Assessing the Liver Fat Score's Efficacy in MASLD Diagnosis: A Comparative Study with MRI-PDFF

Assessing the Liver Fat Score's Efficacy in MASLD Diagnosis: A Comparative Study with MRI-PDFF | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Liver Fat Score's Efficacy in MASLD Diagnosis: A Comparative Study with MRI-PDFF Hongyan Qi, Yi Zhu, Wei Tian, Jinpo Wang, Wugao Qiao, Yu Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6289146/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: This study evaluates the concordance between the liver fat score (LFS) and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) in quantifying hepatic steatosis in Metabolic-associated steatotic liver disease(MASLD) patients. Methods: In this single-center prospective study, 271 adult participants underwent MRI-PDFF assessment between January 2023 and August 2024. Participants were categorized based on MRI-PDFF results into healthy controls (<5%), mild steatosis (5%–9.9%), and moderate-to-severe steatosis (≥10%). Clinical and laboratory data were collected to calculate LFS, and its correlation with MRI-PDFF was analyzed using Pearson correlation and receiver operating characteristic (ROC) curve analyses. Results: LFS showed a significant positive correlation with MRI-PDFF ( r =0.59, P <0.001). For distinguishing MASLD patients from healthy controls, LFS achieved an area under the ROC curve (AUC) of 0.886, with an optimal cutoff value of -0.6508, yielding 85.29% sensitivity and 86.57% specificity. However, LFS demonstrated limited ability to differentiate between mild and moderate-to-severe steatosis (AUC=0.737). Conclusion: LFS correlates well with MRI-PDFF in assessing hepatic fat content and serves as a convenient alternative for MASLD screening. PDFF LFS MASLD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Metabolic-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a chronic condition closely linked to metabolic syndrome ( 1 , 2 ). Presently, MASLD constitutes a rapidly growing health concern worldwide, with a prevalence estimated at 25–30% among adults ( 3 , 4 ). Despite the majority of patients being asymptomatic, the pathological hallmark of MASLD is hepatic steatosis, which can progress to metabolic-associated steatohepatitis (MASH), fibrosis, cirrhosis, or even hepatocellular carcinoma (HCC) if left unrecognized and untreated ( 5 – 10 ). Accurate assessment of hepatic fat content is therefore critical for early detection, disease monitoring, and therapeutic decision-making in patients with MASLD ( 11 ). Although liver biopsy remains the historical “gold standard” for evaluating hepatic steatosis, its invasiveness, sampling variability, and observer bias limit broader clinical adoption ( 12 , 13 ). Consequently, noninvasive modalities have gained prominence, among which magnetic resonance imaging–proton density fat fraction (MRI-PDFF) is recognized as the current “gold standard” for quantitative assessment of steatosis. MRI-PDFF demonstrates strong concordance with histologically measured fat content and exhibits excellent reproducibility ( 14 – 18 ). Nonetheless, its widespread application is constrained by high costs, lengthy scan times, and limited availability of specialized MRI equipment, underscoring the need for simpler and more economical methods of evaluation. In recent years, the liver fat score (LFS)—an index combining anthropometric parameters, medical history, and routine laboratory indicators—has been developed to estimate hepatic steatosis. In 2009, Kotronen et al. ( 19 ) first introduced the NAFLD LFS, which incorporates metabolic syndrome (MS), type 2 diabetes (T2D), fasting insulin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) to predict liver fat content. Subsequent studies have validated the potential utility of the LFS model for diagnosing MASLD, although its specificity and sensitivity remain suboptimal ( 20 – 23 ). Because LFS relies heavily on metabolic and biochemical variables, its accuracy can be influenced by multiple confounding factors, leading to possible inter-individual discrepancies. Evaluating the agreement between LFS and MRI-PDFF is thus essential for determining the reliability of LFS in the diagnosis and stratification of MASLD. Accordingly, this study employs MRI-PDFF as the “gold standard” for quantifying hepatic steatosis in MASLD and compares it directly with the LFS model to investigate their concordance. We anticipate that our findings will help refine noninvasive diagnostic approaches for MASLD, potentially enabling earlier detection, more accurate risk stratification 2. Methods 2. 1. Study Design and Participants This single-center, prospective study was conducted at the First Hospital of Lanzhou University from January 2023 to August 2024, adhering to the ethical principles of the Declaration of Helsinki and approved by the institutional review board (IRB) of the First Hospital of Lanzhou University. Informed consent was obtained from all participants prior to enrollment, and written informed consent was waived. Eligible participants were those aged ≥18 years, had at least one component of metabolic syndrome (MS) (see Table 1) (24), and underwent MRI examination, with subsequent classification based on MRI-PDFF results : MRI-PDFF <5% as healthy controls, ≥5% and <10% as mild hepatic steatosis (mild HS), and ≥10% as moderate-to-severe HS (M-S HS) (25,26). Exclusion criteria included excessive alcohol consumption (≥210 g/week for men, ≥140 g/week for women), other known causes of hepatic steatosis (e.g., viral hepatitis, autoimmune liver disease, or drug-induced liver injury), prior major liver surgery or procedures that could alter hepatic structure and function, and any contraindications to MRI (e.g., implanted cardiac pacemaker or severe claustrophobia). All qualifying participants underwent MRI-PDFF measurement and were categorized according to their MRI-PDFF results (Figure 1). Table 1. Definition of Metabolic Syndrome (MS) Components Term Definition Overweight/Obesity BMI ≥24.0 kg/m², or waist circumference ≥90 cm (men) / ≥85 cm (women), or excessive body fat Elevated Blood Pressure / Hypertension Blood pressure ≥130/85 mmHg, or receiving antihypertensive treatment Prediabetes / Type 2 Diabetes (T2D) Fasting glucose ≥6.1 mmol/L, or 2-hour post-load glucose ≥7.8 mmol/L, or HbA1c ≥5.7%, or diagnosed T2D, or HOMA-IR ≥2.5 Elevated Triglycerides (TG) TG ≥1.70 mmol/L, or receiving lipid-lowering therapy Low High-Density Lipoprotein (HDL-C) HDL ≤1.0 mmol/L (men) or ≤1.3 mmol/L (women), or receiving lipid-lowering therapy 2.2. Clinical Data Collection Baseline characteristics and clinical indicators, including age, sex, height, weight, BMI, blood pressure, fasting glucose, HbA1c, lipid profile (TG, total cholesterol, HDL, LDL), and liver function tests (ALT, AST), were recorded. All blood samples were collected after 8-12 hours of fasting and analyzed using standardized clinical laboratory techniques to measure fasting insulin (FINS) and other biochemical parameters. NAFLD LFS (liver fat score) was calculated using the formula proposed by Kotronen et al. (19): where MS and T2D definitions refer to Table 1. 2.3. MRI-PDFF Examination All participants underwent upper abdominal MRI scanning using a 3.0T MRI scanner (Philips Healthcare, Netherlands) with a six-echo mDixon-quant gradient echo sequence to obtain PDFF quantification images. The scanning parameters were as follows: Field of View (FOV): 375 × 312 × 201 mm; Matrix: 152 × 125 × 67; Voxel Size: 2.5 × 2.5 × 6 mm; Slice Thickness: 6 mm; Flip Angle: 3°; Repetition Time (TR): 5.8 ms; Echo Time (TE1): 1.01 ms. After scanning, images were processed using Philips IntelliSpace Portal (version 10.1) to generate and record PDFF values. 2.4. Image Post-processing and Analysis All PDFF images were independently analyzed by two radiologists with over five years of experience in abdominal imaging, who were blinded to clinical information. To minimize measurement bias, circular regions of interest (ROIs) (~150 mm² each) were manually placed in nine different locations within the liver, based on Couinaud’s classification. Large vessels, bile ducts, artifacts, and focal lesions were carefully avoided. The mean value of the nine ROIs was recorded as the final hepatic PDFF percentage. A PDFF value ≥5% was used to diagnose MASLD . 2.5. Statistical Analysis All statistical analyses were performed using MedCalc and GraphPad Prism. Normality of continuous variables was assessed using Shapiro-Wilk or Kolmogorov-Smirnov tests. Normally distributed variables were presented as mean ± standard deviation (SD) and compared using ANOVA. Non-normally distributed variables were reported as median (interquartile range, IQR) and compared using the Kruskal-Wallis test. Categorical variables were expressed as frequencies (n, %) and compared using the Chi-square test or Fisher’s exact test. Intraclass correlation coefficient (ICC) was used to assess interobserver agreement in ROI placement and MRI-PDFF measurements. Pearson correlation analysis was conducted to evaluate the linear relationship between LFS and MRI-PDFF. Receiver operating characteristic (ROC) curves were generated to assess the diagnostic performance of LFS for MASLD (MRI-PDFF ≥5%), and area under the curve (AUC) was calculated. The Youden index was used to determine the optimal cutoff value for LFS. Additionally, LFS was evaluated for distinguishing mild from moderate-to-severe MASLD. Finally, multivariate logistic regression analysis was performed to identify independent risk factors for MASLD, adjusting for BMI, T2D, lipid profile, and other potential confounders. All tests were two-tailed, and P < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics of Participants A total of 271 participants were included in this study, comprising 67 healthy controls and 204 MASLD patients, including 59 with mild hepatic steatosis (MHS) and 145 with moderate-to-severe hepatic steatosis (MSHS). The baseline characteristics of the study population are presented in Table 2 . Analysis of baseline characteristics revealed that BMI, LFS, ALT, AST, and TG levels were significantly higher in the MASLD groups compared to the healthy controls ( P < 0.05 for all). The mean PDFF progressively increased across groups, from 3.13 ± 1.03% in healthy controls to 7.69 ± 1.54% in Mild HS and 18.49 ± 8.58% in moderate-to-severe HS. Similarly, LFS values followed an increasing trend (-1.51 ± 1.35, -0.14 ± 1.21, and 0.71 ± 1.24, respectively). ANOVA with post-hoc analysis confirmed that BMI, LFS, ALT, AST, and TG exhibited statistically significant differences among the three groups ( P < 0.05). Specifically, LFS was significantly different among all three groups ( P < 0.0001 for all pairwise comparisons), showing a progressive increase from controls to Mild HS and moderate-to-severe HS. BMI values also showed significant differences between all groups ( P < 0.05) For liver enzymes, both ALT and AST levels were significantly higher in mild HS compared to controls ( P 0.05). The TG levels were significantly higher in Mild HS compared to controls ( P 0.05). Additionally, there is no significant difference in age was observed among the three groups ( P > 0.05). Table 2 Baseline Characteristics of the Study Population Health Controls Mild HS M-S HS Sex Male: 41/ Female: 28 Male: 25 / Female: 34 Male: 84 / Female: 59 Age(year) 55.60 ± 13.94 51.32 ± 13.68 50.94 ± 11.52 PDFF 3.13 ± 1.03 7.69 ± 1.54 18.49 ± 8.58 LFS -1.51 ± 1.35 -0.14 ± 1.21 0.71 ± 1.24 BMI((kg/m²)) 24.31 ± 4.55 26.74 ± 3.93 28.09 ± 6.06 ALT(U/L) 25.00 [16.50, 36.50] 31.00 [24.50, 48.50] 38.00 [27.00, 58.75] AST(U/L) 24.00 [18.00, 34.00] 28.00 [22.00, 47.00] 28.00 [22.25, 46.75] TG(mg/dL) 1.25 [0.98, 1.74] 1.74 [1.23, 2.81] 2.00 [1.43, 2.89] 3.2. Quantitative Analysis of MRI-PDFF As illustrated in Fig. 3 , representative MRI-PDFF images from a healthy control, a mild hepatic steatosis case, and a moderate-to-severe hepatic steatosis case are presented. Two radiologists—each with over five years of experience in abdominal imaging and blinded to clinical information—independently measured the PDFF values for all participants. Their measurements yielded an intraclass correlation coefficient (ICC) of 0.92, indicating high interobserver consistency. 3.3. Correlation Between PDFF and LFS The correlation analysis showed a significant positive correlation between PDFF and LFS in the overall study population ( r = 0.59, P < 0.001), indicating a high level of consistency between LFS and MRI-PDFF in the assessment of MASLD. In the subgroup analysis, both the mild and moderate-to-severe hepatic steatosis groups exhibited a moderate positive correlation ( r = 0.51 and 0.31, respectively; P 0.05), implying that the applicability of LFS in the normal hepatic fat range may be limited. As illustrated in Fig. 4 , scatter plots demonstrate the distribution of PDFF and LFS values across the study population. 3.4. Diagnostic Performance of LFS for MASLD Detection and Severity Stratification Figure 5 a illustrates the receiver operating characteristic (ROC) curve for distinguishing MASLD from healthy controls, yielding an AUC of 0.886 (95% CI: 0.842–0.922, P − 0.6508, resulting in a sensitivity of 85.29% and specificity of 86.57%. These findings demonstrate that LFS can effectively differentiate MASLD patients from healthy individuals. In contrast, Fig. 5 b shows the ROC curve for distinguishing mild hepatic steatosis from moderate-to-severe hepatic steatosis, with an AUC of 0.737 (95% CI: 0.670–0.798, P 0.2697) yielded relatively lower sensitivity (63.77%) and specificity (73.68%). Thus, while LFS effectively distinguishes MASLD from healthy individuals, its performance in severity stratification is comparatively lower. 3.5. Risk Factor Analysis for MASLD Severity A multivariate linear regression analysis was conducted to identify independent predictors of MASLD severity (healthy control, mild, and moderate-to-severe steatosis). The overall model was statistically significant (F = 23.23, P < 0.0001), indicating that the included variables significantly contributed to the classification of MASLD severity. Among the predictors, LFS exhibited the strongest association with MASLD severity ( β = 0.2937, P < 0.0001), suggesting that higher LFS scores were strongly correlated with more severe hepatic steatosis. BMI was also positively associated with increasing MASLD severity ( β = 0.0176, P = 0.0272), indicating that individuals with higher BMI were more likely to have more advanced hepatic steatosis. In contrast, sex, Age, AST, ALT, and TG were not significantly associated with MASLD severity ( P > 0.05), suggesting that these factors did not independently predict MASLD progression in the study cohort. Collectively, these findings highlight LFS as the strongest predictor of MASLD severity, with BMI also playing a contributory role. 4. Discussion This study systematically analyzed the non-invasive evaluation of metabolic dysfunction-associated steatotic liver disease (MASLD) using MRI-PDFF and NAFLD LFS, focusing on the diagnostic and stratification value of LFS. The results indicate that LFS demonstrates high accuracy in distinguishing MASLD from healthy individuals and is significantly correlated with MRI-PDFF. However, its ability to differentiate between mild and moderate-to-severe MASLD is relatively limited. MRI-PDFF is currently regarded as the gold standard for quantifying liver fat content. However, its application is constrained by high costs, limited accessibility, and the requirement for patient cooperation during scanning, particularly for those unable to perform prolonged breath-holding ( 27 ). In this study, 34 subjects were excluded due to motion artifacts, highlighting the potential limitations of MRI-PDFF in clinical practice. In such cases, LFS emerges as a valuable alternative based on clinical parameters, demonstrating practical utility in scenarios where MRI is unavailable or unsuitable. LFS is derived from routine clinical and biochemical indices (e.g., fasting insulin, AST/ALT ratio) and does not require imaging, making it cost-effective and easily applicable in primary healthcare settings. Our findings further confirm that LFS exhibits good accuracy in MASLD screening and serves as an effective substitute for MRI-PDFF, particularly for individuals who cannot undergo MRI assessments. Correlation analysis in this study demonstrated a significant positive correlation between LFS and MRI-PDFF ( r = 0.58, P < 0.001), suggesting that LFS effectively reflects liver fat accumulation. In the MASLD subgroup, LFS and MRI-PDFF maintained a moderate to strong positive correlation in both mild and moderate-to-severe cases ( r = 0.51 and 0.45, respectively, P < 0.001), indicating that LFS more accurately predicts liver fat content in individuals with pronounced metabolic dysfunction. However, in the healthy control group, the correlation was weaker ( r ≈ 0.18, P > 0.05), implying that LFS may have limited screening capability in individuals without significant hepatic steatosis. This study