7060 Background: Changes in the bone marrow microenvironment are believed to play a major role in the biology of Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). To study the bone marrow microenvironment (BME) in MDS and AML and to compare it with normal BME, we studied the expression profile of 43 immune biomarkers and evaluated the differences in the BME between AML and MDS and compared it to that of normal BME. These 43 immune biomarkers included B- and T-cell markers, cytokines and chemokines. Methods: RNA was extracted from fresh bone marrow aspiration samples from 626 patients with AML, 564 patients with MDS, and 1449 individuals having bone marrow without any mutations or having low level mutations determined to be CHIP (clonal hematopoiesis of indeterminate potential) and considered normal. RNA levels of 42 immune biomarkers were quantified using next generation sequencing. Using a machine learning algorithm, we first selected the relative genes that distinguish between two classes using K-fold cross-validation (K = 12). The selected genes were used to predict one class from the other using random forest classifier. Samples were divided into a training set (67%) and testing set (33%) for each classification. Results: The random forest showed that MDS can easily be distinguished from normal using the expression of 15 genes (CYFIP2, CXCR4, IL1RAP, CD58, CD36, CD19, PAX5, CD79B, ID1, IL8, CD44, IL1R1, CD79A, IL21R, and CD74). The AUC for distinguishing MDS from normal was 0.996 in the training set and 0.931 (95% CI: 0.912-0.949) in the testing set. Distinguishing between AML and normal was also robust and achievable using the expression of only 10 genes (CYFIP2, IL1R1, CXCR4, IL8, IL21R, CD44, CD28, CD79A, and IL7R, and CD8A). The AUC for the training set was 0.994 and 0.972 (95% CI: 0.961-0.983) for the testing set. Eight of these 10 markers were shared with MDS algorithm. Only CD28 and IL7R were specifically needed for AML classification. Distinguishing between MDS And AML was achievable with high reliability with AUC of 0.994 (95% CI:0.992-0.997) in training set and 0.924 (95% CI: 0.896-0.952) in testing set). Only 10 biomarkers were used for distinguishing MDS from AML, nine of which (IL1R1, CYFIP2, CD44, IL1RAP, CXCR4, IL21R, CD74, IL8, and CD36) were used in distinguishing MDS from normal. The only unique biomarker was CD28. Comparing levels of these biomarkers, most of which showed highest level in normal BM, significantly lower level in MDS but the level was even significantly lower in AML (deeper reduction) than in MDS. Conclusions: This data suggests that the BME is significantly different in MDS from AML and both are different from normal. Few immune biomarkers play major role in defining each BME. However, relative increase or decrease between these immune biomarkers dictate the uniqueness of each microenvironment.
2634 Background: Poor response to immune checkpoint inhibitors (ICI) in colorectal cancer (CRC) is believed to be due to lack of immune suppressive tumor microenvironment (TME). In contrast, lung cancer TME is believed to be significantly more immunologically active and responsible for the relative success of ICI in lung cancer. We evaluated the TME in lung cancer and CRC using 43 immune biomarkers quantified using RNA sequencing and developed a model to classify TME into immunologically active (similar to lung cancer) vs inactive (similar to colorectal). These 43 immune biomarkers included B- and T-cell markers, cytokines and chemokines. Methods: RNA was extracted from FFPE samples from 707 patients with lung cancer, 227 patients with CRC, 131 patients with breast cancer, 111 patients with ovarian cancer, and 72 patients with pancreatic cancer. The expression levels of the 42 immunological markers were quantified using next generation sequencing (NGS) as a part of larger targeted RNA sequencing panel of 1408 genes. Using a machine learning algorithm, we first selected the relative genes that distinguish between two classes using two criteria: performance of each gene with K-fold cross-validation (K=12) and second based on stability measure using statistical significance tests. The selected genes were then used to predict one class from the other using random forest classifier. Samples were divided into a training set (67%) and testing set (33%). Results: A Bayesian-based algorithm selected the expression of 20 genes that were significantly relevant in differentiating between immunologically active and inactive TME. Using these 20 genes in Random Forest model, we can distinguish between lung and CRC with AUC of 0.997 (95% CI: 0.992-1.00) in the training set and AUC of 0.923 (95% CI: 0.880-0.966) in the testing set. Testing 131 breast cancer samples showed 23 (18%) with TME that can be classified as immunologically active. Of 111 ovarian samples 13 (12%) showed immunologically active TME and of 72 pancreatic samples, 17 (24%) showed microenvironment classified as active. The 20 genes that were adequate to distinguish between TME active vs inactive are: CD74, FCGBP, IL1R1, CD44, CD274, FCGR2B, IL21R, IL1RAP, IL7R, CD79A, CCL2, CYFIP2, CD19, IL2RA, CD8A, CD79B, ID1, CD22, FZD10, and IL1B, listed in order of their importance. Conclusions: This data shows that lung cancer TME is significantly different from that of CRC. Only 20 immune biomarkers adequate to distinguish between the two TME. The relevant biomarkers included CD274 (PD-L1) as well as one marker for T-cells (CD8A) but three markers for B-cells (CD19, CD22 and CD79B). This suggests that B-cells play a significant role in immunologically active TME and should be explored further as biomarkers for predicting response to ICI therapy.
