The oral microbiome of implant-abutment screw holes compared with the peri-implant sulcus and natural supragingival plaque in healthy individuals

- ¹Department of Oral Microbiology, School of Dentistry, Pusan National University, Yangsan, Korea.
- ²Oral Genomics Research Center, Pusan National University, Yangsan, Korea.
- ³Interdisciplinary Program of Genomic Science, Pusan National University, Yangsan, Korea.
- ⁴Department of Biomedical Informatics, School of Medicine, Pusan National University, Busan, Korea.
- ⁵Dental Research Institute, BK21 PLUS Project, School of Dentistry, Pusan National University, Yangsan, Korea.
- ⁶Department of Prosthodontics, School of Dentistry, Pusan National University, Yangsan, Korea.
Correspondence: Jin Chung. Department of Oral Microbiology, School of Dentistry, Pusan National University, 49 Busandaehak-ro, Mulgeum-eup, Yangsan 50612, Korea. Email: jchung@pusan.ac.kr , Tel: +82-51-510-8245, Fax: +82-51-510-8246

^†MinKee Son and Yuri Song contributed equally to this work.

Received December 15, 2022; Revised March 14, 2023; Accepted April 27, 2023.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/).

Abstract

Purpose

An implant-supported prosthesis consists of an implant fixture, an abutment, an internal screw that connects the abutment to the implant fixture, and the upper prosthesis. Numerous studies have investigated the microorganisms present on the implant surface, surrounding tissues, and the subgingival microflora associated with peri-implantitis. However, there is limited information regarding the microbiome within the internal screw space. In this study, microbial samples were collected from the supragingival surfaces of natural teeth, the peri-implant sulcus, and the implant-abutment screw hole, in order to characterize the microbiome of the internal screw space in healthy subjects.

Methods

Samples were obtained from the supragingival region of natural teeth, the peri-implant sulcus, and the implant screw hole in 20 healthy subjects. DNA was extracted, and the V3–V4 region of the 16S ribosomal RNA was sequenced for microbiome analysis. Alpha diversity, beta diversity, linear discriminant analysis effect size (LEfSe), and network analysis were employed to compare the characteristics of the microbiomes.

Results

We observed significant differences in beta diversity among the samples. Upon analyzing the significant taxa using LEfSe, the microbial composition of the implant-abutment screw hole’s microbiome was found to be similar to that of the other sampling sites’ microbiomes. Moreover, the microbiome network analysis revealed a unique network complexity in samples obtained from the implant screw hole compared to those from the other sampling sites.

Conclusions

The bacterial composition of the biofilm collected from the implant-abutment screw hole exhibited significant differences compared to the supra-structure of the implant. Therefore, long-term monitoring and management of not only the peri-implant tissue but also the implant screw are necessary.

Graphical Abstract

Keywords

Biofilms; Dental implant; Microbiota; Next-generation sequencing

INTRODUCTION

Dental implants are the most functionally reliable and widely chosen treatment option for replacing lost natural teeth [1]. Since their introduction, dental implants have steadily gained popularity due to their ability to restore function, provide aesthetic appeal, and offer stability. After osseointegration, the long-term success of a dental implant depends on several factors, with peri-implantitis being a primary factor that can hinder longevity. Direct causes of peri-implantitis include changes in microbial flora [2, 3]. Excessive occlusal force can be generated by a patient’s spontaneous occlusion and the clinical and prosthetic environment. Alterations in microbial flora can directly cause infection, leading to an inflammatory response and ultimately resulting in peri-implantitis. This condition destroys the alveolar bone that supports the implant, causing a loss of stability and, if left untreated, eventual loss of the implant itself. Numerous studies have been conducted to explore ways to prevent and treat peri-implantitis, including examining the microflora of the implant surface, peri-implant tissues, and subgingival sites [4].

A complete implant-supported prosthesis is composed of an implant fixture that interfaces with the alveolar bone, an abutment that supports the prosthesis, an internal screw for connecting the abutment to the implant fixture, and the prosthesis/superstructure responsible for distributing direct occlusal force. The abutment is designed with a screw hole to facilitate the passage of an internal screw, which connects the abutment to the implant fixture. After tightening the screw with the appropriate torque, excess space within the abutment post and above the screw head becomes apparent. The extent of this space varies depending on the manufacturer, but it is generally limited to less than 50 µm for commonly used implant systems [5]. Moreover, the microgap at the implant-abutment interface may further expand upon loading. Since most oral bacteria have a diameter of less than 1 µm, a microbial pathway is created between the internal part of the implant and its super-structural components [6, 7, 8].