further evaluated the diagnostic performance of LFS in MASLD screening through ROC analysis. The results showed that when using MRI-PDFF ≥ 5% as the diagnostic threshold for MASLD, LFS achieved an AUC of 0.886 in distinguishing MASLD from healthy individuals, demonstrating its practical value for large-scale screening. The optimal cutoff value was − 0.65, yielding a sensitivity and specificity of 85% and 86%, respectively, which is largely consistent with the original study by Kotronen et al ( 19 ). (cutoff value − 0.64, sensitivity 86%, specificity 71%). However, in differentiating between mild and moderate-to-severe MASLD, the discriminative ability of LFS declined (AUC = 0.737), indicating its limited effectiveness in disease severity stratification. This finding aligns with previous studies, suggesting that LFS is more suitable for binary classification (presence or absence of MASLD), whereas for stratified MASLD management, it should be combined with MRI-PDFF or other metabolic indicators for comprehensive assessment. This discrepancy may stem from the nature of LFS calculation and its applicability across different levels of hepatic steatosis ( 28 ). LFS primarily relies on metabolic indicators, which may show minimal variation in healthy individuals, leading to a weaker correlation with MRI-PDFF. However, in MASLD patients, LFS effectively captures the extent of metabolic dysfunction, resulting in strong ROC performance in distinguishing MASLD from healthy individuals. Additionally, LFS values exhibit a wide distribution within MASLD patients, with some individuals with mild MASLD presenting high LFS values and some with moderate-to-severe MASLD showing relatively lower LFS values. This heterogeneity likely contributes to the lower accuracy of LFS in distinguishing different severities of MASLD. Consequently, while LFS is well-suited for identifying MASLD, further stratification requires integration with other metabolic indicators. From a mechanistic perspective, LFS integrates multiple key metabolic parameters, including BMI, fasting insulin, and AST/ALT ratio, reflecting an individual’s metabolic state and its impact on hepatic fat accumulation ( 29 , 30 ). Multivariate regression analysis further confirmed that LFS is the strongest predictor of MASLD severity, with BMI also showing a significant positive correlation ( P = 0.0272), reinforcing the role of obesity in MASLD progression. Notably, AST, ALT, and TG were not independent predictors of MASLD severity in this study, possibly because these markers may not sufficiently reflect hepatic disease progression in early metabolic dysfunction. Despite confirming the utility of LFS in MASLD assessment, this study has certain limitations. First, it was a single-center prospective study with a relatively large sample size; however, the regional and ethnic characteristics of the study population may limit generalizability. Future large-scale, multicenter studies are necessary to validate LFS applicability across diverse populations. Second, while MRI-PDFF was used as the reference standard for MASLD diagnosis, it does not assess liver inflammation or fibrosis. As MASLD progresses to metabolic-associated steatohepatitis (MASH) or cirrhosis, the predictive value of LFS requires further investigation. Additionally, some LFS components (e.g., AST/ALT ratio, fasting insulin levels) may be influenced by laboratory variations and individual physiological states, potentially affecting its consistency across different populations. Thus, refining the LFS model or integrating additional biochemical and imaging parameters may enhance its overall diagnostic accuracy. In conclusion, this study demonstrates that LFS, as a clinical and biochemical-based scoring model, exhibits strong diagnostic performance in MASLD screening and is highly consistent with MRI-PDFF. Future large-scale, multicenter studies should further refine MASLD’s non-invasive diagnostic framework and explore the role of LFS in disease progression prediction and individualized management to enhance early detection, precise evaluation, and long-term MASLD management. Abbreviations MASLD – Metabolic Dysfunction-Associated Steatotic Liver Disease MRI-PDFF – Magnetic Resonance Imaging Proton Density Fat Fraction LFS – Liver Fat Score MASH – Metabolic Dysfunction-Associated Steatohepatitis HCC – Hepatocellular Carcinoma AST – Aspartate Aminotransferase ALT – Alanine Aminotransferase BMI – Body Mass Index TG – Triglycerides HDL – High-Density Lipoprotein LDL – Low-Density Lipoprotein HbA1c – Hemoglobin A1c FINS – Fasting Insulin TR – Time of Repetition TE – Echo Time ROI – Region of Interest SD – Standard Deviation ICC – Intraclass Correlation Coefficient IQR – Interquartile Range MHS – Metabolic Hepatic Steatosis MSHS – Metabolic Steatohepatitis Score AUC – Area Under the Curve ROC – Receiver Operating Characteristic References Eslam, M., Sanyal, A. J., George, J., & International Consensus Panel (2020). MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology, 158(7), 1999–2014.e1. https://doi.org/10.1053/j.gastro.2019.11.312 Rinella, M. E., Lazarus, J. V., Ratziu, V., Francque, S. M., Sanyal, A. J., Kanwal, F., Romero, D., Abdelmalek, M. F., Anstee, Q. M., Arab, J. P., Arrese, M., Bataller, R., Beuers, U., Boursier, J., Bugianesi, E., Byrne, C. D., Castro Narro, G. E., Chowdhury, A., Cortez-Pinto, H., Cryer, D. R., … NAFLD Nomenclature consensus group (2023). A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology (Baltimore, Md.) , 78 (6), 1966–1986. https://doi.org/10.1097/HEP.0000000000000520 Rinella, M. E., Neuschwander-Tetri, B. A., Siddiqui, M. S., Abdelmalek, M. F., Caldwell, S., Barb, D., Kleiner, D. E., & Loomba, R. (2023). AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology (Baltimore, Md.) , 77 (5), 1797–1835. https://doi.org/10.1097/HEP.0000000000000323 Chalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., Harrison, S. A., Brunt, E. M., & Sanyal, A. J. (2018). The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology (Baltimore, Md.) , 67 (1), 328–357. https://doi.org/10.1002/hep.29367 Hahn, L., Reeder, S. B., Muñoz del Rio, A., & Pickhardt, P. J. (2015). Longitudinal Changes in Liver Fat Content in Asymptomatic Adults: Hepatic Attenuation on Unenhanced CT as an Imaging Biomarker for Steatosis. AJR. American journal of roentgenology , 205 (6), 1167–1172. https://doi.org/10.2214/AJR.15.14724 Angulo, P., Kleiner, D. E., Dam-Larsen, S., Adams, L. A., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Keach, J. C., Lafferty, H. D., Stahler, A., Haflidadottir, S., & Bendtsen, F. (2015). Liver Fibrosis, but No Other Histologic Features, Is Associated With Long-term Outcomes of Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology , 149 (2), 389–97.e10. https://doi.org/10.1053/j.gastro.2015.04.043 Geier, A., Rinella, M. E., Balp, M. M., McKenna, S. J., Brass, C. A., Przybysz, R., Cai, J., Knight, A., Gavaghan, M., Howe, T., Rosen, D., & Ratziu, V. (2021). Real-World Burden of Nonalcoholic Steatohepatitis. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association , 19 (5), 1020–1029.e7. https://doi.org/10.1016/j.cgh.2020.06.064 Eslam, M., Newsome, P. N., Sarin, S. K., Anstee, Q. M., Targher, G., Romero-Gomez, M., Zelber-Sagi, S., Wai-Sun Wong, V., Dufour, J. F., Schattenberg, J. M., Kawaguchi, T., Arrese, M., Valenti, L., Shiha, G., Tiribelli, C., Yki-Järvinen, H., Fan, J. G., Grønbæk, H., Yilmaz, Y., Cortez-Pinto, H., … George, J. (2020). A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. Journal of hepatology , 73 (1), 202–209. https://doi.org/10.1016/j.jhep.2020.03.039 Younossi, Z., Anstee, Q. M., Marietti, M., Hardy, T., Henry, L., Eslam, M., George, J., & Bugianesi, E. (2018). Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nature reviews. Gastroenterology & hepatology , 15 (1), 11–20. https://doi.org/10.1038/nrgastro.2017.109 Wong, R. J., Aguilar, M., Cheung, R., Perumpail, R. B., Harrison, S. A., Younossi, Z. M., & Ahmed, A. (2015). Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the United States. Gastroenterology , 148 (3), 547–555. https://doi.org/10.1053/j.gastro.2014.11.039 Tamaki, N., Ajmera, V., & Loomba, R. (2022). Non-invasive methods for imaging hepatic steatosis and their clinical importance in NAFLD. Nature reviews. Endocrinology , 18 (1), 55–66. https://doi.org/10.1038/s41574-021-00584-0 Cusi, K., Isaacs, S., Barb, D., Basu, R., Caprio, S., Garvey, W. T., Kashyap, S., Mechanick, J. I., Mouzaki, M., Nadolsky, K., Rinella, M. E., Vos, M. B., & Younossi, Z. (2022). American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists , 28 (5), 528–562. https://doi.org/10.1016/j.eprac.2022.03.010 Harrison, S. A., Allen, A. M., Dubourg, J., Noureddin, M., & Alkhouri, N. (2023). Challenges and opportunities in NASH drug development. Nature medicine , 29 (3), 562–573. https://doi.org/10.1038/s41591-023-02242-6 Loomba, R., Neuschwander-Tetri, B. A., Sanyal, A., Chalasani, N., Diehl, A. M., Terrault, N., Kowdley, K., Dasarathy, S., Kleiner, D., Behling, C., Lavine, J., Van Natta, M., Middleton, M., Tonascia, J., Sirlin, C., & NASH Clinical Research Network (2020). Multicenter Validation of Association Between Decline in MRI-PDFF and Histologic Response in NASH. Hepatology (Baltimore, Md.) , 72 (4), 1219–1229. https://doi.org/10.1002/hep.31121 Bonekamp, S., Tang, A., Mashhood, A., Wolfson, T., Changchien, C., Middleton, M. S., Clark, L., Gamst, A., Loomba, R., & Sirlin, C. B. (2014). Spatial distribution of MRI-Determined hepatic proton density fat fraction in adults with nonalcoholic fatty liver disease. Journal of magnetic resonance imaging : JMRI , 39 (6), 1525–1532. https://doi.org/10.1002/jmri.24321 Caussy, C., Reeder, S. B., Sirlin, C. B., & Loomba, R. (2018). Noninvasive, Quantitative Assessment of Liver Fat by MRI-PDFF as an Endpoint in NASH Trials. Hepatology (Baltimore, Md.) , 68 (2), 763–772. https://doi.org/10.1002/hep.29797 Noureddin, M., Lam, J., Peterson, M. R., Middleton, M., Hamilton, G., Le, T. A., Bettencourt, R., Changchien, C., Brenner, D. A., Sirlin, C., & Loomba, R. (2013). Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials. Hepatology (Baltimore, Md.) , 58 (6), 1930–1940. https://doi.org/10.1002/hep.26455 Yokoo, T., Shiehmorteza, M., Hamilton, G., Wolfson, T., Schroeder, M. E., Middleton, M. S., Bydder, M., Gamst, A. C., Kono, Y., Kuo, A., Patton, H. M., Horgan, S., Lavine, J. E., Schwimmer, J. B., & Sirlin, C. B. (2011). Estimation of hepatic proton-density fat fraction by using MR imaging at 3.0 T. Radiology , 258 (3), 749–759. https://doi.org/10.1148/radiol.10100659 Kotronen A, Peltonen M, Hakkarainen A, et al. Prediction of non-alcoholic fatty liver disease and liver fat content using metabolic and genetic factors. Gastroenterology. 009;137(3):865-872. doi:10.1053/j.gastro.2009.06.005 Ma, Y., Belyaeva, O. V., Brown, P. M., Fujita, K., Valles, K., Karki, S., de Boer, Y. S., Koh, C., Chen, Y., Du, X., Handelman, S. K., Chen, V., Speliotes, E. K., Nestlerode, C., Thomas, E., Kleiner, D. E., Zmuda, J. M., Sanyal, A. J., (for the Nonalcoholic Steatohepatitis Clinical Research Network), Kedishvili, N. Y., … Rotman, Y. (2019). 17-Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease. Hepatology (Baltimore, Md.) , 69 (4), 1504–1519. https://doi.org/10.1002/hep.30350 El Kamari, V., Hileman, C. O., Gholam, P. M., Kulkarni, M., Funderburg, N., & McComsey, G. A. (2019). Statin therapy does not reduce liver fat scores in patients receiving antiretroviral therapy for HIV infection. Clinical Gastroenterology and Hepatology, 17 (3), 536–542. https://doi.org/10.1016/j.cgh.2018.07.034 van den Berg, E. H., Flores-Guerrero, J. L., Gruppen, E. G., de Borst, M. H., Wolak-Dinsmore, J., Connelly, M. A., Bakker, S. J. L., & Dullaart, R. P. F. (2019). Non-Alcoholic Fatty Liver Disease and Risk of Incident Type 2 Diabetes: Role of Circulating Branched-Chain Amino Acids. Nutrients , 11 (3), 705. https://doi.org/10.3390/nu11030705 Niu, L., Geyer, P. E., Wewer Albrechtsen, N. J., Gluud, L. L., Santos, A., Doll, S., Treit, P. V., Holst, J. J., Knop, F. K., Vilsbøll, T., Junker, A., Sachs, S., Stemmer, K., Müller, T. D., Tschöp, M. H., Hofmann, S. M., & Mann, M. (2019). Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease. Molecular systems biology , 15 (3), e8793. https://doi.org/10.15252/msb.20188793 National Workshop on Fatty Liver and Alcoholic Liver Disease,Chinese Society of Hepatology,Chinese Medical Association & Fatty Liver Expert Committee,Chinese Medical Doctor Association.(2018).Guidelines for the management of nonalcoholic fatty liver disease (2018 update). Journal of Clinical Hepatobiliary Diseases (05), 947-957. Gu, J., Liu, S., Du, S., Zhang, Q., Xiao, J., Dong, Q., & Xin, Y. (2019). Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: a meta-analysis. European radiology , 29 (7), 3564–3573. https://doi.org/10.1007/s00330-019-06072-4 Zeng Jing, Fan Jiangao. Interpretation of the Guidelines for the Prevention and Control of Metabolism-Related (Non-Alcoholic) Fatty Liver Disease (2024 Edition)[J]. Chinese Journal of Arteriosclerosis,2024,32(07):553-557.DOI:10.20039/j.cnki.1007-3949.2024.07.001. Tamaki, N., Ajmera, V., & Loomba, R. (2022). Non-invasive methods for imaging hepatic steatosis and their clinical importance in NAFLD. Nature reviews. Endocrinology, 18(1), 55–66. https://doi.org/10.1038/s41574-021-00584-0 Hernando, D., Sharma, S. D., Aliyari Ghasabeh, M., et al. (2017). Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5T and 3T using a fat-water phantom. Magnetic Resonance in Medicine, 77 (4), 1516–1524. Vilar-Gomez, E., & Chalasani, N. (2018). Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. Journal of Hepatology, 68 (2), 305–315. Saokaew, S., Kanchanasuwan, S., Apisarnthanarak, P., et al. (2017). Clinical risk scoring for predicting non-alcoholic fatty liver disease in metabolic syndrome patients (NAFLD-MS score). Liver International, 37(10), 1535–1543. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6289146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432735082,"identity":"e4677a91-2033-4798-ad60-58e65061ae93","order_by":0,"name":"Hongyan Qi","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Qi","suffix":""},{"id":432735083,"identity":"6a637edc-2daf-4bee-b2ce-65e78dfbae40","order_by":1,"name":"Yi Zhu","email":"","orcid":"","institution":"Philips healthcare，Beijing，China","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhu","suffix":""},{"id":432735084,"identity":"5f0acb71-3946-41e5-91e2-75ccb8fe3cbf","order_by":2,"name":"Wei Tian","email":"","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Tian","suffix":""},{"id":432735085,"identity":"11168132-8e55-4791-bb82-0b8b09f67c9d","order_by":3,"name":"Jinpo Wang","email":"","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Jinpo","middleName":"","lastName":"Wang","suffix":""},{"id":432735086,"identity":"69119804-29bb-4837-8bbf-e0cf2f99c024","order_by":4,"name":"Wugao Qiao","email":"","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Wugao","middleName":"","lastName":"Qiao","suffix":""},{"id":432735087,"identity":"5f60680c-e043-4d42-a993-b6d15830d8cc","order_by":5,"name":"Yu Wang","email":"","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":432735088,"identity":"9762b799-31fe-4a06-a3b0-094e8c54e997","order_by":6,"name":"Yupeng Wei","email":"","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Yupeng","middleName":"","lastName":"Wei","suffix":""},{"id":432735089,"identity":"00338d30-dea5-4b2f-ac77-1c2fe382a963","order_by":7,"name":"Xiande Lu","email":"","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":false,"prefix":"","firstName":"Xiande","middleName":"","lastName":"Lu","suffix":""},{"id":432735090,"identity":"6a468598-14fb-4d32-a409-51bb8055bb47","order_by":8,"name":"Junqiang Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIie3PMQrCMBiG4QQhdfhxjlTqCYSUglNpr2Io1LVjx5RAe4UOnsO5pUMXD9Euuih0dBJTHByTUTDv/D0kP0I22w/GEBYM5RSIUxTDZE4u4WYFnfRrM6LCZRp69Fi6SyPiSJHdSQdkPQoXocjbCR2BVvgnUMTlIshQEuwbHaFqCfRDkho1/Kwl21ERNn+sFR0YEYoVOaRAKC6kGQGubmlCIMAlrpnBLXHVX9njReNt1d+eUx55WqIiDL6P6udziwH0I5vNZvvn3pVRPk8Y0r9dAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Radiology, The First Hospital of Lanzhou University, Lanzhou , Gansu, China","correspondingAuthor":true,"prefix":"","firstName":"Junqiang","middleName":"","lastName":"Lei","suffix":""}],"badges":[],"createdAt":"2025-03-23 15:36:16","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6289146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6289146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79262681,"identity":"2c0edd1f-de57-4cd8-9007-276fb3da860a","added_by":"auto","created_at":"2025-03-26 09:47:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286366,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant Selection Flowchart\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6289146/v1/f32c9086a327ae8502dd610d.png"},{"id":79262682,"identity":"943c0d66-6d7d-4c9b-b6b7-4c1671c34718","added_by":"auto","created_at":"2025-03-26 09:47:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225035,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of baseline characteristics among the three groups: healthy controls, mild hepatic steatosis (MHS), and moderate to severe hepatic steatosis (MSHS).