Background: Determining the need for prostate biopsy is frequently difficult and more objective criteria are needed to predict the presence of high grade prostate cancer (PCa).To reduce the rate of unnecessary biopsies, we explored the potential of using biomarkers in urine and plasma to develop a scoring system to predict prostate biopsy results and the presence of high grade PCa.Methods: Urine and plasma specimens were collected from 319 patients recommended for prostate biopsies.We measured the gene expression levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, B2M, AR, and PTEN in plasma and urine.Patient age, serum prostate-specific antigen (sPSA) level, and biomarkers data were used to develop two independent algorithms, one for predicting the presence of PCa and the other for predicting high-grade PCa (Gleason score [GS] ≥7).Results: Using training and validation data sets, a model for predicting the outcome of PCa biopsy was developed with an area under receiver operating characteristic curve (AUROC) of 0.87.The positive and negative predictive values (PPV and NPV) were 87% and 63%, respectively.We then developed a second algorithm to identify patients with high-grade PCa (GS ≥7).This algorithm's AUROC was 0.80, and had a PPV and NPV of 56% and 77%, respectively.Patients who demonstrated concordant results using both algorithms showed a sensitivity of 84% and specificity of 93% for predicting high-grade aggressive PCa.Thus, the use of both algorithms resulted in a PPV of 90% and NPV of 89% for predicting high-grade PCa with toleration of some low-grade PCa (GS <7) being detected.Conclusions: This model of a biomarker panel with algorithmic interpretation can be used as a "liquid biopsy" to reduce the need for unnecessary tissue biopsies, and help to guide appropriate treatment decisions.
Objective: To investigate the diagnostic value of serum α-enolase (ENO1) in the primary hepatocellular carcinoma. Methods: From May 2012 to March 2017, 163 cases with liver diseases who met the inclusion and exclusion criteria were admitted to the Infectious Diseases Department of the General Hospital of Ningxia Medical University. Among them, 28 cases were of chronic hepatitis B (CHB), 31 cases with liver cirrhosis (LC), 104 cases with hepatocellular carcinoma (HCC), and 18 healthy volunteers (NC). Patient data and serum samples were collected and liver disease related indicators were measured to detect ENO1 levels with enzyme-linked immunosorbent assay (ELISA). The measured indicators were expressed in median. Mann-Whitney U nonparametric test was used to analyze the differences between the data. A Spearman's correlation analysis was used for bivariate correlation analysis. The sensitivity and specificity of ENO1 and alpha-fetoprotein in the diagnosis of liver cancer were analyzed by ROC curve. Results: Serum level of ENO1 in CHB group, LC group and HCC group was significantly higher than normal group. Serum level of ENO1 in HCC group was higher than CHB group (P = 0.001) and LC group (P < 0.01). Area under the curve (AUC) for serum ENO1 and alpha-fetoprotein were 0.782 (cut-off value 75.96, P = 0.000 1) and 0.800 (cut-off value 27.02, P = 0.000 1), respectively. There was a positive correlation between ENO1 and AFP (P = 0.001). The combined detection had significantly improved the detection efficiency (AUC = 0.835). Serum ENO1 was statistically significant (P < 0.05) in HCC tumor size (AUC = 0.663), tumor metastasis (AUC = 0.681), TNM stage (AUC = 0.710, stage I vs. II), and Edmondson grade (AUC = 0.685) (P < 0.05) and the elevated levels of ENO1 had significantly reduced (P < 0.05) the survival time. Conclusion: ENO1 can be a new candidate marker for the diagnosis of early stage HCC and its progression.目的： 探讨血清α-烯醇化酶（ENO1）在原发性肝细胞肝癌诊断中的价值。 方法： 收集2012年5月-2017年3月入住宁夏医科大学总医院感染性疾病科并符合纳入与排除标准的肝病患者163例，其中慢性乙型肝炎（CHB）组28例，肝硬化（LC）组31例，肝癌（HCC）组104例；另收集对照健康志愿者（NC）组18例。采集血清标本用酶联免疫吸附法检测血清ENO1水平，收集患者资料，并检测肝病相关指标，测定指标均用中位数表示，采用Mann-Whitney U非参数检验进行资料间的差异分析；采用双变量斯皮尔曼相关性检验进行相关性分析；采用ROC曲线分析ENO1、甲胎蛋白在肝癌诊断中的敏感性和特异性。 结果： ENO1血清水平在CHB组、LC组、HCC组显著高于正常人群，HCC组ENO1血清水平高于CHB组（P = 0.001）及LC组（P < 0.01）。血清ENO1、甲胎蛋白在本组HCC诊断中曲线下面积（AUC）分别为0.782（界值75.96，P = 0.000 1）、0.800（界值27.02，P = 0.000 1），ENO1与AFP呈正相关（P = 0.001），联合检测能够显著提高检测效率（AUC = 0.835）。血清ENO1在HCC肿瘤大小（AUC = 0.663）、肿瘤是否转移（AUC=0.681）、TNM分期（I、II期比较，AUC = 0.710）、Edmondson分级（AUC = 0.685）中差异有统计学意义（P < 0.05）；高水平ENO1生存期显著降低（P < 0.05）。 结论： ENO1能够成为一种新的早期肝癌及肝癌进展的候选诊断标志物。.