The bacteria that colonize the oral cavity play a significant role in oral health and disease. Over 700 species of oral bacteria have been identified [9]. Although most dental implants exhibit high survival rates, complications can arise, with implant failure primarily attributed to peri-implantitis [10]. Dental plaque formation near the peri-implant soft tissue can trigger peri-implant inflammation, resulting in a progressive loss of supporting bone and ultimately leading to the loss of the implant [11]. While numerous studies have focused on the microbiome associated with peri-implantitis, there have been limited studies examining the microbiome of the internal aspects of the implant fixture and its superstructures in healthy conditions [7]. Therefore, analyzing the microbiome of the internal implant space and its superstructures will enhance our understanding of implant management.

The advent of next-generation sequencing (NGS) has facilitated the simultaneous parallel analysis of hundreds of thousands of specific genes, such as 16S ribosomal RNA (rRNA), amplified by polymerase chain reaction (PCR). This has led to significant advancements in the examination and comprehension of the oral microflora [12]. In this study, microbial samples were obtained from the supragingival regions of natural teeth, the peri-implant sulcus, and the implant-abutment screw hole of participants. Following the sequencing of the V3–V4 variable region of 16S rRNA using NGS, the differences in microbial diversity among the various sampling sites were analyzed.

MATERIALS AND METHODS

Study subjects and sample collection

To evaluate the effect size and statistical power, we utilized micropower [13], a simulation-based method for permutational multivariate analysis of variance (PERMANOVA)-based β-diversity comparisons. The effect sizes (ω2) were calculated using simulated matrices with 80% and 90% power for varying sample numbers per group. In comparison, the effect size in a sample size of 20 subjects per group with an ω2 of 0.0213 is smaller than the effect size in published microbiome studies cataloged by Kelly et al. [13] that were analyzed using unweighted Jaccard distances. Therefore, 20 subjects per group likely provide adequate statistical power for the primary outcome measure.

Microbial samples were collected from a total of 20 healthy implant participants who received treatment at Pusan National University Dental Hospital. Patients with mental illness or suspected of having mental illness, as well as those who were deemed unsuitable for participation in the clinical trial, were excluded. The primary diagnostic criterion for peri-implant mucositis is the presence of bleeding during probing. To ensure that the patient did not have peri-implantitis, we adhered to the guidelines established by the 2017 World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions [14] and utilized previous studies [15] for diagnosis. In brief, peri-implantitis was diagnosed in subjects who had an implant with radiographic evidence of marginal bone loss ≥3 mm and/or probing pocket depth ≥6 mm, accompanied by profuse bleeding. The clinical attachment level of the implant was calculated as the distance from a fixed landmark (implant-abutment junction) to the bottom of the implant sulcus/pocket [16]. The demographic and clinical parameters of the participants are summarized in Table 1.

Table 1
Characteristics of participants

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Microbiome samples were collected from 3 sampling sites: (1) the supragingival surfaces of natural teeth, (2) the peri-implant sulcus, and (3) the implant-abutment screw hole. Dental plaque from the supragingival surface and the peri-implant sulcus was collected by gently rubbing the tooth-mucosal interface using a micro-brush. To sample the dental plaque of the implant-abutment screw hole, the surface of the crown was decontaminated with a sterile cotton ball, and the sealing material was removed. The micro-brush was then inserted into the screw hole and rotated 5 times to collect the microbiome sample. Samples were collected from all participants and stored at −80°C for subsequent processing. The study design was approved by the Institutional Review Board of Pusan National University (Approval No. PNUDH-2018-025), and written informed consent was obtained from each participant prior to the start of the study.

