Alloimmune Monitoring after Islet Transplantation: A Prospective Multicenter Assessment of 25 Recipients

Abstract

Islet transplantation is an effective treatment for selected patients with type 1 diabetes. However, an accurate test still lacks for the early detection of graft rejection. Blood samples were prospectively collected in four university centers (Geneva, Grenoble, Montpellier, and Strasbourg). Peripheral blood mononuclear cells were stimulated with donor splenocytes in the presence of interleukin-2. After 24 h of incubation, interferon-γ (IFN-γ) ELISpot analysis was performed. After a total of 5 days of incubation, cell proliferation was assessed by fluorescence-activated cell sorting (FACS) analysis for Ki-67. Immunological events were correlated with adverse metabolic events determined by loss of ≥1 point of β-score and/or an increased insulin intake ≥10%. Twenty-five patients were analyzed; 14 were recipients of islets alone, and 11 combined with kidney. Overall, 76% (19/25) reached insulin independence at one point during a mean follow-up of 30.7 months. IFN-γ ELISpot showed no detectable correlation with adverse metabolic events [area under the curve (AUC) = 0.57]. Similarly, cell proliferation analysis showed no detectable correlation with adverse metabolic events (CD3⁺/ CD4⁺ AUC = 0.54; CD3⁺/CD8⁺ AUC = 0.55; CD3-/CD56⁺ AUC = 0.50). CD3-/CD56⁺ cell proliferation was significantly higher in patients with combined kidney transplantation versus islet alone (6 months, p = 0.010; 12 months, p = 0.016; and 24 months, p = 0.018). Donor antigen-stimulated IFN-γ production and cell proliferation do not predict adverse metabolic events after islet transplantation. This suggests that the volume of transplanted islets is too small to produce a detectable systemic immune response and/or that alloimmune rejection is not the sole reason for the loss of islet graft function.

Keywords

Diabetes Islet transplantation Alloimmune monitoring ELISpot Cell proliferation

Introduction

Since the year 2000 and the success of the Edmonton protocol¹, pancreatic islet allotransplantation has become increasingly effective in the treatment of selected patients with type 1 diabetes². However, multiple islet grafts are still needed because of poor islet engraftment³, caused mostly by the instant blood-mediated inflammatory response (IBMIR)⁴.

After repeated islet transplants, and once they have achieved insulin independence, patients still lose islet function over time², and there is, as yet, no reliable and reproducible means for the early detection of islet graft rejection. Indeed, because pancreatic islets are transplanted via the portal vein, they are widely dispersed, and therefore needle biopsy is not sensitive enough and too invasive for routine follow-up in all patients⁵. Imaging techniques, such as magnetic resonance imaging (MRI)⁶ and positron emission tomography (PET)⁷, seem promising but are too expensive to be used as a frequent and routine means for follow-up. Interferon-γ (IFN-γ) enzyme-linked immunosorbent spot (ELISpot) analysis has proven to be efficient in the detection of kidney graft rejection in humans⁸ and in the detection of islet graft function loss in mice⁹. ELISpot analysis is cheap and quick to perform (24 h of incubation) and could be promising for the early detection of islet graft rejection. However, the reliability of this analysis still needs to be validated in this regard.

The need for an easy, reproducible, cost-effective, and noninvasive test is therefore necessary for post-islet transplantation follow-up¹⁰. Such a test could be performed via peripheral blood sampling by analyzing cellmediated alloimmunity. In previous experiments¹¹, we demonstrated that patients having lost islet function have an increased alloimmune activity against donor antigens, compared with patients with preserved islet function. It was, however, unclear whether this was the cause or the consequence of the loss of islet graft function, and a prospective study was needed in order to validate and better understand these results.

The aim of the study was to perform a prospective follow-up of islet-transplanted patients, based on peripheral blood samples, in order to determine whether alloimmune monitoring can be used as a reliable means for the early detection of islet graft function impairment.