Myelodysplastic syndrome (MDS) may be induced by certain mutagenic environmental or chemotherapeutic toxins; however, the role of susceptibility genes remains unclear. The G/G genotype of the single-nucleotide polymorphism (SNP) rs1617640 in the erythropoietin (EPO) promoter has been shown to be associated with decreased EPO expression. We examined the association of rs1617640 genotype with MDS.We genotyped the EPO rS1617640 SNP in 189 patients with MDS, 257 with acute myeloid leukemia (AML), 106 with acute lymphoblastic leukemia, 97 with chronic lymphocytic leukemia, 353 with chronic myeloid leukemia, and 95 healthy controls.The G/G genotype was significantly more common in MDS patients (47/187; 25.1%) than in controls (6/95; 6.3%) or in patients with other leukemias (101/813; 12.4%) (all P < 0.001). Individuals with the G/G genotype were more likely than those with other genotypes to have MDS (odd ratio = 4.98; 95% CI = 2.04-12.13). Clinical and follow up data were available for 112 MDS patients and 186 AML patients. There was no correlation between EPO promoter genotype and response to therapy or overall survival in MDS or AML. In the MDS group, the GG genotype was significantly associated with shorter complete remission duration, as compared with the TT genotype (P = 0.03). Time to neutrophils recovery after therapy was significantly longer in MDS patients with the G/G genotype (P = 0.02).These findings suggest a strong association between the rs1617640 G/G genotype and MDS. Further studies are warranted to investigate the utility of screening for this marker in individuals exposed to environmental toxins or chemotherapy.
Background Mutations in the thrombopoietin receptor (MPL) may activate relevant pathways and lead to chronic myeloproliferative neoplasms (MPNs). The mechanisms of MPL activation remain elusive because of a lack of experimental structures. Modern computational biology techniques were utilized to explore the mechanisms of MPL protein activation due to various mutations. Results Transmembrane (TM) domain predictions, homology modeling, ab initio protein structure prediction, and molecular dynamics (MD) simulations were used to build structural dynamic models of wild-type and four clinically observed mutants of MPL. The simulation results suggest that S505 and W515 are important in keeping the TM domain in its correct position within the membrane. Mutations at either of these two positions cause movement of the TM domain, altering the conformation of the nearby intracellular domain in unexpected ways, and may cause the unwanted constitutive activation of MPL's kinase partner, JAK2. Conclusions Our findings represent the first full-scale molecular dynamics simulations of the wild-type and clinically observed mutants of the MPL protein, a critical element of the MPL-JAK2-STAT signaling pathway. In contrast to usual explanations for the activation mechanism that are based on the relative translational movement between rigid domains of MPL, our results suggest that mutations within the TM region could result in conformational changes including tilt and rotation (azimuthal) angles along the membrane axis. Such changes may significantly alter the conformation of the adjacent and intrinsically flexible intracellular domain. Hence, caution should be exercised when interpreting experimental evidence based on rigid models of cytokine receptors or similar systems.