Extraction of genomic DNA and NGS analysis

Total DNA was extracted from each sample using a Gram Positive DNA Purification Kit (Lucigen, Biosearch Technology, Novato, CA, USA) following the manufacturer’s instructions. The final concentration was measured using a NanoDrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and stored at −80°C until use. PCR amplification of the V3–V4 fragment of the 16S rRNA gene was carried out. Purified amplicons were pooled in equimolar amounts and subjected to paired-end sequencing with HiSeq 2500 (Illumina, San Diego, CA, USA). Raw fastq files were demultiplexed and processed using tools available in QIIME2 (version 2019.7). Reads were demultiplexed with q2-demux, and quality filtered and dereplicated with q2-dada2. Representative sequence sets for each dada2 sequence variant were used for taxonomy classification using the naive Bayes machine learning classifier [17]. The raw sequencing data have been deposited at NCBI GenBank under BioProject ID PRJNA657184 (BioSample SAMN15818853–SAMN15818912).

Bioinformatic analysis, statistical analysis, and visualization

For alpha diversity estimation, we calculated 2 metrics: Chao 1, which estimates species abundance, and the Shannon index, which estimates microbial diversity within a sample. To assess the similarity of microbial community structures between samples, we performed a principal coordinates analysis. We calculated analysis of similarities using the Bray-Curtis dissimilarity metric. To evaluate community-level statistical differences between groups for beta diversity, we used the non-parametric PERMANOVA test, implemented in the Adonis function of the R/vegan package, with 1,000 permutations [16]. We identified significant differences in bacterial taxa abundance using linear discriminant analysis effect size (LEfSe) [18] with the default settings. To visualize internal interactions within the microbial community, we employed Sparse Correlations for Compositional data [19] to calculate the Spearman correlation coefficient and corresponding P values between species pairs. We measured clusters of nodes that form coherent structural subsystems of interacting units using the function cluster_louvain in the igraph package [20]. We then visualized the network using Cytoscape [21], with nodes representing genera and connections indicating the existence of correlations.

RESULTS

Patient characteristics

In total, 20 subjects, including 11 male and 9 female, were recruited. The demographic and clinical parameters of the participants are summarized in Table 1. Specimens were collected from subjects at least 1 month after installing the implant prosthesis, with an average interval of 15.25 months (standard deviation, 23.85).

Diversity and abundance of the microbiome

The alpha diversities, which represent the intra-sample microbial diversity, were assessed using the Chao 1 and Shannon indices. Based on these indices, no significant differences in alpha diversities were observed between the sampling sites (Figure 1A). The beta diversities of the supragingival biofilm on natural teeth (Nt_Supra) and the biofilm associated with the peri-implant sulcus (Im_Sulcus) and implant screw (Im_Screw) were analyzed using the Bray-Curtis dissimilarity metric (Figure 1B). Notably, significant differences were found between the Nt_Supra, Im_Sulcus, and Im_Screw groups, indicating that there were substantial differences in microbial composition among the sampling sites.

Figure 1
Comparisons of the bacterial community between Nt_Supra, Im_Sulcus, and Im_Screw. (A) Alpha diversity (Chao1, Shannon, and Simpson indices). (B) Beta diversity. The non-parametric PERMANOVA test was used to evaluate differences in beta diversity between sampling groups. PCoA was performed based on species abundances.
Nt_Supra: supragingival biofilm on natural teeth, Im_Sulcus: biofilm associated with peri-implant sulcus, Im_Screw: biofilm associated with implant-abutment screw hole, PERMANOVA: permutational multivariate analysis of variance, PCoA: principal coordinates analysis.

Following taxonomic assignment, the average relative abundance was assessed. At the phylum level, the 5 most abundant phyla included Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria (Figure 2A). Actinobacteria (32.5%) were more prevalent in the Nt_Supra group than in the other groups (Im_Sulcus: 15.7%, Im_Screw: 14.3%). Firmicutes (50.9%) and Synergistes (3.45%) were more abundant in the Im_Screw group compared to the other groups (Nt_Supra: 28.5%, 0.17%; Im_Sulcus: 37.1%, 0.57%, respectively). Proteobacteria (7.8%) were less abundant in the Im_Screw group than in the other groups (Nt_Supra: 19.1%, Im_Sulcus: 22.1%) (Figure 2A).

