1082	0.2079	0.1015	0.0303	0.6955	0.2431
sigma_e	0.0532	0.1270	0.0592	0.0609	0.3002	0.0639
r2_w	0.1137	0.1605	0.3010	0.2413	0.2357	0.5594
N	231	231	231	231	231	231
Convergence rate	0.0254	0.0376	0.0584	0.0364	0.0634	0.0331
Note: Figures in parentheses are t statistics; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

Variables	Pearl River Delta	Innovative development	Coordinated development	Green development (LCE)	Open development	Shared development
β	-0.0694	-0.2971^***	-0.4331^***	-0.5385^***	-0.0652	-0.2098^***
	(-1.0082)	(-3.5375)	(-4.8539)	(-6.9107)	(-1.0868)	(-5.2699)
lnPFE	0.0227^*	0.1706^***	0.0436^**	0.0383^***	-0.0588	0.0317^***
	(1.7799)	(3.0559)	(2.5867)	(4.3971)	(-1.2639)	(2.6485)
Constants	-0.1975	-1.6853^***	-1.4677^***	-2.3381^***	0.0815	-0.6996^***
	(-1.3974)	(-3.1236)	(-4.2471)	(-6.6362)	(0.4221)	(-4.2287)
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
sigma_u	0.0250	0.0910	0.0886	0.0146	0.0955	0.0966
sigma_e	0.0453	0.0998	0.0371	0.0223	0.1463	0.0343
R²	0.0352	0.1273	0.2946	0.3819	0.0184	0.2790
N	99	99	99	99	99	99
Convergence rate		0.0320	0.0516	0.0703		0.0214
Note: Figures in parentheses are t statistics; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

Variables	East Wing	Innovative development	Coordinated development	Green development (LCE)	Open development	Shared development
β	-0.2963^**	-0.7835^***	-0.3432^***	-0.3366^***	-0.7307^***	-0.3587^***
	(-2.5859)	(-4.5828)	(-3.4461)	(-3.5979)	(-5.1721)	(-8.7568)
lnPFE	-0.0065	0.2317^***	0.0042	0.0301	-0.7279^***	0.0980^***
	(-0.5041)	(3.2111)	(0.3864)	(1.4694)	(-5.3161)	(2.8763)
Constants	-0.5247^**	-3.9314^***	-0.9345^***	-1.4676^***	0.4750	-1.9395^***
	(-2.1022)	(-4.2013)	(-3.0298)	(-3.1434)	(1.6791)	(-5.8026)
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
sigma_u	0.0307	0.1624	0.0541	0.0071	0.3033	0.0865
sigma_e	0.0420	0.1409	0.0276	0.0325	0.2023	0.0618
R²	0.1945	0.3690	0.3383	0.4251	0.4323	0.8068
N	44	44	44	44	44	44
Convergence rate	0.0319	0.1391	0.0382	0.0373	0.1193	0.0404
Note: Figures in parentheses are t statistics; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

Variables	West Wing	Innovative development	Coordinated development	Green development (LCE)	Open development	Shared development
β	-0.5144^**	-0.8149^***	-0.4438^***	-0.3857^**	-0.8672^***	-0.1544
	(-2.5889)	(-4.8703)	(-2.8907)	(-2.6611)	(-4.4591)	(-1.4413)
lnPFE	0.0758	0.2121^***	0.0697^*	0.0104	-0.2923	-0.0242
	(1.6693)	(3.6584)	(1.9309)	(0.5898)	(-1.3485)	(-0.4604)
Constants	-1.4451^**	-4.2697^***	-1.6446^**	-1.5629^**	-3.0394^**	-0.3996
	(-2.2926)	(-4.6078)	(-2.6117)	(-2.4918)	(-2.4689)	(-0.6006)
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
sigma_u	0.0916	0.1416	0.0856	0.0275	0.8703	0.0244
sigma_e	0.0674	0.0937	0.0417	0.0435	0.6079	0.0686
R²	0.2228	0.4651	0.2987	0.2269	0.4155	0.3923
N	33	33	33	33	33	33
Convergence rate	0.0657	0.1533	0.0533	0.0443	0.1836
Note: Figures in parentheses are t statistics; , , and ** refer to the significant level of 10%, 5%, and 1% respectively.

AIMS Bioengineering

Histologic and histomorphometric evaluation of minicono abutment on implant surrounding tissue healing and bone resorption on implants placed in healed bone. An experimental study in dogs

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Abstract

1. Introduction

2. Measurement of low carbon economy and quality of economic growth

2.1. Construction of the indicator system

2.2. Methods and data

2.2.1. Measurement method

2.2.2. Data resources

3.1. Convergence of quality of economic growth

3.1. σ convergence

3.2. β convergence

3.3. Club convergence

3.4. Relevant variables and data description

4. Convergence analysis of economic growth quality and low carbon economy

4.1. σ convergence analysis

4.1.1. σ convergence analysis of economic regions

4.1.2. σ convergence analysis of "One Core, One Belt, One Area"

4.2. β convergence analysis

4.2.1. Absolute β convergence analysis

4.2.2. Conditional β convergence analysis

4.3. Analysis of club convergence

5. Conclusions

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Abstract

1. Introduction

2. Measurement of low carbon economy and quality of economic growth

2.1. Construction of the indicator system

2.2. Methods and data

2.2.1. Measurement method

2.2.2. Data resources

3.1. Convergence of quality of economic growth

3.1. σ convergence

3.2. β convergence

3.3. Club convergence

3.4. Relevant variables and data description

4. Convergence analysis of economic growth quality and low carbon economy

4.1. σ convergence analysis

4.1.1. σ convergence analysis of economic regions

4.1.2. σ convergence analysis of "One Core, One Belt, One Area"

4.2. β convergence analysis

4.2.1. Absolute β convergence analysis

4.2.2. Conditional β convergence analysis

4.3. Analysis of club convergence

5. Conclusions

Use of AI tools declaration

Conflict of interest

Appendix

References