High resolution melting real-time PCR for genotyping of Giardia lamblia assemblages A and B regardless of parasite load
- Open Access
- 01.12.2025
- Research
Erschienen in:
Abstract
Introduction
Giardia lamblia (Syn. G. duodenalis, G. intestinalis) is a globally widespread intestinal zoonotic protozoan, with transmission occurring via the fecal-oral route. The World Health Organization estimates that 280 million people are infected with Giardia annually [1]. Hygiene and sanitary conditions influence the prevalence of this parasitic disease [2]. Therefore, in low and middle-income countries with unstable sanitation conditions and ineffective drinking water treatment plants, the prevalence of giardiasis can exceed 90% [3]. In developed countries, such as Europe and North America, the prevalence ranges from between 2 and 7% [4‐6]. In Brazil, G. lamblia infection is widespread, and the estimated point prevalence may exceed 50% in low-income areas [7].
Infection can cause direct damage to enterocytes and developmental delays in children, regardless of symptom manifestations and infectious assemblages [8, 9]. G. lamblia is phylogenetically divided into assemblages A to H. Assemblages A and B have high zoonotic potential and are commonly reported in humans and other animals, including pets such as dogs and cats [10].
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To genotype Giardia, specific targets for assemblage-specific primers were initially used in conventional and real-time PCR (qPCR) [11‐13]. Subsequently, techniques such as PCR-RFLP [14] and microarrays have also been employed [15].
Currently, the most commonly used multilocus approach for Giardia genotyping is gene sequencing using genes that encode the proteins triose phosphate isomerase (tpi) [16], glutamate dehydrogenase (gdh) [17], and beta-giardin (βgia) [14]. This methodological strategy requires a significant financial investment and prolongs the time required to obtain results, as it requires genetic analysis. Some studies have suggested the use of next-generation sequencing (NGS) for genotyping [10]. However, these methodologies are costly, especially in developing countries, where infection rates are higher and research funding is often limited.
This limitation restricts the knowledge of the frequency and distribution of these assemblages, as well as the understanding of transmission dynamics and association of these assemblages with virulence, symptom manifestation, pathogenesis, and drug resistance [18]. In this context, less expensive and easier-to-handle techniques could promote the expansion of research on this topic.
Real-time PCR with high-resolution melting (qPCR-HRM) is a simplified approach for genotyping studies, mutation mapping, and sequence complementarity; it is characterized by low cost, ease of handling, and high sensitivity/specificity, making it an attractive tool for genotyping and application in diagnostic laboratories [19, 20].
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qPCR-HRM has been used to differentiate Leishmania spp. [21] and discrete typing units of Trypanosoma cruzi [22]. Recently, qPCR-HRM was used to differentiate between G. lamblia assemblages isolated from dogs and humans [23]. In this context, our objective was to validate the qPCR-HRM technique to differentiate G. lamblia assemblages A and B in clinical stool samples with a low parasite load using the β-giardin gene. This tool can be strategic for strengthening public health policies for the surveillance and control of giardiasis.
Materials and methods
Samples
A total of 76 stool samples from children under 5 years of age, diagnosed as positive for Giardia spp., were selected for this study. These samples were stored at −20 °C in the biorepository of the Laboratório Interdisciplinar de Pesquisas Médicas at the Institute Oswaldo Cruz – FIOCRUZ, Rio de Janeiro, Brazil.
The procedures were approved by the Ethical Committee for Human Research (Instituto Oswaldo Cruz, Fundação Oswaldo Cruz) (CAAE: 28771820.6.0000.5248, CAAE: 90779017.7.0000.5248, CAAE: 24712319.6.0000.5248 and CAAE: 672238423.5.000.5428).
DNA extraction
DNA was extracted from all stool samples (n = 76) using the QIAmp Fast DNA Stool Mini Kit (Qiagen) according to the manufacturer’s instructions, except that the lysis temperature was raised to 95 °C and the final elution was reduced to 100 µL.