Figure 2
Relative abundance of bacterial species in the microbial samples collected from Nt_Supra, Im_Sulcus, and Im_Screw. (A) Average phylum abundance. (B) Average genus abundance of the top 30 genera.
Nt_Supra: supragingival biofilm on natural teeth, Im_Sulcus: biofilm associated with peri-implant sulcus, Im_Screw: biofilm associated with implant-abutment screw hole.

In total, 67 genera were detected. The most abundant genera in the Nt_Supra samples included Rothia, Streptococcus, Actinomyces, Neisseria, and Leptotrichia, which constituted more than 58% of the total sequences. In the Im_Sulcus samples, Streptococcus, Rothia, Neisseria, Haemophilus, and Veillonella represented more than 55% of the total sequences. In the Im_Screw samples, Streptococcus, Parvimonas, Porphyromonas, Fusobacterium, and Peptostreptococcus comprised more than 38% of the total sequences. Depending on the sampling site, differences in bacterial abundance were noted. Actinomyces (6.29%) was more abundant in the Nt_Supra group than in other groups (Im_Sulcus: 1.1%, Im_Screw: 0.3%). Parvimonas (9.6%), Peptostreptococcus (5.7%), Lactobacillus (4.5%), Dialister (3.9%), Atopobium (3.6%) and Cutibacterium (2.4%) were more abundant in the Im_Screw group than in the other groups (Nt_Supra: 0.37%, 0.31%, 0.03%, 0.26%, 0.08%, 0.001%, Im_Screw: 0.73%, 0.69%, 0.85%, 0.48%, 0.29%, 0.001%, respectively) (Figure 2B).

Comparison of the diversity of microorganisms between samples

Differences in bacterial abundance between sampling sites were analyzed at the genus level using the LEfSe algorithm. In surface samples, Actinobacteria were significantly more abundant in the Nt_Supra samples than in Im_Sulcus samples (Figure 3A). Since the alpha and beta diversity analyses revealed similar patterns of microbial composition between the Im_Sulcus and Nt_Supra samples, they were grouped together as the surface microbiome for further analysis. In a comparison of the surface and screw microbiomes, the Firmicutes phylum and bacterial class Bacteriodia, which belongs to the Bacteroidetes phylum, were significantly more abundant in the surface microbiome. In the screw microbiome, the phylum Proteobacteria, which includes the Betaproteobacteria class and Gammaproteobacteria class, was significantly more abundant (Figure 3B). At the genus level, Parvimonas, Peptostreptococcus, Dialister, Atopobium, Pyramidobacter, Cutibacterium, Olsenella, and Phocaeicola were significantly more abundant in the screw microbiome, while Rothia, Streptococcus, Neisseria, Haemophilus, Leptotrichia, Actiomyces, Capnocytophaga, and Lautrophia were significantly more abundant in the surface microbiome (Figure 3C).

Figure 3
Distinct taxa identified in Nt_Supra, Im_Sulcus, and Im_Screw samples using LEfSe analysis. (A) Cladogram constructed using the LEfSe method to indicate the phylogenetic distribution of bacteria that were enriched in the Nt_Supra and Im_Sulcus samples. (B) Cladogram of bacteria that were enriched in the surface (Nt_Supra and Im_Sulcus) and screw samples (Im_Screw). (C) LDA scores of the top 40 bacteria shown within groups at the genus level.
Nt_Supra: supragingival biofilm on natural teeth, Im_Sulcus: biofilm associated with peri-implant sulcus, Im_Screw: biofilm associated with implant-abutment screw hole, LEfSe: linear discriminant analysis effect size, LDA: logarithmic discriminant analysis.

