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Curcumin downregulates human tumor necrosis factor-α levels: A systematic review and meta-analysis ofrandomized controlled trials

Volume 107, May 2016, Pages 234-242 Pharmacological Research Review Author links open overlay panelAmirhosseinSahebkarabArrigo F.G.CicerocLuis E.Simental-MendíadBharat B.AggarwaleSubash C.Guptaf a Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran b Metabolic Research Centre, Royal Perth Hospital, School of Medicine and Pharmacology, University of Western Australia, Perth, Australia c Medicine and Surgery Sciences Dept.,University of Bologna, Italy d Biomedical Research Unit, Mexican Social Security Institute, Durango, Mexico e Cytokine Research Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX USA f Department of Biochemistry, Faculty of Science, Banaras Hindu University, Varanasi, India Received 17 January 2016, Revised 23 March 2016, Accepted 24 March 2016, Available online 26 March 2016. crossmark-logo Get rights and content Abstract Background Tumor necrosis factor-α (TNF-α) is a key inflammatory mediator and its reduction is a therapeutic target in several inflammatory diseases. Curcumin, a bioactive polyphenol from turmeric, has been shown in several preclinical studies to block TNF-α effectively. However, clinical evidence has not been fully conclusive. Objective The aim of the present meta-analysis was to evaluate the efficacy of curcumin supplementation on circulating levels of TNF-α in randomized controlled trials (RCTs). Methods The search included PubMed-Medline, Scopus, Web of Science and Google Scholar databases by up to September 21, 2015, to identify RCTs investigating the impact of curcumin on circulating TNF-α concentration. Quantitative data synthesis was performed using a random-effects model, with weighed mean difference (WMD) and 95% confidence interval (CI) as summary statistics. Meta-regression and leave-one-out sensitivity analyses were performed to assess the modifiers of treatment response. Results Eight RCTs comprising nine treatment arms were finally selected for the meta-analysis. There was a significant reduction of circulating TNF-α concentrations following curcumin supplementation (WMD: ⿿4.69 pg/mL, 95% CI: ⿿7.10, ⿿2.28, p < 0.001). This effect size was robust in sensitivity analysis. Meta-regression did not suggest any significant association between the circulating TNF-α-lowering effects of curcumin with either dose or duration (slope: 0.197; 95% CI: ⿿1.73, 2.12; p = 0.841) of treatment. Conclusion This meta-analysis of RCTs suggested a significant effect of curcumin in lowering circulating TNF-α concentration. Graphical abstract Unlabelled figure Download high-res image (143KB)Download full-size image Keywords Curcumin TNF-α Curcuma longa Inflammation Randomized controlled trial Meta-analysis 1. Introduction Evidence from both preclinical and clinical studies over the past several years has indicated that chronic inflammation is closely linked with numerous human chronic diseases such as cardiovascular, pulmonary, autoimmune, degenerative diseases, cancer, diabetes, and Alzheimer disease [1]. The inflammatory cytokine, tumor necrosis factor-α (TNF-α) is one of the major molecular mediators of chronic inflammation [2]. Thus blockers of TNF-α such as monoclonal antibodies and circulating receptor fusion protein have been developed. However, these blockers are highly expensive and produce adverse effects [3]. Therefore, agents that are safe, cost effective and readily available are required. Curcumin (diferuloylmethane), a naturally occurring polyphenol, is one such agent that is derived from turmeric (Curcuma longa L.). Clinical trials have demonstrated the efficacy and safety of curcumin supplementation in several human diseases such as osteoarthritis [4], metabolic syndrome [5], solid tumors [6], chronic obstructive pulmonary disease [7], anxiety and depression [8], rheumatoid arthritis [9], psoriasis [10], pruritic skin disease [11] and hypertriglyceridemia [12]. The underlying mechanism for curcumin clinical efficacy seems to be modulation of numerous signaling molecules. Numerous studies from both in vitro and animal models have