Genotyping of clinical isolates by Sanger sequencing
DNA was subjected to PCR to amplify β-giardin using primers G7 and G759 [14]. The PCR product was further processed with by nested PCR using primers βgia F and βgia R [24]. The efficacy of PCR and nested PCR products was verified by electrophoresis on a 1% agarose gel. The nested PCR product was purified using the Nucleo Spin® Gel and PCR Clean-up kit, following the manufacturer’s instructions.
The purified product was sequence using the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) at the FIOCRUZ DNA Sequencing Platform (P01-001 A) in a 3730xl DNA Analyzer (Applied Biosystems).
The electropherograms obtained from sequencing were analyzed, and their quality was checked using the Chromas 2.6.6 software. Sequence characterization was performed using the Basic Local Alignment Search Tool with nucleotides - BLASTn. The nucleotide sequences of β-giardin from G. lamblia were aligned using the CLUSTAL W algorithm in the Molecular Evolutionary Genetics Analysis (MEGA) 10.0 software [25]. Phylogenetic analysis was conducted using MEGA 10.0, with distance estimation based on the Jin and Nei equation (Kimura 2-parameter model) [26]. The sequences of the new isolates (PX111222-PX111273) were aligned using reference sequences of G. lamblia from GenBank belonging to assemblages A (KF963547, KR075937, MG736240, OQ968785) and B (MK 982544, KP687756, KY320578, OL632306), and the assemblage E (OR455136) and G. muris (AY258618) used as “outgroups”. The respective accession numbers of the sequences are shown between the vertical bars in the phylogenetic trees. Phylogenetic trees were constructed using the Neighbor-Joining algorithm [27]. For each construction, the validity of each branch was checked using bootstrap analysis (1000 repetitions).
qPCR-HRM standardization
For qPCR-HRM standardization, synthetic DNA sequences (GBlock) of the conserved β-giardin gene from assemblages A and B were used (Table 1). The primers BG5 and BG7 described by Tan et al. (2015) [28], which amplified a fragment of a single-copy gene specific to Giardia, were aligned with sequences from assemblages A and B, and degeneration was inserted into the reverse primer to ensure hybridization with assemblage B sequences (BG5: AAG GAG GCC CTC AAG AGC and BG7-deg: CTC TGC GAC YTT CTC GTT GA). The region amplified by the primers corresponded to a 102 bp segment. The concentration of the primers and DNA was calculated for a final volume of 10 µL. PCR mix was prepared as follows: HOT FIREPol Evagreen HRM Mix (no ROX) [1X], primers BG5 and BG7- deg at a concentration of 200 nM, and 2 µL of DNA and water to complete the 10 µL volume.
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The PCR cycling conditions were as follows: 40 cycles of 95 °C/15 s, 58 °C/60 s, following an initial denaturation of 95 °C/10 min.
Table 1
Nucleotide sequences of the synthetic DNAs of assemblages A and B of Giardia lamblia used in PCR standardization with 126 base pairs. Differentiated nucleotides between assemblages A and B are highlighted in red. The primer hybridization regions (BG5 and BG7-deg) are highlighted in yellow. Legend: pb (base pairs) Differentiated nucleotides between Assemblages A and B are highlighted in red. The primer hybridization regions (BG5 and BG7-deg) are highlighted in yellow
qPCR-HRM was performed under the following conditions: 40 cycles of 95 °C/15 s, 58 °C/60 s was run, after an initial denaturation of 95 °C for 10 min, each consisting of 95 °C/15 s, 60 °C/15 s, followed by an increasing temperature from 60 °C to 95 °C at intervals (ramps) 0,025 °C per second.
To evaluate the differentiation potential between assemblages A and B, a dilution curve ranging from 10⁶ to 10¹ copies/µL of synthetic DNA was used. Synthetic DNA concentrations corresponding to cycle threshold (CT) values between 15 and 25 were selected for the subsequent experiments.
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For qPCR-HRM analysis, DNA samples previously genotyped by Sanger sequencing were used under the following conditions: (1) directly, using DNA obtained from extraction; (2) DNA diluted 10-fold; and (3) DNA amplified by PCR targeting the β-giardin gene (primers G7 and G759, as described in Sect. 2.3), followed by purification and dilution at 1:10, 1:100, 1:1,000, and 1:10,000 ratios.