Network analysis

Using the abundance profiles of the microbial communities in the samples, microbial interaction networks were created to provide information on potential patterns of interaction between microbes. The network analysis results showed distinctive patterns in network topology depending on the sampling site. In the surface samples, 3 major clusters were observed; 2 clusters were predominantly composed of bacterial species belonging to the Firmicutes phylum. The first cluster included Filifactor, Selenomonas, and Peptostreptococcus, and the second cluster included Parvimonas and Bulleidia. The third cluster was primarily composed of Proteobacteria, including Neisseria, Haemophilus, and Lautrophia (Figure 4A). In the screw samples, 8 clusters were identified. The first major cluster comprised dense aggregates of bacterial genera, mainly belonging to the Proteobacteria phylum, including Cycloclasticus, Alcanivorax, Colwellia, Porticococcus, Amylibacter, and Alteromonas. The second major cluster mostly belonged to the Firmicutes phylum, including Parvimonas, Dialister, Mogibacterium, Peptostreptococcus, and Eggerthia and showed dense interaction with the first cluster. The third major cluster consisted mostly of the Proteobacteria phylum, including Lautropia, Neisseria, Haemophilus, and Cardiobacterium. The fourth major cluster consisted mostly of Firmicutes and Bacteroidetes, including Streptococcus, Veillonella, Selenomonas, Prevotella, Porphyromonas, and Allopretotella. In the fourth cluster, Fusobacterium and Treponema were also included (Figure 4B).

Figure 4
Microbiome network analysis of (A) the surface microbiome and (B) implant screw hole. Each network node represents a genus whose color varied with phylum according to the legend. A connection between 2 nodes indicates a correlation between the 2 corresponding genera and the node size represents the relative bacterial abundance. Clusters of nodes that form coherent structural subsystems of interacting units are represented in gray.

DISCUSSION

Advances in dental implantology have led to a 96% long-term implant survival rate [22]. As dental implants have become the standard of care for replacing missing teeth, the prevalence of peri-implant disease has gradually increased, with one study reporting a prevalence of 45% [23]. Risk factors contributing to peri-implant disease include dental plaque from poor oral hygiene, excessive occlusal loading, smoking, genetic factors, history of periodontitis, and systemic disease [24]. Among these factors, anaerobic bacteria in dental plaque have a negative impact on the tissue surrounding the implant, leading to peri-implantitis [25]. To date, there have been numerous reports on peri-implantitis-related microbiota; however, studies on the microbial composition of healthy implants have been limited. Examining the oral microbiome associated with healthy implants can provide a deeper understanding of the microorganisms that naturally exist in and around implants in a healthy state. Therefore, in this study, we analyzed the microbiome of healthy implants and discovered a characteristic microbial profile in sealed implant-abutment screw holes.

Previously, research has been conducted to identify solutions relevant to peri-implantitis by investigating microbial colonization of the implant surface and surrounding tissues [4]. Before NGS sequencing was available, Cosyn et al. [7] utilized the DNA-DNA hybridization technique to study and compare the microbial characteristics of the peri-implant sulcus, internal implant space, and superstructural components. This technique allowed them to identify only 40 culturable species. In the present study, we employed the NGS sequencing technique, which can detect a variety of both culturable and unculturable species. Although DNA-DNA hybridization was able to quantify the amount of each bacterium in samples [7], NGS analysis primarily provided the relative abundance of each bacterium. Consequently, a direct comparison between the 2 studies was not suitable.

When comparing microbial abundance at the phylum and genus levels, distinct features were observed for each sampling site. Between Nt_Supra and Im_Sulcus, bacteria belonging to the Actinobacteria phylum were significantly more abundant in the Nt_Supra samples. Actinomyces are considered one of the early colonizers in initial biofilm formation due to their co-aggregation with Streptococcus spp. [26]. Although several bacteria showed significant differences, the overall abundance between Nt_Supra and Im_Sulcus exhibited similar compositions. Consequently, Nt_Supra and Im_Sulcus were combined as a surface group to compare with the Im_Screw group. In the surface samples, Streptococcus, Rothia, Neisseria, Haemophilus, Leptotrichia, Capnocytophaga, and Lautrophia were more abundant than in the Im-Screw group. Parvimonas, Peptostreptococcus, Cutibacterium, Dialister, Atopobium, Olsenella, Phocaeicola, Pyramidobacter, and Solobacterium were more abundant in the Im_Screw group than in the surface group. The LEfSe analysis further showed that the microbial composition of the Im_Screw samples differed significantly from that of the surface microbiome. This difference may have been attributable to the closed, anaerobic conditions of the implant-abutment screw hole, which was created by sealing this space with restorative materials [27]. Inside the implant screw hole, Parvimonas, Peptostreptococcus, Dialister, and Atopobium were among the most significantly abundant genera. Parvimonas has been associated with the onset of periodontitis [28], promotes the growth of Porphyromonas gingivalis, and facilitates secretion of gingipain by P. gingivalis, thereby increasing toxicity [29]. Peptostreptococcus and Atopobium have also been reported to be present in higher numbers in subgingival plaque in cases of periodontitis [30].