indicated that curcumin can block the action and production of TNF-α [13⿿15]. As referred above, curcumin has been evaluated for its potential against numerous chronic diseases in humans. This polyphenol reportedly possesses activities against all those diseases for which TNF-α blockers are currently being used. Curcumin has been reported to inhibit cell signaling pathways activated by TNF-α. In humans, curcumin has been shown to modulate numerous signaling molecules such as pro-inflammatory cytokines, apoptotic proteins, nuclear factor (NF)⿿κB, signal transducer and activator of transcription 3 (STAT3), adhesion molecules, phosphorylase kinase, transforming growth factor-β, triglyceride, endothelin-1,cyclooxygenase-2, 5-lipooxygenase, C-reactive protein, prostaglandin E2, prostate-specific antigen, creatinine, heme oxygenase-1, aspartate aminotransferase, and alanine aminotransferase [16]. In clinical studies, curcumin has been tested either alone or in combination with other agents. To increase the efficacy and bioavailability, various formulations of curcumin have also been used. In this study, we thoroughly reviewed nine randomized controlled clinical trials (RCTs) in which curcumin effects on plasma concentrations of TNF-α have been studied. The aim was to obtain a conclusive finding on the significance and magnitude of the TNF-α-lowering activity of curcumin in clinical practice. 2. Methods 2.1. Search strategy This study was designed according to the guidelines of the 2009 preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement [17]. Medline (, SCOPUS, Web of Science and Google Scholar databases were searched using the following search terms in titles and abstracts (also in combination with MESH terms): (curcumin OR curcuminoid OR curcuminoids OR Curcuma OR C. longa OR turmeric) AND (TNF-α OR TNFα OR ⿿TNF α⿿ OR ⿿tumor necrosis factor-α⿿ OR ⿿tumor necrosis factor α⿿ OR ⿿tumor necrosis factor α⿿) AND (random OR randomized OR randomly OR randomization OR ⿿randomized controlled trial⿿ OR ⿿randomized trial⿿ OR ⿿randomized study⿿ OR ⿿random number⿿). The wild-card term ⿿*⿿ was used to increase the sensitivity of the search strategy. The search was limited to studies in English language. The literature was searched from inception to September 21, 2015. 2.2. Study selection Original studies were included if they met the following inclusion criteria: (i) being a RCT with either parallel or cross-over design or a post-hoc analysis of a RCT, (ii) investigating the impact of supplementation with curcuminoids or turmeric preparations with a determined amount of curcuminoids on plasma TNF-α concentrations, and (iii) presentation of sufficient information on changes in circulating TNF-α in the study groups. Exclusion criteria were (i) non-randomized trials, (ii)an active control group in the study design, (iii) observational studies with case-control, cross-sectional or cohort designs, (iv) using non-standardized turmeric extracts with unknown curcumin content, (v) trials with treatment durations of <2 weeks, and (vi) incomplete data on circulating concentrations of TNF-α. In case of the latter item, authors of the article(s) were contacted and requested to provide necessary numerical data. 2.3. Data extraction Eligible studies were reviewed and the following data were abstracted: (1) first author⿿s name; (2) year of publication; (3) study location; (4) study design; (5) number of participants in the fibrate and placebo groups; (5) type and dose of curcumin supplement used; (6) duration of treatment; (7) age, gender and body mass index (BMI) of study participants; (8) inclusion criteria defined in the study; (9) systolic and diastolic blood pressures; and (10) baseline and follow-up TNF-α concentrations. 