Validation of genotyping by qPCR-HRM
The concentrations and conditions of the qPCR-HRM standard are described in Sect. 2.4. For the validation of qPCR-HRM, DNA extracted from clinical samples positive for Giardia was subjected to qPCR-HRM and Sanger sequencing, in biological and experimental duplicates for both techniques (as per the protocol described in 2.3). Based on the qPCR CT values, the samples were classified as having low, medium, or high parasite loads, and the level of agreement between the methodologies was determined using the Kappa coefficient. Values of Cohen’s kappa coeficiente, sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy were calculated using the Open-Source Epidemiologic Statistics for Public Health (OpenEpi software V3.01), available at www.openepi.com.
Results
Sanger sequencing
The 76 selected samples were successfully amplified using PCR for the β-giardin target. Using Sanger sequencing, 41 samples were grouped into assemblage A, 11 into assemblage B, and 24 did not present sufficient electropherogram quality for genotyping (Fig. 1).
Fig. 1
Tree constructed from nucleotide sequences of the gene encoding the beta-giardin protein (β-giardin gene) of Giardia lamblia. The isolates were obtained from 52/76 stool samples of children positive for Giardia lamblia stored in the biorepository of the Laboratório Interdisciplinar de Pesquisas Médicas at FIOCRUZ (symbol ▲). The tree was constructed using the neighbor-joining algorithm, with reliability assessed by bootstrap analysis with 1000 replicates
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Synthetic DNA standardization
Standardization of qPCR-HRM began with the use of synthetic DNA for G. lamblia assemblages A and B. To test the differentiation potential of assemblages A and B by qPCR-HRM, the reaction was performed using synthetic DNA from the assemblages at a temperature of 60 °C, at concentrations of 106 to 101 copies/µL, using the BG5 and BG7-deg primer pair (Fig. 2). Considering the amplification CT values (between 15 and 25), concentrations of 106, 105, and 104 copies/µL were selected for the following experiments (Fig. 3).
Fig. 2
Amplification curve of Giardia lamblia using primers BG5 and BG7-deg. Real-time PCR with high-resolution melting was performed using different concentrations of synthetic DNA from each assemblage. (A): Assemblage A at concentrations of 106 to 101 copies/µL; (B): Assemblage B at concentrations of 106 to 101 copies/µL
Good reproducibility of the curve shapes was observed for assemblages A and B (Fig. 3A and B, respectively). The curves differed between the assemblages (Figure C), demonstrating potential for differentiation by qPCR-HRM.
Fig. 3
Aligned high resolution melting (HRM) dissociation curves, using primers BG5 and BG7-deg and synthetic DNA from assemblages A and B of Giardia lamblia, at concentrations of 106 to 101 copies/µL. (A): Aligned curves for the control (synthetic DNA) of assemblage A (red); (B): Aligned curves for the control (synthetic DNA) of assemblage B (blue); (C): Differentiation of aligned HRM curves for the controls (synthetic DNA) of assemblages A (red) and B (blue)
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Standardization of the qPCR-HRM technique using DNA samples of Giardia lamblia previously characterized by Sanger sequencing
Considering that the assay was standardized with synthetic DNA, we initially used DNA extracted directly from stool samples for qPCR-HRM. Five DNA samples previously characterized as assemblage A and another five as assemblage B by Sanger sequencing were diluted 10X and used (Fig. 4).
Fig. 4
qPCR-HRM reaction for the genotyping of Giardia lamblia. (A): DNA from assemblage A samples diluted 10X; (B): DNA from assemblage B samples diluted 10X. In both (A) and (B), the aligned HRM curves are shown on the left, and the difference Plot graphs are shown on the right. The assemblage A sample with the highest melting temperature was used as a reference. Assemblage A samples are indicated in red, assemblage B samples are indicated in blue, and undefined assemblages in other colors
A pattern of overlapping curves was observed, making it impossible to differentiate between the assemblages. The main hypothesis for this observation was that the impurities in the material directly from the fecal sample could interfere with high-resolution dissociation, even after a 10X dilution.
To reduce material impurities, increase the concentration of DNA in the sample, and improve the differentiation of assemblages in qPCR-HRM, we used the amplified product from the first set of PCR, as aforementioned. For this purpose, the same five samples used in the previous experiment were amplified with the G7 and G759 primers and diluted 10X, were used (Fig. 5).