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6289146/v1/baf21e2c14cfce51f2500b7f.png"},{"id":79262690,"identity":"6db7610d-e6fa-4c27-889b-906dfc3c154d","added_by":"auto","created_at":"2025-03-26 09:47:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2258929,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative MRI-PDFF images of hepatic fat quantification in different steatosis severity groups. Axial MRI-PDFF maps from (a) a healthy control (PDFF \u0026lt;5%), (b) a mild hepatic steatosis case (PDFF 5–9.9%), and (c) a moderate-to-severe hepatic steatosis case (PDFF ≥10%).\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6289146/v1/153ac7e84d52184e17664b87.png"},{"id":79262684,"identity":"75a6ac2f-2ec3-4a48-80e8-8cfdb921e924","added_by":"auto","created_at":"2025-03-26 09:47:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":344834,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots showing the correlation between PDFF and LFS in different groups. (a) Overall population. (b) Healthy controls. (c) Mild hepatic steatosis. (d) Moderate-to-severe hepatic steatosis.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6289146/v1/a984e9d4cb8a5b6ea17db123.png"},{"id":79264664,"identity":"49663ec6-aff4-470b-9aaa-87c5408d8b2a","added_by":"auto","created_at":"2025-03-26 09:55:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":281189,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for LFS in MASLD detection and severity stratification. (a) Differentiation between MASLD and healthy controls. (b) Differentiation between mild and moderate-to-severe hepatic steatosis.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6289146/v1/e2c10bb027ebad326f8fba4e.png"},{"id":79266569,"identity":"d7059004-b268-47be-bb38-b4a6b755db1b","added_by":"auto","created_at":"2025-03-26 10:11:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4933091,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6289146/v1/16771ee0-c443-41d4-b70c-cef47f2ce30c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessing the Liver Fat Score's Efficacy in MASLD Diagnosis: A Comparative Study with MRI-PDFF\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a chronic condition closely linked to metabolic syndrome (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Presently, MASLD constitutes a rapidly growing health concern worldwide, with a prevalence estimated at 25\u0026ndash;30% among adults (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Despite the majority of patients being asymptomatic, the pathological hallmark of MASLD is hepatic steatosis, which can progress to metabolic-associated steatohepatitis (MASH), fibrosis, cirrhosis, or even hepatocellular carcinoma (HCC) if left unrecognized and untreated (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Accurate assessment of hepatic fat content is therefore critical for early detection, disease monitoring, and therapeutic decision-making in patients with MASLD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough liver biopsy remains the historical \u0026ldquo;gold standard\u0026rdquo; for evaluating hepatic steatosis, its invasiveness, sampling variability, and observer bias limit broader clinical adoption (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Consequently, noninvasive modalities have gained prominence, among which magnetic resonance imaging\u0026ndash;proton density fat fraction (MRI-PDFF) is recognized as the current \u0026ldquo;gold standard\u0026rdquo; for quantitative assessment of steatosis. MRI-PDFF demonstrates strong concordance with histologically measured fat content and exhibits excellent reproducibility (\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Nonetheless, its widespread application is constrained by high costs, lengthy scan times, and limited availability of specialized MRI equipment, underscoring the need for simpler and more economical methods of evaluation.\u003c/p\u003e \u003cp\u003eIn recent years, the liver fat score (LFS)\u0026mdash;an index combining anthropometric parameters, medical history, and routine laboratory indicators\u0026mdash;has been developed to estimate hepatic steatosis. In 2009, Kotronen et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) first introduced the NAFLD LFS, which incorporates metabolic syndrome (MS), type 2 diabetes (T2D), fasting insulin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) to predict liver fat content. Subsequent studies have validated the potential utility of the LFS model for diagnosing MASLD, although its specificity and sensitivity remain suboptimal (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Because LFS relies heavily on metabolic and biochemical variables, its accuracy can be influenced by multiple confounding factors, leading to possible inter-individual discrepancies. Evaluating the agreement between LFS and MRI-PDFF is thus essential for determining the reliability of LFS in the diagnosis and stratification of MASLD.\u003c/p\u003e \u003cp\u003eAccordingly, this study employs MRI-PDFF as the \u0026ldquo;gold standard\u0026rdquo; for quantifying hepatic steatosis in MASLD and compares it directly with the LFS model to investigate their concordance. We anticipate that our findings will help refine noninvasive diagnostic approaches for MASLD, potentially enabling earlier detection, more accurate risk stratification\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2. 1. Study Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, prospective study was conducted at the First Hospital of Lanzhou University from January 2023 to August 2024, adhering to the ethical principles of the Declaration of Helsinki and approved by the institutional review board (IRB) of the First Hospital of Lanzhou University. Informed consent was obtained from all participants prior to enrollment, and written informed consent was waived. Eligible participants were those aged \u0026ge;18 years, had at least one component of metabolic syndrome (MS) (see Table 1) (24), and underwent MRI examination, with subsequent classification based on MRI-PDFF results : MRI-PDFF \u0026lt;5% as healthy controls, \u0026ge;5% and \u0026lt;10% as mild hepatic steatosis (mild HS), and \u0026ge;10% as moderate-to-severe HS (M-S HS) (25,26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExclusion criteria included excessive alcohol consumption (\u0026ge;210 g/week for men, \u0026ge;140 g/week for women), other known causes of hepatic steatosis (e.g., viral hepatitis, autoimmune liver disease, or drug-induced liver injury), prior major liver surgery or procedures that could alter hepatic structure and function, and any contraindications to MRI (e.g., implanted cardiac pacemaker or severe claustrophobia). All qualifying participants underwent MRI-PDFF measurement and were categorized according to their MRI-PDFF results (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Definition of Metabolic Syndrome (MS) Components\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOverweight/Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eBMI \u0026ge;24.0 kg/m\u0026sup2;, or waist circumference \u0026ge;90 cm (men) / \u0026ge;85 cm (women), or excessive body fat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eElevated Blood Pressure / Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eBlood pressure \u0026ge;130/85 mmHg, or receiving antihypertensive treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePrediabetes / Type 2 Diabetes (T2D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eFasting glucose \u0026ge;6.1 mmol/L, or 2-hour post-load glucose \u0026ge;7.8 mmol/L, or HbA1c \u0026ge;5.7%, or diagnosed T2D, or HOMA-IR \u0026ge;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eElevated Triglycerides (TG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eTG \u0026ge;1.70 mmol/L, or receiving lipid-lowering therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eLow High-Density Lipoprotein (HDL-C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eHDL \u0026le;1.0 mmol/L (men) or \u0026le;1.3 mmol/L (women), or receiving lipid-lowering therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e2.2. Clinical Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics and clinical indicators, including age, sex, height, weight, BMI, blood pressure, fasting glucose, HbA1c, lipid profile (TG, total cholesterol, HDL, LDL), and liver function tests (ALT, AST), were recorded. All blood samples were collected after 8-12 hours of fasting and analyzed using standardized clinical laboratory techniques to measure fasting insulin (FINS) and other biochemical parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAFLD LFS (liver fat score)\u003c/strong\u003e was calculated using the formula proposed by \u003cstrong\u003eKotronen et al. (19):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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1y71bCqqq6vJ888/TwwNDcmCBQsIl8tVz0LeeecdEhwcTHbs2KGeRH7//Xfi6+tLjI2NyXvvvUeamprUs2jF4/HI3Llzib6+PnnzzTeJQCBg0vLy8sjrr79OzMzMiKOjI9m8eTNpa2tT2b43hYWFZPz48URPT49MmjSJZGdnq2fp0ZdffklMTU1JbGwsSUpKUk8mR48eJSEhIWTp0qWksbGR+XtqaiqZMmUKcXJyIitWrFA5DvX19WT27NnEwsKCfPvtt0QoFDJp/ybV1dVk3rx5BAAZOXIkSUtLY9Kam5vJihUriImJCeFwOOTbb78lIpFIZXtt6urqmH1OmzaNlJeXM2lNTU3k888/J/b29sTBwYH89NNPzLFtamoiW7ZsIc7OziQ6OpqcPn1aZbsVK1YQU1NTEhcXRxISEohcLmfSddXW1kaSkpLIqVOnSE1NjXryQ5HJZCQpKYmsWbOGDBo0iAAgAMgbb7yh8t0fVHNzMzlx4gR5/fXXiaenJwFAXF1dya5du0hXV5d6dkIUx37u3LkEAHn22WdJRUUFk9bY2EjWrFlD7O3tiaOjI9m7d+9jdV7L5XKyc+dO4uDgQMaNG0fu37+vnkWFRCIhn3zyCfH29iYmJiYkPDycnDhxgkgkEvWspKOjg/z444/Ez8+PACAODg5k48aN6tkIIYQUFBSQl156iTg4OJA33niDVFVVMWkCgYAsW7aMmJqako8++oh0dHSobKuLhIQEEhgYSFgsFnnttddIdXW1ehaSnp5Ohg0bRmxsbMj7779P6urq1LP868nlcnLr1i3y+eefkyFDhjDX1ZIlS0hpaal69j+tra2NvPnmm8TV1ZUYGBiQ0NBQcuzYMfVspLm5mbz33nvExsaGrFmzhnR2dqpn+Ufi8/lk69atxM7OjowaNYrk5OQQoih733//faKvr08iIiLI9evX1TdVIRAIyObNm4mVlRWZMWMGKS4uVs/yUOrr68mFCxfIpUuXSHt7u3qyzqqrq8nixYuJmZkZWbZsGXMN5+TkkIkTJxIAZObMmSpl54MSCARkz549xNHRkQAgQUFB5MSJE+rZCCGE7Nmzh3h7e5Pg4GAyZ84c8vLLL2u83n33XZKVlaW+6SPX2tpKVqxYQSwsLMgrr7yiUr8ihJC0tDQydOhQEhISonI/37dvH7G1tSXDhw9XKec3b95MXFxcyH//+1/S2tpKamtryTPPPMPUJQoKCpi8dXV15D//+Q/hcDjE1NSU/Pzzz1rLfqWsrCwyZMgQYmFhQb7//nuVtMTEROZ9wsPDyfHjx3Wq62jT2NjI3IMHDhxIbty4oZ6FNDU1kffee4/o6+uTUaNGkeTkZPUs/3plZWVk+vTpBACZOHGiynnQ0NBAli9fTgwMDIihoSHZvn07EYvFKts/rKtXr5LBgwcTKysrwmKxyIIFC7SWUTdv3iRRUVGkX79+JDExUT35H6ugoIA888wzxNLSkqxdu5b5e2JiIomJiSEsFou8/fbbpKWlRWU7dcp7xcSJE0llZeVD1cf/lzT7qT0kZfew4cOHw9fXF0VFRUhLS1PPBqlUCmNjY3h5eWmN9GpTUFCAP/74A05OTvD394eFhYXW1qvulGNkTExMEBQUpNJCoTRgwAA8++yzWiPGEomE6WY/ePBgZnhAXyQSCeRyOYyNjWFra6uSFhQUhNWrV2P69OngcrnYv38/MjIydBpXAkXkVHn8Bg0aBA8PD/UsPZLJZOBwOPDy8tLaLdTLywtTp07F4MGDmVZTkUiECxcu4PLly4iJicErr7yiEj22srLCyy+/jFdffRUxMTE9trb+0xkZGSEyMhIzZ87E2rVrERUVxaRZW1tj9uzZCA8Ph1QqRVVVFTo6OlS2VycSiXDx4kVcuXIFvr6+WLBggUp3IhsbG7z66quYOnUqeDweLl68iObmZgBASkoKdu/eDYlEgldeeQXjx49X2e6FF17AkCFDkJaWhgsXLjzUjKT19fX48ccfsXz5cmRlZaknPzCxWIykpCTs2LEDN2/ehL+/P8LDw2FiYgI7O7uHbhVsbm7GyZMnsWPHDjQ0NCA2NhY2NjYwNjaGo6OjSrRaSXlOX7lyBX5+fliwYIHKdWRra4uFCxdiypQpaGhowIULF5hj35vOzk5UV1ejqalJPUkrkUiEyspKNDU1QSaTqSdrRRQTif3000+Qy+V46aWXEBISop5NhUQigUQiQVBQEDw8PNDR0YGysjJItawWkJubiwMHDkBfXx+mpqZwcXHROuRJIpEgPj4eZ86cgb+/PxYtWgQ3Nzcm3dDQELNnz8aSJUswZMiQh+pB4e3tjejoaBgYGEBfX19rl+rk5GTU1dVhypQpWL58OZycnNSz/KtJJBLcvHkTO3bsQGJiInx8fBAREQFjY+M/dV31RiwWQy6XY8CAAQgNDQWPx8OtW7cgEolU8kkkEujr68PFxQUREREareH/VPn5+di/fz8MDAzwyiuvMNeHjY0N5s+fj6ioKGRnZyM5OZnpyq2NiYkJZs2ahaFDh+LChQv4/fffNYblPYzc3Fx89tlnWLduHRoaGtSTdXb58mWcPn0aYWFhmD9/PnMNh4aG4uWXX4a1tTVu3ryJnJwcncuv7gghuHnzJn744QfIZDIYGxtrvcaVpFIpTE1NMWHCBPz888/Yu3evxuubb75BeHi4+qZaSaVS1NfXo66uTufPX1dXh7q6Oo1zvS9JSUk4fPgwPDw88NZbb6nUC+VyOfLy8lBbW4tp06Yxwx/q6uqQkpICsViMfv36MfVXsVgMAwMDWFlZoV+/frC0tASHw0FsbCxeeOEFrF69GgEBAcz+nZycMGfOHAQEBKCrqwtVVVUQCoVMujrlOWtmZgZra2uVYzN06FB8+eWXeOqpp5CdnY2DBw+ivLy829a6s7GxQUxMDFOX19aTID8/H6mpqYiKisLq1asxaNAg9Sz/esbGxujfvz9mz56NNWvWqNzv7e3tMXv2bAQHB0MkEqGqqgpdXV0q2z+szs5O2NvbY9iwYXBzc0NqaioKCwvVs0Eul8PMzAzBwcF/aljI44QQghMnTiA+Ph7Dhw/Hiy++yKQNHToUL774IthsNuLj41FcXNzrXAmzZs3CxIkTcePGDfz666+93hMehZ5L3AdUX1+PtrY2DBgwADExMWhtbUV2drbGMieVlZXg8/nw9/fX6QFWJpPhwoULuHv3LqZMmQJvb2+IxeI+b5aNjY2or68Hh8OBo6Oj1gJmxowZWLlyJYYOHaqehIaGBnR0dMDd3R1BQUFaHya04fP5aGhoYMbOqI99dXBwwMiRI+Hs7IzKykqkpaXpfENpbm5GU1MT011c16AEn89HTU0NRCIR7OzstAZDoqOjsWrVKsycOZO52dfV1SEvLw9CoRA2NjYa72doaIhx48bhyy+/RFxcnErav4mNjQ3eeOMN7N27V+v4RGNjY1hbW0NPT0+nykRnZyfu3LkDLpeL0NBQlRu2kpmZGRwcHMBms9HU1MRcR7m5uSgpKYGHhwf69++vEXRxcHBA//79oa+vj9TUVFRUVKik60pPT6/XCtmDaG9vx40bN+Dr64vvvvsOq1evRmxsLDgcDqRSaa8FaG+Ki4uRlZWFOXPm4Mcff8TSpUvh4eEBWS+T4wgEAty+fRsNDQ0ICwvr9djr6empHPveFBYW4ttvv8XWrVtRXV2tnqxCKpXiwoULWLt2La5du9Zrhaw7gUCAS5cuobCwEIMGDUJ0dLR6Fg0tLS2ora2FtbU13NzcwOfzUVFRoXEjamxsRHx8PDo7O+Hi4gJTU1NYW1trfahuamrC/fv30dbWBisrK40u92w2G3Fxcfjiiy/w9NNPq6TpytbWFsOHD4e5uTmKi4s1HmZKS0tx/vx5uLq6Yu7cuToPu/k3EQgEuHHjBjw9PbFlyxasWbMGgwYN+tPXVW8aGxvB5XIREhKC4cOHQy6XIzs7G3V1dSr5uFwuamtr4e7uDh8fnz4D9v8EcrkcBQUFKCsrg5ubGwIDA1XKSCcnJ6ZRIzs7u89ywNraGmPHjoWhoSHT1fvPYrFYYLPZf6rs7urqQk5ODlpbWxEQEKAx1tXX1xcBAQFoaWlBVlaWTuWjutLSUhw9ehRisRivvPIKfHx8+jxnlRON6XKP7Ut7ezsOHDiAL774AsnJyb2+LxTzT61fvx4HDx7UKIt6I5PJcPXqVRQVFSEyMlKjsUksFkMmkyE8PBwTJkxgunCXl5fj9u3bAABPT09mZRdCCCwtLREQEABXV1cAgJ2dHZYvX47du3cjIiKi297/j4mJCaytrcHhcCDvY3K3pqYmNDU1wdjYmKl/dBcQEIARI0bAxMQEubm5uH//vkq6rlgsFjM8tra2FmVlZSrpIpEIf/zxBxoaGvDiiy/qdK/7N3JwcMC7776Ln376SeucZ91/297qPQ+CEILq6mro6elhxIgRCAgIQFVVFXJzc1X2L5fLUVpaCrlcjqCgoH/NPbipqQlZWVmQSCQIDg6Gi4uLSnpgYCA8PT1RW1uLnJycXutvdnZ2GDRoELq6upj5Yh4nD3+X6EYkEqG4uBhNTU3w8vKCn58fCCHMeK3ulDdGV1dXnZarSktLQ2ZmJqKjozF27FhYWFiAx+Ohubm5x5NdKpWiqKgIFRUVsLCw0Dp5FQBmwkL1Qk4ul6O+vh5dXV1wcnLS+sDck9bWVnC53F7fNyoqCv369UNraytKS0s1KuM9aWxsZAIAPe1bm8rKSuTn54MQAmdnZ62tMXp6ejA0NIS+vj5TWVPeaAkhaGxs1NqarNxO/Rj+m7AUE0b2NI65vb0djY2NkMvl4HA4fVZ2CSFMCyyfz++1hZnFYqnsj8vlorOzEzY2NlrHzRsbG8Pb2xvGxsaorKx8qAJHWYnsqeX1QVlYWGD+/PlYunQpAgICYG5uzsxN0NM1rIvg4GC88847mDFjBuzt7WFkZMQE6nraL1HM6ArFtdrXsUcv++pOLBYjIyMDP/zwA7Zv385MKqjNxYsXsX79eiQlJUEoFOp8jFtaWnDlyhW0tLQgLi6uzzFlhBCUl5ejoqICjo6OCAwMhFQqZYKb3cXHx+PcuXMYN24cbGxs0NnZCQcHB41eTFDslygq401NTb2WC7oGTtUZGxsjKioKLi4uzJJZSu3t7di3bx9aW1sxf/58DBs2TGXbnig/t66U3/FBtvk7mZmZ4aWXXsIbb7yBoKAgWFhY/CXXVU/kcjnKyspQXV0NBwcHBAcHw9DQEKWlpbh//75Kr5KSkhLk5+fD3t6+xzHH3T3uxxqKAHxWVhb4fD4cHBxgbW2tkm5gYABvb2+YmZmhurq61zIAivzDhw+Hq6srbt++jYyMDPUsD4zFYoHD4eh0H+pJWVkZcnNzIVdMzqZeT7O0tGR6EZaVlaGtrU0lvS+EEBw7dgy3b9/GjBkzMHz4cJiYmGjtlaSue/n9ZxDFQ84vv/yCjRs3IjU1VT0LIz8/H19//TWOHj0KHo/3QHWdwsJCZGRkwNraGnFxcRoBewMDA0yaNAlbtmzBgAEDmL83NjaCx+PB1NQUzs7OTN3DwMAAEydOxObNm5mHYhaLBSMjox7rJ3w+H42NjZBKpX2eF8p6np2dndbrVl9fH4MHD4a3tzfq6upQVVWlnkVnQUFBCA0NRVNTE9LS0tDZ2cmknTlzBjdu3MC4ceMwb948nXuRPUgZ/08oc/qqe7a1taGpqUmn31ZXfD4fhYWFEIlECAgIgLu7OwQCAbKyslBTU8PkEwgESEtLQ0dHB9zc3HR6b+Uxf1wRQlBYWIjCwkKmB5t647G9vT08PT0hFApRWlraZ6+LkJAQhIaGory8HJmZmX9J+fVX0a3m2QeBQMC0MDs4OCAuLg6enp7Izc3F3bt3mXxSqRRlZWUwMDBA//79NQ6sura2Nhw5cgRcLhczZ86Evb09CCFMV/ieCIVCVFRUoL29HSEhISoR7JycHHz66ae4cuVKj70IampqkJ+fD6FQCDs7O50Ln66uLuTm5oLL5SIgIEBrV3soonaWlpaQSqWoq6vTqIxro1w7tqmpSWurW0/kcjnq6upQU1MDV1dXhISEMA8cHR0d+Oabb7Bz5060traqbwpnZ2emhfTu3bs4cOBAn5WaJ41EIkF+fj4qKyvh4OCg0zrsJiYm6N+/P+zs7FBeXq71mIrFYjQ1NUFPTw+BgYHMzdjMzAwGBgbo6urS2vKivGEoW697mjX576Svrw83NzcmaKWc0fjPMjc3h6enJ3N9qq/0oU33Y19WVtbjsW9sbASbzUZQUJBGRV8bf39/vPbaa3BxccGOHTvw/fffa933xYsXsWrVKpSXl2PevHkYMWKERqWwJy0tLUhJSYGhoSGCg4P7nOBRWb7w+XyV4VMymUzl+FdWVjITgA4dOhQyxWzTQUFBWnsAWFtbIygoCAYGBsjJycG+ffv67Ap6584dzJ8/HwsWLFC5J/SExWLB09MTwcHBTERerJiR+s6dO7h16xamTp2KZ599ts/7SEpKClavXo3Jkydj1KhRmDx5Mr766iutvWMIIUhMTMRbb72FMWPGYPTo0Rg7diymTp2KFStW4MaNGyoV1UeJw+HAzc2NaXnp6urq9b74Z4nFYtTU1KC9vR1WVlYYMGAAQkJCUFtbi6SkJJXjUl1dDYFAgP79+2v0HOuuqqoKGzZswOTJkzF69GiMHj0aixYtQkJCgnrWR66xsRFlZWVgs9kq5Y6SgYEBvLy8YGFhgZqamj6DrywWC/7+/vD09ERjY+NDt6b+1SorK1FdXQ0rKyu4u7trBPEsLS3h5eUFFouFioqKBw4A/PHHHzh16hT8/Pwwb9482NjYQCKR6PQgxmaze3wYehDm5uaYP38+xowZg8uXL2PdunVagwD5+flYt24dTp48iVGjRmHmzJlag6I9yczMRFFREby8vLSuvKKnpwcbGxt4enqqlGPNzc0QCATw8PCAr68vU9azWCxYWVnB09NTo3epNsoJsevq6uDu7t7rcJyWlhZkZ2ejq6sL4eHhWhsYoDh2JiYmaG9vVxkS0dXVhR9//BHPPvssvv76a2a1qJ5YWVkxE0dnZWWhtrYWUJx/586dg6urK956660+7781NTXYtWsXZs+ejVGjRmHcuHF44403kJSUpLWeUVZWplHmPP3003jttdeY541/ApFIhPz8fKZBNTIyUqdzoi8tLS2oq6sDIQReXl4YNGgQrKyscO/ePeTk5DD5BAIBSkpKVHo+9eTq1atYsmQJRo0axayAsX79euY3f1zIZDKUlpaCx+PBzs5Oo/UfilZ9Dw8PiEQiVFRU9BkA8PLyQv/+/dHR0YF79+5prbc/Kg/XPKOmtbUV7e3tzIzy9vb2CAsLQ05ODu7evctE8AQCAVpbW2Fra4uAgACNwlBdUlISbt68iYiICMTExKCpqQkikQhSqRTt7e0QCARaH7Y6OjpQU1MDFovFjFWEonJ34cIF/PrrrwgKCurx/cvLy1FUVAQbGxtERUXpfFG1t7cjNzcXnZ2dCAwM1HryoNv6qsqHNfUbrDaNjY3Iy8sDFD0IdO1uI1WMdWtra0N0dDQGDhzIpKWlpWHv3r3MEnbqTExMMG7cOMTHx+P69ev48ccfweVy8corrzzwMoZQPCynpKTg+PHjWgMOfTEyMsLIkSMxZcqUPh9+/i4NDQ04c+YM+Hw+lixZgtGjR/f52QwNDTFq1CgcPXoU165dw+HDhxEQEMCcp52dnTh06BAuXLiAkJAQzJgxg7kJOjg4wNDQsMeWV2VLAIvFglAofGweVh4XRkZGzLG/fv06c+yVY+sEAgEOHTqEixcvIjQ0FM8//7zWlhB11tbWeP7552FsbIyVK1fihx9+ACEEy5YtY2ZiP3/+PFauXInq6mq89dZbWLJkiU77VqqtrUVzczOsra37rBRBUflraGhgZoXW09ODubk52tvbmV5HhBCcOXMG9+/fx+zZs+Hk5ISWlhaYmJjAy8tLa3DCyMgITz31FCZNmoSTJ0/i559/RnNzM1599VU89dRT6tkBRZl68OBBsFgsTJgwQaW1qyfm5ubw8/ODXC5HVVUV2tvbUVxcjO3btyM8PByzZ8/uNRDK5/Px22+/4fDhw0yZbG5ujoSEBNy9excdHR1YtmwZE+QQi8WIj4/HN998Az6fj9DQUFhaWqKyshJXr15FfHw8TExMEBYW1us9obOzE4mJiTh9+rTOvbu6s7CwYIIVj5POzk40NTXB0dERbm5uCA4ORmxsLC5fvow7d+6goaEBFhYWkEqlaG5uhpGREYKCgrQeK5lMhlOnTuHQoUNoaGiAj48PAgMDmRUgCgoK8Pbbb2Py5Mlaz0F1NTU1OHXqFNLT09WT+qR8EJ89e3avwxJ5PB6qqqrA4XBgYWGh0RLMZrNhZmYGfX198Hg8jWER2hgbGzOB0erqaohEIp2+7/9SVVUVeDweTExMtNav9PX1YW5uDplMhpqaml57Uamrr6/Hrl27IBaLsXjxYqYlua/ArVwuh1gsxv379/HFF1+goaEBQqEQhoplWydMmKB1OFdP9PX1ERUVhVWrVoHFYuH06dMAgBUrVjAt67m5uVi3bh3Onj2LZ555Bh999NEDLTFKCEFxcTEzZEbZZb8vbW1tKCgoQFtbG1xdXXXeTpuqqiqcOXMGUqkUL7/8MgYPHtxjfZPL5TJLZ4eEhPR4j1G2mnM4HBgZGTENShKJBImJiTh16hQ6Ozsxb968XstnKB6OrKysUFtbCx6PBwsLC2zduhUdHR144403emxEU7p+/Tp27tyJ/Px8eHl5oV+/figoKMChQ4dQVVWFTz/9lLnXEMUcOt988w0zF1FgYCAzlOrSpUtoampCaGhoj8GP7qqqqnDixAlkZ2erJ/VJT08PAQEBmDNnzkP/vrW1tThz5gyEQiGWLFmC4cOH9/jbPoiGhgbIZDJ4enoygT5PT09kZGQgMzOTGdbX1taGjo4O+Pn59bgyg3J1pvj4eJiamiIkJASGhobIzMzEDz/8gOrqaqYHW18kEgnu3r2L48ePP3DQEYqydsyYMZg0aVKPx0mmWDmJz+drDfJCUX83MzODRCJBTU0Nc532xMbGhlm14f79++Dz+Q/Uq/x/6S/pAVBeXo76+noEBgbC3t4eVlZWCA0NBZvNRl5eHioqKkAU3VHr6uqYCUZ6IxQKce7cOVhbW2PWrFkwNDSEgYEB84Pw+fweH27q6+tRVFQEoVCI5ORkrF+/Ht999x1WrVqFw4cPw8zMDJ6enj2eBPX19cySbupLX/RGGXhgK9Ze1XbzhKIi1dnZCRaLBTMzsx4jst21traipqYGFhYWiI6O7vP4KQkEAuTm5qKjowMVFRXYvn07vvvuO3z77bfYtGkTmpub4evr2+OEUVFRUfj4448xdepUtLS0YPv27VizZg2uXr36wK1NRLGebX19PWprax/q1dbWplNLwd+hra0NBw8exJUrVzB27FgsWrRIa4upOpZiPfZly5ZhwoQJuHPnDj788ENs2LAB3333HT755BNs3boVTk5OWLVqFUaMGMFUNkNCQhAcHIySkhKcPXuWWYMViojw9evXcenSJQgEApibm2stwPpiZWUFQ8Wwjt5a7/6JWCwWfH19NY79119/je+++w6ffvoptm7dChcXF6xateqBb6qTJ0/GZ599Bi8vL2zfvh3bt29HXl4eLly4gDVr1qC0tBTLli3DsmXLHujhXyaToa6uDiwWCzY2Nn22ekNRZuTn58PFxQV+fn6wsLCAXC4Hl8tlKu23b9/GH3/8AX9/fzzzzDOor69HTU0NLC0te23pCgwMxHvvvYe5c+dCLpfjwIEDWLVqFU6dOqV1TBxLMZRF+dKFvr4+goKCYGNjw7QKnTx5EoQQvPDCCyoTD6prb2/HkSNHsG7dOpSXl+PFF1/Etm3bsHXrVvznP/+BVCrFlStXUF9fz2xTU1ODQ4cOISUlBS+++CL27NmDTZs2YdeuXVi8eDH0FZMj9lVeE0LQ0dGBuro6jfJLl1ddXR0EAoH6bh+5hoYGZhlZFxcXGBkZITQ0FNbW1igpKWHGsNfX16O8vBwGBgawtbXVGOIiEonw+++/Y8WKFbh58yYmT56Mbdu2YdOmTdizZw9eeOEF3L59Gzt37kRJSYnKtj2RSCRoamrSOJa6vGpqasDj8XrsEajU0dEBPp8PouiGrn4f6v73nnpoqZPL5cxDUmtr60MFxrtTDgPR19fXuY6grq2tjTn/tLWgkm5dpzs6OnQ+V8ViMQ4dOoTc3FyVAJcu9QgrKyuYmZmBx+MhPz+fubauXr2Kzz//HGvXrsWNGzf6/A3VhYSE4NNPP8W0adOYngB3795FZmYmNmzYgFOnTmH8+PFYuXLlAz38KzU0NEAikcDU1FTnSn9tbS3y8vIgk8ng6Oj40D0empqasHfvXty+fRtTp07F/Pnze73nNDc3o76+HiYmJnB2dtZ6j1H+5kKhEBwOB6ampioNHsqyXdcy3svLC76+vuDz+bh58yYOHz6MvLw8PP300xg+fLh6doZMJsPt27exdu1a/P777+jfvz+++uorbNmyBd988w0CAwORlJSkMomxSCTC+fPncfToUQQEBGD79u3YtGkTduzYgS+++ALu7u4ghOj8O/3ZMqexsfGBz1el1tZW7Nu3D4mJiZg0aRIWLFjwQMOCe5Ofn4+uri4EBwfDzMwM3t7eCAoKgkQiYeYGkclkKCoqQmtrK6ysrLTWE2tqavD9999j48aNYLPZ+PDDD5lyfuPGjXB1dcXevXtx/vx5nXpBE0LQ2dn50M8PdXV1fQYO5HI5WlpamDqMtvJPLpcz5Z+yIbo3RkZGsLCwYAIGffUY+DvpXrPtRWlpKSoqKjB48GCm0OjXrx88PT1RXl6O9PR0+Pn5IS8vD/X19QgICOj15iSTyRAfH4+amhpMmzYNI0aMABRdLxwdHZmoo3rFQqmqqgqFhYWQyWRMFxlCCFpaWtDU1IQxY8b0eLHIFeMcm5ub4efnBxsbG50LMx6Ph/LyclhYWMDT07PHglvZJb/75HF9qa2tRWVlJUxNTWFvb6/zQ0lraytycnKY1puzZ8+CEAKRSISamhq4u7v3GL2DIto1duxYeHh4wNnZGUePHsW5c+fQ0tKC9evXY8iQIeqb9MjAwACjRo36y1q2WltbUVJS0udFrQ2LxYK9vT38/PweqsVFLBbjxIkT2LVrF4KDg/Hxxx/32Q2qOw6HgzFjxqC+vh48Hg9ZWVmorKwEFBXNtrY2jBo1CmFhYZAoZtSGIiDzyiuvYO3atdi9ezcMDAwwevRocDgcNDY24vr167h27Rq6FGvY93SeK7sv1XQb06VUU1PDdO1MTEzUWmCZmprC19e314fEx1X3Y9/Q0ICsrCymO7jy2I8ZMwahoaEqx15XzzzzDORyOb744gv88ssvKCoqYuZjePPNN7FkyZI+W0bUCYVCpkultbV1nw+hUJRH2dnZzDCGxsZG6OvrQygUMtH7Q4cOoaOjAx9++CEsLS2RlpaG+vp6DB48uNcHbA6HgyFDhsDDwwOurq44cOAAEhIS0NjYCAMDA4wfP16lhVQ5+SmLxdK595JyqFhwcDCysrKwZcsWBAUF4e2330ZkZKR6doZMJsP169fx3XffQS6X46OPPsL8+fOZe5OVlRWsrKxgYmKi8tvW1NQgOzsbVlZWKi0S1tbWmDFjBiQSCQYPHqy1Rbs7U1NTTJ8+HdOnT1dP+kerVTyYxMXFMQHjwMBAhIeHIzU1Fffu3cNTTz2FiooKFBcXM/drdUVFRfj2229RXFyMDz74AEuWLFEJQI8cORJHjx5FRUUFysvL+1zpAooHiZUrV6r/+S+laz0AinO3p/t/dyxFl24Oh4POzk60tbVpPWbdtbe3o6SkRGsPsMzMTPB4PHA4HFy4cEEjIM1isWBtbQ1/f/9ez2NdviuLxYKxsbFO90+ZTIbk5GScOHECQ4YMwcKFC5nr0VixAoCenh5MTU0hl8tRW1sLY2NjWFlZgc1mIywsDNOnTwebzca0adOY6zMxMRHr1q3Dr7/+irKyMnz77beIiorS6J3Rm7CwMKxYsQJ6enqIj4/H559/Dj09PWRkZGDixIn473//qzF5n66UQaCeelNo09DQgLq6OtjY2CAoKEinsl6dUCjEoUOHcPDgQQwYMAAffPBBn63pVVVVqKurY3r4aKtjyuVyVFZWorm5GZaWlir3MQ6Hg9DQUMTExCAyMlJrAEGdr68vBg4ciMzMTBw4cAC2trZ48cUX8eyzz2p9f6Xi4mJs2bIFd+/exQsvvID//ve/zFBfZQOjmZmZyrnZ1dWFtLQ0CIVCBAUFMRMuQlHmvPrqq0w9Vxc+Pj5Ys2aN+p//50QiEX777Tfs27cPUVFR+PDDDx+o90tv5HI5cnNz0drayrRqm5iYIDo6GmfOnEFOTg5yc3MRERGBrKwsCIVCuLm5aZQlyh5ee/bsgZeXFz755BOVgI63tzdGjRqFrKws5Ofno6Wlpc/rw8DAAGPHjsXYsWPVk/5SupR96Fb+9XWe6+vrw8rKCoQQ8Pl8nQKef5e+nzz7IFeMMReLxfDy8mIqVOHh4RgwYAB4PB4SExPRopiN2tTUtM8ulFVVVdixYwdEIhEcHR1RV1eH6upqFBQUoKmpiYnA9BSdqaurA5fLhbe3N7Zu3YqrV6/i2rVrWLNmDcLCwhAVFdXjg0tbWxtyc3PR3t4OZ2fnHh+gtCkpKUF5eTlcXFwQEBDQYwFWU1OD6upqODs7Y9CgQb0eCygiX0VFRaiqqoK1YmZuXYIGhBA0NTWhpqYGHA4Hb775Jq5du4Zr167h0KFDGDp0KMLDw3sNACgFBgbiiy++wIcffggXFxfcunULe/bseWTjpQghKCgowLp167Bo0aIHfi1ZsgTbt29/oO6LSjKZDJcuXcKOHTvg7OyMdevWPfAqCEKhEHv37sX3338PS0tLbNu2jfltfvrpJ4wYMQJHjhzBokWLcPfuXaa1ycTEBC+88AK+/PJLhISE4MCBA1i8eDFee+01XLp0CQMGDMCECROYQFH3m1x3zc3NOHjwoMZxWbRoET755BPcvHkTNTU1+PbbbzXSFy1ahJUrVz5U17fHQfdjb21tja1btzLHfs+ePXjqqafw22+/YfHixbh3755GS58upk6dio8++ggmJib49ddfce/ePbzwwgt44403dH4A7k4qlUIoFDI3nb6CEkQxcWdzczOcnZ1hYWEBa2truLq6Qk9PD11dXUhKSmJu5nFxcRAKhSgrKwMhBBEREX1WGAHA3d0dK1euxKpVq+Dn54f79+9j165dzH6Uhg4diuPHj+PYsWMPFDRUzrbe2NiImpoahIWFIS4urseyFYoH1aNHj6KgoACTJk3CnDlzYGBgAJlMhsrKSiQmJqK1tRW+vr4qLWLGxsYwNzdHXV0d/vjjD5SXlzM369jYWHzxxRdP7GzUhBDweDzw+Xy4ubkxLT5eXl4YOnQopFIpbty4gdraWqYLaXh4uEb5IxQKcevWLWRnZ8PHxwcjR45UaQyQy+Uq48HlfXQN/zvp6ekx511PlUQWiwVCCMzNzXttbVXqXokUi8XMeOrelJWVYdOmTRpl8qJFi/D111+jsLAQubm5+OijjzTSFy9ejC1btvQ6PIHD4TD1C23fU/kd9fT0YG1trbX1rztCCMrKyrB161Y0NDQwY+FrampQVVWFiooKZmhnXV0dzp07h//+97+4ePEiE3wOCQnB+++/j3feeUclODds2DC8++678PPzw507d5jGiQcVEhKCFStWIC4uDufPn8eZM2cQGRmJ//73vyrL/j4IZUMLFA8vutTZoOiKz+Vy4ezsjOjo6AfuxSeRSHD69Gn8/PPPCA4Oxtq1a3sNmEJxf8nLy0NjYyP8/Pzg7e2t9beXyWQoKytDY2MjAgMDVY6NsbEx3nnnHVy9ehUrV67stZFPycLCAiEhIdDX10dBQQHs7OwwfPjwXq+drq4uXL58GZcuXUJgYCBeeeUV5uGfz+cjMTERhYWFcHR0hLu7O7Mdm82GjY0NxGIxbt26hZs3bzItvVZWVvjoo4/w4osv6vw7PQpSqRTnzp3D7t274evri7Vr1yImJkY920Pr6OhAbW0tDA0NVYZDDRo0CMHBwSgoKMDdu3fR3t6O2tpauLi4aA2OVVZWIj4+Hu3t7Rg2bBhiYmJUzieZTAapVMqUJY8TXcs/5fnUV/kHRb0divuftufWR+VPn+lVVVUoKSmBlZUVgoODmRukh4cHoqOjwWKxkJWVhaKiItQourB3vyjViUQi3Llzh5no6b333sOYMWMwfvx4TJs2jRlfxOfztU4y0tDQgOzsbAiFQoSEhCAqKgp2dnaws7ODv78//Pz84Ovr22NUtVGxzJGZmRlCQkJ07g4kFApRW1sLoVDY4+zZUBRe9+/fR0tLC+zt7eHm5tZntLq9vR01iqEFwcHBvY436U4oFCInJwe1iuWYYmJiYGtrCzs7OwQGBjLjLnsrbLuztrbG4sWLsXDhQrDZbNy4cQMpKSkP3Y3pz2CxWAgODsZnn32GAwcOPPBr3759ePPNN3v8nXpz6dIlrF+/HnK5HO+99x7i4uK0FhQ9EQqFOHbsGL7//nsQQrBgwQJMnDiROU9HjBiB9957D/3798eNGzdw7NgxlUqNubk5ZsyYgf379+PEiRM4ePAgfv75ZyxfvhwDBw5Ee3s7urq64ObmplEBV7K1tcX8+fM1jsuBAwewceNGDB8+HG5ubvjwww810g8cOICvvvpK65JDj7uuri4cOXIEW7duBYvF0jj2I0eOxPvvv4/o6GgkJCTg2LFjD90t18jIiCnslZX7h61csBWrMhBF9+K+rrn29nZm5Y/g4GCmt5Grqyva29uRlpaGrVu3wtnZman0tCpWMNHT04OLi4vOvRRMTEzw4osvYunSpbCwsMCtW7dw69YtpnIFxU3V3Nwc5ubmvT68q2Oz2cxLuSZvX8GPnJwcpKWlwdLSEv3792fmPbhw4QKWL1+OAwcOIDIyEgsXLlQJ8IaEhGDx4sWwsrLC7t278fbbb+Ps2bNMK1738a5PmpaWFhQUFICtGB+sfCixtrZGdHQ0LC0tkZeXh7y8PFRVVYHNZqs0CCjV19fj9u3b6OzsRP/+/eHj46OSLpVKmfkeulfEHgc2NjZwcnLSqdKq10sPxe6U17NYLAaHw9E4Xtr4+Pjgo48+0iiTDxw4gE8++QRBQUEICwvDpk2bNNL379+P5cuX9zg/ERTLGdrY2PQYfOn+3XX5njKZDNnZ2bh9+zYqKiqwceNGTJw4EePGjcPTTz+N5cuXo6ysDHV1dVi1ahWWLVvG9GJT7ltPsaqItt4GwcHBGDBgAFgsFgoLCx8qAABFOUa6zVIuEol6PAa6Uv6eyglMddHQ0IC2tjZYW1vD2dn5geoWMpkMp0+fxsaNG2FhYYF33323z4d/KB6clUEhZcBYGx6Ph5ycHIjFYri6uqr0MFEGsywsLGBiYqLz59bT0wOLxYKXlxezJGRvqqurkZycjK6uLkRGRjIPqqmpqfjyyy/x2WefgRCCxYsXq8xVZWpqipdffhnDhg3DzZs38eabb2Lbtm1M7z/lMOPHFSEE586dw4YNG2BoaIh33333L334h6JnRY1iwvDuvQqUZYqyF0VZWRl4PB6cnJy09ljKzs5GTk4OHB0dERsbq3E+KRuElQ/Sup4r/2tsNhsuLi7MHCd90aX8g6LeiQcMBP4d/vQnqa6uRlNTE5ydnWFnZ8f8kN0fVisrK3Hv3j0UFhbCzMys1y42WVlZOHjwIEaPHo2NGzfi448/xttvv423334bb775JiZOnAhTU1N0dHRorQRzuVyUlpZCX18fvr6+Khd0UFAQ3n//fYwZM6bHVnflzOnW1tbw9vbW+ccSCARMy4erq2uP+7937x5u3rwJAOjfv3+fS3lB0SuBy+XC0NAQfn5+OkeEBQIBiouLIRAI4OnpqfKwa2hoiNdeew3z5s3T6CbYG0tLS4waNQpBQUFoUyxBoutNUiwW4/Lly5g1axbGjBnzwK/Jkydj9+7dzMRaFhYWCA8PR1xc3AO/Bg0ahICAgAcu8NPT07FhwwZ0dHTgnXfewYQJEx648GpubsbFixdRVFQEPz8/xMbGagSBlD1VZDIZkpKSUK22prSRkRGzrfL7+Pv7g81mMz0+QkJCemxtNjAwgI+Pj8ZxiYuLw8iRI+Hm5gZzc3MMGDBAIz0uLg6RkZE6RfgfN7oc+/DwcObY37x5U+PY6+Lq1avYsGED2traMHbsWISGhuL48ePYunWryrhzXRkYGDDdpPl8vsrDtTatra2oUCyDqnxosbS0hKenJ5qbm7F3716kp6dj6NChiIqKAlFMkFReXg4jIyOVslwXpqamGDFiBCIjI9HV1YXGxkadbqB9qampQXFxMVgsFkLUVnTRRiQSISUlBbW1teDz+di8eTPGjh2LuXPnYuvWrRAKhXj77bexYcMGxMbGqgQjjI2NMXXqVHz77bcICQnBmTNn8OGHH+Krr77SeSw6FK0oR44cwaRJkzTKMF1eM2bMwKlTp9R3+0jxeDxUV1fD1tYWjo6OKg9m/v7+8PX1RVNTE1JSUpCdnQ0Wi6V1aajGxkYUFxfD3NwccXFxGpVHZRfjjo4O+Pr69vkwoFRWVoaVK1dqHEtdX++88w4zh0FP7BSzP4vFYtR1m/1cSSKRgMvloqurCzY2Njo1HhBC0NbWBqlUCiMjI53u7crGCfUyWfmysbGBnZ0dRo0apZGmbMnrbXiCm5sbHB0dmYCg+oOrQCBgev7Z29v3WNdR0tPTQ2RkJL7//nscOHAAK1euxLJly5h63fTp02FnZwcLCwtMnDgRq1evxtatWzF27FitD/zq2Iq5avT19SGRSB6q3CktLcW6detw+/ZtxMXFYciQIcjKysLXX3+tdXUAXbBYLObYdHZ26jQnhDLQ1tbWBjs7ux7nZtKGEILk5GRs2LAB+vr6eP/993Uebsnn89HQ0AADAwO4urpqDUSJRCLEx8cjMzMT5ubmiI2NfaAestoIBAJmLLmXlxdCQkI07sfqCgsLkZ2dDZFIhIsXL2LWrFmYMmUKVqxYgYyMDIwbNw7fffcdnn/+eZXric1mo1+/ftiwYQNefPFF5Obm4osvvsDy5csRHx+v9XmiNyUlJfjvf/+rUZbo8ho7dizef/99lJWVqe9WK6JY/WbDhg0ghDANo38luWL4szK4070BwMrKium5nZ+fj3v37qGqqgr2WpZ5VdYleDwegoKCmIbg7lpaWlBRUQETExNmHpm+iEQiXLp0Cc8//7zG8dTlNWXKFOzdu7fXLvgcDgdeXl6wtrZGU1OTyhxbSsprhcPhwN7eXuu10p1UKmUaq83MzB6oAeR/Tben215wFeuSOzk5abSq+/v7Y+DAgWhpacHvv/+OsrIy+Pr69tjlvK2tDVevXkVVVRXmzp2LxYsX45VXXsHixYuxePFiLF26FGPGjIGRkRETOVLX2NiIuro6ODg4ICIiQuXmZGdnh+joaLi4uGjdViqVIjMzExUVFbCzs9P6YMzlcnHmzBmcPn1aZWx0ZWUl0tPTwVLMmKntpiiRSHDq1CmkpKRg8ODBmDt3rkZkTJvCwkLk5OTAyMgI7u7uGp9dIBAgISEBv/zyi8qY7q6uLqY1pl+/fipRfwMDA/Tr1w8BAQEqlQG5XI7i4mL8+uuvSElJYf7enVwuh75iJmBra2uNz9MTlmK8o3LW9Qd9BQcHw8HBQaMw+bs0NDTgxx9/RG1tLRYtWoQZM2boVEFRp5zIBN0mbVJnYmICV1dXcDgcNDc369QKTQhBeno6ioqKEBoaigEDBjzU52ttbYVIJIJMJtOp0vJP0tnZiQbF8lw9HXtjY2O4uLiAw+GgqalJp2Pf3fXr1/Hpp58iOTkZzzzzDFavXo2VK1fCxMQEmzdvxr59+x74uOrr68PJyQlyuRzNzc19zi7f3t4OLpcLW1tb5kFETzHGViAQoKysjFkyic1mQyKRoLKyEjweDz4+PlrL6KqqKhw5cqTHJdrkcjnYbDZMTU1hY2Oj9dg+CEII6uvrUVlZCZZi7HJfJBIJeDwehEIhQkNDMXbsWISEhKBfv3545pln8OGHH2LlypWIiYnRGtw1MzPDrFmzsGHDBnz00UeQSCT4/vvv8dNPP+k8XIjNZsPOzg7BwcEaZZgurwfplfV3aW1tRVNTExwcHDQeSlxcXDB48GCw2WxcunQJaWlpcHZ2RmBgoMYx5vF44HK5TI9B9ftkV1cX8vPzIZVKERUVpXMAwNDQEO7u7hrHUpdXaGgovLy8NOov6qysrODs7Ay5Yoy6+sRVEokEZWVl6OzsRFhYmE7BfRaLxcxhY2Fh8VA90rpTBhMkEskDl1tKDg4OsLe3Z7oDq5c1bW1tKFMs5xwVFaURxFGnp6cHb29vTJs2DbNmzcL8+fOZOt3ixYsxb948ODg4wMrKCtOnT8crr7yCadOmwdPTU+e6BRTlhVwxPPRBFBUVYe3atdi7dy8CAwOxcuVKrFmzBnFxcTh+/Di+/vprlJaWqm/WJ5ZiniE2m830Wu1LU1MTc2xDQ0M1rrXeVFZW4ocffoBAIMDSpUsxefJknR828vPzkZeXBzMzM61LP0Lx2Y4cOYLKyko888wzeOaZZx6qftGd8lwSCASwUkw+3JeWlhY0NzfD0dERo0ePRnR0NIKCgjBy5Ei8/vrr+Pzzz/HMM8/0GICLjo7GihUrsHHjRvTr1w9Hjx7F+vXrVZa404WRkRE8PDw0yhNdX56enjp9XyiGtf34449oaWnBkiVLMG3atAduvOoLIYRp7ND27BMZGYnQ0FAUFxfjwoUL4PP5CA4O1ggCCYVCVFdXo6urCz4+PlrL8Lq6OpSWlsLJyUnnYS56enp/6vkhKCioz+cHPT09ODk5wdrampkUUz0A2tjYiMrKSlhZWTG933ojVMy5xOFw4OzsrPNv/rcgf9LXX39NYmNjyebNm4lMJlNJ6+joIFu2bCEmJibE1NSUuLu7k+3bt6vk6e7ChQtk9OjRZN68eaSyslI9mYhEIrJjxw7i4OBARo4cSVJTU9WzkJ9++onY2dmRgQMHkvT0dPXkXnV0dJBly5YRDodDZs2aRSoqKtSzkJMnT5KxY8eSFStWkLa2NubvJ06cIO7u7sTT05OcOnWKiMVile0IIeTYsWPE09OTODg4kD179qgn9+jHH38klpaWJCoqiiQlJWkc55ycHPL888+Tl156ieTn5zN/T09PJ4MGDSJ2dnbk559/JlKpVGU7bYRCITl48CCJjo4mmzZtUk8mhBDy22+/ERcXFxIXF0cyMzPVk/+VOjs7yU8//UQiIyPJxo0biVwuV89CMjIyyL179wifzydEcT7duHGD/Pbbb+TmzZvM+VJbW0vmzZtH9PT0yJQpU0hBQYHangiRy+Vkw4YNhMVikZiYGJKRkaGeRUN2djaZOHEisba2Jhs3biQtLS3qWXRSVFRE5s+fT8LCwsilS5fUk/+0qqoqsnjxYmJpaUlWrVpFWltb1bMwysrKyIkTJ8iJEydIcXExkUgk6lkYCQkJJCYmhvj6+pLff/9d4zohhJCamhoyd+5coqenR6ZOnUoKCwvVsxC5XE6++uorwmKxSGxsLMnKylLP0qOkpCQSFxdHrK2tyZtvvqmy/4MHDxJ/f3/i7u5OtmzZwpwnukpJSSEODg7E2NiYnDlzRj1ZxdWrV8nw4cPJa6+9RqqqqghRXNvr168nAEhISAi5du0ak5/L5ZJ33nmHmJubk9dff51UV1d32xshMpmMnDt3jsTFxZEPPvhAJU3pypUrJCAggISEhJD4+HiVa6S6upqcPHmSnDp1SmPfPRGLxWTXrl3EycmJhIaGkvPnz6tn0dDe3k6WLVtGDA0NyZtvvqmeTAghpLS0lNy4cYMIBALmb/fv39daln311VfE2NiYhIeHk3v37qknP1Zqa2vJ0qVLiYWFBfnkk09Ic3OzehZGa2sruXHjBjlx4gQpLCzs9boihJDjx4+TuLg4snz5ctLY2KiSJhaLycmTJ4mHhwcxMDAgtra25IMPPiAikUglHyGEnD59mnh7e5OgoCASHx+vnkyOHDlCPDw8SFhYmE6/999JJBKRnTt3EisrKxIcHEwSEhJU0mtqasiUKVOIiYkJ+eKLL4hQKFRJ16arq4tMmTKFACDvvvuuevIDi4+PJ0899RQZM2YMKS0tVU/WSUtLC1m2bBkBQKZNm0ZqampU0uPj40lgYCDx9PQkv//+u8p13traSq5cuUJ+++03cu/ePZVrrCc3btwgERERxNfXl5w8eVI9mbS2tpKEhARy/fp1rce0rKyMPP/888TAwIC89tprWuuOPamsrCSvvvoqMTU1JRMmTCBXr15l0u7du0fGjx9PLCwsyJIlS7TeK/qyZ88eYm9vTwYMGKBT+XHp0iUSEhJC/Pz8yKlTp7TWM7Rpa2sjmzdvJpGRkWTXrl3qyYQo7h/p6emkvb1dPYls3LiRmJiYkMGDB5PU1FSN9xUIBGTjxo3E3NychISEkCtXrqikE0KIRCIh9+7dI7/99htJSkoiXV1d6lk03L59mwwbNoxYWlqSdevW6XS+7N+/nzg5OZEBAwaQkpIS9WQiEAhIfHy8ynlQX19PkpOTNb678lw2MDAgP/zwQ5/l4KPQ0dFBfvjhBxIZGUl++OEH9WRCCCFpaWkkNTVV5ZlELpeT0tJScvr0aXLlypVe7wdE8T5vvPEGGTZsGDl+/Lh6MuHxeOSNN94gAIilpSXp16+f1vOgo6ODvPnmm8TIyIgsWrRI45g3NDSQRYsWERMTE7JkyRJSV1enkv6oVVVVkenTpxMA5PXXXycdHR0q6b/88gtxcHAgUVFROl3TVVVVZMmSJcTMzIy8++67pKGhQT3LI6PZBPIA2tvbkZmZidbWVjg4OGhE+5UT/jk6OkIgEDDdULXh8Xg4evQo8vLyeuxaxGaz0d7eDqlUioaGBqYlT0koFCI7OxuNjY06LTWorr29HXV1dTAyMlIZW6TE5XJx6tQpNDU1Yfjw4SpRq/r6eggEAvj4+KB///4qrV+1tbX47bff8Nlnn0EmkzFLkOmCEIK6ujpIJBIEBwcjMjJS5Tgrx7ZmZmZiwIABKjN3FxUVobCwEHp6erC1tdUpmi6VSlFRUYG6ujqtM+zX1NQgKSkJHA4HQ4cO7bNL7r+BXC5Hamoq9uzZA0II7OzskJ2djYyMDOaVkJCATz/9FNu2bWPG0jU0NOCrr77C7NmzsWnTJubvNjY2GDVqFBwcHJCdnY07d+5odEu6ffs2Lly4AAMDA4wZM0bjXOxOKpUiOzsb27ZtQ2ZmJp577jnMmDGjxwj4o6ZscZbJZH3OipqQkIAFCxbg5ZdfxuXLlzW63XYnV0wgJhaL0dbWplEeQXHsR44cCXt7+x6PfXJyMi5evAhDQ0OMGTOm1zlLlDo7OxEfH49Vq1ahuLgYr7/+Oj755BP4+/szeWbNmoUVK1bA0NAQ33zzDfbv34/GxkaNCHNPbGxsEBwcjK6uLhQXF2t87u7y8vJQXl4Oa2trppwyNDREYGAgpkyZgtdff11lQrva2lrcvHkTIpEIdnZ2GhF5uVyOqqoqZoUI9ck/GxoakJCQAJFIhCFDhsDf318l0n7nzh28+uqrePXVV3H37l2VbXsiEAiQmJiIhoYGBAUFqUz+1RO2Yi12Qgi4XK5K+SWTyXDnzh1s27YN586dY3pw8fl8fP/99/jss8+YlTiUPDw8mHuWSCTS+bd6FB7kuqqqqsL69evx8ssv4/fff1fpzaaOEIKcnBxUV1fDzs5Oo/u4vr4+goOD4eXlBbFYDLFYDCvFzPbqXF1d4ebmBh6Ph8LCQpXjeevWLWzatAlcLhfPPfecyvjdx4GBgQHCw8MRHByMsrIyXL16VWW8+b1795CZmYmAgADExMT02dJDCEFFRQWqqqpgZGT0l83k/WdZWVkhNjYWDg4OSE1NRWJiInMuicViJCQkoLa2FgMHDkRQUJDKdV5RUYFPP/0Us2fPxo4dO9DY2Nhtz9qxWCzIZDKIxWKtrXMVFRVYs2YNlixZgvj4eJU0iUSCGzdu4NatW3BycsKIESO0tl6qk8lkKC8vx/r163H8+HGMHj0a69atU+kyHxMTg9WrV2PIkCH47bffsGnTJuTl5T1QV/GwsDB4eXmhqqoKOTk5fZYfd+/eRWlpKSwtLWFvb6/1eKhTDhM8cOAADA0NYWVlhaysLJX6ycWLF/HRRx/hp59+0ujWLJFIUF9fD7lcjtDQUERERDDvK5VKUVZWhh9//BGbN2+Gl5cX3n33XQwYMEBlH1D03tm+fTtmz56Nb775RqPuqE16ejqys7Ph6OiIqKgojR5B2piamsLAwAAtLS0ak1lWVFRgz5492Lt3L1OWS6VSXLx4Ee+99x5u3Lihkt/Z2Rmurq4wMDBgJqJ8nMgUyx3u3bsXbDZb62975coVfPzxx9i5c6fKfVkul+P69etYuHAhPvnkkz6HONXW1iI7OxtyuVzr85ednR3CwsJgamoKPp8PIyMjrT1UjI2NmXnWlHMKKLW3t2PPnj345Zdf4Ofnh5kzZ2p9r0fJ0dERAwcOhImJCW7duoU7d+4waW1tbUhISIBAIMCQIUO09pRUV1VVhYyMDBgZGSEqKqrP1Q7+Tpo1ZB2JRCIkJCQgLS0NNTU1KCsr01qJ8PDwYNZPtbKy0vixiWItxTNnzuDcuXMQiUQwMjLSKGQlEgkKCgpw48YNNDc3o66uDrm5uRAKhSCEQCwWIzExEbdv3wYU3YSUy9/1hRACoVDIjF+USqWQy+VoaGgAVzEja0ZGBtatW4fDhw/D3NwcLi4u0NPTY24kd+7cQXNzMwwMDJiJBEtLS3Ht2jVs3LgR77//Pvh8PjOnQW/zIChJFOtu3rt3D52dnWCz2cwKB8r979ixA99++y2EQiHc3d2ZiccKCgpw5coVNDc3QyAQID8/Hzwer8/ucTKZDFwuF62trTh9+jR2797NvGdKSgq++OILHDlyBLGxsXj55Zd7DOj8m9TX1+PkyZO4efMmMjMzsXTpUgwdOlTlNXHiRJw7dw4dHR3Mg6fyvITaJEDKB8uRI0eioaEBP/zwA86ePatyrm3btg0pKSl4+umn8corr2g8zMtkMrS2tqKqqgoXLlzAqlWrcPXqVcydOxefffbZnwrMEEKYbqR9nS+6EgqFaGlpQUNDA9LT01FQUICOjg7cuHEDCQkJaGhoYIYedCeTySAUCpkhCd0rUHK5HAKBAE1NTaivr0daWhrqFOtDX7hwAenp6eDxeEzQEIpue2PHjsXIkSPB5XI1jn16ejq2bduG1NRU5tjrco4XFBRg48aNyMrKwpIlS7Bs2TKNrrEcDgezZ8/GJ598Ag6Hgy1btjzQ2EMrKyuMGjUK5ubmuHPnjsbDKroFg+Lj45kJWrvP0zFhwgTs378fixYtYr6X8qamnNwpPz8f5eXlKmNpiWIW+NbWVly5cgU//PADysvLweVycf/+fWzatAk//fQT/Pz88Oqrr2qUb3K5HEKhEEKhsM9ziigmRktKSsLdu3chl8tRX1+PwsLCPuc+MDIywoABA+Dv74/c3FxcvHgRXC4XDQ0NuHr1Kr766itUV1dj9uzZTIC4srISRUVFuH79Og4ePIji4mJwuVzk5OQgPj4eMpkMsbGx8PHx0alC/ncSCoVobW1FQ0MDMjIyUFBQAIFAgKSkJFy/fr3H60pZRil/456+l0Qiwb1795CUlMTMx9Da2qrxIOPk5IR+/fqBzWbDxMSkx5VqPD09meUgL126hMTERHC5XFy7dg1btmxBQUEBJkyYgBkzZvQ4gemjFBwcjJkzZ8LS0hIHDx7EwYMHUVNTg/j4eOzfvx96enpYuHAhBg8erL6pBolEgqSkJNTW1iI2NvahZ5vvThkElXRbSeFhDBs2DFOnTkVzczN+/PFH/P7776irq8OxY8dw5swZBAUFYdGiRRr3me73vL4+g0wmQ2NjI3JyclBfX4/6+nrk5eWBz+erlBHK+U+Kioqwbds2XLlyhSmvz5w5g507d0IikWDOnDl46qmndBp61N7ejp07d+Lw4cMYPnw4VqxYofX4Dxw4kFkd4JdffsEvv/yiU1d+pYiICISHhzOTX/b0gCmVSpGRkcHMTN/S0oL8/Hy0t7f3egyhKL9OnjyJ1NRU3LlzB/Pnz8eQIUNU6ifPPfccrly5ojKxIhS/UWZmJlJSUiAUCqGnp4fa2lpwuVzk5+fj/PnzWLlyJTZs2AB7e3t88cUXmDt3rtYHP0IIcy/rXt/Rpnt9vrW1FXw+H/n5+TrNKRUYGIhBgwaBz+fjjz/+QFFREbhcLtMIcuLECYwfP575Pdva2lBYWMgsNXjz5k1wuVxUVVXhypUrqKysRIhimFhfQbu/W01NDU6cOIG7d+8iNTUVixYt0vhtp0yZgkuXLjHPCEosFgt6enoghIAQ0mMZTwhBa2sr/vjjD+Tm5jJBSW31ksDAQPj6+gKK+T+0NbLq6ekhLi4O/fr1Q2FhIc6ePYvy8nJUVlYyE5FaWVlh3rx5GDp0qE4Nk38nfX19jB8/HuPGjUNRURG2bt2K+Ph4VFdX48CBA4iPj8eQIUOwYMECnYZsFRQUIDU1lZkQUT2A/kipdwnQ1eXLl8moUaOInp4eAUBiY2PJ1atXNbrQ8Pl88uWXXxJzc3Py0ksvkbKyMpX0zs5Osn79euLl5UUAEBaLRUaOHEmuXLmisq/c3Fwya9YsYmxsTAAw73n27FkiFovJvXv3yLhx44i+vj4BQAwMDEhMTAw5f/68xmdSJxKJyB9//EHGjx9PWCwWAUCcnZ1JWFgYCQ0NJaGhocTPz4+YmJgQFotFli5dynRrKSsrIx9//DGxt7cnAIiZmRkJDAwkoaGhJDg4mAQFBZFx48aRVatWkYSEBI3uMD2Ry+UkOzubLF68mJiZmREAxNramgQHBzOfKTg4mNjY2BAAZNSoUSQ3N5cQRZeTd999l5ibmzPHytHRkaxfv77X7tZE0X1qy5YtZPDgwcTGxoY4Ojoy7xcWFkYGDhxIPvzwQ5KZmanRTezf6saNG2