2.4. Quality assessment A systematic assessment of bias in the included studies was performed using the Cochrane criteria [18]. The items used for the assessment of each study were as follows: adequacy of sequence generation, allocation concealment, blinding, addressing of dropouts (incomplete outcome data), selective outcome reporting, and other potential sources of bias. According to the recommendations of the Cochrane Handbook, a judgment of ⿿yes⿿ indicated low risk of bias, while ⿿no⿿ indicated high risk of bias. Labeling an item as ⿿unclear⿿ indicated an unclear or unknown risk of bias. 2.5. Quantitative data synthesis Meta-analysis was conducted using Comprehensive Meta-Analysis (CMA) V2 software (Biostat, NJ) [19]. Plasma TNF-α concentrations were collated in pg/mL.Net changes in measurements (change scores) were calculated as follows: measure at end of follow-up⿿measure at baseline. For cross-over trials with a 2 ÿ 2 design, each treatment arm was analysed separately, and net change in each arm was calculated by subtracting the value after control intervention from that reported after treatment. Standard deviations (SDs) of the mean difference were calculated using the following formula: SD = square root [(SDpre-treatment)2 + (SDpost-treatment)2 ⿿ (2R ÿ SDpre-treatment ÿ SDpost-treatment)], assuming a correlation coefficient (R) = 0.5. Where standard error of the mean (SEM) was only reported, standard deviation (SD) was estimated using the following formula: SD = SEM ÿ sqrt (n), where n is the number of subjects. Selection of fixed-effects and random-effects (using Der Simonian-Laird method) models was performed in cases of heterogeneity values <50% and ⿥50%, respectively [20]. Heterogeneity was quantitatively assessed using I2 index. In order to evaluate the influence of each study on the overall effect size, sensitivity analysis was conducted using leave-one-out method, i.e. removing one study each time and repeating the analysis [21⿿23]. 2.6. Meta-regression Random-effects meta-regression was performed using unrestricted maximum likelihood method to evaluate the association between calculated WMD and potential confounders including dose and duration of supplementation with curcumin. 2.7. Publication bias Potential publication bias was explored using visual inspection of Begg⿿s funnel plot asymmetry, fail-safe N test, and Begg⿿s rank correlation and Egger⿿s weighted regression tests. Duval & Tweedie ⿿trim and fill⿿method was used to adjust the analysis for the effects of publication bias [24]. 3. Results 3.1. Flow and characteristics of included studies First, after multiple database searches 267 published studies were identified and the abstracts reviewed. Then, 38 non-original articles were excluded. Next, 213 did not meet the inclusion criteria and were also excluded. Thus, 16 full text articles were carefully assessed and reviewed; of which eight trials were excluded for not measuring plasma TNF-α concentrations (n = 5), incomplete data on serum TNF-α levels (n = 2), and short treatment duration (n = 1). Finally, eight studies were eligible and included in the systematic review and meta-analysis. The study selection process is shown in Fig. 1. Fig. 1 Download high-res image (487KB)Download full-size image Fig. 1. Flow chart of the number of studies identified and included into the meta-analysis. Data were pooled from eight eligible studies comprising nine treatment arms which included 549subjects, with 275 in the curcumin arm and 274 in the control arm (participants enrolled from the cross-over trial were considered in both arms). Included studies were published between 2008 and 2015. The clinical trials used different doses of curcumin. Two studies investigated curcumin 300 mg/day [25,26], three studies investigated curcumin 1 g/day [27⿿29] and two studies investigated curcumin 1.5 g/day [4,30]. The range of intervention periods was from four weeks [7] up to three months [26]. Study design of all included studies was parallel-group, except one which was cross-over design [28]. Selected studies enrolled subjects with obesity [26,28], type 2 diabetes [25,26], type 2 diabetic nephropathy [30], major depressive disorder [27], knee osteoarthritis [4], metabolic syndrome [5], and sulfur mustard-exposed veterans [7]. Demographic and biochemical characteristics of the evaluated studies are presented in Table 1. Table 1. Demographic characteristics of the included studies. Reference Study design Target Population Treatment duration n Treatment groups Age, years Female (n, %) BMI, (kg/m2) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Glucose (mg/dl) Total cholesterol (mg/dl) LDL cholesterol (mg/dl) HDL cholesterol (mg/dl) Triglycerides (mg/dl) C-reactive protein (mg/l) Baseline TNF-α (pg/ml) (22) Randomized, double-blind, placebo-controlled Type 2 diabetic nephropathy 2 months 20 Curcumin 1.5 g/day 52.9 ± 9.2 11 (55.0) ND 129.0 ± 15.5 79.0 ± 7.4 179.0 ± 65.5 214.2 ± 66.5 114.1 ± 34.6 43.8 ± 12.6 236.2 ± 146.5 12.8 ± 2.9 20 Placebo 52.6 ± 9.7 7 (35.0) ND 130.5 ± 25.3 80.5 ± 5.3 169.5 ± 76.3 193.3 ± 45.7 108.3 ± 39.9 39.8 ± 9.5 220.4 ± 106.9 16.4 ± 14.2 (20) Randomized, double-blind, placebo-controlled cross-over Obesity 1 month 15 Curcumin 1 g/day ND ND ND ND ND ND ND ND ND ND 2.63 ± 2.81 15 Placebo 2.72 ± 1.17 (18) Randomized, double-blind, placebo-controlled Overweight or obese with type 2 diabetes 3 months 50 Curcumin 300 mg/day ND ND ND ND ND 131.1 ± 31.9 ND ND ND 157.7 ± 49.6 1.72 ± 0.74 50 Placebo ND ND ND ND ND 147.2 ± 37.1 ND ND ND 186.9 ± 66.4 2.14 ± 0.84 (17) Randomized, placebo-controlled Type 2 diabetes 8 weeks 23 Curcumin 300 mg/day 55.5 ± 10.7 11 (47.8) 24.6 ± 2.4 130.4 ± 18.5 81.8 ± 10.0 155.0 ± 17.9 195.0 ± 41.1 120.3 ± 42.1 38.7 ± 7.6 176.3 ± 27.6 4.10 ± 2.10 21 Placebo 49.7 ± 8.1 10 (47.6) 23.9 ± 2.3 126.3 ± 15.4 80.7 ± 7.4 161.1 ± 19.9 196.9 ± 35.7 125.2 ± 34.9 36.3 ± 7.6 170.1 ± 47.5 3.60 ± 1.57 (19) Randomized, double-blind, placebo-controlled Major depressive disorder 6 weeks 50 Curcumin 1 g/day 44.1 ± 8.0 0 (0.0) ND ND ND ND ND ND ND ND ND ND 50 Placebo 45.2 ± 7.6 0 (0.0) ND ND ND ND ND ND ND ND ND ND (7) Randomized, double-blind, placebo-controlled Sulfur mustard-exposed veterans 4 weeks 39 Curcumin 1.5 g/day 50.9 ± 7.2 0 (0.0) 28.0 ± 4.8 ND ND ND ND ND ND ND 6.98 ± 1.83 28.03 ± 2.63 39 Placebo 53.9 ± 8.6 0 (0.0) 25.9 ± 4.0 ND ND ND ND ND ND ND 8.54 ± 1.64 26.13 ± 4.01 (48) Randomized, double-blind, placebo-controlled Knee osteoarthritis 6 weeks 19 Curcumin 1.5 g/day 57.3 ± 8.7 14 (73.6) 28.7 ± 3.1 ND ND ND ND ND ND ND 5.56 ± 1.74 31.75 ± 3.93 21 Placebo 57.5 ± 9.0 17 (80.9) 29.6 ± 4.4 ND ND ND ND ND ND ND 5.00 ± 0.00 31.99 ± 3.99 (21) Randomized, double-blind, placebo-controlled Metabolic syndrome 8 weeks 59 Curcumin 1 g/day 44.8 ± 8.6 23 (46.0) 25.4 ± 2.4 135.5 ± 13.1 88.3 ± 7.8 155.4 ± 40.8 220.2 ± 37.7 190.4 ± 20.0 31.5 ± 4.6 199.6 ± 23.4 6.52 ± 2.16 79.24 ± 8.55 58 Placebo 43.4 ± 9.7 27 (54.0) 22.8 ± 5.3 135.7 ± 14.7 88.7 ± 8.1 136.9 ± 52.4 184.0 ± 17.3 157.0 ± 17.2 35.4 ± 6.5 185.6 ± 38.4 7.10 ± 1.80 77.48 ± 6.54 Values are expressed as mean ± SD. *Final condition. Abbreviations;: ND, no data; BMI, body mass index. 3.2. TNF-α assay methods Different assays methods were used to measure serum TNF-α concentration. Most of the studies[25⿿27,29,30] measured serum TNF-α levels by enzyme-linked immunoassay. Ganjali et al. [28] analyzed TNF-α concentrations using the Biochip Array Technology on the Randox Evidence Investigator (Randox Laboratories, Belfast, Northern Ireland) by chemiluminescent immunoassay. 3.3. Risk of bias assessment Most of the included studies were characterized by lack of information about the random sequence generation, allocation concealment, and blinding of outcome assessment. In addition, some trials did not provide sufficient information of blinding of participants and personnel, andone of them showed high risk of bias [25]. On the other hand, all evaluated studies had a low risk of bias according to selective reporting. Details of the quality of bias assessment are shown in Table 2. Table 2. Quality of bias assessment of the included studies according to the Cochrane guidelines. Reference Random sequence generation Allocation concealment Selective reporting Other bias Blinding of participants and personnel Blinding of outcome assessment Incomplete outcome data (22) U L L L L U L (20) U U L L U U U (18) U U L U U U U (17) U U L U H U L (19) L U L U U U U (7) L L L L L L L (48) U U L L U U L (21) U U L U U U L L, low risk of bias; H, high risk of bias; U, unclear risk of bias. 3.4. Effect of curcuminon circulating TNF-αconcentration Overall, the impact of curcumin on circulating TNF-α concentration was reported in eight RCTs comprising nine treatment arms. Meta-analysis suggested a significant reduction in plasma TNF-α concentrations following curcumin supplementation (WMD: ⿿4.69 pg/mL, 95% CI: ⿿7.10, ⿿2.28, p < 0.001) (Fig. 