Materials and Methods

Study Design and Patient Selection

The study was designed as a prospective, longitudinal cohort of immune monitoring after islet transplantation. It included all patients with type 1 diabetes mellitus admitted on the islet transplant waiting list at four university centers (Geneva in Switzerland, and Grenoble, Montpellier, and Strasbourg in France) between February 2010 and January 2015. Islet transplantations were performed alone [islet transplant alone (ITA)], after kidney transplantation [islets after kidney (IAK)], or simultaneously with a kidney transplantation [simultaneous islets and kidney (SIK)]. Patients without posttransplant follow-up (removed from the study after moving to another city, death while on the list, or still on the list), those with <12 months posttransplant follow-up, and those with no available donor splenocytes were excluded. The present study was approved by the institutional ethics committee of the Geneva University Hospitals [09-185 (NAC 09-066)]. All patients included in the study provided informed written consent.

Sample Gathering and Processing

Blood samples were collected prior to the first islet transplantation, every 3 months for the first year after transplantation, and every 6 months thereafter. Each sample included 5 ml of blood in citrate-coated tubes. They were shipped to Geneva at room temperature (RT). Peripheral blood mononuclear cells (PBMCs) were isolated by centrifugation, at 400 × g, in a Ficoll-Paque Plus density gradient (GE Healthcare, Uppsala, Sweden). The pellet was suspended in 1 ml of complete Roswell-Park Memorial Institute medium (cRPMI)-1640 (Gibco, ThermoFisher Scientific, Waltham, MA, USA) +10% heat-inactivated fetal bovine serum (FBS) (Gibco, ThermoFisher Scientific) + 1% 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) 1 M (Gibco, ThermoFisher Scientific) + 1% penicillin–streptomycin (Sigma-Aldrich, Buchs, Switzerland) + 1% sodium pyruvate (Gibco, ThermoFisher Scientific) + 1% β-mercaptoethanol 50 mM (Bio-Rad, Hercules, CA, USA). PBMCs were frozen in two aliquots of 500 μl, added to 500 μl of FBS + 20% dimethyl sulfoxide (DMSO) (Sigma-Aldrich), in liquid nitrogen for long-term storage. At the time of the analysis, PBMCs were simultaneously placed in a 37°C water bath, until partial thawing. Warm cRPMI was added to each aliquot of PBMCs to complete thawing. The diluted PBMCs were washed and suspended in cRPMI. Trypan blue (Sigma-Aldrich) staining was performed on all PBMCs in order to plate the correct amount of cells/well. In the few samples with cell viability <75%, a second aliquot was thawed. The same procedure was applied to donor splenocytes. Additionally, splenocytes were irradiated (5,000 rad) in order to prevent cell division.

Culture and Stimulation

PBMCs were plated in cRPMI in U-bottom 96-well plates (Corning, Durham, NC, USA) at 2.5 × 10⁵ cells/100 μl/well (2.5 × 10⁶ cells/ml)^12,13. In order to reactivate T lymphocytes and natural killer (NK) cells after their cryogenic states, a low concentration of interleukin-2 (IL-2) (BioLegend, San Diego, CA, USA), 10 U/ml/well, was added in some wells overnight¹⁴. IL-2 stimulates only PBMCs that expressed the high-affinity trimeric or low-affinity dimeric IL-2 receptor in vivo¹⁵. These receptors are present on T-cell receptor-activated T lymphocytes and, at a lesser rate, on NK cells. The following conditions were used: negative control (PBMCs only), control (nonspecific stimulation of PBMCs by IL-2), and one-way standard mixed lymphocyte reactions (stimulation by IL-2 and coculture with each individual donor splenocyte, plated at a stimulating splenocyte to responding PBMC ratio of 1:1^12,13). A positive control with CD3/CD28 bead polyclonal stimulation of PBMCs was analyzed in selected patients. Incubation was performed at 37°C with 5% CO₂.

Cells were transferred and incubated in enzyme-linked immunosorbent spot (ELISpot) plates for IFN-γ secretion analysis for 18 h. They were subsequently transferred back into flat-bottom 96-well plates for total culture duration of 5 days.

ELISpot Assessment

ELISpot plates (MAIPS4510 Sterile MultiScreen-IP 0.45 μm; Merck Millipore, Schaffhausen, Switzerland) were prepared and coated for 24 h with human anti-IFN-γ capture antibody (dilution of 1:250), according to the manufacturer's instructions (Human IFN-γ ELISpot Ready-Set-Go!; eBioscience, Vienna, Austria), before cell plating. After 18 h of incubation, the plates were washed and human IFN-y detection antibody was applied. Spots were revealed after adding avidin-horseradish peroxidase (avidin-HRP) (eBioscience) followed by 3-amino-9-ethyl carbazole (Sigma-Aldrich). They were analyzed using an automated system (CTL-ImmunoSpot S6 Macro Analyzer; Cellular Technology Ltd., Bonn, Germany). One spot counted as one IFN-γ-secreting cell. Results were expressed in number of IFN-γ-secreting PBMCs (i.e., number of IFN-y spots) after subtraction of the number of IFN-γ-secreting PBMCs in the negative control wells.