TQoEHMseztNXv2bFJcXEyIoqvxxIkTCQAyffp0ja7+5eXl5PPPPydhYWHEy8tL5XeNjIwkn3zyidYubkTxG7/zzjtk4MCBZMCAAWTZsmXkwoULhMfjqWd9YIWFhWTevHkkKCiIXLx4UT35gUmlUnLx4kXy2muvkUGDBhEvLy9iYGBAABA9PT3i5uZG+vfvT9544w2SmJioMlRl7969xNLSkpibm5Mff/xRpTtWQ0MD2b59O5kxYwaJiIggjo6OzO9gampK/P39yciRI8mGDRtUyh25XE7KysrImjVrNI59UFAQiYqKIp9++ukDdaFNTU0lb7/9Nvnss8+0Dh3qTiAQkG+++YaMHj2anD9/XutwIW1kMhlJT08noaGhxN3dnZw+fVo9CykqKiJLliwhJiYmBACxsbEhW7du7fG6l8vlZOfOncTNzU3l2C1fvlylq75EIiF79+4lo0aNIvb29sTOzk6lLIqJiSFvvvkmSU5O1jrU6Pjx48TGxobY2tqSEydOqCerEIlE5MiRIyQsLIwpjw0NDcnIkSPJ2bNntQ7t6K6trY0cO3aMjB8/nnh6epLQ0FDSv39/MnnyZLJ69WqSkpKiso+ioiLy/vvvEw8PD+Ls7EyCgoKY6zA6Opq5Dvt637+bTCYjf/zxB1m6dCmJi4sjXl5exNDQkLmuXF1dSXR0NHn99ddJQkKCyn3w/v37ZOrUqcTd3Z38+uuvPZ6D2dnZZM6cOcx+3dzcyP79+zW6RUokEvLrr78SZ2dnMmzYMJKSkqKSriSTyUhpaSn56KOPiK+vL/H29maO9ZgxY8hXX31FCgoKtJ5Dj4umpiZy4MABMnjwYOLo6EhCQkKIt7c3GTJkCDlw4IDOQ3uamprICy+8QPT19cmaNWt06jLdl+vXr5MhQ4aQESNG9Hjv0IVMJiNlZWVk7dq1JDAwkLi5uZHQ0FDi6elJpk+fThISErQO8cjMzGTulQsXLuy1LKyuriarV68mnp6eTNnj4OBAPvjgA5Xha11dXeTYsWNk4sSJxNvbW+NeOX78eLJt2zZSW1ursv/eNDc3ky+//JIsW7ZMYyiHOrlcTq5fv05Gjx5N1q9f/8BD6/bt20dsbW1JTEyMRv1XqaioiEyfPp0YGRkRAITNZpOwsDCyc+fOPuuMv//+O+nXrx9zDHt7dR8iIZPJSFpaGnnppZeYerWtrS1zbAMDA0lYWBiZNm0a2bx5M0lPT++1Lt3W1kZeffVVAoA8++yzhMvlqmdhZGZmkhkzZhBTU1Pms3l7e5Ovv/66zzqMSCQi6enpZMmSJSQwMJAEBQWRsLAwMnLkSPLGG2+QU6dOqRyz1tZWsnv3bhITE0McHByYMickJISEhoaSOXPmkOvXr+s0/ODvdunSJRIdHa3xO2p7LViwgJSXlzPbymQysnfvXuLh4UGmTJnSY3nQ0dFBDh48SHx9fZl9TZ8+neTk5KhnJVVVVeTVV18lZmZm5PPPP++xXiEUCsn58+fJxIkTiZOTEwkODiYhISEkKiqKzJs3j5w4caLPIQmPkkQiIffv3yfvvfce8fLyIl5eXkw5v3DhQpKenq5TfaCrq4ssW7aMsNls8s477/R5Lf/dWKS3MF0vSktLkZ6eDqFQCDabzUwI4+npqRJhVHYdzcjIgLe3N4KDg1UitBKJBHfv3kVxcTEMDAxACGG6SnTfF4/Hw61bt9DW1gYDAwPI5XKmS56fn5/K8kLK2WBZLBYGDhwIHx8fra0RSjKZDBUVFcjMzGSiaJ2dnSqtTXp6ekyXl6CgIKZXQ2trKzIzM1FdXc10wRQIBJDL5WCxWDA0NIS3t/dDzZqubNXh8XjgcDgQCoXo7OxkIqvK/ZuYmMDT0xNRUVEwMjJCW1sbUlNTUVFRAWNjY8jlcnR1dSE4OBjR0dG9RjllMhlqampQq1jPubm5melFYWhoCDc3N0RGRmq0bv6b1dbWIiMjQ6eJ2zw9PREWFgYzMzMIBAKkpaUxyzCGh4drRM6bm5uRlpaGyspK5nzjcDjMso3qPWaU2trakJycjOrqapiamqJ///4q3c3/jI6ODuTm5qK5uRmRkZE6dansjVwuR2lpKfLz85muhiYmJuBwOJDJZMw57ebmhrCwMLi5uTHXa3l5OdLT0wEA/fr1g6enJ9O1uLOzE7m5uSgtLUVzczP09fVholh+SCQSoaurC8bGxvDz80NoaKhGLwrlsVeuQw3Fsffw8EBMTMwDtUDy+XzweDxYWVnptJ1ysidvb2/Y2Nj0GJ1XJxQKsXbtWnz//fd49tln8eWXX6q0tjc3NyMlJQX19fUwMDCATCZjJsHp6bpPS0tDVlYWjIyMmGPn5eWFqKgo5nwliqFINTU1aGhoQFNTEwQCAQgh0NfXh7OzMyIjI1WGIHVXU1ODe/fugcViISYmptelTGUyGfLz85Gamgo2mw0OhwOJRAJDQ0NERERoDC/QRiKRIC0tDTk5ORCJROBwOHByckL//v013rurqws1NTUoKSkBl8tlvheHw4Grqyuio6M1ejQ8DohiffW8vDzU19dDJpPB2NgY+vr6zHUll8uZ68rd3Z25rpRLeQ4YMAArVqzQOlETFPege/fuMdcXIQTh4eEIDAzUaGnl8Xi4d+8eLC0tER0d3WtLR21tLe7evQsul8t8bj8/P4SHh2tcp48jiUSClJQU5Ofno6urCxwOB35+fhgyZEiP11l3EsVa7f/5z3/g4eGBzZs3a+1W/aAaGhqQk5MDPT09xMbGagzleVDNzc24e/cu0yJobGyMqKioHpeW4/P5SElJQWNjI3x9fRESEtJjt+729nakp6ejpKSEKXs6Ozvh7u6OYcOGqUzK2NHRgZKSElRUVKC+vp5pSTczM0NgYCBCQ0MfqGutRCJhllbuaVLo7mQyGbKysmBlZdXjJHk9aWhowEcffYRff/0V7733Hj7++GON36W5uRmJiYlM/VbZ2y04OBhhYWG9TvhWUVGBrKwsnXq7+vn5MZNvEsUkqxkZGWhubgabzYZQKERXVxcIIdBTTBobEBCAiIgIld9DG2UvhpKSEri5uaF///49bsPlcpn6vLLXqlQqhb+/v8bk3T0pKSlBamoqmpqaQAiBlWKm+vDwcJX7g1QqBY/HQ2lpKaqrq9Ha2gqZTAaWYmLZsLAwhIWFqez7cVFZWYmsrCwIBAL1JA0+Pj4I6bZEa3NzM9555x2kpaXhrbfewrx587T+HsreGOnp6eBwOCCEwMbGBtHR0RqrSEmlUuTl5aG0tJSpj/X0bCWVSpGVlYXMzEymbmtvb4+wsDAEBgb2eQ9/HKjfpywsLDBgwACdh2sdP34cb7/9NhwdHbF7925ERkY+Vt/7oQMAFEVR1N+vuLgYH3zwAeLj4/Huu+/ik08+eaxuKtTj7f79+/j+++/R0tKC999//7Eba/8kuHXrFt555x1wuVysXbsW06dP7zVgQv2zJScn47333kNpaSk2btyIuXPnqmehqL+MQCDAuXPnsHfvXgwdOhSLFi3SeJin/rcyMjLw2muvoaysDOvWrcPChQvVszxy7NWrV69W/yNFURT1eLKxsYGzszNKS0tx+/ZtCAQCmJiYwNra+oFapqgnU1lZGdhsNqZNm/aXtDpTuqutrcXly5exY8cOVFdX47XXXsOcOXMeqPWa+udxdXWFk5MT8vPzcffuXXR2dsLKygrW1tY9tqBS1MPq6upCaWkpwsLCMGPGjCeqx+6jVl9fj2vXrmHTpk2or6/H0qVLsWDBgl578TwqtAcARVHUP1BmZia+++473L17F0OGDMHChQsRGRlJgwAU9RhqbGzE8ePHsW/fPnA4HCxcuBAzZ87U2i2X+veRy+VITEzE999/j9LSUowbNw7z58/XaXUTiqIef83NzTh8+DCOHDkCsViMV199FbNnz9ZpSMujQAMAFEVR/1CNjY24efMm7t69i6ioKIwZM+YfMYaaop4khBBmOTZjY2M8/fTT8PHxeSxbhaj/HblcDi6Xi+vXryMjIwODBw/GtGnT1LNRFPUPlJubiwMHDsDBwQHjxo2Dv7//Y13G0wAARVHUP5hUKgWXy4W+vj6sra01JmejKOrRa2trQ1tbG8zMzGiQ7gknFArR0NAAY2PjHif6pSjqn6WjowONjY2wtLR84EnfHwUaAKAoiqIoiqIoiqKoJwCdfYSiKIqiKIqiKIqingA0AEBRFEVRFEVRFEVRTwAaAKAoiqIoiqIoiqKoJwANAFAURVEURVEURVHUE4AGACiKoiiKoiiKoijqCUADABRFURRFURRFURT1BKABAIqiKIqiKIqiKIp6AtAAAEVRFEVRFEVRFEU9AWgAgKIoiqIoiqIoiqKeADQAQFEURVEURVEURVFPABoAoCiKoiiKoiiKoqgnAA0AUBRFURRFURRFUdQTgAYAKIqiKIqiKIqiKOoJQAMAFEVRFEVRFEVRFPUEoAEAiqIoiqIoiqIoinoCsAghRP2PFEVRFEVRFEVRj5JYLAYA6Ovrg8ViqSdrkEqlEIvFkMvlAAAWiwUOhwM2mw09PT3o6emBEAKZTAapVAq5XM7k7Z4ul8uhfETqvi2Hw9Hpc1DU44wGACiKoiiKoiiKeqzcuXMH3333HUxMTPDGG28gMjJSPYuKiooKHD16FBcvXgSfzwcAGBgYwNXVFcHBwZgwYQIGDRqE2tpaHDhwABcuXIBIJIJUKgWLxYK+vj709fUhk8mYIAKLxYKBgQFMTU3x1FNP4bXXXoONjY36W1PUPwodAkBRFEVRFEVR1GOjvLwcP/74I3755RecOnUKJSUl6lkYYrEYly5dwocffoiLFy8iMDAQU6ZMwZQpUxAQEIArV65g+/btSE1NBQA0NjbizJkzSEhIgI2NDaZMmYIJEyZAKpUiISEB2dnZ6N+/P6ZMmYLBgwejpqYGf/zxB4qLiyGVStXfnqL+cWgAgKIoiqIoiqKox4JMJsPdu3dx7do1GBsbw9LSEiKRSD0bIyUlBRs2bEBiYiLGjx+P9evXY8WKFVixYgW+/PJLPP3005BIJGCz2QCA0tJSNDY2YuzYsdiwYQNWrFiBzz77DGPHjoWJiQmmT5/O/P3rr7/G1KlT4enpiSFDhsDCwkL97SnqH4cGACiKoiiKoiiKeixkZGTg+vXr8PHxgZ+fH8zMzHocd9/V1YULFy4gMTERcXFxmDt3LszMzJh0a2trLFiwAK+//jpiY2MhFouRlpYGqVSKF154Af7+/gCAnJwcpKWlwcjICOHh4dDT+79HpM7OTrDZbNjb2yMsLAxGRkbMvinqn4oGACiKoiiKoiiKeuQkEgnOnDmDgoICTJ8+Ha6urhAKhZBIJOpZAQA1NTUoLCyEVCqFpaUlTExMVNINDQ0xduxYrFmzBtHR0ejq6oKJiQmeeuopjBo1CgYGBgCA3NxcZGZmwszMDG5ubszfWSwWXFxcEB4eDkdHR5V9U9Q/FQ0AUBRFURRFURT1yCUmJqKgoABDhw7FiBEjYGJigoaGBjQ3N6tnZbBYLBBCwOVy0dTUpJ4MKGbyZ7FYMDMzw+LFi7F+/Xq4u7sz6fX19Whra4ONjQ2cnZ2Z4QJmZmZ49dVX8dVXX8HNza3bHinqn4sGACiKoiiKoiiKeqQaGxtx+PBhdHZ24vnnn4eFhQWzXJ9MJlPPDgBwdXVFSEgIACA5ORk///wz6urq1LMx2Gw2bGxsYG9vzwwrkMlk4PF4kMlk8PPzg6enJ5Omp6cHKysrODg4gMPhqO2Nov6ZaACAoiiKoiiKoqhH6tq1a0hLS0NwcDBCQkIgl8shFoshkUjA5/PR2dmpvgmMjY0xfvx4jBo1Cs3NzdixYwdWrFiBO3fuqGftUfdhBO7u7rCzs1PPQlH/KjQAQFEURVEURVHUI9PS0oJz587By8sLM2fOBJvNhoGBAczNzQEAbW1tEAqF6psBAKKiovDpp5/i+eefR2trK/bs2YNVq1bh4sWLPc4d0F1JSQmKi4thaGgIR0dH6Ovrq2ehqH8VGgCgKIqiKIqiKOqREIlEuHz5MlpbWzF79mz0798fAODo6AgHBwcYGBhAT0+PmZlfnYGBAUaOHIkvv/wSr732GpycnHDp0iV89tlnuH37tnp2DbW1tWhqaoKbmxsCAwN7XHGAov4ttF9JFEVRFEVRFEVR/2NFRUXYtWsXpFIpOBwOiouLUVRUhLS0NHC5XMhkMubVG19fX3z++ef44IMP4Obmhtu3b2Pv3r2or68HIUQ9O6OmpgZ8Ph9+fn4ICwvrMdBAUf8W9AynKIqiKIqiKOpvJxAIkJycjIyMDCQnJ+Pdd9/FpEmTMHnyZLzwwgv4448/0NXVBT6fj7a2NvXNNVhaWmLRokV45ZVXwOFwkJSUhNTU1B6DB11dXaivr4dIJIK9vT3s7e3Vs1DUvw4NAFAURVEURVEU9bdLTk7G4cOHMWPGDGzfvh3r16/HmjVrsGbNGqxcuRJTpkyBsbExBAIBpFKp+uZamZmZYfjw4QgMDER7ezv4fD7kcrl6NkBtAkAHBwcYGxurZ6Gofx0aAKAoiqIoiqIo6m/V1NSEq1evorW1FfPmzcOsWbMwY8YMzJo1C7NmzcJLL72EQYMGwdDQEIQQsNlsAIBcLkdeXh727NnT42z/EokELBYL5ubmsLGx0bqEHyEEXC4XVVVVsLOzQ0hICJ0AkHoi0AAARVEURVEURVF/G0IIrl27hsTERMTExMDf3189C7q6uiAWi8FisdDZ2QmBQAAoHu5v376N7777Dnfv3lXfDADQ0NAAHo8HZ2dneHp6ah3XL5PJkJeXh7q6Ovj7+yMkJIROAEg9ETSvBoqiKIqiKIqiqP+R6upqHD58GJWVlYiLi4ONjY16Fujp6aGtrQ0SiQT19fVobGwEFAGA8vJycLlcFBQUgM/nq2xXWlqKa9euwcjICKNGjYK7u7tKupJAIMDNmzfR2NgIGxsbrZ+Bov6NaACAoiiKoiiKoqj/OblcjsbGRvz222+4dOkSxGIx5HI5Ojs7VfIJhUKkpaXh2rVraGtrQ1VVFbKysiAQCCCTydDU1AQ+n48TJ07gxx9/RGlpKUpLSxEfH481a9bgwoULeOqppzB37lyYmZmp7BsAOjs7ce3aNSQnJ4MQgqqqKmRmZqK9vV09K0X967BXr169Wv2PFEVRFEVRFEVRf6X29nZs3rwZ27dvR2NjIyQSCQQCAQIDA+Hq6sp01c/MzMS6deuQmJgIqVQKsViMtrY2+Pj4wMPDA3V1dWhtbQWPx8OdO3dw/PhxHDp0COfPn4dQKMQLL7yA119/HX5+fuofAQBw+/ZtrFq1Cnl5eZDJZODz+aisrISjoyP8/f21DhmgqH8LFultYUyKoqjHkFwuR1dXF0xMTOh4PapHUqkUIpEIpqam6knUE6qzsxNsNhuGhobqSdQj1tXVBRaLBSMjI/Uk6l9ELBYjIyMD1dXVMDAwACEExsbGCA0NhZOTE3NP5/F4SE9PR1dXF/T19SGXy2FgYIDg4GC4uLiAx+Ohvr4eTU1NaG1thVAoBAAYGBjAxcUFISEhsLa2Vnv3/6+2thZpaWmQy+XgcDiQSCQwMDBAYGAgvL29ad2C+lejAQCKesJJJBIcO3YMvr6+GDBggHryYyU/Px/Hjh1De3s7Jk2ahGHDhtGb9L+UVCrFxYsXcfv2bQwdOhRPP/20epY+tbe349KlS0hKSkJQUBCef/552Nraqmej/uUIIbh8+TKuXr0KS0tLzJo1C76+vurZqIdUWFiI06dPg8PhYM6cOXByclLPopOUlBQcP34choaGmDhx4mN/P6IoivqnogEAivofEYlEyMzMRHp6OgQCAQwMDODr64uYmBjY29urZ39gIpEI9+7dAyEEwcHBsLOzU88CKCbaSUlJ0ZgkR6mqqgpHjx7F0qVLsWTJEvXkx4JEIsHly5dx9OhR5ObmIioqCvPnz8egQYOYPLm5uUhNTYVEIoGenh6EQiG6urrQvYhjs9kwNTWFvr4+oqOjER4eDiiO5c2bN1FeXs50+5NKpfDy8sKQIUNU1gXm8/lISkpCXV0dOBwOWCwWQkJCEBMToxKMaGtrQ0pKCoqKimBoaAiRSARXV1cMGDAADg4OTD6lqqoqJCcno7q6mvmbtbU1nJyc4OnpCU9Pz8eiJVsgECA9PR3379+HoaEhMztzaGgoBg8ezCy1lJ+fj4yMDKZVTyqVIiAgAP3799fpexQXF+P1119HRkYGPvvsMyxduhRQ/Fa3bt1CaWkpsySUVCqFp6cnhgwZAhMTE2YfAoEAly9fxr59+8DlcjF06FDMmTMHUVFRTJ7HUV1dHe7cuYPS0lIAgI2NDSIiItCvXz/mOz8ouVyO3Nxc3Lt3Dy0tLWCz2XB3d0dMTAw8PDzUs/eoq6sLSUlJaGlpQXR0dI/dax8XdXV1OHHiBM6ePQuhUIixY8di7ty58PT0VM+qob29HUlJSejq6tLpOBFCkJWVpVLestlsODg4wNnZGd7e3vDw8IBIJEJeXh7u37+PtrY2puXSyMgIJiYmkMlkzJrnLBYLxsbGMDExgbOzM2JjY2FlZaX2zj1raGhAcnIyeDwe9PX1IZPJQAjB0KFDERgYCCh+07S0NBQWFoLFYjEtrZGRkQgJCem1K7REIsF3332HdevWYciQIdi2bZvKceJyubh16xaampqY99fT00NERITGdZiVlYWff/4Zd+/ehbOzM2bMmIGJEyfCwsJCJd/jqKKiAnfu3EF1dTVYLBYcHBwQGRmJ0NBQ9aw6yc/Px71798Dj8QAAhoaGCAwMRGxsLCwtLdWz96i2tha3bt2CqakpBgwYQAOgFEX9H0JR1F+ura2N7N27l0yePJmEhYWRyMhI4uHhQYKDg8nq1atJSUkJkUql6pvprKKiguzcuZMMGDCATJs2jdy+fVs9C+P06dPEy8uLAOjxxWazyZ49e9Q3fSwIhUJy5MgREh4eTgYPHkwOHjxIBAKBejZy9uxZMnjwYOY7mZubk+DgYBIZGcm8fH19CZvNJkZGRmTr1q3Mtm1tbWTt2rXEzc2N2d7JyYmsWLGCNDc3q7xPVVUVefPNN4mVlRWT98UXXyQ8Hk8lX21tLfnkk0+ImZkZAUBMTEzI66+/TvLy8lTydXV1kcTERPLee++RwYMHk379+pHIyEjSr18/4uLiQtzc3Mibb75JioqKVLZ7VBobG8n69etVvr+trS35+uuviVAoZPKdP3+eDB8+nMnj4uJCNmzYoHGctBGJROTAgQPE3d2dzJkzhxQWFjJp7e3tZN26dSq/laOjI/nkk09IU1OTyn6UampqyFdffUV8fHzI008/TVJTU9WzPDays7PJ8uXLSWxsLImIiCDh4eHE1dWVTJgwgZw4cYLw+Xz1TfokEonI2bNnyezZs0l4eDhzLfj4+JBly5aR9PR0IhKJ1DfT6ty5cyQgIIDY2NiQXbt2qSc/VhoaGsgHH3xAPDw8yIsvvkhu3rxJ5HK5ejatRCIR+eWXX4i7uzuxs7Mj+/btU8+iorGxkRw9epTMmzePREdHM2VOUFAQcXR0JOHh4eT7778nUqmU1NbWksWLFzPXTkREBImMjCQuLi7MOe3t7U0iIyNJaGgoc61NmDBBo/zoS25uLnnxxReJoaEhs++IiAhy6dIlJk9LSwvZvHmzyjUVFxdHTp061ed9qqSkhEyZMoX4+PiQX375RSN/bm4umTt3LjE2NlZ5/19++UUln5LyXJ0wYQIJCAgg33//PWlra1PP9li5ffs2eeONN0hUVBSJjIwkISEhxN3dnUyfPp1cvnxZ6/2qJ+3t7eTSpUtkwYIFpF+/fsz9wMvLiwwYMIB88803pK6uTn0zrZT3NTMzMxIbG0vu3LmjnoWiqCcUDQBQlIJEIiEtLS2kqamJiMVi9WSdSSQS8uuvvxIfHx8yePBgcvnyZdLc3ExOnTpFBg4cSCwtLcmyZctIZWWl+qZ9am9vJ7dv3yZLly4ltra2BAB59tlnSUZGhnpWxunTp4mfnx9hs9nE0tKS2NjYaLwCAwPJ0aNH1Td95GQyGbl8+TIJCwsjQUFB5Ny5c0QikahnI0RRcTx06BDx9PQkBgYGZOnSpSQ/P580Nzczr/j4eDJw4EASHh5Ozp8/z2wrl8sJj8cj7777LtHX1ydmZmbkww8/JHV1dRoPDDKZjBQVFZE5c+YwleoBAwaQ+Ph4jXwFBQVk3rx5xNDQkLz00kskPz9fpYIskUjI2bNnSVxcHOnXrx/ZuXMnqa+vJ83NzaS+vp689957RF9fn4wZM4YUFBSo7P9RUX7/hQsXEj09PaKvr0/Wrl1LmpubVY6VSCQi27dvJ3Z2dsTc3JysWLGCNDQ0EJlMprI/bWpqashbb71FXFxcyMaNG0lXVxeTJpfLSWNjI3NsTE1NyfLly0ltbW2v+xaJROTLL78ktra2ZOrUqQ91/Wkjl8tJZ2cn4fF4f/pBhcvlkvnz5xNbW1vy3nvvES6XS6qqqsgnn3xCbGxsSGhoKDl8+LD6Zr2SyWQkISGB9O/fnwQGBpIDBw6Q5uZmkpSURKZMmUJMTU3J9OnTSWZmpvqmGnJzc8mkSZMIFAGyxzVoSBTX1tq1a4mNjQ157rnnSFlZmca13Jv79++TcePGEQDEysqKbN68WT0Lo6mpiXz99dfE39+fTJo0iVy8eJEpc1JTU8nTTz9NOBwO+eCDD4hMJiP3798no0ePJnZ2dmTjxo2Ex+MRLpdL1q1bR4yMjEh4eDizj+LiYrJs2TJiYmJCli5dSmpqatTfvldSqZRcvnyZDB06lAAgQUFB5PTp0yoBH7lcThoaGsiSJUuInp4eCQwMJL/99ptKQK8nJ0+eJCEhIWT06NFagxNSqZSkpqYyx9LPz4/s27dP5ZpWJ5PJyL1798jgwYOJl5cX2bdvX6/X9qMil8uZAIiTkxNTDhYWFpKlS5cSc3NzMmjQIHLx4kX1TbVSBj6Dg4NJREQE2b17N6mrq2PqD8OHDycODg5kxYoVfQZSJRIJOXbsGPH19SUAyJAhQ8i9e/fUs1EU9YTquV8XRT1hioqKsGLFCixduhRFRUXqyTpLT0/Hrl270NnZiXnz5mHEiBGwtrbG5MmTsWjRIhBCcPr0aWRnZ6tv2qvm5mb88MMP+Oijj5Ceng42mw0WiwVnZ+de166VyWQwMzPDpEmTcPLkSSQmJmq8Tp8+jbFjx6pv+sgVFBRg165dqK+vx1tvvYUxY8YwXczVGRgYwM3NDVZWVnBycsKIESMQGBgIa2tr5hUeHo5+/fohMjJSZRgGi8WCnZ0dnJ2doa+vDz8/P4wfP15lQiIlPT09ODk5wdnZGf3794erqytqamqQnJwMqVSqks/R0RFeXl5wdHTE8OHD4e/vr9KFu6KiAjt37kRBQQGee+45zJkzB46OjrC2toajoyOmTZuGsWPHwsvL66HH1f7V9PT0YG5uDjMzMxgbG2PUqFGYPHkyrK2tVY6VgYEBLCwsYGhoyBxPe3v7XrsTK1VXVyMjIwN2dnYICwtTmRSMxWLB1tYWzs7OMDAwYPbt7Ozc674NDAywaNEizJgxAzdu3MDevXvR2tqqnu2BSaVSnD17Fi+//DK2b98OmUymnkUnUqkUv/zyC37//XdER0dj/vz5cHBwgJubG1577TWMGDEC9+/fx/nz59HU1KS+eY8qKiqwY8cOlJSUYMaMGXjmmWdgbW2NIUOGYOnSpXB0dMSFCxeQnJysvqmKqqoqfPvtt7h+/ToAwNjYuNfj/SjJ5XJcu3YNhw4dgre3N95++214eXlpXMs9aW9vx507d9DQ0AAXFxfY2NhALperZ2PcunULu3fvBofDwcKFCzFmzBimzImMjMTMmTMRGxsLLy8v6OnpoaCgANXV1Zg8eTKee+452NnZwdLSEiKRCAYGBhg0aBCioqJgbW0NX19f+Pr6wtPTEwMHDnzgLtxsNhuWlpbQ19eHhYUFpkyZguHDh8PAwIDJw2KxYGFhAXNzc5iYmGDAgAEYPnx4nxMlisVipKamorW1FWFhYVqHSLDZbLi5ucHW1hZsNhsDBw7E8OHDe53oT09PD/3798dbb70FFouFvXv3IjMzUz3bI9fR0YGff/4ZiYmJeOqpp/Diiy/C2toa/v7+WLZsGfr37487d+7gjz/+QFdXl/rmGm7cuIFt27aBx+Nh5syZmDlzJpycnGBtbY1Jkybh5ZdfBgD8+uuvGvcbdYmJifjuu+9QUlICKIYQ6Hr+UxT17/d43r0p6hEQCATIy8tDZmYmBAKBerLOlOP+/fz8MHz4cOaBlc1mY/DgwQgJCUFdXR1SU1PR1tamvrlWXV1d+PXXX5GTk4Pp06fjo48+wvjx42FiYgIOh6NSmVNHCIGenh7s7e0RGxuLkJAQjVdgYKDO4wrFYjH++OMP7N+/n6lc9EYikeDEiRM4fPiwyvh2Xdy4cQO///47QkNDMXny5D4rpK2trejo6IClpSXc3d3Vk2Fubo633noL77//Pvz9/VXSxGIxmpubQQhBUFAQwsLCVNK7a29vR0tLC6KiojBs2DCIRCKkpqaiublZJV9rayva29vh6emJ4OBglQcmsViMzMxMpKSkwMzMDLGxsRprFUdHR+Orr77Cf/7zn8dqHGxbWxsqKiogk8kQHBwMZ2dn9SyAYvyxQCCAra0t3Nzc1JO1ksvlyM/PR2VlJYKDg+Ht7a2eBSKRCE1NTZDL5X3+Vt3Z2tpi6tSpMDc3x6+//vqnAn1KhBDU19cjNTUVZWVlKnNOPIjm5mbEx8ejo6MDw4cPVxk77Obmxjw0ZWZmoqCgoNcH0u6Kiopw69YtODg4YOTIkSrXeUxMDPr374/Ozk6kpKSgvr5eZVulrq4uXLx4EVeuXMHw4cPh5+cHuVz+wN+1vLwcBw8exIULFyAWi9WTNRQUFGDPnj24efOmTg9QSp2dnTh06BDy8vIwfvx4DBkyRD1Lr5KTk3H06FEMGDAAYWFhvZavLS0tSE5ORnFxMTw9PREdHa0S5NPT08OUKVOwZcsWTJkyBUKhkJm/4tlnn2XmIsjJyUFiYiJYLBb8/f2ZB+TOzk7IZDIYGxvD3d29zzJQm/r6etTU1MDMzAxhYWFa5+Dg8/ng8XiQy+Vwd3fXaZ4aLpeLzMxMGBsbIzo6WmX+je5aW1vR2toKExMTREdHw9XVVT2LBhaLhWeeeQaDBg1CUlISTp06pZ7lkauvr0dCQgIAYMSIEfDy8mLSgoODmfMuLS2Nmc+jJ2KxGDdv3kRqaioCAwMxYcIEmJubM+kcDgdxcXGIjo5GdXU1EhMTewwE1tfX49dff0VtbS0mTZrEBLAe9Hq9ffs2fv75Z6SkpKgnaXXt2jXs27cP+fn56kkURT1maACAohTYbDbMzMxgYWHx0BNttbS0ICsrC+3t7XB3d9dorTE3N0dgYCDYbDaKioqYCX76oqenB19fX8ybNw9Lly7FhAkT4OHhATabDalU2ufDACEEYrH4L2nxlMlkSEhIwJo1a7BlyxZUVVWpZ2GIxWIcOXIEn3/+Oc6ePatzwAOKybsSEhIgkUgwfPjwPiukQqEQBQUF4HK5sLS01PrArK+vj7CwMPTr108j4FFbW8s8WHl5efXYq4IQgsrKSlRWVsLd3R3R0dFMq15xcbHKb1FeXo7c3FzY2dlptODLZDJwuVwIhULIZDKtLccmJiYIDw9/6Imk/ldaW1tRU1MDFosFR0dHrRV/qVSK2tpaEELg5+encS30pLm5mZk8MDY2Fi4uLupZIBQKUVtbCxaLBU9PT533DUXFPC4uDpWVlbhx4wba29vVszwwQ0NDWFpaaj0OupDL5cz5Y2pqCnd3d43WOg8PD2bpq6Kiol5b/5Q6OzuRnZ0NHo8HJycnODo6qqQbGhrC398fJiYmKCsr6zFAl5ycjF9++QVhYWGYO3cu3N3dIZFI1LP1qa6uDj/99BNWrVqFM2fOaD3nlUpKSrBx40Zs2bIFOTk5vebtTjnZYXJyMlxcXDBw4MAHKs95PB7i4+Mhk8nw3HPPwdPTE83NzeDz+Vo/g0AgQGNjI3MNayuLbW1tERsby6xxPmTIEKxYsQJDhw5lgoI1NTUoLi6GoaEhnJ2dmQCAvr4+RowYgffffx9BQUFqe9YNl8sFj8eDmZkZHBwctPbcaGtrQ21tLSwtLeHj49NjT6vu7t+/j6KiInh5eWlM6Ncdn89nymUPDw/o6+urZ9HKxMQEgwcPhr6+PhISEnq91/zdJBIJcnNzUVFRAWtra60BTh8fH9jb26Ourg4lJSW9PoB3dHSgqKgIEokEfn5+WieptLGxQWhoKFgsFnJzczUCzkq//fYb7t69i2eeeQbPPPMMTE1NdSov1GVlZWHDhg346quv+uyxGB8fjzVr1uDAgQPgcrnqyRRFPWY07wIURT20yspKFBUVgc1mw9XVVaObo4mJCby9vWFkZISqqqoeb+DqDA0N8fTTT2Ps2LHQ09NDa2srRCJRrxUKdRwOR6VF4WHp6+sjJiYG1tbW2L9/P7Zs2YKamhr1bBAKhThy5AjWrl2Lzs5ODBw4sM+H+O5SU1Nx9+5deHt7Y8SIEX1WGltbW1FYWAixWMx0/VcqKirC1atX0dDQoLKNEiEE1dXVKCsrY4YDaKskQ/FgW15ejoaGBlhaWiIiIgJubm6oqqrC7du3mRm9oQgA1NTUICAgQCPgwOFw4OLiAhMTE3C5XMTHx/fYAvugCCFITU3F7t278dNPPyEhIQEdHR0AgKamJhw7dgw//PAD9u3bh9TUVGa7mpoaJm3//v3MGsnqlC16dnZ2zPmsrqOjA1wuF2ZmZggJCdHo3dCTrKwsJCcnw93dHXFxcRrbEUJQV1eHiooKQPFw9SAPeI6Ojhg5ciRYLBauXLnS40Pv30kkEiE/Px88Hg92dnYaD+oA4OzsDHd3d7S1taGyslKnCj2Px0N+fj6kUilcXFw0rn8OhwNvb29YWVmhrq5O6/lXW1uLAwcOoLm5GYsWLUJQUBAU8wepZ+2Tk5MTYmJiUFJSgi+//BJnzpzR2hOgvLwcX331FU6cOIGAgACEhYXp3PLd2dmJGzdugMfjYciQIQgJCVHP0iNCCC5evIjs7GxMnz4dERER0NfXR1tbGzo6OrReC+bm5kyQKj8/H9euXeu1t4Kyi//s2bNVgowtLS3o6uqCs7MzPD09mfJOX18f/fv3x5w5c3rsadMXHo8HkUgEd3d3uLm5ab1e+Hw+mpub4e7uDh8fH/VkDXw+H1evXkV7ezvi4uJ63EYqlaKiogL19fUwMzN7oBUMoOilEhERgdLSUty6dUun8/7v0NnZidzcXLS2tsLR0VFrENLNzQ3Ozs5obm5GdXV1r9eMepr6/6E4d6ysrJgyUFtA/9atW/jtt9/g4+ODBQsWwNLS8qGv1+DgYPj6+uLixYtYt24dcnJy1LMAAK5fv46VK1eioKAAsbGxWoMXFEU9XrTXcCmKeij19fWoq6uDmZnZ/2vvvsOavP7/8T8TkoDsIUuWIFvBCdZZiijWOqvWtlpX33W1al0d1rd1fbSD9mttXW+1taXaqrWto+K2bgEBQRBkzwRISAKBBLLO7x9y/8gCtLZ1nMd1cV1enpMQ7uS+c5/XeZ3XgYeHh9EsiqWlJdzc3GBhYYHKykqzKXyPEiEEGo0GEokEycnJuHbtGi5duoQrV64gKyvL5E1EezgcDiZNmoQNGzYgJCQE3377LbZt26YXBFAoFDh69Ci2bNkChUKB5cuXY+7cuQ8UAMjNzUVxcTHc3Nw6tf2ZVCoFn8+Ho6MjhgwZwtwsq9Vq7Nu3Dxs3bkR5ebnJGyGNRoPa2lrmZs7Pz89oBlanubkZIpEIVlZWcHNzQ//+/TFw4EBme0CJRAK0HneRSMQETAwHX1wuF3369MGgQYOg1Wrxww8/YNu2bX85LV0qleLGjRs4dOgQduzYgWXLluG///0vbty4AYFAgHPnzuHQoUNISEjA3LlzsXHjRmRnZ6OiogKXLl3CL7/8gs8//xxvvvkmNmzYYJRurlarUVlZCbFYDG9vb/To0cPoc47WdfyVlZWwsbExOYtvilqtxrVr11BQUIChQ4eaHLxpNBpm5s3FxeWB1najde16z549wWazkZ6e/ljMVjU3N6O0tBQKhQLu7u4mB0lOTk5wdnZGU1MTKioq0NLSYtjFSF1dHSoqKsDlcuHp6Wk0iOZwOHBzc4O1tTWqq6uNAgAqlQoHDhxASkoKJk6ciBEjRsDS0tLkoL0z/P398f7772PRokUoLy/Hpk2b8Mcff+g9X1lZGT755BMcPnwYQ4cOxZo1azBkyJAOA4A69fX1SE1NRWNjo9l16eYUFRXhxIkT0Gg0GD16NCwtLdHc3AxCCFQqlcmsBwcHBwwaNAhhYWGoqKjAV199hQMHDpgNNpqiVCpRXl4OuVyO7t27t3v9eVBtl+sEBgaanKlWqVQoLi6GWCw2O5g1VFxcjAsXLsDV1RUxMTEmlxWg9f3Qfc/4+vqaDG61Jzg4GCEhIaitrUV6enqnPvf/hMbGRpSVlUGtVsPDw8Po+o7W4KSDgwOkUikqKyvbPW9sbGyYY1NbW8t8j7RlaWkJLpcLQggaGhqMMuqqq6uxc+dOqNVqzJgxAxEREVCpVCYDV50xbNgwbNy4EXFxcTh16hS2bNmilwmg0WiYmf+cnBzMmTMHy5Yt01sKQVHU44kGACiqFY/HA5vNBpvNbnfNZ3tEIhFEIhFYLJbZGzgWiwVCCKRSaaczAP4KHo8HCwsLFBYWYuvWrXj33Xfx1ltvYfr06Xj99dexe/duVFVVmRwYtyc+Ph7r169HcHAw9u3bx2QCNDc34/fff8emTZvQ2NiIjz76CNOnTzd7g2iO7jV1dtaoqqoKAoEAPB4PGo0GFRUVqKysxIkTJ/Dnn3+Cx+PB1dXV5PvS0tKCoqIiSKVSREZGtrumvL6+HpWVlcyNuouLCyIjI2FlZcUMTNF6411TUwNLS0u4u7ubHMB4e3tj3rx5GDFiBBoaGvDZZ59hxYoVuH79+kPPdBUUFODixYsIDg7GN998gzFjxqCqqgrHjh3DwYMHUVlZiU2bNmHHjh3o2bMnrl+/ju3bt+PIkSNQKpXYsmULtm/fjuDgYNy4cQMXLlzQu+muq6tDVlYWGhoa0LVrV5PvDWndD72goABdu3bt9CCstLQU6enpsLa2xtChQ41m/9EaJCgvL0d9fT0iIyMRERFh2KVDdnZ2sLGxQW1t7V8OwvF4PHA4HLBYLFhYWJgMhnSkpaUFVVVVUCgUZjNP0LoUSKvVQiQSMRkd7ZFKpRAIBCCEmPzctyWTyVBXV6d3Hbh16xaOHz+OgIAAzJo1i9mj/q9wcXHB8uXLsWjRIpSVlWHTpk04ffo0mpubUV5ejs8++wwHDx5ETEwM1q9fjz59+hg+RbsUCgWKioqgVqvh6ur6QNfypKQk8Pl8xMXFwdfXFxwOB2w2G2q1GmKx2OSgDAAGDhyIxYsXIygoCNnZ2Vi5ciU+++wzlJaWGnY1qby8HNnZ2VAqlXB3dzf5uX9YhYWFzJpsDw8Pk8tU6uvrcfv2bYjFYvj6+nYYqG1ubkZ6ejqqqqrQr18/hIWFGXZhNDQ0oLS0FFwuF88991yn1v+35eDgAGdnZ+YabSoI82+Qy+WoqqqCUqns8JxVqVQQiUSQy+WGzQwrKyv4+fnBzs4O5eXlKCws1GvXtC4ZE4lEIITAwsJCLygul8tx/PhxpKamYsyYMRg9ejTQei1+0O/2tvr27Yv169cjLi4OJ0+exObNm3H37l00NzfjypUrWL9+PbKysrBo0SIsWbIEbm5uhk9BUdRjyPxVi6KeMUqlElqtFlqttt1IfXt060BhJoUPbf7/r34xd5a3tzd69+6NwMBATJ48Gdu3b0diYiJWrVoFAPjiiy+wevVq3L9/3/Ch7WKz2YiLi8P69esRGBiIxMREfPnll9i2bRsSEhLQ2NiIZcuWYdq0aQ91Q6u72XZwcOhw9h+t61GrqqpQU1ODjRs3YvTo0YiPj8eSJUtw7949+Pr6GqXh60gkEqSnp6OxsRG+vr7o2rWrYRcGn89HdnY2XF1dmWUGoaGh8Pf3R3V1NbKyspi013v37sHKysps4MHCwgLPP/88EhISMHPmTBBCcOrUKaxYsQIXLlx4qJmbkJAQzJo1C5MnT0Z4eDjs7OzQ2NiIlJQUsNlsTJ8+HSEhIUyBPZFIhLNnz0Kr1eLFF1+Ev78/QkND4efnB6VSycyA6tTV1aG0tBQWFhbw9fU1GdghhKC0tBRyuRwhISFGBRfNyczMRG5uLgICAhAZGWlyMK2bLVWpVPD29u5wsGIKl8tlAhd/NQCgUqmgVqtBWjNtHmaArHus7ppg6rrQ9v+1nSzopdVqH+h6pNVqmc+pSCTC3r170dLSgpkzZzIp3qRNMKG9gU97nJycsHTpUixYsABlZWX4/PPPsXPnTnzyySc4cuQIhgwZgvfff/+BB/9oDabw+Xyg9drRWWVlZbh58yZ69eqFN954A2gdlLm6uqJLly5Qt1NrxcHBATNmzMDWrVsxdOhQ1NfX43//+x/Wrl2L/Px8w+5GBAIBKioqYGNjA19f3wcKWnSksrIS1dXVzDp1U+9ZU1MTSkpKYGVlhcjIyA4HcWKxGNeuXQOLxUK/fv2M6pu0pZv9trKygq+v7wN/F1hYWDD1XPh8/kMHRh813Tmr+7ep8+tBz9m4uDjExsYiPz8fX331Fe7cucO0paWlISEhAT/++CNUKpVRptCdO3eQmJiIiIgITJ06FdbW1nrnM4vFMvned0bv3r2xbt06jBgxAklJSUhISMDu3bvx6aef4u7du5g7dy7mz5/f7ueAoqjHy8NdDSjqCVVeXo4NGzYgLi7O6GfBggW4ceMG8vLysHDhQqP2uLg4LF68GLm5uYZP+0B0N9AODg4mZ08fteDgYKxcuRJr167FtGnTMHDgQAwaNAjz58/HqlWrwOPxcOjQIRw/ftzsDJc5HA4HI0eOxIYNG9CrVy/89NNP+Oqrr6BSqfD+++9j9uzZJovxdaS5uZmZLenMMVKpVMySirCwMCxZsgRLly7F0qVL0bNnT3h6eiIyMtLszadMJkN5eTlsbW0RGhra7sChrq4OVVVVcHNzY/62gIAADB48GM3NzUx15pqaGkgkEgQFBbV7Y8Tj8RAZGYnVq1dj/fr18PDwQHJyMrZv34579+4Zdu+Qvb09fH194eTkhIqKChQWFkIkEqFHjx4YN24cszSCEMJkJYSEhGDMmDFMW21tLerr62Fvbw8vLy+97IWioiLcu3ePKWzWttaCTktLC2pra8HlcuHr69up95AQgszMTNTV1SEyMtJkqjJa1zNnZ2dDLpfDzc2t3ffKHC6Xy6y/lslkZgd2OkqlEhcuXMCrr75qdE2Ij4/H1q1bUVlZiSNHjmDUqFFGfcaOHYu9e/e2G1jsaHCA1j5sNhsuLi4mAy8Pi7Rm2uiOSUtLC3766SdkZmbi5ZdfxtSpU5m+NjY2sLCwALt1O8i6ujrs3bsXZ8+efaCCis7OznjnnXewZMkSVFdX48svv8Thw4cRExOD//73vxg0aJDhQzpFqVSiqakJLBbL5Gy3KU1NTThw4ADkcjkmTZrEBJW4XC7s7OyYTID2BlB2dnYYM2YMEhISMHv2bMjlchw+fBg//PBDh8usqqurUVtbC29vb/Tr1w9dunQx7PJQWlpakJWVBT6fj6CgIERGRpr8G2QyGcRiMZycnODl5dVhwLWqqgpZWVlwd3dHeHi4yefUKSsrQ35+PrhcrtlMqI7o0utlMlmnAmxisRj/+9//jM7Dzv688cYbTHX/9pgb+Lelu866uLh0+HkMCQnBe++9hwkTJuDKlSuYMWMG4uLiMG7cOBw5cgTOzs4ICAiApaUlunXrxlxXi4uLkZiYCLVajYULFzJZUWw2G1ZWVmCz2UwNoKysLOzevRuZmZmdOpY6kZGRWLduHV566SVcvHgRX3zxBfLz8zF//nwsWbLE7PXakEAgwLx58zBlypQn+iclJcXwT6OoJ4r5qzZFPYUsLS3h4+NjtA1eeHg4AgICYGdnhy5duiAgIMCoPTw8HP7+/u3enNnb2zODQgsLC6NZX12aMIvFAo/HM1k87VGzsbFBz5490bdvX70BsKWlJV566SWMHDkSKpUK169fZ2bOHgSbzcaoUaPQu3dvCAQC8Pl8ODk5ITY2tlMDP1NUKhVzY2VqFthQZWUlU4E/JiYGK1aswLx58zBv3jyMGjUKPXr0MFvZWqVSoaioiKmA3bYAlyGFQoHi4mKoVCqEhoYy77WHhwcGDx4MKysrpKamoqCgAAKBAGw2G6GhoZ0arPn7++Odd97Bu+++C3d3d5w9exanT582WufZWRqNBqWlpSgtLYWbmxvGjRuHwMBAoPWmtKSkBCUlJfDz88OMGTP01ttXVVUx9RS8vLz0jltFRQXKy8vh4OCAkJAQk5/htlWv28umaCsnJwdXrlyBtbU1Bg4caPY84/P5qKqqgpOTEwIDA82+V+1hsVjM43Qz7+3RBeyCg4ONrgnh4eHw9PQEj8eDi4uLUVt4eDjCwsLg5uZmdD3Q0Q0QeDye2Zk6NpvNPN7KyqpTs8TW1tZwdHTUu+601fb/uVwuc324e/cuduzYgczMTJw5cwZLlizBggULsGDBAnz44Ye4f/8+pFIpdu7cidmzZ2PNmjVISUlpN8BhioeHB1588UXY2toyAby+ffviueeeM3qtnUXazMyaOo6mFBYW4uDBg7h27Rp27dqFhQsXYsGCBZg3bx4OHTqEpqYmKBSKTi27iIqKwurVqzFz5ky0tLTgyJEjSE5ObvfYtK0/Ehwc3OEAvLMUCgUKCwuZrUgDAgKMjqtWq0VJSQmqWrcJ7Oia3dDQgIsXL6K0tBT9+vUzW/wPrdfW0tJSZv2/r6+v0e/vDN3x6OyAlcPhMMGJh/kJCgrqMHBtaWkJFxcXcLlcs+es7txisViwsrLq8Fpl0bpV8Mcff4xNmzYhPj6euX4MGjQIcXFx8PT0hIWFBby9veHo6Ai1Wo3Tp09j3759uHfvHvbu3Yu3336b+fzu3LkTUqkU9+/fx4cffoh58+bh66+/RklJieGv71BkZCST4aLbkWH48OEmt9w1RywWY8+ePTh69OgT/fOgGZMU9bgxvmJR1FPM3d0dc+bMwbZt24x+Nm3ahH79+qFHjx7YuHGjUfu2bduwfPnydgvcdOvWDd26dUNLS4vJbaPUajUzi9GlS5cObwj+bhwOB927d2fSxJuamgy7dEitVuP333/H9evX4eHhAS8vL5SXl+PgwYN6hQEfhKWlJXPT1966SZ3CwkIUFhbC3t7e6IY0MjISkydPRkhIiN7/68jlcuTk5KCurg7Ozs4mZ7R1RCIRioqKYGdnB39/f2YQxuPxEBYWBm9vb/D5fNy5c4dZd6vb+qszHB0d8frrryMmJgYqlQrZ2dkPnJWho1QqUV1djebmZgwZMgTR0dFMm1arRVZWFkpKShAaGooBAwYwbRqNBgUFBZBKpejevTu8vLyYm3bdzH5LSwucnJxMzr4TQlBVVYXi4mI4OzujR48ehl2MaLVaXLx4Ebm5uejTpw8GDx5s2AVofW1CoRBNTU3w9/eHv7+/YZdO0Z2HaFP7oz3c1kKOGzZsMLomfPPNN5gzZw48PT0xZswYfP3110Z9Pv/8c4wfP97s+W5lZYWAgABYW1ujoaHBZBV5uVwOuVwODodjNjhiyNXVFd27d4dGo4FUKjVKn9ZoNJDJZFAqlbCysmKKBLJYLMTGxmLKlCmwtbXF/fv3mYBRVVUVWlpaoFarIRAIoNFoEBcXhwEDBnQq0NVWRUUFDh8+jLq6Ovj4+MDJyQlnz57FyZMnH7rYm0Xr2mjSuvVpR2pra3H48GHY2Nhg4MCBUCqVKC4uRklJCUpLS0Fa11vX19d3OsMhKCgIb7zxBsLDwyEQCJCbm2v2tcjlcpSVlUEmk8HJyclkMbmHJZVKmZo0Li4uJjOglEolSkpKUFNTA39//3azldCaAXThwgVYWVkhNja23aJ+crkctbW1sLCwQHh4eIdLC8zRnQ+6wXZH7O3tMWHCBKPzsLM/a9eubXdbQwCwtbVFQEAAuFwu6uvr9XZ/0dEFjng8XqfPWQDo1asXli1bhi+++ALbtm3DZ599hpdffhldunRBfn4+OBwOQkJCmCBF165dMXnyZIwcORJSqRSFhYXM+SoUCqFWq6FQKFBZWYmuXbtizJgxCAwM7PC6ZyglJQVJSUnMUhWFQoFDhw4hJyfHsKtZLi4uWLhwIaZNm/ZE/zzslpwU9dggFEURQgi5c+cOmThxIomOjiYZGRmGzZ1SUlJCXnvtNcJms8mcOXOIUCjUa+fz+WTRokXE1taWvPrqq+T+/ft67Z1VW1tLVq5cSezs7MiiRYsIn8837NIpEomEfPzxx8Te3p7ExsaS5ORkwy7tam5uJr/++isJDw8nbm5uZNWqVSQhIYE899xzxMnJiXzwwQdGx6CzZs+eTQCQyZMnE61Wa9isZ//+/cTHx4eEh4eT48ePGza3i8/nk7fffpvY2dmR1157rd33JDMzk8yYMYOMHz+e5Obm6rVVVFSQmTNnEisrKzJlyhQyatQoEh8fT1JSUvRev1KpJHw+n5SVlRGVSqX3HIQQIhaLyapVq4idnR2ZN28eqaqqMuzSKUKhkKxcuZK4u7uTdevWkaamJqatubmZvP/++8TR0ZH85z//IbW1tUybRCIhs2fPJlZWVmT58uVELpczbSKRiCxevJjweDwydepUUlFRwbTpKJVK8sMPPxAPDw/y2muvkcrKSsMuRkQiEZk1axbx8fEhCQkJhs2M+vp68sknnxBHR0cyfvx4kp2dbdiFyGQyUlxcTAQCgWET4+7du8Tf358AIPv27TNsfiAqlYrs3r2bBAcHk5UrVxKNRmPYpUNKpZL88ssvxNfXl3h6epKff/7ZsAs5deoUiYiIIN26dSN79uwhzc3Nhl2MCIVC8t577xEej0dGjRpF8vLy9NobGxvJ5s2biZOTE3n++efJzZs39dpNycjIIEOGDCGenp5k//79hs2dVlNTQ5YsWUJsbW3J8OHDyVdffUWWLl1K3NzcSP/+/cnJkycf6ljm5OQQb29vAqDD16fRaMhvv/1GevfuTRISEohMJjPsQr7//nvi6elJhg0bRtLT05n/b2pqIqWlpaS6ulqvv86dO3dIfHw86dq1K9m1a5feedRWUVERGT9+POHxeGTVqlUmX8PDSk9PJ8OGDSPW1tZky5YtJo9ndXU1WbJkCXFxcSFbt24ljY2Nhl30/PzzzyQoKIi89NJLRp8nQwUFBWTq1KnE3t6erF+/nkgkEr12rVZLRCIRKSwsJPX19Xptba1Zs4YAIMOGDXvo75NHTS6Xk927dxMXFxcSGhpKzp49a9iF/PTTT6R79+4kMDCQ/PLLLyaP/4M4cOAA8fLyIgMGDCApKSmGzUbUajX54YcfiKenJxk6dChJS0sz7NIpWq2WJCcnkxEjRhBbW1sye/Zssm3bNjJu3DhiZ2dHZsyY0eFngaKox8uDhf8o6inWtkhPR2uCzXF0dES3bt3AYrFQU1NjlL7d2NiIkpIScDgc9O/fX2/dnFarRVNTEyQSCZqamoyyB9rSpRXCIDXYkO45ZTKZ2TTnlpYWaDQacLlckyny5mi1Wvzxxx9Yu3YthEIh5s6di2XLluGdd97BBx98AA8PD+zfvx+JiYkPNYutSx1vaGgwO3uGNvtMNzQ0IDQ0tN0K/qbIZDKmSrXhe2JIKBSipqYG7u7uRimiLi4uGD58OOzt7XH58mXk5eUhICAAgYGBeu9PZWUltm7dik2bNpk8Ls3NzaisrASLxYKXl5fR79FllzQ2Nrb7GREKhcjIyIBKpYKnp6fe+tOGhgYIBALY2NggNDRUL01ftx2cjY0NvL299WauCCFQq9Xg8Xh6NRDaqq+vx5UrV0AIQf/+/Tu1BWBxcTFyc3Ph6enZ7sybSCTCrVu3IJfLERAQYHJ5QVJSEtauXYtTp04ZNjFaWloglUqZmdG/ou11g7QW0ntQHA4HHh4ecHZ2ZnaPMKQrcNm9e3f069dPb0s/pVKJ+vp61NfXo6WlhXk9NjY28PLygqWlpcmtxXSz3UqlEr179+5UsUZO644HrNblAw9DJBJhy5Yt+P777xEeHo6VK1di/vz5WLt2LV5//XXcv38f/+///T/cunXL7HXLHEtLS+Yc7mjtfXFxMc6ePQsXFxcMGTLE5Ay5bv2/trVArE56ejo+/PBD/Pjjj3r9dSQSCQQCARwcHNCtWzezSzaqq6tRVVUFDw8P9O3bt1NZFLoMlvr6+nar4mtbi0Da2dnBzc3N5IxvYWEhUlJSmHOvvd/f0NCAu3fvorGxEf3792939h8A8vPzkZGRARsbGwQEBBgtF5JIJNi7dy/+7//+z+wssu56h9Zsqgf5jvo78Xg8dOvWDQ4ODhCLxSa3fSwrK4NYLEZwcDB69+4NNpsNQggUCgUkEglkMplRVo45xcXF+OOPPyCXyzF27FhmOVd7dNkwuvP1YY9damoqPvroI6SmpmLixIlYsWIF5s+fjzVr1iAqKgrHjx/Hrl27mB1wKIp6/Bl/G1AU9dDs7e0RHh4OR0dHFBUVGW3lU1JSgry8PLi7uyMiIkJvUFZbW4s1a9YgNjYW69atY9bYmWI44Dd1Y4fWm8stW7bgzTffREZGhmEzampqkJGRAbVajd69e3d6uza0DrQ2btyIuro6fPDBB1i2bBmz1/iLL76ItWvXwtXVFQkJCfj222+NBh8d8fHxAY/Hg0wmg0AgMGxmiMViZGdnQyaTwcvLq8MUVkPl5eW4d+8eLCws4Ofn126hpoKCAlRWVsLb29voRtnKygr9+vWDt7c3hEIhJBIJunbtapTSqzvmeXl5JlOK6+rqIBAI4ObmhvDwcL3fo1arcfLkSUyePBkrVqxAcXGx3mPb0g1AbG1t9QbKhBCmMBeHw2ECVmgdMGRmZiI/Px8BAQGIiopq84z/f+CJtBayNPW5S0tLw/Xr1xEREYGhQ4cafVYNNTc3Iy0tDQKBAD179mx3SzHdtnb29vbo06ePUUqxVCrFsWPHIBQK271BFgqFaGlpgbe3918OADwKLBYL/v7+CAoKglwuR3Z2NkQiEdPe3NyMzMxMNDQ0oGfPnkbLXM6fP4+JEydiwoQJOHHiBJOObGVlhdDQUHh4eKCyshJ5eXl6QSM+n4+7d+/C2toavXv3ZooAtkfduuNB27X2D0IoFOLLL7/E/v37ER0djU8++QTx8fGwtLSEs7MzVqxYgVmzZiE1NRXr16/HzZs3DZ+iXTY2NggJCQGPxwOfzzcKwuoolUqcPn0a58+fR0REhMnlJLq/kcViQSKR6AVmdFtWlpSUmAz68Pl8yGQy9OjRA4GBgWaDJbdv30ZhYSE8PDzg7+/f4fkCAFlZWVi4cCFeffVVXL582ewgsu25aup5m5ubcenSJZSVlWHkyJEdpjUXFxcjIyMD9vb2iI6ONrq2GRIIBJBKpfDx8WG2Sm0rLy8Px48fh52dHVOA1JBQKERtbS2sra0RFBRkNpDyT7OwsEBgYCACAgIgkUiQk5OjVyNCIpEgMzMTKpUKkZGR8PPzA1qP+Y4dOxAbG4tFixbh7t27bZ7VtKamJvz44484ffo0Bg8ejNdee63DWg1osyuR7nw19zlpT1paGtavX4/bt2/jzTffxNq1a9GrVy/weDxERUXh448/RlRUFL777jt8+eWXKC8vN3wKiqIeQ8Z3bxRFPTQ2m43nnnsOw4YNw/3797F3716mkntFRQV++ukniMViTJs2Df3799d7bHNzM7KysnDnzh1kZWW1u/Zdo9Ggrq4OSqUS9+/fN7vWXi6X4+7duzhy5IhRhF4gEGDPnj24du0a+vXrh7Fjx3ZqMKRSqXDkyBF89NFHEAqFWL58OebOnas3GOPxeJg4cSJWr14NR0dHfPnll9izZw+EQqHec7WnZ8+eCA4OhkAgQFpamtmZrry8POTm5kKr1TIzdZ2lUChw584dVFZWAq03/OZIJBLcuXMHVVVVUKvVRrMpLBYLPj4+CA0NBZvNhoWFBZycnIxu/GtqalBeXo68vDwcPXpUb713cnIyNm7ciLy8PEyePBlDhgzRu3EnhIDP5+PKlStIS0szO7hRKBS4d+8eRCIRQkJC9OpWaLVaVFdXMwPpttvoEUIgFoshlUrh5OQEDw8P5OfnIyEhAbdv34atrS369++PLl264OLFi7h27RrzWAA4cuQI/vvf/0Imk2H69OmIjIzUazeloaEBqamp0Gg0iIiIMBrUt1VeXg4+nw9XV1eEhYXpHZva2lps374dZ8+ehbu7u9nZbKlUirS0NLBYLERHRz9wwOjv4ubmhjFjxsDV1RW//vorjhw5wtywnzt3DklJSQgJCcG0adOMai9UV1fj6tWruHz5MiorK5mBOYvFQq9evTBq1CiIRCJ8++23uH79OtB63A8dOoS8vDyMHTsWsbGxJgeJhlpaWtDY2Ij6+vp2r1GmFBcXY/Pmzdi9ezeio6OxZs0avPDCC3qDOm9vb7z33nt4/fXXcevWLaxbtw6XL182e/4bcnBwQFRUFOzs7HDnzh2zQbKCggIcPXoUdXV1iIiIMBn8IISgsLAQYrEYEokE1dXVTFtZWRkEAgGuXr2KM2fO6D3myJEj+OKLL8DlcjFjxgyTwQW0XlNu376N+vp6Zna4M6RSKVJTU3Ht2jXU1NSYveb5+/ujd+/eEIlEOHnypN6xEIlESEhIwI4dOxAQEIAZM2aYzKhpKzc3FwUFBejevTvCwsKMrm1ttbS0oLi4GDKZDAEBAUbnY3p6OrZt24bCwkIEBwebPQ9zcnKQnZ0Nd3d3REVF6WW+/Nt8fHwwZswY8Hg8HDx4ECdOnGDajh07hj///BMDBw7Eyy+/rFd0ND8/H3fu3EFqamqHgfHMzEysXLkS3333HeLi4vDxxx8jODi4U+cqi8VCU1MTmpqaTNYk6sjly5exevVq3Lx5E7NmzcK7776r9z6yWCwMHz6cqZmwf/9+JCQkoKioSO95KIp6/FisW7duneF/UtSzqLKyEidPnkRtbS0mTpwILy8vwy6dokv5lMlkuHHjBm7evImMjAwkJiaisLAQr7zyCt566y2j1GipVIrff/8dpaWlCAgIwOjRo/UGZzk5OThx4gSOHDmCAwcO4NSpU2hsbERxcTHu37+PrKwsFBYWMtWJLVq36pLL5SgoKEBWVhbS0tKYQj6HDh1CWloa+vTpg8WLF2PYsGFmi5S1pVKp8Ntvv6GgoAAzZszAwoULTc5G6AoVOTs749KlS7CxscHw4cONBi/mODg4IC8vD3/++Sfc3d0RFxenN+huaWnB1atXsXHjRiQnJwOtadJNTU0IDAzs8PeIRCIkJiZi69atEIvFkMvl4PP58PLyMpo9rqiowDfffINvv/0WDQ0NqK2tRUhICHx9ffVeE5fLBZ/Px/nz5+Hm5oaZM2ciODhY77mKioqQkpKCoqIiJthz5coVnDx5EkePHkVJSQnGjBmDBQsWGBWcZLFYyM/Px/Xr1+Hv74+JEyeaDNrU1tYiMTER165dQ2xsLMaPH8/M1kmlUiQmJiIpKQmDBw/GtGnT9AY/UqkUly9fxr179yAQCCAQCKBWqxEREYFu3brBzc0NarUaaWlpSE1NRWpqKs6dO4dffvkFf/zxBzQaDd5880288sorHb4HGo0GKSkp2Lt3L/z9/TFr1iyTWShyuRwXLlzA1q1bcefOHWY29saNG0hKSkJSUhJ++OEH7Nu3DzKZDJMmTcKIESNMfp6Li4uxc+dO8Pl8LFy4EEOGDDHZr7M0Gg1u3ryJs2fPIjAwEGPGjDGZGdERNpsNDw8PODo6oqCggAnyJCUl4ciRI/Dw8MCyZcsQHx9vNPBKT0/HsWPHAADx8fHo378/M6jWLQNAaxGv69evIzMzEz/99BNu3ryJuNbtTTua/ZXJZDh16hS++uorXL16FSqVipntCwwM7FSRs/z8fPz222/w9PTEBx98gJiYGMMuQOu537NnT1RVVSEjIwPR0dEIDw83+rtN4XA4sLS0xNmzZ5Gbm4vo6GhmWzS0vl+nT5/G5s2bce7cOSgUCnA4HAQEBMDDw4N572pqarB7925s374dEokEjY2NkMlkCAwMhIeHB+7cucNky2RkZCA7Oxtnz57F0aNHcfz4cbBYLMydOxfTpk0zubSgoqICn376KbOvu0AggEQigaura4db8dXW1jIz/1OmTEFQUJDJz1yXLl3g7u6OmpoaJCcn613/f/nlF1y5cgWBgYFYunQpYmJiTD6Hjlgsxr59+5Cbm4vp06cjNjbW7HlTU1ODAwcOYNeuXairq4NEIkF+fj4uXLiApKQkHD9+HPv27cOZM2dga2uL2bNnIyQkxOSg9vDhwzh8+DCioqLwzjvvdJh18E/SLQOwsbFhdjFJT0/H77//jpMnTyIkJATLly/HsGHDmL9NqVTizJkzSE9Ph7OzM8aOHWsUICopKUFiYiJznRaLxRg5ciTefvvtdpdItVVeXo7vvvsOe/bsQUVFBWpra1FZWckUZjV1rA3pgrwjR47E+++/b/LaDIDZYeLWrVuQSqV44YUXzGZ0UBT1eKABAIpqpVQqoVQq4e/vj2HDhrVbDb49FhYW8Pf3R79+/cDhcCASiVBdXQ0Oh4NJkyZh8eLFJmc71Go1Ghsb4ejoiOeff56ZxdIpKSlBWloa7t27x6SW9urVi6luLxQKYWlpyVRut7CwgJWVFXx8fODn5wcnJyeIxWJUV1dDKBSCy+Vi9OjRmD9/Pp5//nmzN3OGCCHg8XgYNmwYxo4d2+6skYWFBUJCQuDi4oL+/fsjPDy80ymc1tbWaGpqwp9//on6+nrExsbqBUSUSiUzIxUSEoJevXrB1dUVtra26NOnj8mBcVtSqRQZGRlQKBSIjIxEaGgorK2tERISgvA2W+Kh9YY2PT0dNjY26NWrF7p164bQ0FCmCrQOm82Gra0t2Gw2nn/+eYwZM8bo5p/D4cDV1RWBgYFwdnZm1o+KxWJ4eXlhxowZmDdvnslaBHK5HEePHkVBQQFefPFFjBw50iitFq0ZAHV1dUzFZ13Kpq6tqKgIbm5umDx5MqKjo5kgBqt1uztea2V8jUaD8PBwzJgxg6nmb29vj8jISDg5OUEmk4HP50MoFEIul6NPnz5YsGABXnvtNaO/2xSpVIoDBw7g0qVLePXVVzFp0iSTn8Pm5mZkZ2ejuroagYGBCAkJgUKhQE1NDYRCIYRCIbRaLcLCwhAbG4sJEyaY3X3g3Llz2LdvH4KDg7F48eKHDvTpEELQ1NQEHo+HgQMHmt1rvTNsbW0RHR0NHx8fyGQyZns4f39/LF26FGPGjDE5MNTt3tGnTx/ExsYioM2WlywWCx4eHoiKioKNjQ3q6upQXV0NlUqFF154AStWrDC7Q0ZbcrkcqampKCkpQVhYGHr27AkbGxt06dIFAwcONFoSYwohBD4+Phg/fjwGDRrU7iDEyckJAQEB8Pb2xuDBg+Hq6tqp46r7DBcXF+PatWtwdXVFTEwM8/nXarW4desWioqKEBoaivDwcDg5OSE0NBS+vr7M8RWLxbhy5Qp4PB569eqFsLAwuLu7M7tP6NLWAwICwOPxIBAIIBQKoVAo0KdPH7z99tuYMmWK2SVFNTU1zOuLiIhAUFAQrK2t4efnh6CgIKMMo7b+/PNPXLx4EREREZg2bVq712AvLy+EhYWBy+VCKBQyr5PD4WDUqFFYunRpp5bq3LhxA7t374aLiwuWL1/e7nkjFouRkZEBQgjCw8Ph7u4OkUjEnKtisRi2traIjIzE2LFjMXr0aJNBZKFQiK1bt0IgEOCtt97CyJEjDbv86xwdHTFw4EC4u7ujvr6eqf0TERFhNPhH6znQ0NAADoeDoUOHYujQoXrfa2hdPqLbatHT0xMLFizAnDlz2s2OMlRdXY1bt27pfb9pNBp07979ga5Rffv2xYQJE4yC0Yb8/Pzg4eGBnj17YsCAAWY/9w9KLBbj6tWrKC0thb29vdnnFQgEuHz5MtLT05GXl4e7d+/i/v374HA4Ju8FmpqakJ2djWvXriE1NRUpKSlIT09Hbm4u8vPzkZWVheTkZNy+fZtZsldYWIiGhgZm+0eKeqIZVgWkKIp6XJSVlZFFixYRZ2dn8uGHHxKRSGTY5ZmhVCrJ1atXSUxMDJk5cybJysoy7PLEycrKIrGxsaRHjx7k2LFjhs2P3L1798i4ceOIi4sL+frrrx9pxXXq8ZKSkkIGDRpE/P39ycGDBw2bn1gVFRVk9uzZZPjw4eTkyZMd7pDyKGi1WrJ582bi6upK3nzzzX/kvGlubiaffvopcXV1JS+//DIpKioy7EI95ZqamsjOnTuJu7s7CQ8PJ2fOnDHswkhNTSUTJkwgVlZWBABhs9lk4MCB5LfffjPsSkjrd8HUqVMJAOb5w8LCSNeuXQkAwuPxSEBAAAkPDyeBgYHE1taWcLlcMnv2bLM7f1DUk6RzIUCKoqh/gY+PD2bPno2IiAgkJibi2LFjJvdIfxboMhYGDhyIJUuWPPBuB4+jwsJCFBcXIygoyGjZxaNECIFEIsHu3btx+fJljB8/Hq+++mqnshSoJ1P//v0xZ84cqNVq7N69G6mpqZ2uI/C40mq1SE1NhaOjI5YsWYL4+PgOZ+4fBbFYjMzMTHC5XPTp0+dvP2+am5tx/vx5fP/99+jWrRvmzJljlCZPPf0yMzOxf/9+1NTUQCqVtlsvISIiAu+88w5TdyYoKAiLFy/GqFGjDLsCrdkluuKb69evx5kzZ3Ds2DFMnToVAJjChmfOnMGBAwcwYsQIcDgceHt7P1Z1KCjqYdEAAEVRjy0Wi4W+fftiw4YNCA0NRUJCArZs2YKSkhLDrk89BwcHTJw4Ee+++y769ev3j9z4/53q6+uRnp4OmUyGQYMGGdXEeFR0Kd+rVq1ibvDWrFnTbto09eRjs9mYNm0aPvzwQ4jFYqxYsQI///yzyZ03nhQsFguDBw/GsmXL8NJLL7W7TOBRIYQgLy8POTk58PPzQ3R0tGGXR6qmpgY7duzAmjVr4OTkhLVr1yIuLu6Jv95RD0YsFuPWrVsQiUTw9PSEg4NDu0UMLS0tERgYCHd3d3C5XAwfPhwxMTFmlwzk5OSgvr4e06ZNw+TJk+Ht7Q0PDw8QQmBnZ4fhw4cjOjoa3t7e6NOnD0JCQhAUFITnnnvusapDQVEPiwYAKIp6rHE4HAwfPhybN2/GuHHjcO3aNWzatAkXL17sdNXspwGPx4OPjw88PDyeipvhzMxMnDlzBjY2NoiOjoa9vb1hl7+svr4eP//8Mz755BNUVlZi8eLFWL16tdE2etTTyd7eHm+88QbWr1+P7t27IzExEZs3b0Z+fr5h1ycCi8WCu7s7fH19Tdb++DvIZDKcPHkSJSUliIyMbHebzr8qJSUF69atQ1JSEoYMGYKNGzdi3Lhx/9jfSj0+zp49i/PnzyMmJgY9evQAl8vt8HtPKpWivr4ednZ2GDBgANzd3Q27AAAaGxtRUFAAOzs7TJkyhQkGp6am4tatW8yWk7rfJ5fLodFoYGtrCz8/P5N1WCjqSUMDABRFPRGioqLw/vvv4z//+Q/CwsI6XUyQevwoFAqcOXMGaWlpGDBgAMLDwztdlOpBsFgs2NjYYMiQIVi2bBmWLl1KB//PGFtbW0yaNAmrV6/GxIkT4enpSW/gO4kQgtzcXPz222+wtrbGsGHD/tbZTy6Xi4CAAEyfPh2rV6/GCy+8QIutPYP4fD4uXboES0tLZoAukUg6zN6RSCQQCoVwdnY22qGnLTabjbi4OKxatUpvV4WSkhKUlJTAzs4OHh4ezGfP0tIS8fHxmD9/frvFLynqScIiz9IUGkVRFPWvk8lkOHjwIFJSUvDyyy9j5MiRNKBDUY8ZrVaLjIwM7Nu3j9mhxM/Pz7AbRT0yarUau3btwrlz5zB9+nRERUVhxYoVOH/+PD7++GOsWLHC8CFA6/bEBw8exMqVK+Hr64tvvvkGgwYNMuzWrs8++wzr169HdHQ0tm3bprd9KEU9bR79lAtFURRFtcPOzg7z58/Hvn378NJLL9HBP0U9hthsNvr3748dO3bgo48+ooN/6m+Xk5ODkydPwtbWFiNGjABaB/eEEKjVarN1AHSFKhsbG+Hr6/vANV5kMhnKy8uh0WgQGBgIHx8fwy4U9VShAQCKoiiKoiiKov41arUaJ06cQGNjI2JjY+Hi4sIsDVMqlRCJRGhsbDR8GNCa/l9aWgorKysMHjwYbm5uhl3aVVRUhLy8PBBC4O7uDhsbG8MuFPVUoQEAiqIoiqIoiqL+NQUFBUhOTsaQIUMwefJkAICNjQ26du0KLpcLlUplNgNAIpGAz+czhfoedABfUVEBPp8PR0dHeHt7/y01aSjqcUI/4RRFURRFURRF/SuEQiEOHDgAGxsbvPLKK3B0dAQAuLi4wN7eHhwOB2w22+zAvLi4GIWFhejSpQvc3NzMFgA0p7KyEmKxGIGBgYiMjDT7eyjqaUE/4RRFURRFURRF/Svu3r2L77//HikpKdixYwfee+89vPfee1iyZAkuXLgAhUIBhUIBuVxu+FAolUqUlJSgoaEBfn5+8Pb2NuzSLq1Wi5qaGjQ1NcHHxwc9evTocMtBinrS0QAARVEURVEURVH/uNLSUpw4cQLOzs7w8/NDYWEhbt68iZs3byItLQ0KhQJarRZSqRRNTU2GD0dDQwNqampgZWWF8PDwBy4AKBaLUV5ejpaWFjg7O/+tW11S1OOCBgAoiqIoiqIoivpHaTQanDt3DpcuXcK7776L48eP4+LFi7h06RIuXbqE06dPY968ebCzs4NKpTKZml9TU4PS0lJwOBz4+PiAy+XqtWu1WkgkElRWVkKhUOi1oTX9Pz8/HzweD927d4elpaVhF4p66hifSRRFURRFURRFUX+jnJwcXLlyBd7e3hgyZAjs7OxgYWEBDocDDocDGxsbWFlZgcViQavVghBi+BTIyclBZmYmHBwc4O/vb7StbG1tLXbu3InNmzejsLBQr02r1YLP56Ompgbdu3dHZGQkLCws9PpQ1NOIBgAoiqIoiqIoivrHNDU14dixY0hOTkZUVBS6detm2AVKpRJarRYAUFdXB5FIZNgFfD4fUqkU/v7+6NWrl1EGQE5ODk6ePAl7e3s4OzvrtbW0tCAlJQVVVVXw9fWFr6+vXjtFPa1oAICiKIqiKIqiqH/M3bt38euvv0KpVKJfv36wtbU17AKNRoOioiI0NTWhtrYWYrFYr12hUKC4uBgtLS0IDg5GSEiIXvvt27exY8cOlJeXo2fPnnBzc9NrF4lESE5OhkKhYLIOKOpZYLFu3bp1hv9JURRFURRFURT1KCkUCvzxxx/YsmULbt68iYaGBri4uKBHjx5wdnZmKvCXlZVh586d2Lt3LxobG5nZ/5CQELi6uqKmpgaJiYnYs2cPpFIpGhsbUVJSgitXruDixYtISkrCrl27cP78eXTt2hWzZs2Cv78/8/z5+fn49NNPcfToUWi1WlRXV6O5uRmenp5wd3enOwFQTzWaAUBRFEVRFEVR1N9OrVYjPz8fbDYbI0eORFxcHJqbmyGVSvX6SaVSVFZWom/fvoiPj8fIkSPB5XJRW1sLtC4hqK2tRUREBEaNGoVu3brh1q1bTAHBmzdvwtLSEvHx8XjjjTcQEhKiN6gXi8UQi8WIiYlBfHw8oqKiIJFIUF1d3eZVUNTTiUVMVdSgKIqiKIqiKIp6xNRqNQghzICcEAILCwu9Kv9arRYajQYsFgssFguEEL1+hBBoNBoAYIoE6uoF6LBYLLDZbOanLXPPz2azaSFA6qlHAwAURVEURVEURVEU9QygSwAoiqIoiqIoiqIo6hlAAwAURVEURVEURVEU9QygAQCKoiiKoiiKoiiKegbQAABFURRFURRFURRFPQNoAICiKIqiKIqiKIqingE0AEBRFEVRFEVRFEVRzwAaAKAoiqIoiqIoiqKoZwANAFAURVEURVEURVHUM4AGACiKoiiKoiiKoijqGfD/AZf2pGb/8aKpAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere MS and T2D definitions refer to Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. MRI-PDFF Examination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants underwent upper abdominal MRI scanning using a 3.0T MRI scanner (Philips Healthcare, Netherlands) with a six-echo mDixon-quant gradient echo sequence to obtain PDFF quantification images. The scanning parameters were as follows: Field of View (FOV): 375 \u0026times; 312 \u0026times; 201 mm; Matrix: 152 \u0026times; 125 \u0026times; 67; Voxel Size: 2.5 \u0026times; 2.5 \u0026times; 6 mm; Slice Thickness: 6 mm; Flip Angle: 3\u0026deg;; Repetition Time (TR): 5.8 ms; Echo Time (TE1): 1.01 ms.