Baseline characteristics in CAD and non-CAD group
Table 1 presents the clinical characteristics and laboratory parameters of 613 NAFLD patients (336 with CAD and 277 without CAD). Compared with non-CAD patients, CAD patients are older, more likely to be male and smokers, and have higher rates of diabetes and hypertension. Laboratory data shows that compared with non-CAD patients, CAD patients have higher ALT, AST. And the levels of Scr, TG, FPG and some inflammatory markers (WBC, MON, NEU) were higher, while the HDL-C of the CAD group was lower. On the other hand, the MHR, NHR, and NLR of CAD patients were higher than those of non-CAD patients (P < 0.05) (Fig. 1). There was no significant difference in BMI, TC, LDL-C, and LYM between the two groups (P > 0.05).
Table 1 Baseline characteristics of NAFLD patients with and without CAD.
|
CAD(n = 336)
|
non-CAD(n = 277)
|
P value
|
Age (Male)
|
57.67 ± 10.64
|
54.67 ± 9.87
|
< 0.001
|
Gender(years)
|
235(66.9)
|
137(49.5)
|
< 0.001
|
Smoke[(n%)]
|
184(54.8)
|
85(30.7)
|
< 0.001
|
HT[(n%)]
|
223(66.4)
|
119(43)
|
< 0.001
|
DM[(n%)]
|
115(34.2)
|
46(16.6)
|
< 0.001
|
BMI (kg/M2)
|
25.09(24.21,27.55)
|
25.64(24.69,27.34)
|
0.124
|
SBP (mmHg)
|
132.49 ± 19.41
|
130.51 ± 18.84
|
0.071
|
DBP (mmHg)
|
80.69 ± 13.21
|
79.46 ± 12.10
|
0.218
|
ALT(U/L)
|
28.30(18.60,40.75)
|
21.7(16.00,31.70)
|
< 0.001
|
AST(U/L)
|
24.7(19.20,35.50)
|
21.55(17.90,26.25)
|
< 0.001
|
Scr(mmol/L)
|
68.01 ± 15.94
|
64.30 ± 12.59
|
0.002
|
TC (mmol/L)
|
4.47 ± 1.14
|
4.49 ± 1.09
|
0.804
|
TG (mmol/L)
|
2.31 ± 1.77
|
1.83 ± 0.81
|
< 0.001
|
HDL-C(mmol/L)
|
1.08 ± 0.25
|
1.19 ± 0.27
|
< 0.001
|
LDL-C(mmol/L)
|
2.39 ± 0.71
|
2.38 ± 0.74
|
0.998
|
FPG (mmol/L)
|
5.86(5.03,7.29)
|
5.33(4.94,5.94)
|
< 0.001
|
WBC (109/L)
|
7.01(5.83,8.81)
|
6.02(5.15,7.54)
|
< 0.001
|
MON (109/L)
|
0.51 ± 0.16
|
0.44 ± 0.14
|
< 0.001
|
LYM (109/L)
|
1.81(1.46,2.26)
|
1.92(1.57,2.28)
|
0.149
|
NEU (109/L)
|
4.32(3.33,5.76)
|
3.44(2.72,4.66)
|
< 0.001
|
MHR
|
0.47(0.35,0.61)
|
0.37(0.27,0.48)
|
< 0.001
|
NHR
|
4.14(3.01,5.53)
|
2.89(2.22,4.09)
|
< 0.001
|
NLR
|
2.32(1.70,3.22)
|
1.78(1.37,2.53)
|
< 0.001
|
Values are expressed as mean ± standard deviation, no. (%), or median (interquartile range). P < 0.05 (two-sided) was defined as statistically significant. Bold values indicate statistically significance. |
CAD: coronary artery disease; BMI body mass index, HT hypertension, DM diabetes mellitus, SBP systolic blood pressure, DBP diastolic blood pressure, ALT alanine aminotransferase, AST aspartate aminotransferase, Cr creatinine, TC total cholesterol, TG triglycerides, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, FPG fasting plasma glucose, WBC white blood cell, MON monocytes, LYM lymphocytes, NEU neutrophils, MHR Monocyte-to-HDL-cholesterol ratio, NHR neutrophil-to- HDL-cholesterol ratio, NLR neutrophil-to-lymphocyte ratio. |
Logistic regression analysis of MHR, NHR and NLR in CAD and non-CAD patients
We used univariate and multivariate logistic regression to analyze the association between CAD and different inflammatory markers (Table 2). Whether in univariate analysis (Model 0) or after adjustment for age, sex, ALT, AST, Scr and FPG (Model 1), or further adjustment for smoking, history of hypertension and diabetes (Model 2), the levels of TG and HDL-C are all related to CAD. At the same time, we found that MHR (OR = 15.57, 95% CI: 4.68–51.85, p < 0.001), NHR (OR = 1.36, 95% CI: 1.21–1.53, p < 0.001), NLR (OR = 1.28, 95% CI: 1.09–1.49, p = 0.002) and log-transformed MHR (OR = 3.14, 95% CI: 1.84–5.38, p < 0.001) were independently associated with CAD (Table 2 and Fig. 2).
Table 2 Association between MHR, NHR NLR, and CAD in logistic regression Models.
|
Model 0
|
Model 1
|
Model 2
|
OR (95%CI)
|
P
|
OR (95%CI)
|
P
|
OR (95%CI)
|
P
|
TC (mmol/L)
|
0.98(0.85–1.13)
|
0.804
|
|
|
|
|
TG (mmol/L)
|
1.34(1.15–1.56)
|
< 0.001
|
1.27(1.07–1.51)
|
0.007
|
1.26(1.05–1.50)
|
0.012
|
HDL-C(mmol/L)
|
0.17(0.09–0.33)
|
< 0.001
|
0.17(0.08–0.38)
|
< 0.001
|
0.19(0.08–0.42)
|
< 0.001
|
LDL-C(mmol/L)
|
1.00(0.80–1.25)
|
0.998
|
|
|
|
|
WBC (109/L)
|
1.31(1.20–1.43)
|
< 0.001
|
1.25(1.13–1.38)
|
< 0.001
|
1.23(1.11–1.36)
|
< 0.001
|
MON (109/L)
|
17.04(5.59–51.94)
|
< 0.001
|
6.93(1.89–25.44)
|
0.004
|
5.43(1.42–20.80)
|
0.013
|
NEU (109/L)
|
1.40(1.26–1.55)
|
< 0.001
|
1.28(1.14–1.44)
|
< 0.001
|
1.28(1.13–1.43)
|
< 0.001
|
MHR
|
21.56(8.35–55.65)
|
< 0.001
|
18.42(5.69–59.65)
|
< 0.001
|
15.57(4.68–51.85)
|
< 0.001
|
NHR
|
1.47(1.33–1.64)
|
< 0.001
|
1.36(1.22–1.53)
|
< 0.001
|
1.36(1.21–1.53)
|
< 0.001
|
NLR
|
1.54(1.33–1.79)
|
< 0.001
|
1.24(1.07–1.43)
|
0.004
|
1.28(1.09–1.49)
|
0.002
|
LogMHR
|
4.08(2.69–6.18)
|
< 0.001
|
3.52(2.09–5.91)
|
< 0.001
|
3.14(1.84–5.38)
|
< 0.001
|
Model 0, non-adjusted model. |
Model I was adjusted for age, sex, ALT, AST, Scr and FPG. |
Model II was adjusted for age, sex, smoking history, hypertension, diabetes mellitus. |
ROC curve analysis of MHR, NHR, NLR and CAD
We further evaluated the performance of MHR, NHR, and NLR in predicting CAD by ROC curves (Fig. 3). The AUCs (95% CI) of MHR, NHR, and NLR were 0.660 (0.617–0.703), 0.683 (0.641–0.725), and 0.643 (0.600-0.687), respectively. After incorporating well-established risk factors (sex, age, smoking, hypertension, diabetes, TG, HDL-C, LDL-C), the AUCs of these models increased to 0.821, 0.830, and 0.828, respectively, greater than that of the original model.
Clinical and biochemical characteristics according to MHR tertiles
The study population was further divided according to MHR tertiles: Q1 (MHR ≤ 0.35, n = 204), Q2 (0.35 < MHR ≤ 0.51, n = 205), Q3 (MHR > 0.51, n = 204), as shown in the Table 3. Analysis of variance showed that the group with higher MHR had a lower age, a higher proportion of males and smoking, and among laboratory indicators, the high MHR group had ALT, AST, Scr, TG, HDL-C, WBC, MON, LYM, NEU, NHR, NLR was higher than the low-level MHR group, while TC and HDL-C were lower than the low-level MHR group. At the same time, the prevalence of CAD in the high-level MHR group was higher than that in the low-level MHR group (P < 0.001) (Fig. 4).