Abstract Background and Aim: A reliable test for the detection of hepatocellular carcinoma (HCC) could improve disease management. Recent reports suggested a link between abnormalities in the ubiquitin‐proteasome system (UPS) and HCC. We investigated the potential of using UPS markers, along with HCC markers, to differentiate HCC from chronic liver disease (CLD). Methods: Sera from 135 HCC and 262 CLD patients were retrospectively analyzed for levels of UPS markers (proteasome, ubiquitin, and proteasome enzymatic activities) and the conventional HCC markers alpha fetoprotein (AFP), AFP‐L3, and des‐gamma‐carboxyprothrombin (DCP). Multivariate logistic regression analysis was used to develop a model for differentiating HCC from CLD. The model was developed using a subset of 98 HCC patients and 104 CLD patients with advanced fibrosis or cirrhosis (Metavir F3‐4) and then validated using an independent set (37 HCC and 44 CLD (F3‐4)). Results: A UPS signature model incorporating six markers (trypsin‐like, caspase‐like, chymotrypsin‐like, and normalized chymotrypsin‐like activities of proteasomes; AFP; and DCP) accurately differentiated HCC from CLD (area under the curve = 0.938 [95% confidence interval, 0.884–0.991]). When analysis was restricted to patients with tumors ≤ 3 cm, the UPS model exhibited higher sensitivity (83.1% vs 51.8%) and specificity (90.2% vs 83.7%) than the three conventional markers, with good positive predictive values (34.2% vs 15.1%). These results were confirmed in the independent validation set. Conclusion: The UPS signature in combination with AFP and DCP provides sensitive and specific differentiation of HCC in patients with CLD. The importance of the UPS in HCC suggests that therapeutic approaches targeting the UPS should be explored.
e15064 Background: Current recommendations for colorectal cancer testing include KRAS and NRAS for anti-EGFR therapy, and BRAF mutational analysis with microsatellite instability (MSI) testing for prognostic stratification and Lynch syndrome. We developed a multi-modality colorectal cancer profile useful for clinical management. Methods: 133 Colorectal cancer samples were profiled with our platform, which includes 1) 21 gene mutation analysis using deep sequencing (ca. 15K reads), 2) MSI status, 3) MLH1 Promoter Methylation, 4) MET amplification and PTEN deletion by FISH, and 5) PD-L1 expression (30% of samples) by IHC. Results: 99% of tumors contained at least 1 mutation found by NGS. The most commonly observed mutations were TP53(85%), KRAS (49%), PIK3CA(26%), BRAF(19%), EGFR(16.5%), NRAS(8%), FGFR3(6%), HRAS(5%), KIT (5%), SMO(5%), JAK3(5%), and ERBB2(5%). 39 patients had a TP53 mutation allele frequency consistent with germline mutations, raising the possibility of a Li–Fraumeni syndrome. Most tumors (51%) also had at least 1 abnormal non-sequencing result. The most common findings were PTEN deletion (25%), MLH1(17%) methylation, MSI(11%), PDL1 (17.5%) overexpression. PTEN/MET/PDL1 was anticorrelated to MSH/MLH status. (p < 0.05). Our integrated profile (NGS, FISH, MSI, MLH) robustly recapitulated hypermutation profiles that were associated with BRAF+/MLH+/MSH+ profiles using comprehensive WGS in a recent TCGA study. In addition, 25%(10) were KRAS-/MLH-/MSH-/BRAF- of unknown prognosis without PTEN deletion status. The integrated profile also identified ~2 percent (3/131) of patients as candidates for Lynch Syndrome testing (MSH+/MLH-/BRAF- and < 50 years). Conclusions: Multimodality Colorectal Cancer profiling identified patients with potential new targeted therapy. Specifically, we identified a significant number of cases that were PTEN, PI3KCA, HRAS, FGFR3, MET, KIT, ERBB2, and PDL1 positive that change treatment options. In addition, to identifying patients with prognostic status (e.g.BRAF+, MSI+/MLH+), we identified important candidates for Lynch syndrome testing or possibly Li-Fraumeni Syndrome who would benefit from alternative treatments and different management.