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Fig. 5
qPCR-HRM reaction for the differentiation of Giardia lamblia assemblages from 10X diluted PCR product. (A): PCR product from assemblage A samples diluted 10X; (B): PCR product from assemblage B samples diluted 10X. The aligned HRM curves are shown on the left, and the difference plots are shown on the right. Assemblage A samples are indicated in red, assemblage B samples are indicated in blue, and undefined assemblages in other colors
The assemblage differentiation curves in qPCR-HRM using the PCR product showed considerable improvement compared with the curves obtained from the direct use of DNA. However, noise was still observed in the curves (non-specific peaks). Therefore, we used the purified amplified product from a PCR set with the following dilutions: 100X (Fig. 6A and B), 1,000X (Fig. 6C and D), and 10,000X (Fig. 6E and F). Ten previously amplified DNA samples were used (Fig. 6G).
Fig. 6
qPCR-HRM technique reaction for genotypic characterization of Giardia lamblia from a purified and diluted PCR set products. (A) and (B): Purified PCR product samples from assemblages A and B diluted 100X; C and D: Purified PCR product samples from assemblages A and B diluted 1,000X; E and F: Purified PCR product samples from assemblages A and B diluted 10,000X. On the left, aligned HRM curves are presented, and on the right, the derivative form of the HRM curves is shown, highlighting the melting temperatures of each assemblage (peaks of the curves on the right); G: Difference plot from purified PCR product samples from assemblages A and B diluted 10,000X. Assemblage A samples are indicated in red, and assemblage B samples are indicated in blue. The assemblage A sample with the highest melting temperature was used as a reference
The use of the purified PCR product diluted 10,000X in qPCR-HRM resulted in a better quality for the differentiation of assemblages A and B. Greater differentiation was observed between the aligned HRM curves of the respective assemblages. Considering the obtained results, the qPCR-HRM reaction for genotyping was standardized under the final experimental conditions.
Validation of the qPCR-HRM technique for genotyping of Giardia lamblia in stool isolates from children
The 52 DNA samples that were successfully genotyping by Sanger sequencing were subjected to qPCR-HRM, according to the protocol standardized in the previous stage (workflow: PCR, purification and 10,000X dilution). Samples with high (n = 15), medium (n = 43) and low (n = 18) parasite loads were used. Of these samples, 42 were differentiated with assemblage A and 10 with assemblage B (Fig. 7).
Fig. 7
Validation of the qPCR-HRM technique for genotypic characterization of Giardia lamblia from the product of a purified PCR set diluted 10,000X. (A): Aligned high-resolution dissociation curves (HRM), in red samples characterized as assemblage A and in blue samples of assemblage B; (B): Derivative curve of assemblages A in red and assemblage B in blue; C: Difference plot of assemblage A in red and assemblage B in blue
The genotyping results obtained using the different techniques were compared. Of the 52 samples genotyped by Sanger sequencing, only one showed divergence in qPCR-HRM strategies (MQ36), which was grouped as assemblage B by sequencing and as assemblage A by qPCR-HRM. This group of DNA-tested samples included there were samples with low, medium and high parasite loads (Table 2). Thus, qPCR-HRM showed a diagnostic accuracy of 98.08% (89.88–99.66), sensitivity of 100% (91.43–100), specificity of 90.91% (62.26–98.38), positive predictive value of 97.62% (87.68–99.58), and negative predictive value of 100% (72.25–100). Notably qPCR-HRM analyses performed as a “blind test” and, subsequently, the results were compared with those of sequencing.
Of the 24 DNA samples with unsuccessful sequencing, 62.5% (n = 15) had a low parasite load, 37.5% (n = 9) had a medium load and none had a high load. In qPCR-HRM, 23 DNA samples were differentiated into assemblage A and one into assemblage B (Table 2).