The analysis of interactions among the oral microbiota provided information on potential patterns of interaction between microbes. Network complexity was found to be distinct depending on the sampling site. The oral cavity is colonized by a complex community of indigenous microorganisms, in which the microbiota and their internal interactions form a network system to maintain composition [31, 32]. Many of the observed interactions may be due to microbes sharing a similar ecological niche. In the surface samples, 3 major clusters were observed, constituting the majority of the oral microbiome [33]. In the screw samples, 8 major clusters were observed. One cluster consisted mostly of Firmicutes and Bacteroidetes, including Streptococcus, Veillonella, Selenomonas, Prevotella, Porphyromonas and Allopretotella. In this cluster, Fusobacterium and Treponema were also included. Periodontal pathogens, including P. gingivalis, Treponema denticola, Tannerella forsythia, Prevotella intermedia, Fusobacterium, and Campylobacter, have also been reported to be associated with peri-implant disease [34, 35]. These microorganisms predominantly belong to the orange or red complex bacteria according to the bacterial classification system introduced by Socransky et al. [36]. Al-Ahmad et al. [37] investigated the ratios of red and orange complex bacteria in the subgingival bacterial complex of patients with peri-implantitis and found that the ratio of periodontopathogens in areas affected by peri-implantitis was significantly higher compared to healthy implants. Another cluster mainly composed of Spirochaetes, including Psychroserpens, Aquaticialea, Olleya, and Cloacibacterium was noted. Spirochaetes are known to comprise up to 50% of the polymicrobial population in subgingival plaque in periodontitis [38]. Since oral bacteria within the internal implant space can access the superstructural components through the microgap, the microbiome, including periodontopathogens, within the screw space can serve as a microbial niche. These niches can protect the bacteria from host defense mechanisms, allowing the bacteria to persist for an extended time, proliferate, and induce peri-implantitis.

A variety of materials have been tested for sealing the screw hole in order to minimize microbial leakage and colonization. These sealing materials include cotton pellets, gutta percha, polytetrafluoroethylene tape, and volumetrically proportioned impression material [39]. However, methods and materials that effectively seal the implant-abutment screw hole with antimicrobial effects have yet to be introduced [39]. Moreover, microleakage from the implant-abutment interface increases the likelihood of peri-implant infection, which may negatively impact the long-term success of an implant [40].

In the present study, we found that the microflora inhabiting the sealed implant-abutment screw hole exhibited a significantly different microbiome composition compared to that of the surface microbiome. The sealed screw hole can trap harmful bacteria that may lead to inflammation. In fact, various pathogenic bacteria have been discovered within the screw hole. Therefore, it is essential to carefully manage the sealing of the screw hole to prevent microleakage from the implant-abutment interface. As a result, our study emphasizes the importance of regular maintenance and close monitoring of both the implant-abutment screw hole and peri-implant tissues. Additionally, by examining the healthy oral microbiome, we can determine the types of bacteria present, their interactions, and their role in maintaining oral health. This knowledge may then be applied to develop innovative approaches for preventing and treating implant-related diseases, such as peri-implantitis.

Notes

Funding:This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (NRF-2017M3A9B6062021).

Conflict of Interest:No potential conflict of interest relevant to this article was reported.

Author Contributions:

Conceptualization: Hee Sam Na, MinKee Son, Jin Chung.
Data curation: MinKee Son.
Formal analysis: Hee Sam Na, Yeuni Yu.
Funding acquisition: Jin Chung.
Methodology: Si Yeong Kim, Yuri Song.
Project administration: Jin Chung.
Resources: Jung-Bo Huh, Eun-Bin Bae, Won-Tak Cho.
Writing - original draft: Hee Sam Na, MinKee Son, Yuri Song.
Writing - review & editing: Yeuni Yu, Si Yeong Kim, Jung-Bo Huh, Eun-Bin Bae, Won-Tak Cho, Jin Chung.

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