'/%3e%3cpath d='m221.969 53.125-5.156 9.656 5.156 9.625 5.156-9.625-5.156-9.656zm0 24.375-5.156 9.625 5.156 9.656 5.156-9.656-5.156-9.625zm-18.382-2.547 8.132 5.156 8.105-5.156-8.105-5.156-8.132 5.156zm20.526 0 8.106 5.156 8.131-5.156-8.131-5.156-8.106 5.156z'/%3e%3ccircle cx='221.969' cy='75.069' r='3.906'/%3e%3ccircle cx='221.969' cy='100' r='10.455'/%3e%3cpath fill='none' stroke-width='1.5' d='M219.344 89.875c0 3.114-.022 4.924-.031 6.625-2.52.112-5.006.377-7.438.781m20.188 0a62.126 62.126 0 0 0-7.438-.781v-6.625m-12.781 12.688a61.391 61.391 0 0 1 10.125-.844c3.45 0 6.83.293 10.125.844'/%3e%3cpath d='M211.75 117.688c-.992 17.082-3.009 34.479-9.656 47.124l10.812-4.375c3.777-14.328 4.57-32.842 5.719-41.593l-6.875-1.156zm20.438 0-6.875 1.156c1.148 8.75 1.942 27.265 5.718 41.594l10.813 4.375c-6.648-12.646-8.665-30.043-9.656-47.125z'/%3e%3cpath d='M221.953 154.688c-12.913 0-22.399 6.086-22.969 21.593-3.156-5.555-16.51-23.025-28.968-20.687-7.41 1.39-13.957 11.666-12.844 23.437-6.135-17.63-24.107-20.519-37.219-10.094 11.642 9.573 16.821 37.835 26.625 50.094h150.75c9.804-12.259 15.014-40.521 26.656-50.094-13.112-10.425-31.083-7.536-37.218 10.094 1.113-11.771-5.466-22.047-12.875-23.437-12.459-2.338-25.781 15.132-28.938 20.687-.57-15.507-10.086-21.594-23-21.594z'/%3e%3cg stroke-width='1.5'%3e%3cpath fill='%23000' d='M297.107 219.026c0 5.58-33.662 11.718-75.138 11.718-41.476 0-75.138-6.137-75.138-11.718s33.662-8.502 75.138-8.502c41.476 0 75.138 2.92 75.138 8.502z'/%3e%3ccircle cx='221.969' cy='114.445' r='3.504'/%3e%3ccircle cx='221.969' cy='122.027' r='3.734'/%3e%3ccircle cx='221.969' cy='130.184' r='4.079'/%3e%3ccircle cx='221.969' cy='139.261' r='4.653'/%3e%3ccircle cx='221.969' cy='149.371' r='5.113'/%3e%3cpath fill='%23000' stroke='none' d='M219.938 159.206c-.553-.007-1.076.459-.938 1.344.163 1.043.367 2.995.563 4.312.22 1.493 1.09 1.13 1.312-.031.221-1.161.132-1.906.188-4.063.027-1.078-.573-1.555-1.125-1.562zm4.062 0c-.553.007-1.153.484-1.125 1.562.055 2.157-.034 2.902.188 4.063.22 1.161 1.091 1.524 1.312.031.195-1.317.4-3.269.563-4.312.138-.885-.385-1.351-.938-1.344zm-7.687.562c-.506.07-1.03.58-1 1.125.055.996.33 2.19.437 3.688.11 1.548 1.202.947 1.313.062.11-.884.235-2.192.125-3.906-.042-.643-.323-.925-.657-.969a.842.842 0 0 0-.218 0zm11.093 0c-.334.043-.615.326-.656.97-.11 1.713.015 3.02.125 3.905.11.885 1.202 1.486 1.313-.062.107-1.499.382-2.692.437-3.688.03-.544-.494-1.054-1-1.125a.842.842 0 0 0-.219 0zm-15.437 1.75c-.463.12-.889.677-.75 1.313.276 1.272.531 2.678.531 3.969 0 1.106.945.711 1-.063a59.74 59.74 0 0 0 .156-3.906c0-1.115-.474-1.432-.937-1.313zm19.687 0c-.329.091-.625.477-.625 1.313 0 1.493.102 