2). Fig. 2 Download high-res image (793KB)Download full-size image Fig. 2. Forest plot displaying weighted mean difference and 95% confidence intervals for the impact of curcumin on plasma TNF-αconcentrations. Lower plot shows leave-one-out sensitivity analysis. Leave-one-out sensitivity analysis showed that this effect size is robust and not sensitive to any single study(Fig. 2).Subgroup analysis for the studies administering bioavailability enhanced curcumin preparations (WMD: ⿿5.33 pg/mL, 95% CI: ⿿10.84, 0.019, p = 0.058) versus those administering unformulated curcumin (WMD: ⿿3.84 pg/mL, 95% CI: ⿿7.00, ⿿0.683, p = 0.017) revealed a numerically larger effect size in favor of the former, though the difference was not of statistical significance (Fig. 3). Fig. 3 Download high-res image (538KB)Download full-size image Fig. 3. Forest plot displaying weighted mean difference and 95% confidence intervals for the impact of bioavailability-enhanced (upper plot) and unformulated (lower plot) curcumin preparations on plasma TNF-α concentration. 3.5. Meta-regression Random-effects meta-regression was performed to evaluate if changes in plasma TNF-α concentrations are dependent to dose and duration of treatment. Meta-regression analysis did not suggest any significant association between the plasma TNF-α-lowering effects of curcumin with either dose (slope: ⿿0.005; 95% CI: ⿿0.02, 0.01; p = 0.336) or duration (slope: 0.197; 95% CI: ⿿1.73, 2.12; p = 0.841) of treatment (Fig. 4). Fig. 4 Download high-res image (487KB)Download full-size image Fig. 4. Meta-regression plots of the association between mean changes in plasma TNF-α concentrations with dose (upper plot) and duration (lower plot) of curcumin supplementation. The size of each circle is inversely proportional to the size of the respective study. 3.6. Publication bias The funnel plot of standard error versus effect size (mean difference) was slightly asymmetric but no potential publication bias requiring ⿿trim and fill⿿ correction was suggested (Fig. 5). Fig. 5 Download high-res image (180KB)Download full-size image Fig. 5. Funnel plot detailing publication bias in the studies reporting the impact of curcumin on plasma TNF-α concentrations. Consistently, the presence of publication bias was excluded by Egger⿿s linear regression (intercept = ⿿4.30, standard error = 2.43; 95% CI = ⿿10.05, 1.46, t = 1.76, df = 7, two-tailed p = 0.121) and Begg⿿s rank correlation(Kendall⿿s Tau with continuity correction = ⿿0.25, z = 0.94, two-tailed p-value = 0.348) tests. The ⿿fail-safe N⿿ test showed that 492 studies would be needed to bring the WMD down to a non-significant (p > 0.05) value. 4. Discussion Results of the present systematic review and meta-analysis of RCTs indicated that curcumin could significantly reduce plasma TNF-α concentration. Furthermore, curcumin formulations with improved bioavailability exhibited better activity as compared to unformulated preparations. The blockers of TNF-α such as infliximab, adalimumab and etanercept have been approved by the United States Food and Drug Administration for inflammatory chronic diseases. However, one of the major problems with most of these TNF-α blockers is that they produce numerous side effects in humans. Some of these adverse effects are development of liver injury, lymphoma, leukopenia, neutropenia, thrombocytopenia, and pancytopenia [30]. In addition, these blockers are highly expensive. Thus, alternatives that are safe, affordable and effective are needed. Based on the present results, curcumin may serve as an inexpensive, orally bioavailable and safe polyphenol that can effectively reduce TNF-α level in humans. Originally discovered as an anticancer agent, TNF-α is now linked with an array of pathophysiological conditions, including cancer, neurologic diseases, cardiovascular diseases, pulmonary diseases, autoimmune diseases, and metabolic diseases. Our analysis from 8 RCTs comprising nine treatments indicated that curcumin can significantly reduce serum/plasma concentrations of TNF-α in patients with depression [27], solid tumor [6], osteoarthritis [4], pulmonary complications [7], end-stage renal disease [29] and metabolic syndrome (unpublished). Although the exact