Table 2
Giardia lamblia assemblages differentiated using Sanger sequencing and PCR with high performance dissociation curve (high resolution melting [HRM]), according to the parasitic load of the sample. In qPCR-HRM the difference of TM means was 0.898 (TM assemblage A: 85.783, TM assemblage B: 86.681)
Parasite Load | Sanger | qPCR-HRM | ||||
|---|---|---|---|---|---|---|
Assemblages (n) | Assemblages (n) | |||||
Low | A | B | Inconclusive | A | B | Inconclusive |
(n = 18) | (n = 2) | (n = 1) | (n = 15) | (n = 16) | (n = 2) | (n = 0) |
Medium | A | B | Inconclusive | A | B | Inconclusive |
|---|---|---|---|---|---|---|
(n = 43) | (n = 30) | (n = 4) | (n = 9) | (n = 39) | (n = 4) | (n = 0) |
High | A | B* | Inconclusive | A* | B | Inconclusive |
|---|---|---|---|---|---|---|
(n = 15) | (n = 9) | (n = 6) | (n = 0) | (n = 10) | (n = 5) | (n = 0) |
Discussion
In developing countries, there is a vicious cycle in which a higher burden of socially determined parasitic diseases coincides with limited investment in research on the biological, biochemical and molecular characteristics of the pathogen-host-environment relationship. High sequencing costs limit knowledge of assemblages in transmission cycles. In this study, we present the standardization of qPCR-HRM using the β-giardin gene target as a lower-cost genotyping strategy to differentiate human assemblages A and B of G. lamblia from human fecal samples.
A survey conducted in May 2025 in the MEDLINE database of the U.S. National Library of Medicine (PUBMED) indicated that 10 published studies used HRM for G. lamblia genotyping, with the earliest dating back to 2011 [29]. The intergenic spacer (igs), glutamate dehydrogenase (gdh), triose phosphate isomerase (tpi), or β-giardin (βgia) targets have already been successfully reported for the differentiation of assemblages from samples of human [23, 28‐32], dogs [23, 28, 30], molluscs, and water [33]. In contrast to prior studies, our work introduces a qPCR-HRM approach using the β-giardin gene that was successfully applied to human samples with varying parasite loads, including low-load samples, which are typically challenging for molecular genotyping.
A major methodological challenge addressed in this study was the optimization of the qPCR-HRM assay for samples with different parasite DNA levels. The initial protocol, based on the direct amplification of DNA extracted from fecal material, was hindered by the presence of inhibitors and impurities inherent to stool samples. These components interfered with the precision of the HRM analysis, leading to overlapping and non-specific melting curves that preclude assemblage discrimination.
To overcome this limitation, we incorporated an intermediate step using the purified product from the first-round of PCR amplification. This adjustment significantly improved the curve profiles and minimized the background noise, especially in low-burden samples. By testing additional dilutions of the purified product, we found that a 1:10,000 dilution yielded the most consistent and distinguishable melting curves for assemblage A and B, even for samples with low parasite load. This finding underscores the importance of optimizing both DNA quality and quantity in HRM-based genotyping and highlights the adaptability of the protocol to a wide range of sample conditions.
Importantly, despite the additional steps required, the proposed method is cost-effective, technically accessible, and suitable for diverse field conditions (qPCR-HRM: $0.48/sample, considering only the main reagent: mastermix HOT FIREPol EvaGreen qPCR Mix Plus, Without ROX–REF 083100001, https://globalbios.com/). These characteristics make it a promising alternative to more expensive sequencing methods (Sanger sequence: $30.08/sample - foward and reverse -, considering only the main reagent: BigDye™ Terminator v3.1 Cycle Sequencing Kit–REF 4337455, https://thermofisher.com), particularly in endemic regions where sample volume and limited resources can constrain surveillance efforts.
Validation of the qPCR-HRM assay using Sanger sequencing as the reference method demonstrated excellent concordance (98.08%, 51/52 samples tested), with a Cohen’s Kappa coefficient of 0.9404 (0.669–1), independent of parasite load. Interestingly, all samples yielding inconclusive sequencing results had low to moderate parasite loads, but were successfully genotyped by qPCR-HRM. Regardless of the amount of genetic material present in the sample, qPCR-HRM could differentiate the assemblages, corroborating the sensitivity of the technique even for samples with a low parasite load. Previous studies have shown a high sensitivity and reproducibility not only between qPCR-HRM and gene sequencing but also between qPCR-HRM and PCR-RFLP [30, 34].