Cell Proliferation Assessment by Fluorescence-Activated Cell Sorting (FACS)

After 5 days of culture, cells were treated for proliferation analysis. Fixation, and permeabilization for intracellular staining, was performed with the usual buffers according to the manufacturer's instructions (eBioscience). Cell surface antigen staining was performed with the following antibodies (BioLegend): anti-CD3 (UCHT1; 0.2 mg/ml), peridinin chlorophyll protein complex/cyanin5.5 (PerCP Cy5.5); anti-CD4 (RPA-T4; 0.05 mg/ml), allophycocyanin/cyanin7 (APC/Cy7); anti-CD8 (HIT8a; 0.5 mg/ml), Alexa Fluor 700 (A700); anti-CD56 (HCD56; 0.1 mg/ml), phycoerythrin/cyanin 7 (PE Cy7).

Intracellular proliferation labeling was performed with Ki-67 (B56; 0.25 mg/ml), phycoerythrin (PE) (BD Biosciences, San Diego, CA, USA). Data were acquired using FACS flow cytometer Gallios IV (Beckman Coulter, Nyon, Switzerland) and analyzed using the Kaluza Analysis 1.3 software (Beckman Coulter).

Cell proliferation was analyzed for each lymphocyte subtype: T-helper lymphocytes (CD3⁺/CD4⁺), cytotoxic T lymphocytes (CD3⁺/CD8⁺), and NK lymphocytes (CD3-/CD56⁺).

Assessment of Clinical Adverse Metabolic Events

During follow-up, clinical and biological data were collected by each participating center. For the purpose of the study, adverse metabolic events were defined as a decrease in overall β-score (as previously defined by Ryan et al.¹⁶) of at least one point and/or an increase of insulin intake ≥10%. IFN-y secretion and cell proliferation were analyzed with respect to the occurrence of an adverse metabolic event at 6, 12, and 24 months after transplantation. They were also analyzed with respect to transplant type (ITA, IAK, or SIK).

Statistical Analysis

When not specified, data are presented as median ± interquartile range. Nonparametric Mann–Whitney U test, parametric Student t-test, or Kruskal–Wallis tests were used where appropriate. Receiving operating characteristic (ROC) curves were performed for overall analysis of IFN-γ ELISpot data and individual FACS-analyzed cell proliferation in order to determine areas under the curve (AUC). Statistical analyses were performed using Prism 6 (Graphpad, La Jolla, CA, USA) and SPSS statistics software 22 (IBM, Zürich, Switzerland). All p values are two-tailed, and significance was set at p < 0.05.

Results

Patient Characteristics

Blood samples were obtained from 25 patients (Fig. 1). Mean follow-up was of 30.7 months (±2.2 months). Mean age was 48 years (±1.9 years), and the male/female ratio was 8:17 (Table 1). Fourteen patients underwent ITA, 7 patients IAK, and 4 SIK. Patients in the cohort received three (two to three) islet infusions and 14,097 (12,368–16,599) islet equivalents/kilogram (IEQ/kg). Transplanted patients had one (zero to two) human leukocyte antigen (HLA) match and five (four to six) HLA mismatches with each islet graft (Supplementary Table 1; supplementary material available at https://figshare.com/s/be6ed687d2dead84f945). Induction immunosuppression was based on antithymocyte globulin at first islet transplantation and on anti-CD25 antibody for subsequent transplantations.

Figure 1.

Inclusion and exclusion criteria. Included, n = 25; excluded, n = 18.

Table 1.