Table 3 Demographic and clinical characteristics of participants by MHR
|
Tertile 1
|
Tertile 2
|
Tertile 3
|
P
|
Age (Male)
|
58.22 ± 9.03
|
57.11 ± 10.52
|
53.61 ± 11.03
|
< 0.001
|
Gender(years)
|
71(34.8)
|
123(60.0)
|
178(87.3)
|
< 0.001
|
Smoke[(n%)]
|
40(19.6)
|
87(42.4)
|
142(69.6)
|
< 0.001
|
HT[(n%)]
|
110(23.9)
|
119(58.0)
|
113(55.4)
|
0.696
|
DM[(n%)]
|
46(22.5)
|
53(25.9)
|
62(30.4)
|
0.195
|
BMI (kg/M2)
|
25.24(23.84–27.04)
|
25.26(24.17–27.04)
|
25.95(24.82–28.28)
|
< 0.001
|
SBP (mmHg)
|
133.30 ± 17.84
|
131.39 ± 20.42
|
130.1 ± 19.08
|
0.236
|
DBP (mmHg)
|
79.58 ± 11.96
|
79.63 ± 13.10
|
81.19 ± 13.08
|
0.348
|
ALT(U/L)
|
22.20(16.30–32.80)
|
23.10(15.50-34.35)
|
30.30(20.03–41.05)
|
< 0.001
|
AST(U/L)
|
22.20(18.40–28.30)
|
21.25(17.40-27.95)
|
25.20(20.60–34.30)
|
< 0.001
|
Scr(mmol/L)
|
61.26 ± 12.14
|
65.08 ± 15.48
|
72.69 ± 13.74
|
< 0.001
|
TC (mmol/L)
|
4.65 ± 1.18
|
4.48 ± 1.01
|
4.23 ± 1.09
|
< 0.001
|
TG (mmol/L)
|
1.58(1.17–2.24)
|
1.75(1.30–2.35)
|
1.89(1.38–2.59)
|
0.001
|
HDL-C(mmol/L)
|
1.29 ± 0.25
|
1.10 ± 0.22
|
0.96 ± 0.20
|
< 0.001
|
LDL-C(mmol/L)
|
2.46 ± 0.75
|
2.39 ± 0.69
|
2.32 ± 0.72
|
0.152
|
FPG (mmol/L)
|
5.53(5.02–6.32)
|
5.54(4.98–6.46)
|
5.65(4.91–7.16)
|
0.685
|
WBC (109/L)
|
5.40(4.61–6.69)
|
6.62(5.68–7.63)
|
7.79(6.77–9.38)
|
< 0.001
|
MON (109/L)
|
0.34(0.28–0.40)
|
0.47(0.41–0.52)
|
0.61(0.53–0.72)
|
< 0.001
|
LYM (109/L)
|
1.70(1.42–2.08)
|
1.94(1.50–2.26)
|
2.00(1.61–2.49)
|
< 0.001
|
NEU (109/L)
|
2.96(2.51–4.03)
|
3.87(3.10–4.95)
|
4.86(3.82–6.24)
|
< 0.001
|
NHR
|
2.38(1.87–3.24)
|
3.52(2.74–4.40)
|
5.07(4.01–6.50)
|
< 0.001
|
NLR
|
1.75(1.34–2.53)
|
2.01(1.58–2.87)
|
2.47(1.74–3.24)
|
< 0.001
|
CAD[(n%)]
|
83(40.7)
|
110(53.7)
|
143(70.1)
|
< 0.001
|
Values are expressed as mean ± standard deviation, no. (%), or median (interquartile range). P < 0.05 (two-sided) was defined as statistically significant. |
Association between MHR and Gensini score.
The distribution of Gensini scores was examined according to grouping by MHR (Fig. 5). Chi-square test for trend showed that the high-level MHR group had a higher proportion of patients with very severe and severe stenosis, and the proportion of severe and very severe patients in the three groups increased with the increase of MHR (P < 0.05) (Fig. 5). Meanwhile, Table 4 illustrates the relationship between MHR and Gensini score. We set the MHR in the first tertile as the control group, and the OR value between MHR and different degrees of coronary stenosis was statistically significant. The ordered regression analysis showed that even after adjusting for common risk factors such as age, sex, and smoking the third tertile had a 2.95-fold higher risk of going up one step in the Gensini score grouping than the first tertile (OR = 2.95, 95% CI: 1.78–4.89, p < 0.001). However, when other classical risk factors were considered, the OR value of the second tertile became insignificant (OR = 1.31, 95% CI: 0.79–2.17, p = 0.298), and secondly, according to different Gensini scores After grouping, Kruskal–Wallis analysis showed that the MHR levels among different groups also showed an increasing trend (P < 0.001) (Fig. 6).
Table 4 Binary and ordinal regression analysis on the risk of Gensini score of NAFLD patients according to MHR
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a7u7uVshJFUS4gU+tX17eYXrWYDQaDXPAYBAGOj48ruzaVv21Z/krmg9t0Oq18L+78/Bzj8RgvLy/6+P/8+QPXdde+Kwr81zWozmen00G73Uaaprlge5/LU85XvU1P1BQhhB5xhMLoJ/WVDAByI/PU9LLRemWjBlEx3N9cf9noKcdxciP9pMyPPHzPcSlqtFXZdsu2993FcayHyledZ1PZKMvidQYg//d//1dPU6PC1LVU2yn7+o6qa7lv1FcalJ0TMw/DGM1qTi/mw6pRg8UyW1xPWRkNw3BlpJ+U/408rBvpVtx387jMMmdut2p735EaXb3JuZBydeSnGh1qliVVVorX1ixL5nRzHWXXd19ZUu5x2zgR3p7GxuPx2pFF343qQty2/U6SBBcXFxu3QNH2arVaWCwWa1uLvpter4fJZLIV++15Ho6PjzdqgaKvxS5C2nvtdhtnZ2db16y9aRci0b+yWCy2qqsN+G+E6TYEV/S9McAiwtvLt8fHx1vzsxCtVqt2iPx3ZX5NA78vZ/fZtr1V11q9V7ktrUHm1zRsS921T9hFSERERNQwtmARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNex/mliJ+iVuIiIiol3xN18V2kiAxe8qJSIiIvoPuwiJiIiIGsYAi4iIiKhhDLCIiIiIGsYAi4iIiKhhDLCIiIiIGsYAi4iIiKhhDLCIiIiIGsYAi4iIiKhhDLCIiIiIGsYAi4iIiKhhjQdYvV4PlmWh1Wo1vep3i6IIlmXBsiwkSfKvd4eIiIj2RKMBlud5mM/nkFKi1WohiqImV6/1er21606SBIPBAGEYIgxDjMfjT9kXIiIioqLGAqwkSTCdTuH7PgDg8fERy+WyqdXnzGYzLJdLWJaFXq9Xmubi4gJCCPT7fTw/P2M+n3/KvhAREREVNRZg3d7eAgB+/vyJLMuQpimOj49X0nmeB8/z9N8qQFJdi71eT8+vM5lMIKXEaDTS3YBKkiRI0xS/fv0CANzd3UEI8dfHSERERLSJxgKs6XQKx3Fg2zb+/PkDADg6Osql6fV6mE6nWCwWAIDT01MAbwHRfD5HGIYYjUbv2m673YaUElJK/a5VMdibz+ff4p0wIiIi2g+NBFhZlgEAzs7OAAC/f/8GAPT7/Vy62WwGx3GQpikA4PDwEBcXF2i32wCAwWAA4K11alNJkugWLCkl2u02siyDECIX7F1cXPzFERIRERFtrpEA6/X1FUC+e9B13dK0tm0DeAvKbm9vdRAmpYQQAp1OZ6Ntep4Hy7IwHo91C5by+PiY6x4EVoM9IiIios/SSIB1cHAA4C14ur6+BlDdCqXeyxJC4PLyEgB0993NzY1O12q1cu9VmXq9Hk5PTyGlxGw2W5nfarVwdHSU63okIiIi+ir/08RKbNuG67o6IDJbk4qOjo4QhqFuUcqyDL9+/dLLqlGIdcqCKtNoNNItYea2iIiIiL6CJeuiISIiIiJ6N/5UDhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNWwvAqxWqwXLstBqtUrnZ1kGy7JgWRaCINB/W5YFz/MAAJ7n5aZHUZT7bC6/jud56PV6jR4jNaeYB8xrvqkkSVbyEBGt1qXqX5IkG6/DrH83qXOJ/gm5J1zXlQBkHMcbzXMcR4ZhmEsXhqF0HEd/TtNUmqcwDEMJYGW5sm2Z66HvJ45jKYTQn33fr8w/ZQDINE1lHMdyj4oZ0UaKdamqF9M0XbusWe+GYZgrp0TfyV60YCmu62I8Hn/a+vv9PoQQeH5+rkwzmUzg+/6n7QN9jqurKwDAy8vL2rRZlgEAbNtGu92GlPJT941o211eXgIAXl9f16Z9fX2FEALAW537+Pj4qftG9FF7FWBdXl5iPp/nmqKjKML5+Xkj6w+CAGma4ufPn42sj74P1c3X7/f1NLNrWXX5JkmiK392DxJtxvM8CCHQbrf1tLLyFUUROp0O0jRl9yB9e3sVYNm2Ddd1cXt7q6ctl8tcoTYNBoPcOwKDwaA0nZr/9PQEKSVs2/6U/aevpSpxy7JwfHy80hIlhECapnp6EARot9tI0xQAIKXEZDL58v0m2gbz+VyXr4uLi5WWqLLy1e/3EccxhBCQUuqWZaLvaK8CLAA4Pz/HdDpFlmVIkgSnp6eVacMwhJRS/wvDsDSdlBK+7+uuIdoNqhJ3XRdPT0+5eaoVVAgBy7Iwn89X0hBRNcdxIKWE4zgrr1WwfNEu2LsAq91uw3EcXF9f4/7+Ptfl8zfUkxSbrHfPZDLBYrFYGUWoAjD1j61VRO83m80wHA5XRhGyfNG227sACwBGoxGm02nj61UVxXuG89N2WCwWGAwG+iagugLNgJrvWxF9TBzH6HQ6uheA5Yt2whePWvwnhBASQG6YvRAiN4xe/VPD8dU/13WllP8NI1b/1FcyFNdrTi8b0m+un1/V8D0V84AaOm5OT9NUDxc3pxXzUxiGejkiWq1Ly6ZLKUvLV1m967ouv6qBviVLSo4hJyIiImrSXnYREhEREX0mBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNSwvQiwWq0WLMtCq9UqnZ9lGSzLgmVZCIJA/21Zlv4Fd8/zctOjKMp9NpffZF8sy0KSJI0f6z7wPA+9Xm+jtL1eb+W6mNegapksy3Lp113XdemyLKvMf4qZp9Ydn5lPi2mLeTHLso3P13ezabmqSpckSWV5W5cPdpGquzbRarVy5QD4L4+WKeYzdX5VHVq1TFU+VmV3k3026+d1eaXuGNS61PaCINj4fG0Lda7W1UdAdX6pO4fvqZ933r/+temvon6pPY7jjeY5jiPDMMylC8NQOo6jP6tfezfnA1hZztxOmqZSSil935d7dPobo66VeR3KqGvjum5uunldfd+XQojcfCFE7voJISQA6ft+7fbWpXMcZ2Vbxf0193XdNqvmCSF0PnYcR/8dhmHt9r+jqmPZNF2xfJp/r8sHuwhAbf1kUvWTqq+k/K9+K6u34jiuPL+u61Zu09yG4zi6DPi+n8u7xX0xhWGo16/2oyqv1B2Duby5v47jrC3/28L3fX2OwzBcqR9NVfml7hxuWj/vi725w7uuK13XLb3wTQVYUr5V9psWxrpKg6r5vr+2ABcDJaV4vczr7vt+6bXbtIKtSheG4btv4mZFWKTya3F9cRznphXza9XxfUfrjmWTdMVz6LquPv66fLDLqsqFKU3T0gBLytVAylyvSlusF4vXyJxuXtOqdav1b3p9yuruTY5BSqnvEcXlzePbZsXzuO4eVJVf6s7hJvXzvtiLLkLl8vIS8/k811UQRRHOz88bWX8QBEjTFD9//tx4Gdu2G9k2/Uc1ad/c3OS6HlR3h9ntIYTQfw+Hw3ddu00tl0v8+PHj3csdHx+XTr+8vISUEt1uN9dMf39/n2v2Pzw8xOPjo/58dXWF4XD47v34F9Ydyybp7u7ucufw+PgYT09Pa/PBvru+vsbV1dXG6aMoQqvV0nXZw8ND7nweHBwgTdOV5V5eXnLX9ODgoHY76+abDg8PN06rBEGAy8vL0nnj8RjX19fvXud3kmUZ0jTNnUchBF5fX//hXu22vQqwbNuG67q4vb3V05bLJdrtdmn6wWCQe79qMBiUplPzn56eIKXcKGhKkgS+73/sQKjWcrlEt9vFaDSClBLz+RxBEMC2bQgh8OfPn5VlkiSBEKLxgNfzPEwmk3cvd3d3V3mTU/s4mUzgum7unZN1++84zta8+7fptahLd3R0VJq+Kh/su488cC6XS5ydneWmbfJ+z8nJCdI0XZsfsyxDt9t9V9msqtPrtgFU56WTkxMsFot3rfO74kP919mrAAsAzs/PMZ1OkWUZkiTB6elpZdowDCHfulEhpUQYhqXppJTwfX/lhdA6t7e373pKpM1lWYbT01Ndyfq+j6enJwBvrVrD4VAHxWmaot1u4+XlZaObwnusy19VoijCaDTaKO3l5aU+tk29vLy8e592TVU+2Hd1D5xVsiwrDWTXsW0bvu+j0+nAsiwIIeA4zkq66+vrjR9SPM/DbDZ7975s0mpX1gpHVGfvAqx2uw3HcXB9fY37+3v0+/1G1qsK5yajzXq9XmVTNP0927bx/PxcOq/dbuuA2XXdT21FvL291a2gnU4HaZrCsixkWZYb9WSOuEmS5N03OdUN9uPHj9xT9svLC7rdbnMH9IU2PZa6dGdnZ1gul3re09OTDni/Mh9siyiKMJ1OcyMrhRDvHkV3cnKC+XyuP7++vpYGTsBbvamugxBi5cHC87yVFrXiaG4lCIIPPdBkWZY77vl8jsFgUDv6cRuplluzxZAPFp9r7wIsABiNRphOp42vdzabYTgc1lZInudhNBrpZtpdK8Tfwfn5ee5do9+/f69U0iqoUYFx1Ts+f2MymeibRxzHEELoLmRznnrizrIM4/FYP60HQbC2VdTzPP3emKooVQU6Go1Ku3s+8n7KV9v0WOrS/fz5U5dzdRMtPlAV88E+6/f7uRZ74O0GvO4htPhAY9s2HMfR9eB4PMbFxUXtOizLwng8zt3sVcCkpqm60txHtW9qW+rze+pV27Zz63QcB2EYrrSa7cJ7er9+/dKvyARBANd1//Ee7bgvepn+n1JD6GGMFFKjQtRoCPVPjZ5R/9QoJDVyC8bQVfNzcUgxSkYlmftRlYbqmdfHHKniOM7KiDnzWill05SykUKO4+Tyhrke83NVOqVqJJVSzE/m8alRWWqf6/KPSlu1H9tU5KuOpThsv+6Yq8pjXT7YVWb9o467mLdM5jmWUq7Uleb0slFjxTrUXEccx7n11ZW7ddeqWDeb+aD4dThVx1DcdtkI8rqvNNgm6tya9VFZPijLL1LWn8Oq+nlfWVL+36MK0Z5T3bu72poRRRGen5