\u003c/p\u003e\n\u003cp\u003eAfter scanning, images were processed using Philips IntelliSpace Portal (version 10.1) to generate and record PDFF values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Image Post-processing and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll PDFF images were independently analyzed by two radiologists with over five years of experience in abdominal imaging, who were blinded to clinical information. To minimize measurement bias, circular regions of interest (ROIs) (~150 mm\u0026sup2; each) were manually placed in nine different locations within the liver, based on Couinaud\u0026rsquo;s classification. Large vessels, bile ducts, artifacts, and focal lesions were carefully avoided. The mean value of the nine ROIs was recorded as the final hepatic PDFF percentage. A PDFF value \u0026ge;5% was used to diagnose MASLD .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using MedCalc and GraphPad Prism. Normality of continuous variables was assessed using Shapiro-Wilk or Kolmogorov-Smirnov tests. Normally distributed variables were presented as mean \u0026plusmn; standard deviation (SD) and compared using ANOVA. Non-normally distributed variables were reported as median (interquartile range, IQR) and compared using the Kruskal-Wallis test. Categorical variables were expressed as frequencies (n, %) and compared using the Chi-square test or Fisher\u0026rsquo;s exact test. Intraclass correlation coefficient (ICC) was used to assess interobserver agreement in ROI placement and MRI-PDFF measurements. Pearson correlation analysis was conducted to evaluate the linear relationship between LFS and MRI-PDFF. Receiver operating characteristic (ROC) curves were generated to assess the diagnostic performance of LFS for MASLD (MRI-PDFF \u0026ge;5%), and area under the curve (AUC) was calculated. The Youden index was used to determine the optimal cutoff value for LFS. Additionally, LFS was evaluated for distinguishing mild from moderate-to-severe MASLD. Finally, multivariate logistic regression analysis was performed to identify independent risk factors for MASLD, adjusting for BMI, T2D, lipid profile, and other potential confounders. All tests were two-tailed, and P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of Participants\u003c/h2\u003e \u003cp\u003eA total of 271 participants were included in this study, comprising 67 healthy controls and 204 MASLD patients, including 59 with mild hepatic steatosis (MHS) and 145 with moderate-to-severe hepatic steatosis (MSHS). The baseline characteristics of the study population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAnalysis of baseline characteristics revealed that BMI, LFS, ALT, AST, and TG levels were significantly higher in the MASLD groups compared to the healthy controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). The mean PDFF progressively increased across groups, from 3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03% in healthy controls to 7.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54% in Mild HS and 18.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58% in moderate-to-severe HS. Similarly, LFS values followed an increasing trend (-1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35, -0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21, and 0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24, respectively).\u003c/p\u003e \u003cp\u003eANOVA with post-hoc analysis confirmed that BMI, LFS, ALT, AST, and TG exhibited statistically significant differences among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, LFS was significantly different among all three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for all pairwise comparisons), showing a progressive increase from controls to Mild HS and moderate-to-severe HS. BMI values also showed significant differences between all groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003eFor liver enzymes, both ALT and AST levels were significantly higher in mild HS compared to controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, both ALT and ALS did not significantly differ between Mild HS and moderate-to-severe HS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The TG levels were significantly higher in Mild HS compared to controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but also no significant difference was observed between Mild HS and moderate-to-severe HS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAdditionally, there is no significant difference in age was observed among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild HS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM-S HS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale: 41/ Female: 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale: 25 / Female: 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale: 84 / Female: 59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.60\u0026thinsp;\u0026plusmn;\u0026thinsp;13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.32\u0026thinsp;\u0026plusmn;\u0026thinsp;13.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.94\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI((kg/m\u0026sup2;))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.09\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00 [16.50, 36.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.00 [24.50, 48.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.00 [27.00, 58.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.00 [18.00, 34.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.00 [22.00, 47.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.00 [22.25, 46.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 [0.98, 1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74 [1.23, 2.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 [1.43, 2.89]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Quantitative Analysis of MRI-PDFF\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, representative MRI-PDFF images from a healthy control, a mild hepatic steatosis case, and a moderate-to-severe hepatic steatosis case are presented. Two radiologists\u0026mdash;each with over five years of experience in abdominal imaging and blinded to clinical information\u0026mdash;independently measured the PDFF values for all participants. Their measurements yielded an intraclass correlation coefficient (ICC) of 0.92, indicating high interobserver consistency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlation Between PDFF and LFS\u003c/h2\u003e \u003cp\u003eThe correlation analysis showed a significant positive correlation between PDFF and LFS in the overall study population (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a high level of consistency between LFS and MRI-PDFF in the assessment of MASLD. In the subgroup analysis, both the mild and moderate-to-severe hepatic steatosis groups exhibited a moderate positive correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51 and 0.31, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that as hepatic fat content increases, LFS scores also tend to rise. However, in the healthy control group, the correlation between PDFF and LFS was relatively weak (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), implying that the applicability of LFS in the normal hepatic fat range may be limited. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, scatter plots demonstrate the distribution of PDFF and LFS values across the study population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Diagnostic Performance of LFS for MASLD Detection and Severity Stratification\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea illustrates the receiver operating characteristic (ROC) curve for distinguishing MASLD from healthy controls, yielding an AUC of 0.886 (95% CI: 0.842\u0026ndash;0.922, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating excellent discriminative ability. The corresponding Youden index was 0.7186 when the optimal LFS cutoff was \u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;0.6508, resulting in a sensitivity of 85.29% and specificity of 86.57%. These findings demonstrate that LFS can effectively differentiate MASLD patients from healthy individuals.\u003c/p\u003e \u003cp\u003eIn contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb shows the ROC curve for distinguishing mild hepatic steatosis from moderate-to-severe hepatic steatosis, with an AUC of 0.737 (95% CI: 0.670\u0026ndash;0.798, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Although this AUC suggests a moderate level of discriminative power, the Youden index was 0.3745 and the associated cutoff value (\u0026gt;\u0026thinsp;0.2697) yielded relatively lower sensitivity (63.77%) and specificity (73.68%). Thus, while LFS effectively distinguishes MASLD from healthy individuals, its performance in severity stratification is comparatively lower.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Risk Factor Analysis for MASLD Severity\u003c/h2\u003e \u003cp\u003eA multivariate linear regression analysis was conducted to identify independent predictors of MASLD severity (healthy control, mild, and moderate-to-severe steatosis). The overall model was statistically significant (F\u0026thinsp;=\u0026thinsp;23.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that the included variables significantly contributed to the classification of MASLD severity.\u003c/p\u003e \u003cp\u003eAmong the predictors, LFS exhibited the strongest association with MASLD severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2937, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting that higher LFS scores were strongly correlated with more severe hepatic steatosis. BMI was also positively associated with increasing MASLD severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0176, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0272), indicating that individuals with higher BMI were more likely to have more advanced hepatic steatosis.\u003c/p\u003e \u003cp\u003eIn contrast, sex, Age, AST, ALT, and TG were not significantly associated with MASLD severity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that these factors did not independently predict MASLD progression in the study cohort. Collectively, these findings highlight LFS as the strongest predictor of MASLD severity, with BMI also playing a contributory role.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically analyzed the non-invasive evaluation of metabolic dysfunction-associated steatotic liver disease (MASLD) using MRI-PDFF and NAFLD LFS, focusing on the diagnostic and stratification value of LFS. The results indicate that LFS demonstrates high accuracy in distinguishing MASLD from healthy individuals and is significantly correlated with MRI-PDFF. However, its ability to differentiate between mild and moderate-to-severe MASLD is relatively limited.\u003c/p\u003e \u003cp\u003eMRI-PDFF is currently regarded as the gold standard for quantifying liver fat content. However, its application is constrained by high costs, limited accessibility, and the requirement for patient cooperation during scanning, particularly for those unable to perform prolonged breath-holding (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In this study, 34 subjects were excluded due to motion artifacts, highlighting the potential limitations of MRI-PDFF in clinical practice. In such cases, LFS emerges as a valuable alternative based on clinical parameters, demonstrating practical utility in scenarios where MRI is unavailable or unsuitable. LFS is derived from routine clinical and biochemical indices (e.g., fasting insulin, AST/ALT ratio) and does not require imaging, making it cost-effective and easily applicable in primary healthcare settings. Our findings further confirm that LFS exhibits good accuracy in MASLD screening and serves as an effective substitute for MRI-PDFF, particularly for individuals who cannot undergo MRI assessments.\u003c/p\u003e \u003cp\u003eCorrelation analysis in this study demonstrated a significant positive correlation between LFS and MRI-PDFF (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that LFS effectively reflects liver fat accumulation. In the MASLD subgroup, LFS and MRI-PDFF maintained a moderate to strong positive correlation in both mild and moderate-to-severe cases (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51 and 0.45, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that LFS more accurately predicts liver fat content in individuals with pronounced metabolic dysfunction. However, in the healthy control group, the correlation was weaker (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), implying that LFS may have limited screening capability in individuals without significant hepatic steatosis.\u003c/p\u003e \u003cp\u003eThis study further evaluated the diagnostic performance of LFS in MASLD screening through ROC analysis. The results showed that when using MRI-PDFF\u0026thinsp;\u0026ge;\u0026thinsp;5% as the diagnostic threshold for MASLD, LFS achieved an AUC of 0.886 in distinguishing MASLD from healthy individuals, demonstrating its practical value for large-scale screening. The optimal cutoff value was \u0026minus;\u0026thinsp;0.65, yielding a sensitivity and specificity of 85% and 86%, respectively, which is largely consistent with the original study by Kotronen et al (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). (cutoff value \u0026minus;\u0026thinsp;0.64, sensitivity 86%, specificity 71%). However, in differentiating between mild and moderate-to-severe MASLD, the discriminative ability of LFS declined (AUC\u0026thinsp;=\u0026thinsp;0.737), indicating its limited effectiveness in disease severity stratification. This finding aligns with previous studies, suggesting that LFS is more suitable for binary classification (presence or absence of MASLD), whereas for stratified MASLD management, it should be combined with MRI-PDFF or other metabolic indicators for comprehensive assessment. This discrepancy may stem from the nature of LFS calculation and its applicability across different levels of hepatic steatosis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). LFS primarily relies on metabolic indicators, which may show minimal variation in healthy individuals, leading to a weaker correlation with MRI-PDFF. However, in MASLD patients, LFS effectively captures the extent of metabolic dysfunction, resulting in strong ROC performance in distinguishing MASLD from healthy individuals. Additionally, LFS values exhibit a wide distribution within MASLD patients, with some individuals with mild MASLD presenting high LFS values and some with moderate-to-severe MASLD showing relatively lower LFS values. This heterogeneity likely contributes to the lower accuracy of LFS in distinguishing different severities of MASLD. Consequently, while LFS is well-suited for identifying MASLD, further stratification requires integration with other metabolic indicators.\u003c/p\u003e \u003cp\u003eFrom a mechanistic perspective, LFS integrates multiple key metabolic parameters, including BMI, fasting insulin, and AST/ALT ratio, reflecting an individual\u0026rsquo;s metabolic state and its impact on hepatic fat accumulation (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Multivariate regression analysis further confirmed that LFS is the strongest predictor of MASLD severity, with BMI also showing a significant positive correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0272), reinforcing the role of obesity in MASLD progression. Notably, AST, ALT, and TG were not independent predictors of MASLD severity in this study, possibly because these markers may not sufficiently reflect hepatic disease progression in early metabolic dysfunction.\u003c/p\u003e \u003cp\u003eDespite confirming the utility of LFS in MASLD assessment, this study has certain limitations. First, it was a single-center prospective study with a relatively large sample size; however, the regional and ethnic characteristics of the study population may limit generalizability. Future large-scale, multicenter studies are necessary to validate LFS applicability across diverse populations. Second, while MRI-PDFF was used as the reference standard for MASLD diagnosis, it does not assess liver inflammation or fibrosis. As MASLD progresses to metabolic-associated steatohepatitis (MASH) or cirrhosis, the predictive value of LFS requires further investigation. Additionally, some LFS components (e.g., AST/ALT ratio, fasting insulin levels) may be influenced by laboratory variations and individual physiological states, potentially affecting its consistency across different populations. Thus, refining the LFS model or integrating additional biochemical and imaging parameters may enhance its overall diagnostic accuracy.