3.132.157 3.906.055.774 1 1.169 1 .063 0-1.291.254-2.697.53-3.97.14-.635-.286-1.192-.75-1.312a.551.551 0 0 0-.312 0zm-59.093.171c-.555-.018-.943.734-.563 1.563.608 1.327 1.254 2.165 1.875 3.594.553 1.271 1.401.478 1.125-.407-.277-.884-.577-1.87-1.406-3.75-.311-.705-.699-.989-1.031-1zm98.812 0c-.332.011-.72.295-1.031 1-.83 1.88-1.13 2.866-1.406 3.75-.277.885.571 1.678 1.125.407.62-1.43 1.266-2.267 1.875-3.594.38-.83-.009-1.581-.563-1.563zm-94.812.063c-.408.124-.656.642-.407 1.25.498 1.216 1.217 2.463 1.719 3.781.442 1.161 1.426.832 1.094-.219-.332-1.05-.43-1.665-1.094-3.656-.332-.995-.905-1.28-1.312-1.156zm90.5 0c-.35.074-.752.41-1 1.156-.664 1.99-.763 2.606-1.094 3.656-.332 1.05.651 1.38 1.094.22.502-1.319 1.22-2.566 1.718-3.782.25-.608.002-1.126-.406-1.25a.592.592 0 0 0-.312 0zm-85.5.969c-.515.097-.913.882-.563 1.843.443 1.217 1.072 2.368 1.625 3.75.553 1.383 1.471 1.104 1.25.22-.221-.886-.492-2.352-1.156-4.563-.29-.968-.757-1.327-1.156-1.25zm80.812 0c-.4-.077-.866.282-1.156 1.25-.664 2.211-.935 3.677-1.156 4.562-.222.885.696 1.164 1.25-.219.553-1.382 1.182-2.533 1.625-3.75.35-.961-.049-1.746-.563-1.843zm-93.187.687c-.557.057-1.065.965-.72 1.656.554 1.106.904 1.483 1.438 2.657.553 1.216 1.159.274.938-.5-.222-.775-.505-1.676-.781-2.782-.203-.81-.541-1.065-.875-1.03zm105.562 0c-.334-.034-.672.221-.875 1.031-.276 1.106-.56 2.007-.781 2.782-.222.774.384 1.716.937.5.534-1.174.884-1.55 1.438-2.657.345-.69-.162-1.599-.719-1.656zm-67.031 2.798c-.584 0-1.208.774-1 1.5.331 1.161.844 2.19 1.219 3.687.331 1.327 1.274.666 1.218-.218-.055-.885-.207-2.089-.593-3.97-.146-.704-.494-.999-.844-1zm28.5 0c-.35 0-.699.295-.844 1-.387 1.88-.538 3.084-.594 3.969-.055.884.887 1.545 1.219.218.374-1.496.887-2.526 1.219-3.687.207-.726-.416-1.5-1-1.5zm-14.25 1.187c-.72 0-.819.966-.844 1.75-.083 2.572-1.15 5.069-2.062 6.313-.913 1.244-2.256.913-3.5.25-1.245-.664-1.986-1.16-3.313-2.156-1.327-.996-2.334-.414-.75 1.843 4.617 6.58 9.625 12.205 9.625 22.938 0 1.388.242 1.812.844 1.812.601 0 .875-.424.875-1.812 0-10.733 4.976-16.357 9.594-22.938 1.583-2.257.577-2.839-.75-1.843-1.327.995-2.069 1.492-3.313 2.156-1.244.663-2.588.994-3.5-.25-.913-1.244-1.98-3.741-2.062-6.313-.026-.784-.125-1.75-.844-1.75zm-35.281-1.61a.6.6 0 0 0-.188.031c-.269.085-.448.41-.344.97.186 1.001.88 2.966 1.157 3.905.276.94 1.432.74 1.156-.53-.277-1.273-.262-1.657-.594-3.095-.181-.786-.747-1.269-1.187-1.28zm70.562 0c-.44.012-1.006.495-1.187 1.281-.332 1.438-.318 1.822-.594 3.094-.277 1.272.88 1.472 1.156.531.276-.94.97-2.904 1.156-3.906.104-.56-.075-.884-.343-.969a.6.6 0 0 0-.188-.03zm-91.531.406c-.579.046-.994.773-.406 1.844.633 1.157 1.72 2.608 2.218 3.438.498.829 