Pretransplant Patient Demographic Data

	Total
Patients (n)	25
Gender (male/female)	8/17
Age (years)	49.5 (43–54.5)
Body weight (kg)	65 (58–69)
Beta score	1 (0–2)
Fasting glucose (mmol/L)	8.06 (5.7–11.72)
HbA1c (%)	8.11 (7–8.83)
Daily insulin intake (U/kg)	0.48 (0.39–0.56)
Stimulated C-peptide (nmol/L)	0
OHA^∗ use	-
Type of transplant received
Islet transplantation only	14
Islet after kidney transplantation	7
Simultaneous islet and kidney transplantation	4

∗

OHA: oral hypoglycemic agent.

Transplantation Outcome

Detailed clinical outcomes are presented in Supplementary Tables 2 to 4. Briefly, 19/25 patients (76%) reached insulin independence. Five experienced an adverse metabolic event at 6 months (28.6% of ITA, 0% of IAK, and 25% of SIK), 10 at 12 months (35.7% of ITA, 71.4% of IAK, and 0% of SIK), and 8 at 24 months (36.4% of ITA, 33.3% of IAK, and 50% of SIK). Only four patients (14.3% of ITA, 14.3% of IAK, and 25% of SIK) did not present any adverse metabolic event during follow-up.

ELISpot Assessment and Adverse Metabolic Events

Patients with adverse metabolic events did not show significantly different IFN-γ secretion compared with those without adverse metabolic events at 6 months [21.2 spots (17.5–37.5) vs. 57 spots (33.8–79.5); p = 0.100], 12 months [34.7 spots (21.9–57.8) vs. 30.9 spots (17.8– 55.7); p = 0.945], and 24 months [61 spots (34.8–81.4) vs. 23 spots (6.8–60.3); p = 0.076] (Fig. 2). Similarly, the number of IFN-γ spots did not predict adverse metabolic events (AUC = 0.571) (Fig. 3). Unstimulated wells showed very few spots overall [0 spots (0–0.5)].

Figure 2.

Number of interferon-γ spots on ELISpot membranes, analyzed with respect to the occurrence of adverse metabolic events. 6 months: p = 0.10; 12 months: p = 0.95; 24 months: p = 0.076.

Figure 3.

Receiving operating characteristics (ROC) curve for interferon-γ secretion assessment by ELISpot analysis, correlated with the occurrence of adverse metabolic events. Area under the curve (AUC) = 0.57.

Cell Proliferation and Adverse Metabolic Events

At 6 months, there was no significant difference in cell proliferation against donor splenocytes between patients with and those without metabolic event for CD3⁺/CD4⁺ [19.8% (17.1–28.1) vs. 31.5% (23.1–40.4); p = 0.272], CD3⁺/CD8⁺ [38.6% (36.8–43.7) vs. 50.8% (41.9–68.5); p = 0.243], or CD3^-/CD56⁺ [51.3% (50.1–78.8) vs. 75.3% (61.4–88.3); p = 0.272] cells (Fig. 4). At 12 months, proliferation remained similar between metabolic groups for CD3⁺/CD4⁺ [24.1% (17.8–36.1) vs. 27% (19.7–34.5); p = 0.732], CD3⁺/CD8⁺ [40.6% (23–48.7) vs. 47% (30.8–52.6); p = 0.395], and CD3^-/CD56⁺ [76.6% (67.3–89.3) vs. 68.1% (59.6–83.4); p = 0.333]. At 24 months, however, patients with adverse metabolic events demonstrated a trend for increased proliferation when compared to patients without adverse metabolic events for CD3⁺/CD4⁺ [43.3% (28.2–46.2) vs. 16.4% (11.5–37.9); p = 0.078] and CD3⁺/CD8⁺ [62.9% (52.5–67) vs. 34.5% (20.1–62.2); p = 0.083] cells. CD3^-/CD56⁺ cell proliferation, however, remained similar between the two groups [78.3% (76–84.3) vs. 67.9% (55.4–80.9); p = 0.33].

Figure 4.

Proliferation in each cell subtype, analyzed with respect to the occurrence of adverse metabolic events. CD3⁺/CD4⁺ at 6 months: p = 0.27; 12 months: p = 0.73; 24 months: p = 0.09. CD3⁺/CD8⁺ at 6 months: p = 0.24; 12 months: p = 0.4; 24 months: p = 0.08. CD3^-/CD56⁺ at 6 months: p = 0.27; 12 months: p = 0.33; 24 months: p = 0.33.

Overall, the CD3^-/CD56⁺ cell subset tended to proliferate more than CD3⁺/CD4⁺ and CD3⁺/CD8⁺ cells (Fig. 4).