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates that LFS, as a clinical and biochemical-based scoring model, exhibits strong diagnostic performance in MASLD screening and is highly consistent with MRI-PDFF. Future large-scale, multicenter studies should further refine MASLD\u0026rsquo;s non-invasive diagnostic framework and explore the role of LFS in disease progression prediction and individualized management to enhance early detection, precise evaluation, and long-term MASLD management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eMASLD\u003c/strong\u003e – Metabolic Dysfunction-Associated Steatotic Liver Disease\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMRI-PDFF\u003c/strong\u003e – Magnetic Resonance Imaging Proton Density Fat Fraction\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLFS\u003c/strong\u003e – Liver Fat Score\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMASH\u003c/strong\u003e – Metabolic Dysfunction-Associated Steatohepatitis\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHCC\u003c/strong\u003e – Hepatocellular Carcinoma\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAST\u003c/strong\u003e – Aspartate Aminotransferase\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eALT\u003c/strong\u003e – Alanine Aminotransferase\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMI\u003c/strong\u003e – Body Mass Index\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTG\u003c/strong\u003e – Triglycerides\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHDL\u003c/strong\u003e – High-Density Lipoprotein\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLDL\u003c/strong\u003e – Low-Density Lipoprotein\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e – Hemoglobin A1c\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFINS\u003c/strong\u003e – Fasting Insulin\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTR\u003c/strong\u003e – Time of Repetition\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTE\u003c/strong\u003e – Echo Time\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eROI\u003c/strong\u003e – Region of Interest\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSD\u003c/strong\u003e – Standard Deviation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eICC\u003c/strong\u003e – Intraclass Correlation Coefficient\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIQR\u003c/strong\u003e – Interquartile Range\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMHS\u003c/strong\u003e – Metabolic Hepatic Steatosis\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMSHS\u003c/strong\u003e – Metabolic Steatohepatitis Score\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAUC\u003c/strong\u003e – Area Under the Curve\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eROC\u003c/strong\u003e – Receiver Operating Characteristic\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eEslam, M., Sanyal, A. J., George, J., \u0026amp; International Consensus Panel (2020). MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology, 158(7), 1999\u0026ndash;2014.e1. https://doi.org/10.1053/j.gastro.2019.11.312\u003c/li\u003e\n \u003cli\u003eRinella, M. E., Lazarus, J. V., Ratziu, V., Francque, S. M., Sanyal, A. J., Kanwal, F., Romero, D., Abdelmalek, M. F., Anstee, Q. M., Arab, J. P., Arrese, M., Bataller, R., Beuers, U., Boursier, J., Bugianesi, E., Byrne, C. D., Castro Narro, G. E., Chowdhury, A., Cortez-Pinto, H., Cryer, D. R., \u0026hellip; NAFLD Nomenclature consensus group (2023). A multisociety Delphi consensus statement on new fatty liver disease nomenclature. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(6), 1966\u0026ndash;1986. https://doi.org/10.1097/HEP.0000000000000520\u003c/li\u003e\n \u003cli\u003eRinella, M. E., Neuschwander-Tetri, B. A., Siddiqui, M. S., Abdelmalek, M. F., Caldwell, S., Barb, D., Kleiner, D. E., \u0026amp; Loomba, R. (2023). AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(5), 1797\u0026ndash;1835. https://doi.org/10.1097/HEP.0000000000000323\u003c/li\u003e\n \u003cli\u003eChalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., Harrison, S. A., Brunt, E. M., \u0026amp; Sanyal, A. J. (2018). The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(1), 328\u0026ndash;357. https://doi.org/10.1002/hep.29367\u003c/li\u003e\n \u003cli\u003eHahn, L., Reeder, S. B., Mu\u0026ntilde;oz del Rio, A., \u0026amp; Pickhardt, P. J. (2015). Longitudinal Changes in Liver Fat Content in Asymptomatic Adults: Hepatic Attenuation on Unenhanced CT as an Imaging Biomarker for Steatosis. \u003cem\u003eAJR. American journal of roentgenology\u003c/em\u003e, \u003cem\u003e205\u003c/em\u003e(6), 1167\u0026ndash;1172. https://doi.org/10.2214/AJR.15.14724\u003c/li\u003e\n \u003cli\u003eAngulo, P., Kleiner, D. E., Dam-Larsen, S., Adams, L. A., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Keach, J. C., Lafferty, H. D., Stahler, A., Haflidadottir, S., \u0026amp; Bendtsen, F. (2015). Liver Fibrosis, but No Other Histologic Features, Is Associated With Long-term Outcomes of Patients With Nonalcoholic Fatty Liver Disease. \u003cem\u003eGastroenterology\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e(2), 389\u0026ndash;97.e10. https://doi.org/10.1053/j.gastro.2015.04.043\u003c/li\u003e\n \u003cli\u003eGeier, A., Rinella, M. E., Balp, M. M., McKenna, S. J., Brass, C. A., Przybysz, R., Cai, J., Knight, A., Gavaghan, M., Howe, T., Rosen, D., \u0026amp; Ratziu, V. (2021). Real-World Burden of Nonalcoholic Steatohepatitis. \u003cem\u003eClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), 1020\u0026ndash;1029.e7. https://doi.org/10.1016/j.cgh.2020.06.064\u003c/li\u003e\n \u003cli\u003eEslam, M., Newsome, P. N., Sarin, S. K., Anstee, Q. M., Targher, G., Romero-Gomez, M., Zelber-Sagi, S., Wai-Sun Wong, V., Dufour, J. F., Schattenberg, J. M., Kawaguchi, T., Arrese, M., Valenti, L., Shiha, G., Tiribelli, C., Yki-J\u0026auml;rvinen, H., Fan, J. G., Gr\u0026oslash;nb\u0026aelig;k, H., Yilmaz, Y., Cortez-Pinto, H., \u0026hellip; George, J. (2020). A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. \u003cem\u003eJournal of hepatology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(1), 202\u0026ndash;209. https://doi.org/10.1016/j.jhep.2020.03.039\u003c/li\u003e\n \u003cli\u003eYounossi, Z., Anstee, Q. M., Marietti, M., Hardy, T., Henry, L., Eslam, M., George, J., \u0026amp; Bugianesi, E. (2018). Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. \u003cem\u003eNature reviews. Gastroenterology \u0026amp; hepatology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 11\u0026ndash;20. https://doi.org/10.1038/nrgastro.2017.109\u003c/li\u003e\n \u003cli\u003eWong, R. J., Aguilar, M., Cheung, R., Perumpail, R. B., Harrison, S. A., Younossi, Z. M., \u0026amp; Ahmed, A. (2015). Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the United States. \u003cem\u003eGastroenterology\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e(3), 547\u0026ndash;555. https://doi.org/10.1053/j.gastro.2014.11.039\u003c/li\u003e\n \u003cli\u003eTamaki, N., Ajmera, V., \u0026amp; Loomba, R. (2022). Non-invasive methods for imaging hepatic steatosis and their clinical importance in NAFLD. \u003cem\u003eNature reviews. Endocrinology\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 55\u0026ndash;66. https://doi.org/10.1038/s41574-021-00584-0\u003c/li\u003e\n \u003cli\u003eCusi, K., Isaacs, S., Barb, D., Basu, R., Caprio, S., Garvey, W. T., Kashyap, S., Mechanick, J. I., Mouzaki, M., Nadolsky, K., Rinella, M. E., Vos, M. B., \u0026amp; Younossi, Z. (2022). American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). \u003cem\u003eEndocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(5), 528\u0026ndash;562. https://doi.org/10.1016/j.eprac.2022.03.010\u003c/li\u003e\n \u003cli\u003eHarrison, S. A., Allen, A. M., Dubourg, J., Noureddin, M., \u0026amp; Alkhouri, N. (2023). Challenges and opportunities in NASH drug development. \u003cem\u003eNature medicine\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 562\u0026ndash;573. https://doi.org/10.1038/s41591-023-02242-6\u003c/li\u003e\n \u003cli\u003eLoomba, R., Neuschwander-Tetri, B. A., Sanyal, A., Chalasani, N., Diehl, A. M., Terrault, N., Kowdley, K., Dasarathy, S., Kleiner, D., Behling, C., Lavine, J., Van Natta, M., Middleton, M., Tonascia, J., Sirlin, C., \u0026amp; NASH Clinical Research Network (2020). Multicenter Validation of Association Between Decline in MRI-PDFF and Histologic Response in NASH. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(4), 1219\u0026ndash;1229. https://doi.org/10.1002/hep.31121\u003c/li\u003e\n \u003cli\u003eBonekamp, S., Tang, A., Mashhood, A., Wolfson, T., Changchien, C., Middleton, M. S., Clark, L., Gamst, A., Loomba, R., \u0026amp; Sirlin, C. B. (2014). Spatial distribution of MRI-Determined hepatic proton density fat fraction in adults with nonalcoholic fatty liver disease. \u003cem\u003eJournal of magnetic resonance imaging : JMRI\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(6), 1525\u0026ndash;1532. https://doi.org/10.1002/jmri.24321\u003c/li\u003e\n \u003cli\u003eCaussy, C., Reeder, S. B., Sirlin, C. B., \u0026amp; Loomba, R. (2018). Noninvasive, Quantitative Assessment of Liver Fat by MRI-PDFF as an Endpoint in NASH Trials. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(2), 763\u0026ndash;772. https://doi.org/10.1002/hep.29797\u003c/li\u003e\n \u003cli\u003eNoureddin, M., Lam, J., Peterson, M. R., Middleton, M., Hamilton, G., Le, T. A., Bettencourt, R., Changchien, C., Brenner, D. A., Sirlin, C., \u0026amp; Loomba, R. (2013). Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(6), 1930\u0026ndash;1940. https://doi.org/10.1002/hep.26455\u003c/li\u003e\n \u003cli\u003eYokoo, T., Shiehmorteza, M., Hamilton, G., Wolfson, T., Schroeder, M. E., Middleton, M. S., Bydder, M., Gamst, A. C., Kono, Y., Kuo, A., Patton, H. M., Horgan, S., Lavine, J. E., Schwimmer, J. B., \u0026amp; Sirlin, C. B. (2011). Estimation of hepatic proton-density fat fraction by using MR imaging at 3.0 T. \u003cem\u003eRadiology\u003c/em\u003e, \u003cem\u003e258\u003c/em\u003e(3), 749\u0026ndash;759. https://doi.org/10.1148/radiol.10100659\u003c/li\u003e\n \u003cli\u003eKotronen A, Peltonen M, Hakkarainen A, et al. Prediction of non-alcoholic fatty liver disease and liver fat content using metabolic and genetic factors. Gastroenterology. 009;137(3):865-872. doi:10.1053/j.gastro.2009.06.005\u003c/li\u003e\n \u003cli\u003eMa, Y., Belyaeva, O. V., Brown, P. M., Fujita, K., Valles, K., Karki, S., de Boer, Y. S., Koh, C., Chen, Y., Du, X., Handelman, S. K., Chen, V., Speliotes, E. K., Nestlerode, C., Thomas, E., Kleiner, D. E., Zmuda, J. M., Sanyal, A. J., (for the Nonalcoholic Steatohepatitis Clinical Research Network), Kedishvili, N. Y., \u0026hellip; Rotman, Y. (2019). 17-Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(4), 1504\u0026ndash;1519. https://doi.org/10.1002/hep.30350\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEl Kamari, V., Hileman, C. O., Gholam, P. M., Kulkarni, M., Funderburg, N., \u0026amp; McComsey, G. A.\u003c/strong\u003e (2019). Statin therapy does not reduce liver fat scores in patients receiving antiretroviral therapy for HIV infection. \u003cem\u003eClinical Gastroenterology and Hepatology, 17\u003c/em\u003e(3), 536\u0026ndash;542. https://doi.org/10.1016/j.cgh.2018.07.034\u003c/li\u003e\n \u003cli\u003evan den Berg, E. H., Flores-Guerrero, J. L., Gruppen, E. G., de Borst, M. H., Wolak-Dinsmore, J., Connelly, M. A., Bakker, S. J. L., \u0026amp; Dullaart, R. P. F. (2019). Non-Alcoholic Fatty Liver Disease and Risk of Incident Type 2 Diabetes: Role of Circulating Branched-Chain Amino Acids. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 705. https://doi.org/10.3390/nu11030705\u003c/li\u003e\n \u003cli\u003eNiu, L., Geyer, P. E., Wewer Albrechtsen, N. J., Gluud, L. L., Santos, A., Doll, S., Treit, P. V., Holst, J. J., Knop, F. K., Vilsb\u0026oslash;ll, T., Junker, A., Sachs, S., Stemmer, K., M\u0026uuml;ller, T. D., Tsch\u0026ouml;p, M. H., Hofmann, S. M., \u0026amp; Mann, M. (2019). Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease. \u003cem\u003eMolecular systems biology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), e8793. https://doi.org/10.15252/msb.20188793\u003c/li\u003e\n \u003cli\u003eNational Workshop on Fatty Liver and Alcoholic Liver Disease,Chinese Society of Hepatology,Chinese Medical Association \u0026amp; Fatty Liver Expert Committee,Chinese Medical Doctor Association.(2018).Guidelines for the management of nonalcoholic fatty liver disease (2018 update). Journal of Clinical Hepatobiliary Diseases (05), 947-957.\u003c/li\u003e\n \u003cli\u003eGu, J., Liu, S., Du, S., Zhang, Q., Xiao, J., Dong, Q., \u0026amp; Xin, Y. (2019). Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: a meta-analysis. \u003cem\u003eEuropean radiology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(7), 3564\u0026ndash;3573. https://doi.org/10.1007/s00330-019-06072-4\u003c/li\u003e\n \u003cli\u003eZeng Jing, Fan Jiangao. Interpretation of the Guidelines for the Prevention and Control of Metabolism-Related (Non-Alcoholic) Fatty Liver Disease (2024 Edition)[J]. Chinese Journal of Arteriosclerosis,2024,32(07):553-557.DOI:10.20039/j.cnki.1007-3949.2024.07.001.\u003c/li\u003e\n \u003cli\u003eTamaki, N., Ajmera, V., \u0026amp; Loomba, R. (2022). Non-invasive methods for imaging hepatic steatosis and their clinical importance in NAFLD. Nature reviews. Endocrinology, 18(1), 55\u0026ndash;66. https://doi.org/10.1038/s41574-021-00584-0\u003c/li\u003e\n \u003cli\u003eHernando, D., Sharma, S. D., Aliyari Ghasabeh, M., et al. (2017). Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5T and 3T using a fat-water phantom. Magnetic Resonance in Medicine, 77 (4), 1516\u0026ndash;1524.\u003c/li\u003e\n \u003cli\u003eVilar-Gomez, E., \u0026amp; Chalasani, N. (2018). Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. Journal of Hepatology, 68 (2), 305\u0026ndash;315.\u003c/li\u003e\n \u003cli\u003eSaokaew, S., Kanchanasuwan, S., Apisarnthanarak, P., et al. (2017). Clinical risk scoring for predicting non-alcoholic fatty liver disease in metabolic syndrome patients (NAFLD-MS score). Liver International, 37(10), 1535\u0026ndash;1543.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The First Clinical Medical College of Lanzhou University, Lanzhou , Gansu, China ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PDFF, LFS, MASLD","lastPublishedDoi":"10.21203/rs.3.rs-6289146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6289146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study evaluates the concordance between the liver fat score (LFS) and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) in quantifying hepatic steatosis in Metabolic-associated steatotic liver disease(MASLD) patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this single-center prospective study, 271 adult participants underwent MRI-PDFF assessment between January 2023 and August 2024. Participants were categorized based on MRI-PDFF results into healthy controls (\u0026lt;5%), mild steatosis (5%–9.9%), and moderate-to-severe steatosis (≥10%). Clinical and laboratory data were collected to calculate LFS, and its correlation with MRI-PDFF was analyzed using Pearson correlation and receiver operating characteristic (ROC) curve analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e LFS showed a significant positive correlation with MRI-PDFF (\u003cem\u003er\u003c/em\u003e=0.59, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). For distinguishing MASLD patients from healthy controls, LFS achieved an area under the ROC curve (AUC) of 0.886, with an optimal cutoff value of -0.6508, yielding 85.29% sensitivity and 86.57% specificity. However, LFS demonstrated limited ability to differentiate between mild and moderate-to-severe steatosis (AUC=0.737).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eLFS correlates well with MRI-PDFF in assessing hepatic fat content and serves as a convenient alternative for MASLD screening.\u003c/p\u003e","manuscriptTitle":"Assessing the Liver Fat Score's Efficacy in MASLD Diagnosis: A Comparative Study with MRI-PDFF","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:46:59","doi":"10.21203/rs.3.rs-6289146/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29e9b4be-30ff-4e61-ada0-3f9ae1782132","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-24T03:29:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 09:46:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6289146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6289146","identity":"rs-6289146","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}