939vjo32m1WlgsFjv7AnWv18NkMtnZ46PPwQCLyNDr9XBxcdHYu3nfRZIkuLi4aLwblAh464JVXeC7xvM8HB8f88GE3m0v38EiqjKbzXBzc/OuEaHfnXq3i8EVfRbbtpGm6c79RIp6D4zBFX0EW7CIiIiIGsYWLCIiIqKGMcAiIiIiahgDLCIiIqKGMcAiIiIiahgDLCIiIqKGMcAiIiIiahgDLCIiIqKGMcAiIiIiatheBFitVguWZaHVapXOz7IMlmXBsiwEQaD/tixL/yq753m56VEU5T6by9cx17NL3xb+Vd57/oIgyF2TJEn08lX5QU0384VlWZXbqEvX6/Vy86q2ae6vSlv3rdhqm0mSlO6/mU+zLNvab9g2z20URR9KZ57TquXKzuOuMeusTfKDWVaKVDlU9aO5DbO8qW1uQl0nc9+KZesjZb7ILB/F6142z/O8ncwfm96zqtKZ+aPs/Kwrs3vhH/7Q9JdSv7gex/FG86p+Ud38hXD1C+TmfNT86nsYhnqe7/v8tfF3eu/5U9fH9309zVxGCCFd180tY+YDM63jOJXbq0qXpmkuT6VpurK9InNfq6jjKsvPYRjKNE1XlgnDUAoh1q77O3BdV58HAPp4hBClx1aXziyzcRyXlt+qemGXFPNesVwUqTqx7HwDKC0LxTKp6sNNbjNCiNL8uUl5MJWVeZPruvqYfN/P7ZvruvocFcuLEKKyXt8Wvu/r4xNC5Oq5qvxfla547yteY3Xdt/2c/a29CrBc1y2tGJoKsKR8y5BVhdusrOI4fnflse/ee/7UNa9KV7yerutWVghxHG8UoJjpijenMAxrb+QqH24aCJUFBqpiKztm3/e/dZ4rVsrF62PeIEx16Yrl2LxhmFFu9KEAACAASURBVNvd9QCrqOpcSlkfjDuOU5qH0jQtXSaO47UBlhnYlC37nuuzrswXmUGkuVxZed/W9gghRK5OKB5bsfwodemK+afsnO9CUPq39qKLULm8vMR8Ps81Z0ZRhPPz80bWHwQB0jTFz58/S+fbtq3/vr295Q+IvtN7zt+m11WtM8syTKdT9Pv9yrTruveK6cz9BYCbmxu02+3K5S4vLyGlRLfb3bhbxZRlGeTbQxOGw+FK983V1RWGw+G71/uZzG4Gte/qGiyXy9w5PDo6Ku0iqks3n89xeHio57VaLby8vHzW4WyV4+Pj0umj0UjnQbP7J0kSzOdzPD09rXR3X19f49evX+/eB1XuAKx0Rx4cHEBKiTiO0el01nY3fbQuV3nn/Pwcw+EQWZbh9vYWNzc3uXS+76/tTvsuzK7VxWIBKaWuL+/v73PX7vDwsPSH4OvS3d3d5fLP8fExnp6ePutwttZeBVi2bcN1Xdze3uppy+Wy8qY3GAxy/f+DwaA0nZr/9PQEKeXKjdWkbijT6XTlBkjrbXr+6q6rmeby8hIA8PDwAMdxKtPe399jNBqt3b+6dHX5wpw/mUzguu67K3Nz/VJKTKfTlYDEcZxv8z5JFEUYj8c6sCpTFQS8J93BwcGH9m+X3d3dlT6gZFmGNE1xenoKKSV830en0wHwlrdd19XzAOgyuFgs8OPHj3fvhyp3ap2O4+h3sFR+brfbiON4bfnbpMybkiSB7/v6c7vdRhiGEELg9PR0ZV0/fvzA3d3dxuv/V5IkQbfb1eWqrN5ZVxdtku7o6OjD+7gv9irAAt6eUtSNJ0kSnJ6eVqYNw1BnUiklwjAsTacqok1ewGy32zp92Q2Q6m1y/jzPw2QyqV2PuvaqAnl+fq6sTNQ21lXedemiKKrNa0WXl5d//UTo+z5eX19Xpn+XFpx+v4/RaMQBH18siqLKYOX19RVCCN2KqIKwLMvw9PSE4+NjPW88HuvrlqbphwJZVe7UOkejUWlrSrvdrm1B3qTMF5W1gi+XS6RpisFgUPqAU7Zv30273cZisdibwRvf2d4FWO12G47j4Pr6Gvf397VdQu+hCuqmrQ5XV1cQQjSy7X1Ud/6m06luVZxOpxgOhysjk8bj8cbXvtvtbtSdW5duuVzmtmeOhqwa0bVp602d7956owJm1SVldgOdnp7mWgyen59xdna2so66dI7j4OHhQc97fHzEycnJZxzKVkiSpLal5+DgAGmals77jG6gqm7fMmZ3vtmzkCTJ2jJf1Ov1dOu1OU09dKVpqrsLt5Ft25BSYjwer4wA/PHjBxaLhf788vKCbre7so66dGdnZ1gul3re09PTux4g98bXve71b5kv5KmXJ82X8tDQS+7YcOREcUQTvc+m56/q5UtFvXhe9aKnmValK7Mu3XuvteM4lSPmlGKeNaVpWrrNuhFD34F60b9sFKH5d1FVOvO6Vl3juvO4S4p5wvf90vNpvshunjNV36llzDqybPCAlJu95G6e/6oX1MMw3PjF9XUvubuum9tXc0CE+rusbt/mOluNmCwbRVh17erSmeen7Fyp9Pv+kvteBFhqFIVZkNVQbnOkiqrYzc8qQ6qKX/0zhyCb6zWnFzOt4zh63rYW1H+p7vxVfY1CcWSQec1QGLFnVhLmEH7zn5lW3aDq0kmZ/3qJOmX5ydxvc9q6vFc1CmzbnqnMa2aeQ9d1V4LasnQqbdl1kbL6nO+aYn1llqG6/FXMR+Z6zCCmbIRqsbyZac3PZrriyLSy6esUAyyzbjDvBWXXvS4/fPdRuO9h1lvmManrq4LoqnRm2rJzZZ7nfQ6yLCkr3i4l2jOe5+H09LSxbuPvJooiPD8/c/QqNS7LMnS73a14R+mjWq3WTh8fNY8BFpGh1Wqt/TqFbZQkCS4uLniDoE8TRRFubm4wm83+9a40rtfr4eLiYmcfvuhzMMAiKti1J9Usy+B53k7e+Oh72cVWUs/zcH5+vnMPXfT5GGARERERNWzvvqaBiIiI6LMxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJq2KcGWK1WC0mSlM7r9XqwLAuWZa2kCYJAz2u1Wp+5i2t5nqf3JYqi3LzvtJ9ERET0fTQeYCVJooOOxWKBdru9ksbzPIxGI0gpAQAXFxe5+cPhUP/d7Xab3kUAQBRF6PV6tWmCIMDp6SmklBBCYDAYfPl+EhER0faxpIpy/lIQBBgOh3AcB7PZbOPler0eHh8f8fj4COAt8Dk5OYFt203sVq0kSdDpdAAAaZrWbtPzPEynUx0UfuV+EhER0XZppAXL8zwAgJTyXcEVADw+PuLXr1/682g0ghAClmUhy7LSZVQrmfl3lmWIoki3ngVBUNk9qbTbbUgpIaVEt9st7QZUsiyD67rv2k8iIiLaT40EWJPJBABgWdbabjdTFEVI0xRXV1d62mKxgO/7AP4L3IrLqFanJElyXZCj0QiO40BKid+/f5d2T1Z5fHyElBLL5XJlu0mSYD6f4/LycuP9JCIioj0mGxbHsQQgAcg0TSvTpWkq6zbvOI4UQpTOC8NQApBhGEoppXRdV/8PQH9+DyFEbp2mumOp208iIiLaT42/5F7sdivrpkuSBN1uF1JKBEFQ2sVm23blyLzDw0MAwPPzM4Ig0C1Lk8kEvu9jOp1WdvUV98N8IV9KiX6/r+dnWQbLsiClxMPDQ+mx1O0nERER7amvjujMFi4A0nEcKeV/LVqqBQmAjOO4dB0qLQDp+76UUkrf9/WyQggZhqFu0SpbTxiGetvrtgFAt1K9Zz+JiIhoP33pF42ao/YU9RUNtm3rr0KwLAtpmla+Q2Xbtn7XSr2/9fT0hOVyqV9+N1uiyvT7/coX8rMsgxAiN029iP+e/SQiIqL91MjXNKivMCgTxzEDECIiItorjX0PFhERERG94W8REhERETWMARYRERFRwxhgERERETWMARYRERFRwxhgERERETWMARYRERFRwxhgERERETWMARYRERFRwxhgERERETWMARYRERFRwxhgERERETWMARYRERFRwxhgERERETWMARYRERFRwxhgERERETVsLwKsVqsFy7LQarVK52dZBsuyYFkWgiDQf1uWBc/zAACe5+WmR1GU+2wuv47neej1eo0eIzWnmAfMa76pJElW8hARrdal6l+SJBuvw6x/N6lzif4JuSdc15UAZBzHG81zHEeGYZhLF4ahdBxHf07TVJqnMAxDCWBlubJtmeuh7yeOYymE0J9936/MP2UAyDRNZRzHco+KGdFGinWpqhfTNF27rFnvhmGYK6dE38letGAprutiPB5/2vr7/T6EEHh+fq5MM5lM4Pv+p+0DfY6rqysAwMvLy9q0WZYBAGzbRrvdhpTyU/eNaNtdXl4CAF5fX9emfX19hRACwFud+/j4+Kn7RvRRexVgXV5eYj6f55qioyjC+fl5I+sPggBpmuLnz5+NrI++D9XN1+/39TSza1l1+SZJoit/dg8SbcbzPAgh0G639bSy8hVFETqdDtI0ZfcgfXt7FWDZtg3XdXF7e6unLZfLXKE2DQaD3DsCg8GgNJ2a//T0BCklbNv+lP2nr6UqccuycHx8vNISJYRAmqZ6ehAEaLfbSNMUACClxGQy+fL9JtoG8/lcl6+Li4uVlqiy8tXv9xHHMYQQkFLqlmWi72ivAiwAOD8/x3Q6RZZlSJIEp6enlWnDMISUUv8Lw7A0nZQSvu/rriHaDaoSd10XT09PuXmqFVQIAcuyMJ/PV9IQUTXHcSClhOM4K69VsHzRLti7AKvdbsNxHFxfX+P+/j7X5fM31JMUm6x3z2QywWKxWBlFqAIw9Y+tVUTvN5vNMBwOV0YRsnzRttu7AAsARqMRptNp4+tVFcV7hvPTdlgsFhgMBvomoLoCzYCa71sRfUwcx+h0OroXgOWLdsIXj1r8J4QQEkBumL0QIjeMXv1Tw/HVP9d1pZT/DSNW/9RXMhTXa04vG9Jvrp9f1fA9FfOAGjpuTk/TVA8XN6cV81MYhno5IlqtS8umSylLy1dZveu6Lr+qgb4lS0qOISciIiJq0l52ERIRERF9JgZYRERERA1jgEVERETUMAZYRERERA1jgEVERETUMAZYRERERA1jgEVERETUMAZYRERERA1jgEVERETUMAZYRERERA1jgEVERETUsL0IsFqtFizLQqvVKp2fZRksy4JlWQiCQP9tWZb+BXfP83LToyjKfTaX32RfLMtCkiSNH+s+8DwPvV5vo7S9Xq/0upjXPMuy3LxiPmm1Wmuvq5lvqqarvFTFzFN1x1fMi+Y2zeOKokhP2/R8fTeblqt16YIgyM1LkkQvU1Uv7CIzX5SpOy/rzlkxn6m6ri7fq/JZXK+ZjzfJA+Z66o4PeMsLVftvbi/LMgRBsHZ920bVH+vy/bq6KEmS0nm9Xm9tXbc3/vWvTX8V9UvtcRxvNM9xHBmGYS5dGIbScRz9Wf3auzkfwMpy5nbSNJVSSun7vtyj098Yda3M61BGXRvXdVfmqXNflheK04UQEoD0fb9yW2a+iOM497daTu1PVd5I0zS3r3XbLK7DPBcAdB4TQui/wzCUQojKY/iOhBD6WjiOU3q9Nkmnzr15Ps1zJoQozSe7BkBtHpSy/ryYZcNxnNz5jOM4V5+Z9afruqXbTNM0d63MMlBM7/t+5fU356k6WOX7srQASstCVXkrHus2830/d46r8n0xH5RdPyHESj3sOE5lvbuP9uYO77qudF239MbcVIAl5Vum27Qw1lUEVM33/bUBVlWlULwRmKpuBOsq2GJeMW/4xfVvmjfMitBUzC9xHOttF/NncR2+72/NjSKO49xNsHhs70mnyn5dwLouP+2KqnJRpnhezPqqeD7NYL5YLxavkVLMy2EY6nJTnPeeG3ZV+TO3U9wfVS9UPXiZx7fNiuem6h5kTjMbBhRVl1TdTxlgvdmLLkLl8vIS8/k81zUXRRHOz88bWX8QBEjTFD9//tx4Gdu2G9k2/Uc16d/c3Kw0cY/HY7iuW9qNNp1O0e/33729+XyOw8ND/bnVauHl5aU07dHR0cbrPT4+XplWzC+3t7c4OTkBACyXy9z8o6OjXPfn1dUVhsPhxtv/l+7v73NdGIeHh3h8fHx3uk3LN8thOfO8uK6ru34WiwWurq4AvJ3jVqul0z48PEAIoZc7ODhAmqa16wbeymu73V6ZV+zC38TBwcG700spEccxOp3OSrfgeDzG9fX1u/fjO8myDGma5s6NEAKvr68radX5T5IEp6enpdfjPXXZvtqrAMu2bbiui9vbWz1tuVzqQl00GAxyffKDwaA0nZr/9PQEKeVGlXWSJPB9/2MHQrWWyyW63S5GoxGklJjP5/r9jfl8juPjY0gpEYYhBoMBsizDw8MDHMf58DY3qdCzLNs4gLu7u9M3sHXM/FYWlJkcx9mad/82DXrq0tWVbzPN5eXlu/ZtHxTPy2QyAfBW35lB7HK5xNnZWW7Zj7zXVnUdHx4eNn4IzrIM3W733QGzSt9utxHHMUajUW7+yckJFovFu9b5XW16bjzPQ6fTwWAwyNUZ19fXG9dN+26vAiwAOD8/x3Q6RZZlOjqvEoYh5Fs3qr4hl5FSwvf9dz1p3d7eMpN+kizLcHp6qm+svu/j6elJXx913vv9vn6Ce35+/tRWjCAI9A1qnSiKVir4MkmSrA2oylS1ru0az/PWnvOyJ3QqPy8qX/q+nxsckmXZX7dmRFFUWRebLVvrXF9fb1zOqrTb7dIAsawVbpdNJhNIKSGEwHg8BtBsj88+2LsAq91uw3EcXF9f4/7+/kNdQmXUTXvdaCfgbZQFn5g/j23beH5+Lp0OfKzLoY7jOHh4eNCfHx8fdbcd8F+XpXmzMkcCml2YSZJs1OoCvHWPmd3Rp6enuLu705+fn59XWha2xY8fP3ItBi8vL+h2u+9KN51O9TmeTqcYDoe5c51lGcbjcWN1wK4oOy9ZlmEwGKDdbuPq6gq+71eOFDs5OcF8PtefX19f17YOL5fLyutQDH6Lo7kVz/Mau/nvYsBt2zaEELnWqDRN19Y1Nzc3ub87nY7u0ZnP5ysjp+k/exdgAcBoNMJ0Om18vbPZDMPhsHZYr+d5GI1GugBzOGvzzs/Pc+8a/f79W1e8ruvqdylURdNut1feV3qPi4sLXQkV30dRAZMKwNX1Vk+HUkrMZjMA/93Y1BN4EAS1+3R3d5e7EfT7fcznc73McDgsfR/QfF/su1KVvrpGo9Go9OZZl85sfXZdF77v63MNAN1uV3+Oomhruk4/W915Mf9Wea/4QGPbNhzH0fXgeDzGxcVF7Tar8nlZy5Z5XVVQFgRBrtX6b+rVKIpKW4bN98q21a9fv/QrMkEQwHXdtcvc3t7q6zebzXI9Oo7jQEr5qfu81b72nfp/Qw21hzFCRI0KMUeP4P+Gcpuf1WgINdIQxlBn83NxmDBKRqOY+1GVhuqZ18ccweI4zsoIOvNamczrYCorDmrYscobZlrzs5k/yvahmJ+KivnJPD6VR828Yn4FhMnMz2WjxbapyKvRaMVzXRyKX5XOZI56K5Z5VAzb3zVmvld5w/z6kHXnxcyj5nTzq0lMZXm+7GsUwjCsHNm4yehOs4wWj6/4dThVx2CW37IyWveVBttGnS/z+M18YJanqnpEyuoRu+vqun1iScnwkwh4e+o9PT3d2S6jKIrw/PzMd/+oca1WC4vFYie71oC31zomk8nOHh99DgZYRIZWq/Wul2q3RZIkuLi4KP2qA6K/lWUZhBA72V3keR6Oj4/5YELvxgCLqKDVau1UIJJlGTzPy71/RNS0XcxnQRDg6OhoZ1u16XMxwCIiIiJq2F6OIiQiIiL6TAywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBq2FwFWq9WCZVlotVql87Msg2VZsCwLQRDovy3L0r/K7nlebnoURbnP5vJ1zPVU/YI8rRcEwdpzvS5dq9UqvQa9Xg9ZluXyhWVZtdvp9Xq5vGFKkmSj613MY3XbVNvr9Xql85Mk0fOyLKtM992Z16B4XjdNZ5bV4jVQyyVJ8in7/52Y50jVa1Wq8pd5LsvyVJIkuXWr9HVU+Siro81ytQmz7NRdU8/zSve/rBx7nreT+UPdF9flhap05nUrOz/ryuxe+Gc/M/3F1K98x3G80TzHcVZ+Rbz46+HqV8fN+Vjz6+Pmr7xv8kvxtEqdd9/3P5zO930JQKZpmpsuhNDXyLw+juNUXi/zWsZxnMsT6nPZtoqK+aZqe67r6mPyfb/0+IQQueXDMJRCiNrtfxfm8ZnnTQhReQ6r0sVxrI87TdPcOVD5o6pe2DVmfqg75qr8pc6XeZ7NPFvMY6