1.359.27.75-.782-.608-1.05-.812-2.257-1.531-3.75-.27-.56-.684-.777-1.031-.75zm112.5 0c-.348-.027-.762.19-1.031.75-.72 1.493-.924 2.7-1.532 3.75-.608 1.051.253 1.61.75.782.498-.83 1.585-2.281 2.219-3.438.588-1.071.173-1.798-.406-1.844zm-144.188 3.33c-.668-.036-.877 1.17-.25 1.968.913 1.161 1.548 1.777 2.594 2.719.83.746 1.07-.222.656-.969-.414-.746-1.014-1.505-1.843-2.75-.455-.682-.853-.952-1.157-.969zm175.875 0c-.303.016-.701.286-1.156.968-.83 1.245-1.43 2.004-1.844 2.75-.414.747-.173 1.715.657.969 1.046-.942 1.68-1.558 2.593-2.719.627-.798.418-2.004-.25-1.969zm-170.843 1c-.533.006-.884.4-.47 1.312.595 1.306 1.378 2.786 1.876 3.781.497.995 2.014 1.33 1.406-.219-.608-1.548-.728-2.343-1.281-3.78-.277-.72-1-1.102-1.531-1.095zm165.812 0c-.532-.008-1.255.374-1.531 1.093-.553 1.438-.673 2.233-1.281 3.781-.609 1.549.908 1.214 1.406.22.498-.996 1.28-2.476 1.875-3.782.414-.913.063-1.306-.469-1.313zm-175.562.343c-.774.008-.84.784-.157 1.281.913.664 2 1.4 3.063 2.25 1.244.996 1.726.08 1.062-.75-.663-.829-1.31-1.67-2.968-2.5-.415-.207-.743-.284-1-.28zm185.312 0c-.258-.002-.585.074-1 .281-1.659.83-2.305 1.671-2.969 2.5-.663.83-.181 1.746 1.063.75 1.062-.85 2.15-1.586 3.062-2.25.684-.497.618-1.273-.156-1.28zm-109.656.719c-.433.06-.807.552-.625 1.187.331 1.161.708 2.392.937 3.594.222 1.162 1.565 1.176 1.344.125-.22-1.05-.332-2.365-.719-3.969-.145-.601-.448-.89-.75-.937a.637.637 0 0 0-.187 0zm33.812 0c-.301.047-.605.336-.75.937-.386 1.604-.498 2.919-.718 3.97-.222 1.05 1.122 1.036 1.343-.126.23-1.202.606-2.433.938-3.594.181-.635-.192-1.127-.625-1.187a.637.637 0 0 0-.188 0zm-63.906-1.985c-.34.094-.536.744-.281 1.656 1.41 5.06 1.842 7.724 1.594 8.969-.25 1.244-.932 1.414-1.844 1a18.844 18.844 0 0 1-2.813-1.594c-.829-.581-1.692.114-.5 1.094 6.055 4.976 10.252 10.664 11.875 17.562.332 1.41 1.218 1.576.969 0-1.372-8.69-1.636-15.833.438-20.312.779-1.684-.006-3.3-1.25-.563-.83 1.825-2.172 2.545-3.25.969-1.079-1.576-3.306-5.78-3.97-7.688-.248-.715-.555-1.046-.812-1.093a.354.354 0 0 0-.156 0zm94.031 0c-.256.047-.563.378-.812 1.093-.664 1.908-2.89 6.112-3.969 7.688-1.079 1.576-2.42.856-3.25-.969-1.244-2.737-2.03-1.12-1.25.563 2.074 4.479 1.81 11.622.438 20.312-.25 1.576.637 1.41.968 0 1.623-6.898 5.82-12.586 11.875-17.562 1.192-.98.33-1.675-.5-1.094-.829.58-1.9 1.179-2.812 1.594-.912.414-1.595.244-1.844-1-.249-1.245.184-3.91 1.594-8.97.254-.911.058-1.561-.281-1.655a.354.354 0 0 0-.157 0zm-124.468 2.422c-.547.007-1 .39-.75 1.22.398 1.328 1.223 2.764 1.5 3.593.276.83 1.165.442 1-.719-.166-1.16-.248-1.853-.47-3.125-.11-.636-.734-.976-1.28-.969zm155.062 0c-.546-.007-1.17.333-1.281.97-.222 1.271-.303 1.963-.469 3.124-.166 1.161.723 1.548 