Cell proliferation level did not allow for the definition of a specific cutoff to predict the occurrence of adverse metabolic events for CD3⁺/CD4⁺ (AUC = 0.542), CD3⁺/CD8⁺ (AUC = 0.548), and CD3^-/CD56⁺ (AUC = 0.501) cells (Fig. 5).

Figure 5.

Receiving operating characteristics (ROC) curve for proliferation levels in each cell subtype, correlated with the occurrence of adverse metabolic events. CD3⁺/CD4⁺ area under the curve (AUC) = 0.54; CD3⁺/CD8⁺ AUC = 0.55; CD3^-/CD56⁺ AUC = 0.50.

Alloimmune Assessment and Transplantation Type

Patients after islet transplantation alone were compared with those having undergone combined islet–kidney transplantation (CIKT), which combines the IAK and SIK groups. Detailed analyses between ITA, IAK, and SIK are shown in Figure 6.

Figure 6.

Analyses according to transplant type at 12 months posttransplantation. ITA: islet transplantation alone; IAK: islet after kidney; SIK: simultaneous islet and kidney. (A) Interferon-γ ELISpot secretion at 12 months for each transplant type. ITA-IAK, p = 0.73; ITA-SIK, p > 0.99; IAK-SIK, p > 0.99. (B) Proliferation in each cell subtype at 12 months for each transplant type. CD3⁺/CD4⁺ cells: ITA-IAK, p > 0.99; ITA-SIK, p > 0.99; IAK-SIK, p > 0.99. CD3⁺/CD8⁺ cells: ITA-IAK, p = 0.83; ITA-SIK, p > 0.99; IAK-SIK, p > 0.99. CD3-/CD56⁺ cells: ITA-IAK, ∗p = 0.003; ITA-SIK, p > 0.99; IAK-SIK, p = 0.08.

IFN-γ secretion was similar after ITA versus CIKT at 6 months [46.8 spots (21.2–62.5) vs. 62.5 spots (30.5–81.5); p = 0.301], 12 months [31.5 spots (15.3–60) vs. 31.8 spots (22.5–57.9); p = 0.866] (Fig. 6A), and 24 months [28.1 spots (15.1–41.5) vs. 54.8 spots (29.6–74.5); p = 0.431]. Proliferation of CD3⁺/CD4⁺ cells did not differ between ITA and CIKT, respectively, at 6 months [27% (20.9–35.3) vs. 32.5% (18.2–41.1); p = 0.814], 12 months [22.8% (18.2–33.6) vs. 32.2% (21.1–37.9); p = 0.565] (Fig. 6B), and 24 months [22.2% (17.8–45.7) vs. 34.3% (16.7–45); p = 0.737]. Similarly, levels of CD3⁺/CD8⁺ cell proliferation did not differ between ITA and CIKT at 6 months [46.5% (39.9–55.2) vs. 54.6% (31.4–69.1); p = 0.966], 12 months [40.1% (26.5–49.1) vs. 47% (35.1–63.9); p = 0.343] (Fig. 6B), and 24 months [34.5% (25.6–63.7) vs. 61.1% (30.1–68); p = 0.394]. However, CD3^-/CD56⁺ cell proliferation remained significantly lower in patients having undergone ITA, compared to those who had a CIKT, at 6 months [63.4% (51.4–71.7) vs. 88.3% (79.3–89.4); p = 0.010], 12 months [66.3% (58.8–77.6) vs. 86.9% (76.9–90.7); p = 0.016] (Fig. 6B), and 24 months [66.4% (50.5–78.3) vs. 78.6% (75.8–89.6); p = 0.018].

Discussion

Altogether, the present results indicate that the cellular monitoring of PBMCs does not allow for the prediction of adverse metabolic events after clinical islet transplantation. Studies on the correlation between preexistent alloantibodies and loss of islet graft function remain divergent. Early studies identified a correlation^17,18, while subsequent ones did not^19–21. One study, from Roep et al.²², found that a strong posttransplant alloreactivity was associated with a very low islet survival rate. However, this increased alloreactivity was only present in patients who did not receive anti-thymocyte globulin (ATG) therapy. This point prevents the generalization of the data as most modern immunosuppression protocols include a depleting antibody after islet transplantation (as shown in the present article).