One discordant result between HRM and sequencing was observed (sample MQ36), which was consistently genotyped as assemblage A by qPCR-HRM and assemblage B by sequencing. Although other studies have not reported differences between the techniques, the considerable number of samples used in this study could justify this finding. This result was confirmed through experimental and biological replicates, thus strengthening the robustness of the data and ruling out technical and sampling errors. Given the absence of double peaks in the electropherogram and the use of a 1:10,000 dilution of the PCR product for the qPCR-HRM assay, the possibility of a mixed infection or the presence of PCR inhibitors can also be excluded.
The most plausible explanation is that assemblage B harbors thermally neutral polymorphisms or nucleotide substitutions located in regions with low influence on the melting profile, making it difficult to distinguish assemblage B from assemblage A using qPCR-HRM. This technical limitation has been acknowledged in the literature and highlights the importance of sequencing as a reference method, particularly in cases with atypical or discordant profiles [20, 35]. Additionally, a multilocus approach could help investigate whether there is a difference in the characterization of the assemblage of this sample (MQ36) among different targets. Thus, the qPCR-HRM protocol proposed herein can distinguish between G. lamblia assemblages A and B.
Considering the sensitivity of qPCR-HRM for genotyping, it is possible to propose a methodological workflow based on the parasite load of the sample (Fig. 8). For samples with high parasite load, DNA dilution (1:10 or 1:100) is recommended because fluorescence signal saturation of the intercalating dye, formation of non-specific products, and loss of resolution for assemblage differentiation may occur. In samples with moderate parasite load, the direct use of the qPCR-HRM protocol may be indicated. For samples with low parasite load, we recommend performing a PCR set, purifying the product, and diluting it (1:10,000) before conducting qPCR-HRM, as presented in this study. Notably, the protocol validated in this study demonstrated sensitivity in differentiating between assemblages A and B, regardless of the parasite load, making it an interesting tool when the parasite load has not been previously assessed.
In this context, determining the best genotyping strategy requires an outline of the central objective. Sequencing, especially NGS, provides genetic information that qPCR-HRM cannot. However, for studies on the molecular epidemiology of assemblage distribution, qPCR-HRM standardized in this study is a valuable tool because it allows the differentiation of assemblages of samples with different parasite loads, in addition to its ease of use, rapid results, and low cost compared with sequencing.
Fig. 8
Methodological workflow of real-time PCR with high-resolution melting for differentiating Giardia lamblia assemblages (A) and (B) based on sample parasite load. This image was generated with the aid of artificial intelligence and adapted by the authors
Lastly, qPCR-HRM does not entirely replace sequencing, as the qPCR-HRM technique has some limitations: (1) although the amplified region is conserved, qPCR-HRM does not provide information about the exact nucleotide composition, (2) it is unable to detect mixed infections, (3) it cannot identify novel sequences or uncharacterized assemblages. The primers used in this study may also amplify other G. lamblia assemblages (D, E, and F), and the Tm values of assemblages B and E are similar. The lack of sufficient validation with assemblage E samples, as well as the absence of D and F, indicates that this potential cross-reactivity represents a limitation of the study.
Nevertheless, we believe that the qPCR-HRM technique has proven to be a good strategy for G. lamblia genotyping, especially for epidemiological studies where assemblage distribution is the focus, with large sample sizes for assemblage identification, regardless of the parasite load, owing to its low cost and ease of use. The use of this methodology will enable the expansion of genotyping studies, particularly in developing countries, and consequently, will help to identify possible transmission routes and guide more rational control measures for giardiasis.
Acknowledgements
The authors are grateful to DNA Sequencing Platform by Capillary Electrophoresis (RPT01A)/FIOCRUZ and Molecular Analysis Platform (RPT09J)/FIOCRUZ.
Declarations
IOC research ethics committee
CAAE: 28771820.6.0000.5248, CAAE: 90779017.7.0000.5248, CAAE: 24712319.6.0000.5248 and CAAE: 672238423.5.000.5428.
Competing interests
The authors declare no competing interests.
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