mechanism of modulation of TNF-α production by curcumin is unclear, there are several possibilities by which this polyphenol can regulate TNF-α production as reported by in vitro and animal studies. Curcumin is a pleiotropic molecule and has been reported to modulate TNF-α-associated inflammatory pathways. One of the important pathways by which curcumin can modulate TNF-α production is through negative regulation of pro-inflammatory transcription factors, NF-kB, activator protein-1, and STAT proteins [32]. NF-kB could be also activated by the modulating effect of curcumin on protein kinases, epidermal growth factor receptor, activity of extracellular signal-regulated kinase 1/2, and PI-3-K/AKT pathway [33,34]. Curcumin has also been shown to influence or even interrupt the signal transduction between TNF-α and its receptor via direct binding, and may thereby suppress inflammation induced by this cytokine [35]. Both non-covalent and covalent interactions were found to contribute to the direct interaction between curcumin and TNF-α [36]. One of the major limitations of curcumin use in humans is its limited bioavailability which appears primarily due to low oral absorption, rapid metabolism, and rapid systemic elimination [29]. Several strategies including use of nanoparticles [38], liposomes [39], micelles [40], structural analogues [41,42], co-administration with piperine [43] and complexation with phosphatidylcholine[44], has been employed to enhance the bioavailability of curcumin. While co-administration of curcumin with piperine is associated with an increased risk of drug interactions, phosphatidylcholine does not introduce risk of drug interactions but enhances the bioavailability via preserving from hydrolytic degradation and increasing intestinal absorption [45,46]. Our analysis indicated that the efficacy of bioavailability-boosted curcumin formulations in reducing circulating TNF-α concentration was higher in comparison to non-formulated curcumin. Several factors may contribute to the improved efficacy of formulated curcumin. For example, Panahi and colleagues examined the efficacy of phosphatidylcholine formulations of curcumin (Meriva®) in patients with solid tumors and found that almost all parameters evaluated in their study including TNF-α were significantly suppressed(6). These observations are further supported from a previous study where absorption of curcuminoids with Meriva® was found to be about 30 folds higher compared with unformulated curcuminoids [47]. Some limitations of the present meta-analysis deserve acknowledgment. First, included studies recruited relatively few subjects and thus further confirmatory evidence from large-scale trials is required. Second, assessment of TNF-α was not among the primary objectives of the included studies, suggesting the need for further studies exclusively performed in populations with elevated baseline levels of this cytokine. Third, included studies were performed in populations with different pathophysiological characteristics, thus increasing the inter-study heterogeneity. Nevertheless, we tried to minimize heterogeneity by applying a random-effects model of analysis and performing subgroup and meta-regression analyses. However, all these limitations, added to the fact that curcumin formulation with presumed different bioavailability have been tested in the different trial could partly explain why we did not observed a clear direct dose-effect nor duration-effect relationship. 5. Conclusion Our analysis indicated a promising impact of curcumin on circulating TNF-α concentration. However, lowering of TNF-α concentrations was independent of curcumin dose and duration of treatment. Our finding is consistent with previous studies that have demonstrated the efficacy of curcumin against several TNF-α-associated diseases [16]. To our knowledge, this is the first meta-analysis demonstrating the beneficial effects of this polyphenol on TNF-α in humans. The most of the currently available TNF-α blockers have a low tolerability, while they are very expensive, thus curcumin could be an interesting therapeutic tool for a large number of TNF-α-associated diseases. 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