1 .719.276-.83 1.101-2.265 1.5-3.594.249-.83-.204-1.212-.75-1.219zm-136.281-1.735c-.635-.114-.727 1.524-.188 2.188.72.885 1.718 1.954 2.719 3.156.83.995 1.342.11.844-.719-.498-.83-1.236-1.711-2.563-3.812-.332-.526-.6-.775-.812-.813zm117.5 0c-.212.038-.48.287-.813.813-1.327 2.1-2.065 2.982-2.562 3.812-.498.83.014 1.714.844.719 1-1.202 2-2.271 2.718-3.156.54-.664.448-2.302-.187-2.188zm-132 3.954c-.394-.028-.729.292-.563 1.094.232 1.116.567 2.043.844 3.094.276 1.05 1 .87 1-.125 0-.996-.178-2.106-.344-3.157-.083-.525-.543-.878-.937-.906zm146.5 0c-.394.028-.855.381-.938.906-.166 1.051-.344 2.161-.343 3.157 0 .995.723 1.175 1 .125.276-1.051.612-1.978.843-3.094.166-.802-.168-1.122-.562-1.094zm-133.406 1.421c-.62.094-1.164.692-.438 1.625 1.161 1.493 2.432 2.54 2.875 3.094.442.553 1.563.349.844-.813-.735-1.186-1.846-2.615-2.344-3.5-.187-.331-.566-.462-.937-.406zm120.062 0c-.272.029-.548.158-.687.406-.498.885-1.61 2.314-2.344 3.5-.72 1.162.401 1.366.844.813.442-.553 1.713-1.601 2.875-3.094.725-.933.18-1.531-.438-1.625a.889.889 0 0 0-.25 0zm-140.719 4.173c-.488-.044-.657.35 0 1.25 1.493 2.046 3.14 5.142 3.25 6.469.111 1.327-.545 1.312-1.156 1.312-1.825 0-2.716-1.332-4.375-1.719-1.659-.387-1.9.486-.719 1.344 6.083 4.424 12.179 9.36 14.625 13.844.995 1.825 2.34 2.492 1.594.75-2.287-5.337-2.841-9.857-2.344-12.594.498-2.737 1.177-4.58 1.094-6.156-.083-1.576-.977-1.468-1.344 0-.249.995-.73 2.314-1.062 2.812-.332.498-1.666.777-2.938-.937-1.272-1.714-4.48-5.015-5.531-5.844-.394-.31-.8-.505-1.094-.531zm161.625 0c-.293.026-.7.22-1.093.531-1.051.83-4.26 4.13-5.532 5.844-1.272 1.714-2.606 1.435-2.937.937-.332-.498-.814-1.817-1.063-2.812-.367-1.468-1.26-1.576-1.343 0-.083 1.576.595 3.419 1.093 6.156.498 2.737-.056 7.257-2.343 12.594-.747 1.742.598 1.075 1.593-.75 2.447-4.485 8.543-9.42 14.625-13.844 1.181-.858.94-1.73-.718-1.344-1.66.387-2.55 1.72-4.375 1.72-.611 0-1.268.014-1.157-1.313.11-1.327 1.757-4.423 3.25-6.47.657-.9.489-1.293 0-1.25z'/%3e%3cpath d='m150.127 212.651 1.95 6.174m2.06-7.344 1.728 6.24m2.605-6.953 1.187 6.365m2.833-7.169 1.27 6.349m3.886-6.858 1.032 6.392m4.452-6.385 1.112 6.378m4.242-8.189.803 6.425m4.234-6.1.803 6.425m4.071-7.074.804 6.424m4.089-6.93.442 6.46m4.786-6.461.384 6.463m4.357-6.79.326 6.467m4.9-6.793.272 6.47m5.74-6.632.272 6.469m79.044.177-1.95 6.174m-2.06-7.344-1.729 6.24m-2.604-6.953-1.187 6.365m-2.833-7.169-1.27 6.349m-3.886-6.858-1.033 6.392m-4.452-6.385-1.112 6.378m-4.241-8.189-.804 6.425m-4.233-6.1-.804 6.425m-4.07-7.074-.804 6.424m-4.09-6.93-.441 6.46m-4.787-6.461-.383 6.463m-4.357-6.79-.327 6.467m-4.9-6.793-.271 6.47m-5.74-6.632-.272 6.469m-7.202-6.89v7.123'/%3e%3c/g%3e%3c/g%3e%3c/svg%3e)