The appearance of posttransplantation de novo donor-specific alloantibodies has shown to be associated with a loss of islet graft function. However, it is still not known whether the apparition of these de novo antibodies is the cause or the consequence of the loss of islets, as they appear in patients with decreased or who were weaned off immunosuppression^11,19,23.

It could be argued that peripheral blood immune follow-up is not sensitive enough in the case of clinical islet transplantation because of the small volume of transplanted cells. The current human data contrast with previous results demonstrating that ELISpot IFN-γ can predict alloimmune rejection after mouse islet transplantation⁸. This discrepancy could be explained by the higher ratio of islets to blood volume in mice compared to humans. Along the same line, further studies need to assess gene expression in peripheral blood cells in order to detect early immune events prior to islet graft loss, as Han et al. have already suggested for cytotoxic lymphocyte genes²⁴.

Another hypothesis to explain the poor correlation between alloimmune activity and adverse metabolic events is that the main loss of islet function over time may not only be due to allogeneic rejection. Autoimmunity can recur despite a well-conducted immunosuppressive regimen²⁵. The presence of autoantibodies at baseline, and CD4⁺ and CD8⁺ T autoreactive cells, or the appearance of de novo autoantibodies predicts early graft failure in most studies^{21,22,26–28}. As with alloimmunity, the article by Roep et al.²² found a correlation between the resurgence of autoreactivity and the loss of islet graft function only in those patients having no history of ATG treatment.

Other aspects such as the liver microenvironment (the impact of low oxygen tension, Kupffer cells, ischemia–reperfusion injury on islets, and gut–liver axis), islet exhaustion, and direct toxicity of immunosuppressive drugs could greatly affect islet function, and much research still needs to be done in this respect.

In the present study, CD3⁺/CD4⁺ and CD3⁺/CD8⁺ cells demonstrated a trend toward higher proliferation levels in patients with metabolic events at 24 months. The reason for this pattern appearing only at 24 months remains unclear. However, it matched our previous transversal data, where most included patients were studied late after islet transplantation, some having already lost islet function⁹.

It has been described that NK cells can mediate solid organ, notably kidney, graft rejection via its activating receptor, NKG2D^29–32. This could explain the fact that CD3^-/CD56⁺ cell proliferation was significantly increased throughout the follow-up in patients having undergone combined kidney and islet transplantation, compared to those with islet transplantation alone. NK cells may therefore be of interest for the follow-up after kidney transplantation alone or simultaneously with islets.

HLA mismatches are frequent in islet transplantation. Indeed, because of the high rate of early islet loss (50%–70%³) and the need for more than 12,000 IEQ/kg/recipient in order to be able to achieve sustained insulin independence¹, transplantation recipients often need more than one donor. Patients in the present cohort had a high rate of HLA mismatches with their donors. This study confirms recent findings from van Kampen et al.³³, suggesting that repeated mismatches do not play a significant part in the occurrence of adverse metabolic events.

One potential limitation of the study is the processing of the blood. Whole blood was shipped to the Geneva center at room temperature. It has been shown that, 24 h after blood sampling, PBMCs display more granulocyte contamination, with a trend toward decreased T-cell function³⁴. However, the appropriate positive and negative controls were used throughout the present study in order to prevent a potential bias linked to an artificially altered function of PBMCs. In addition, cryopreserved PBMCs were used in this prospective study in order to perform batch analyses for each patient. A recent article³⁵ demonstrated a significant difference between fresh and frozen PBMCs with respect to lymphocyte proliferation activity if the number of viable PBMCs is <75% in the frozen cells. In the study, cell viability was estimated to be consistently >75%.

In all, it appears that, based on the current results, cellular alloimmune monitoring using peripheral blood after islet transplantation may not be a reliable diagnostic tool for the early detection of adverse metabolic events, and alloimmune activity may only play a small part in the loss of islet graft function.

Footnotes

Acknowledgments

The authors would like to thank Myriam Haddouche, Laure Nasse, Sophie Bayer, and Philippe Baltzinger for their help in compiling patient data from Grenoble and Strasbourg. They are also grateful to Prof. J. Villard for providing donor splenocytes for selected patients. Christian Toso was supported by the Swiss National Science Foundation (PP00P3_139021). The authors declare no conflicts of interest.

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