o+XHebMffFcZzcttV013Wl67q161lXH5jHB2ClfNWV4+KxbiPz/mXe31zXrTy2qnTFe1/xGqvrvu3n7G/tRQuW4rouxuPxp62/3+9DCIHn5+fS+ScnJ/o3rX78+IGzs7NP25dddn19Ddd1P5yuqiUpCAL8+vVLXyPzN9VGo1Ht7xPatg0AODg4yE1vt9tI03TtvmZZlvu9M/XjzGWm0yl+/vwJAPj58yeGw2HpcZj6/T5+/fq1Uavfv6Kehk9PT3F1dYUoiuA4jj63v379wvX19cpydelub2/1ubBtG61WSz9V27a9kz9OXGUymei/Hcep/EHzdflLnedia9NgMMiVkX6/jziON9o3VW7UutW21T6enp7i9PS0cvksyzAcDjdqNZlMJvB9v3ReVTl+fHzEYDBYfyDfkGqVWy6XmM1myLIM8/lc1zfn5+cYjUYry9Wl+/PnT65udV03V7dIKSGE+MzD2gp7FWBdXl5iPp/nmjOjKML5+Xkj6w+CAGma6sqpyKw8bm9v+evsH7Dp9apLd319XXruh8Nh5bUDVm8oys+fPzGdTpEkCf78+YMwDNfuX5GZN4C3/HFycrKSTuVdlV79r4JG9f/R0dHKsldXVys3y3/N7GaQUkJKqSv05XKZOy9HR0elwXFdusVikTsXtm1XPgDtOnWOoigqvaEC9fnLtm04jgPP8/T5VdcqCIKNHnrKuK6ru58Wi4Uum+Y1XS6Xa39wWUqJNE0xGAw+9CCxrhz7vv+tH1CKVLkajUaQUuoA++HhIRf8HBwclD4E1qW7u7vD8fGxnnd8fIynp6fPOpSttVcBlm3bcF0Xt7e3etpyuax8khsMBrk+/aonGDX/6ekJUsqVm6VJ3VCm0+navm9aVXe9NklXFXglSQIhROW1u7+/r7wp2baNOI7R6XQAYO2NYFNV+1L3ZFgVPCqO43yb90miKMJ4PNaBVRmzEq9Tl+7w8PBD+7eLer0eBoMBOp1OZUtuXf6azWZYLBbodru5Ft67u7vaFqY66sZvWdZKK7F6h2s6ndYGN2ZAmKbphx4k1pXjHz9+4O7u7t3r/WrqXTtVrsrqwaqHxfekK3uIo7y9CrCAt2bO6XSKLMuQJEltpRCGoc6kUsrKlgkpJXzf3+il9Xa7rdOr/aDNeJ6X6+b4SLqqwOvl5aV2EASA2sDu9vYWUkoMh8O/DpyTJNk4sDBt2rr38vLykd1qXL/fx2g04oCPLzSbzXQwW9bduk4QBDrAMl88f3x8/HAgq1rUfN9fyQv9fh9SSriuu3HQpB6kP5Kn1pXjutcEvgvV9c2XzP+9vQuw2u02HMfB9fU17u/vG2ttUK0GmzYhX11dsY/6nabTqW4tnE6nGA6HpSOBqtJFUZSbB7w9ra+rhLrdbm2rkOd5OiCSUupuhjrmiMHiMdzf31d2Vap3uswuQdXydnNzg06no1tb5/P5xqOv/hX1wKFu2Oa1OD09zbUYPD8/l763WJeu2+3i/v5ez8uyDD9+/PiMQ9kqVQ+LdfkrSRL8/v0btm1jMpnkuvY+KssyDAYDtNttXF1dwff90nWaD0zF0b1VZa2uJ6HMR8rxdyalxHK5XBkBeHJygvl8rj+/vr7CcZyV5evSnZ2dYblc6nlPT08fbsHcZXsXYAFvLyxPp9PG1zubzTAcDjd6akiSBK1W692VwD4zWxNd14Xv+7luinXp1NOw2SWVpin6/T4ODw9Ln05brZaeniRJ5bUtdh0UX5Itmkwmej+Kx3B3d1ebL1zXxZ8/fwC8tUKol7hV64RqbXUcp7Tr7Tt2mT0+PuZuCEEQoN/vYz6f65t91TtydenOz8/x+/dvAG/X7/HxcaMu5l13c3NT2dpZlb8A5IIv4L+u2Var9Vcto2YgU5b3oyjS73ipFpqqLrC/CYrWleNNu9a+C1XPnJ6e6oc59S6dqsvG43HpgJq6dOp9NeAt4J1Op401VuyUzxmc+L0IIVaGY6uh3OYwevzfkH7zsxoarIb2whh+an5W6zWnF4dBO46j5/ErGv6OOZRcyuqvUSimM6Hw1Qnm8H5zCL/5z1zWXK+Zx8yhycX1rPuqhjiOV/ZXrcNcr9peVT4qfqWIud/bxCyf5vGrcrounZm27PyXleFdVKznzGNV88xpVfnLPJfFrwEpfo1CcZvFdZjLqjTm1zyYZWqTr2ioqltd182tty5tVTlWy236VRDboOzcqmthlpOqa1B3r6s7j/vEknKPxikT1VDdu7s6ujOKIjw/P+/s8dG/pV6s3lVmazbRJv7nX+8A0XdxdXWFXq+Ho6OjnWvuTpJk7Xd5Ef2NOI53Ngjp9Xqf+h2KtJvYgkVU0Ov1MJlMdub9uCzL4Hle6ftqRE1KkgS3t7cbjfbdFp7n4fz8nO/u0bsxwCIiIiJq2F6OIiQiIiL6TAywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYV8aYCVJAsuy9L8sy/S8IAhKpzfJ8zwEQbA2nbkvrVYrN6/X65VOJyIiIlI+NcDq9Xq5gGY8Huu/hRCwbRsAEEURhsMh4jiG7/u4vr7+lP2ZTCYAsDZAGg6H+u9ut6v/9jwP8/kcUkq0Wi1EUfQp+0lERETbzZJSysZXalkAgDiO0W63AQBZluHh4QH9fr80veM4mM1m8DwPi8UCj4+PTe9WTpZlEEIAANI0zQV7Jycn+rOSJAk6nQ5838fV1RVarRa63a4O2oiIiIiUxlqwoijS3WpSSkgpdXAFANfX1xgMBrAsK9fyo/6+uLgAACwWi9LWpSiK9HS1LSDfnRcEwcbdi7Zt6/3sdrtIkgQAMBqNIIRY6aq8vb0FAPz8+RNZliFNUxwfH298foiIiGiPyAb4vi9d161Nk6apjONYApDmZh3H0Z/V/OK6fN/Xy6VpKtM01cuY6YUQG++zWodapzldbc9xHD3d/Kzmh2G48faIiIhofzQSYEkpZRiGEsDaIEcFJyqoEULowMV1XQlAxnFcuZyap4IqFaD5vr/Rfqr1rNtPx3F0GhWMqW0IIWRDsSkRERHtoMa6CPv9PqSUWCwWtaMBj46OAEC/45Smaa57UAiR61osLvfy8gLP8/S7T7PZDK7rYjgc6m6+Kp7nAQCklGvf8bJtW3dJvr6+Ash3D7quW7s8ERER7a/GRxEW320KggCtVksHNzc3N/B9X6cXQuDw8BBRFCFNU9zc3JSu9/DwEAAwGAxwenoK4C1gSpIk96K553mwLKs02JpMJri6uipdf5ZluffDptMpRqMRAODg4EAfmxrhyJfbiYiIqNJXNJOprj+UvLekuhZR0TWopGm68m6W67q6i3CTbsZ1VNcfCu9lFY+BiIiIqM6nfE3Dd+V5HqbTaek88ysliIiIiP7GvJxdpwAAIABJREFUXgVYRERERF+Bv0VIRERE1DAGWEREREQNY4BFRERE1DAGWEREREQNY4BFRERE1DAGWEREREQNY4BFRERE1DAGWEREREQNY4BFRERE1DAGWEREREQNY4BFRERE1DAGWEREREQNY4BFRERE1DAGWEREREQNY4BFRERE1LC9CLBarRYsy0Kr1Sqdn2UZLMuCZVkIgkD/bVkWPM8DAHiel5seRVHus7n8Op7nodfrNXqM1JxiHjCv+aaSJFnJQ0S0Wpeqf0mSbLwOs/7dpM4l+ifknnBdVwKQcRxvNM9xHBmGYS5dGIbScRz9OU1TaZ7CMAwlgJXlyrZlroe+nziOpRBCf/Z9vzL/lAEg0zSVcRzLPSpmRBsp1qWqXkzTdO2yZr0bhmGunBJ9J3vRgqW4rovxePxp6+/3+xBC4Pn5uTLNZDKB7/uftg/0Oa6urgAALy8va9NmWQYAsG0b7XYbUspP3TeibXd5eQkAeH19XZv29fUVQggAb3Xu4+Pjp+4b0UftVYB1eXmJ+Xyea4qOogjn5+eNrD8IAqRpip8/fzayPvo+VDdfv9/X08yuZdXlmySJrvzZPUi0Gc/zIIRAu93W08rKVxRF6HQ6SNOU3YP07e1VgGXbNlzXxe3trZ62XC5zhdo0GAxy7wgMBoPSdGr+09MTpJSwbftT9p++lqrELcvC8fHxSkuUEAJpmurpQRCg3W4jTVMAgJQSk8nky/ebaBvM53Ndvi4uLlZaosrKV7/fRxzHEEJASqlblom+o70KsADg/Pwc0+kUWZYhSRKcnp5Wpg3DEFJK/S8Mw9J0Ukr4vq+7hmg3qErcdV08PT3l5qlWUCEELMvCfD5fSUNE1RzHgZQSjuOsvFbB8kW7YO8CrHa7DcdxcH19jfv7+1yXz99QT1Jsst49k8kEi8ViZRShCsDUP7ZWEb3fbDbDcDhcGUXI8kXbbu8CLAAYjUaYTqeNr1dVFO8Zzk/bYbFYYDAY6JuA6go0A2q+b0X0MXEco9Pp6F4Ali/aCV88avGfEEJIALlh9kKI3DB69U8Nx1f/XNeVUv43jFj9U1/JUFyvOb1sSL+5fn5Vw/dUzANq6Lg5PU1TPVzcnFbMT2EY6uWIaLUuLZsupSwtX2X1ruu6/KoG+pYsKTmGnIiIiKhJe9lFSERERPSZGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHD9iLAarVasCwLrVardH6WZbAsC5ZlIQgC/bdlWfoX3D3Py02Poij32Vx+k32xLAtJkjR+rPvA8zz0er3K+eb1LF6X4vW1LAtZluWWN/OJmb5Or9erXJ+Zd1R+WrffdenMvFc8D+Y8dcxZltWer+9s03JVlq4uH5jLRVH0Kfv+HW1yvFV5S0mSpHReMZ+puq4uL5vlplhHJ0mycV1prmfd8Zl1cFlaNT+KIgRBsHP5Q9VHVfdDpSofmNeluA7zOhCMnzLfceqX2uM43mie4zgyDMNcujAMpeM4+rP6tXdzPoCV5cztpGkqpZTS9325R6e/MepamdehqHj+fd/X17Y4r7geMx+Y1zuO48ptuq4rfd/Xy5jXNU3TyvxQZK6/Kq+maSpd182lU9uO41gKIXLz1LbDMMzN2wZCCH0OHMcpPR916erygZRv56euvO6aTY7XzINCiNK0QoiVshDHcS7fm/Wn67ql60nTNHc9zLxdrFvr6spi+Qag69miMAwr56ljUOXJPJbitG3l+74+x2EY5uoSU10+MOsm89yY18F13cp175O9ucOrC152k2wqwJLyLTNuWhjrKgKq5vt+bYBVPKdmQTfnpWmau1bFG0ExD5g3cpMQIrdes0JyHGdtQFi2b5uklzJfYfq+nzue4nkqzv/OisFisextkq4uHyhVQcSuWne85jkzHwgVlYeK18IsA8V6sXiNyrYl5du1U+XLzNdqXzbNu1XlVMr/gsyydVU91Kh17kJdXTw3VfegunxgLmNeFzNNGIZ7Va6q7EUXoXJ5eYn5fJ5rbo6iCOfn542sPwgCpGmKnz9/bryMbduNbJv+Y57TYnedOe/Pnz/48eOHTjedTtHv9/X8+XyOw8ND/bnVauHl5WVle2ma4vX1NZdOmUwmkFKuTK/b7yiKMBqNatOajo+PAQBPT094enrS04+OjnLprq6uMBwON17vv3R/f587X4eHh3h8fHxXurp8QOXUOUuSBKenp6XnsJivoihCq9XSaR8eHiCE0PMPDg6QpmnltpSbmxu0220AwN3dnc7XwFseN/P2OgcHByvTsiyDfGtUwHA4zHVdBkEAx3FwcXFR2q05Ho9xfX298fa/oyzLkKZp7twIIXJ1l1KXD1zX1ednsVjg6uoqtwwALJfLXF26r/YqwLJtG67r4vb2Vk9bLpe6UBcNBoPc+wGDwaA0nZr/9PQEKeVGQVOSJPB9/2MHQht7eHioDKCfnp70tX94eIDjOCtpyirqIsdxcnnKpPLCbDZDq9Va+z5Hr9fDYDBAp9PZKCi4u7vTFdzp6Smm0+nafd2Wd/82ffjYJF1dPqA8z/PQ6XQwGAxyeeX6+lrnNdNyucTZ2Vlu2rqHiTLF61gM5DaRZRm63W5pnjCnSSkxnU51Gbu7u4Nt21gsFkjTFNPpNFdWT05OsFgs3r0/39Gm5aoqH0wmEwBv973iQ496B3Q6na59b3If7FWABQDn5+e6YKnovEoYhvqJR0qJMAxL00kp4fv+u56Sb29vSysrapb5VGwqXqvn5+cPtyZOJhNMp1MdaM/nc5ycnKykG41GeH5+rl3XbDbTLV7rnpiLLV39fh+O4+QeCIo3PgClrXC7riof0CrV6iqEwHg8BlDf0p9l2YeCIVMURbV18aaur691ALCO7/u69ebx8RHn5+ewbVs/iBfLalkr3C4rywfAf/WO7/srg3r6/T6klHBdd2tayz/T3gVY7XYbjuPg+voa9/f3jTVjqmBpk6i91+vh8vKyke1SvaqgaZMWDcdx8PDwoD8/Pj6WBk62besg3Pd9uK5buV11IzJHFpaNyKoK5pUkSUpbX1WApm4G2xrE//jxI9di8PLygm63++F07Ip/v5ubm9zfnU5HB+7z+bxypNjJyQnm87n+/Pr6Wto6bCp2KZ2dnWG5XOrPT09POgArjuZWPM97dyulaqGu6v7fJbZtQwiRa41K03Ttg4eZD7Isw2AwQLvdxtXVFXzfLx0lummQu+v2LsAC3loS1nWlfMRsNsNwOKztBvI8D6PRSFf4dUOY6e/UPRUXWzSOjo5WWrUuLi505VJ8z6SM53m4u7urrFwuLi70TUQ9HUopMZvNSvevrsVgPB7r7QRBkNv3JEkghKh84jbfK/uu1LVRN4PRaFR6PjZJ11TryL65vb3FxcUFgP8Cd9WS7ziObmm1bTvX2mPbNhzH+f/t3b1u4k7fBuDb0nsUURRF2MeQIiIUKTAHsAWkSrWSXUfQUNLYSg3SVqmwixxAoEiBrRR7DNiKUJTTmLfIM/MfG9s4WWc3gfuSol3w4M+Z8c/zAaoenEwmaj1l8mXvx48fqo7Oj4/UexXke77vo9PpqPywq15N01R1CwJvZVNvDZ7NZltjafVxZd/Vz58/1XAG3/fhOM7Oz+j5QNKDtKI6MQzDWuvee39vPP2/Y5qmmj0iZ1DIWSFyai602SX6azmTRc40hDbVWX+dnyaMghkp+n6UpaFq+vXRZzLZtr01s6lsJl7+qw6kouKgX/d8Wjl7Rl7X/MwkOZtK/lXNQsrnQz1fyGVRFG3lO/08yHNT9XUM36nI6+dPP7f5qfhl6aSyfKCXx0OY8VR0vPLcya8vyNdxRfIzOsu+wiRfh8rP5stC2YyzqrpUJ2fqFu27/nU4+vqKyohe1vPbq/pKg+9Gni/9HLwnH5SdRz1/7cu5+lOGEP97DCE6cK7rotPp7O3slzAMsdlsvm23IX1dlmVhuVzubVdsr9fDdDrd2+Ojz8EAi0hjWdZeDoiO4xjX19eFX3VA9KfSNIVpmtjH24nrujg9PeWDCb0bAyyiHMuy9ioQSdMUrusWjvUiaso+5jPf93FycrK3rdr0uRhgERERETXsIGcREhEREX0mBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDTuIAMuyLBiGAcuyCpenaQrDMGAYBnzfV/83DEP9Krvrupn3wzDMvNY/X0VfT/4X5Kk+3/d3nuuqdPIaFun1ekjTNJMvDMOo3E4cx6VpZf6TealMPo+VbbMqnb4fcntpmqLX61Vu+6vSr0HZ9dqVTi+r+TInPxfH8afs/1ein4e6+SGfB/V1FJWrOI4z+Vym36XX6xWuV8/jdepLPX3ZNa1TB+vrcF137/KHPN+78kFZul3lstfr7azvDsI/+5npv0z+UnrRr7IXLbNtu/BXxPVfjZe/Oq4vx45fodd/5b3oF+hpN3nePc/7UDqU/Eq8EG+/CC/f16+PbduV16tsX/R85DhOad4QQmwtK9teWbp8fjRNU+1XEATCNE3xHTiOo/YbgEiSRAjxdjzy/3ll6aIoUsedJEnmHMjzVVYv7JMkSYTjOOp1nfIj60VJP5dyHXpezOcxWR/uus1EUVR4/vX87zjOzvy763jkPlXVwWV1uF4vfHd6+fI8r/S8VaWrKpe2bQsAmfx2qA4qwHIcp/Cm1VSAJUT2ppanZ8IoimpVCLRNXss6N4iydEUVZlVlk7+56PQbiX6N8/mjah35wCGKosIKvSpdEASZSi1/w6s6vq8gH/jmy5vneYWVdlW6/PUvKteHEGDllZ1LSdZPev7N5598gFIUSEVRtDPAMk1z5w25qK4tWl71gCvT6fumH49cR1UQvw/0Y6w6r2Xp6pRLWfceuoPoIpRubm6wWCwyzb1hGOLq6qqR9fu+jyRJ8OPHj8LlrVZL/X8+n/PX2T+g7vX6yHUdjUal1w5AaRfz2dkZhBAIggCmaar89fv3b5imqdIdHR0hSZLCdeh5A3jLH2dnZ+9Kt9lsMl0ex8fHmbTD4RCj0ajs8P4JvUtTvD3wqR/WXa1WmeM9OTkp7NKpSrdcLnFycqKWtVotbDabzzqcb+X09LR02Xw+3yoLz8/PeH5+Vq/18+r7PhzH+dB+LJdLCCGwXC4ru6z0slRECIEkSTAYDEqHD1TVwbe3t3AcB6ZpFnaBep5Xa1jCVybrJnke5L/5clWVrm65pAMZgyW1Wi04joP5fK7eW61WaLfbhekHg0GmT38wGBSmk8ufn58hhNi6CerkDWU2m7GP+gOqrtdH0klxHMM0zdJr9/T0hPF4XLhMfqbf7yMIAkwmE7WsLCiroyofFaU7Pz/HYrGorOxs2/4y40nCMMRkMlGBVZGqIKBuunygScDj42PpA57v+7i5udl6v9PpYDabla6v0+l8aF9k/l2v11iv14X58/fv35lyVbaOVquFJEkqHyTK6uDlcgngLVCLogij0SizL+fn53h8fHzfwX1BuwLVOunqlstDd1ABFgBcXV1hNpshTVPEcVxZKQRBoCp/2UJRRAgBz/NqRfHtdlull/tB9biui+l02lg63cvLS+UkCAC1AjbZ+vIn4jiuVYHl07Xb7cwT+MXFBbrd7tbnXl5e/ngfm9Dv9zEejznh4y8Lw7D0YUFeh6Lgvt/vw7btzAPn5eUlgLfgqIlAdjKZFObP1WpVu2zJB+myPFVWBydJogLLdrsN27a39mW9Xr/ncOjAHVyAJQvO7e0tnp6eGrkhAlBPg3WbkIfDYe0nCXozm81U5T6bzTAajQq7FOqmq6vb7b6rO1fenM7OzrBYLNT7r6+vsG0bQHYmU37fnp6eKrsqq9JNp9NMi1BRS8RXIm923W53a0ZSp9PJtBhsNht1Q9dVpet2u3h6elLL0jTF+fn5ZxzKtxDHcWXr7v39PUajEQzDUPWTPiPv4eFBdcUB+JRhDvlAzbKszANTfnZvWYvsrhbgfB1smiZeX1//YM+/vna7jSRJVFCZpmlhy31VurrlkrAno/Zq0AfcyYGX+VkRTQxyx45Blvo+cBbhx9UZ5F6VLj/IvWwAuv5e2cBzned5mTR6PirKU0Xq5ouydDJfls3M+soDuuWEk6JZhNgxALkonX5dy65x2bnaN0mSbA1OLjufMn3RLULWn/mZY0V5u84gd13++ti2nRloXafMR1FUa4B1vg7WB+0XHfu+1Nl6nVhVj1al21UuOcj9zUEEWHKWil6RyqmlsgKQf3LmjPzTZyPp7+szx/T16u/nK205fRXAXhTUf6lodljZDNF8BaLnB/2moE831mcl6X+SHgTo17WossrnpSpFs0vlvuQDwny6OtPiv9szlV4+9ePPz3ArS6enLboRFJXhfZSvr/Q6SJ67/PHngwx5HouC1PwMVn29+TypX7uyNEKIrf2tCrD1a5yvB/SveNhVB+vL89v66rNw30PWgfo5KMoHRen0tEXlTb9XHnqQZQhRMrqU6MDI7t19nd0ZhiE2m83eHh/9W3Im6L6yLItjsOhd/u9f7wDRVzEcDtHr9XByctLY2LyvIo5jjMdj3iDo00RRtLdBSK/Xq5zFSFSELVhEOb1eD9PptPbXJHx1aZrCdV08PDz8612hPRfHMebz+btn8X5lruvi6urqXV/7QgQwwCIiIiJq3MF9TQMRERHRZ2OARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNQwBlhEREREDWOARURERNSwvxpgWZYFwzDUX6/XU8tc1y18v0mu68L3/co0aZpm9tEwjMxn9GPYtS4iIiI6TJ8aYPV6PRWExHGMJEkyy8fjMYC3oGY2m6n3Ly8vP2V/ptMpAMAwDFiWVZjm/v5+673hcAgACMMwcwzn5+efsJdERET03f3fZ6zUMAwAQBRFaLfbAID5fA4hhErjuq5adn9/n1n2mYbDIYbDoWqpAoAkSdBqtQAAz8/Pal/iOMbLy4v67Gaz+Wv7SURERN9XYy1YYRiqrjMhBIQQKoACgJubG/V/13VVaxIAjEYj9dmq9ctWJ7ktAPB9P9Nll6Zprf1ttVpqP7vdLuI43trP+XyOfr8P4C3Ykvv5WV2YREREtB8aCbB838dqtVIBSxHZQpSmKU5PT9X7aZpCCAHHcQC8BV9F6x8MBkiSBGma4uzsTC0bjUZwHAdCCPz69UttZxd9rNVyuVTBoPx8GIa4urpS6Y+OjiCEgGmaWCwWCMOw1naIiIjo8DQSYA2HQ3Q6ncqxTdLt7a0a0wT8F9BMp1OYplnYAjUcDuF5HgDg9fUVrVZLBWS2bWM2m8H3fazX6537Klu8ut2uCgiLgrLVapVpgZNplsslgLfuQiIiIqIijXUR9vt9CCGwXC5Vy1A+WMq3CuVZllXaAnVycgIAeHl5yXQxPjw8wHEcjEYj1c1XRraOCSEqg7F8F6ZO7p/cHyIiIqK8xmcR5sc26V9lkG8ViuMYhmEgjmOkaYrFYpEZA6U7Pj4GAAwGA3Q6HQBvgVAcx5lgSH7dQ1GwNZ1OM61nRfJdmMB/rV7AW5BomqYam0VERESU9ymzCCW9lcj3/a3gSQZbFxcXAFA5Q+/o6AiO42y1LE0mEywWC9i2jXa7jfl8/kf7fHt7u7UN+XUMhmHAtu1aXZFERER0uAxxQN874Lpu5vu2dPpXShARERH9iYMKsIiIiIj+Bv4WIREREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDGGARERERNYwBFhEREVHDDiLAsiwLhmHAsqzC5WmawjAMGIYB3/fV/w3DgOu6AADXdTPvh2GYea1/fhfXddHr9Ro9RmpOPg/o17yuOI638hARbdel8i+O49rr0OvfOnUu0T8hDoTjOAKAiKKo1jLbtkUQBJl0QRAI27bV6yRJhH4KgyAQALY+V7QtfT309URRJEzTVK89zyvNP0UAiCRJRBRF4oCKGVEt+bpU1otJkuz8rF7vBkGQKadEX8lBtGBJjuNgMpl82vr7/T5M08RmsylNM51O4Xnep+0DfY7hcAgAeHl52Zk2TVMAQKvVQrvdhhDiU/eN6Lu7ubkBALy+vu5M+/r6CtM0AbzVuev1+lP3jeijDirAurm5wWKxyDRFh2GIq6urRtbv+z6SJMGPHz8aWR99HbKbr9/vq/f0rmXZ5RvHsar82T1IVI/rujBNE+12W71XVL7CMMTFxQWSJGH3IH15BxVgtVotOI6D+Xyu3lutVplCrRsMBpkxAoPBoDCdXP78/AwhBFqt1qfsP/1dshI3DAOnp6dbLVGmaSJJEvW+7/tot9tIkgQAIITAdDr96/tN9B0sFgtVvq6vr7daoorKV7/fRxRFME0TQgjVskz0FR1UgAUAV1dXmM1mSNMUcRyj0+mUpg2CAEII9RcEQWE6IQQ8z1NdQ7QfZCXuOA6en58zy2QrqGmaMAwDi8ViKw0RlbNtG0II2La9NayC5Yv2wcEFWO12G7Zt4/b2Fk9PT5kunz8hn6TYZL1/ptMplsvl1ixCGYDJP7ZWEb3fw8MDRqPR1ixCli/67g4uwAKA8XiM2WzW+HplRfGe6fz0PSyXSwwGA3UTkF2BekDN8VZEHxNFES4uLlQvAMsX7YW/PGvxnzBNUwDITLM3TTMzjV7+yen48s9xHCHEf9OI5Z/8Sob8evX3i6b06+vnVzV8Tfk8IKeO6+8nSaKmi+vv5fNTEATqc0S0XZcWvS+EKCxfRfWu4zj8qgb6kgwhOIeciIiIqEkH2UVIRERE9JkYYBERERE1jAEWERERUcMYYBERERE1jAEWERERUcMYYBERERE1jAEWERERUcMYYBERERE1jAEWERERUcMYYBERERE1jAEWERERUcMOIsCyLAuGYcCyrMLlaZrCMAwYhgHf99X/DcNQv+Duum7m/TAMM6/1z9fZF8MwEMdx48d6CFzXRa/Xq0yjX8cwDLeW69c8TdPMMj2f6Oup0uv1Sren5x2Zn3bZtT19v/RzoR+X3I80TXeer6+qbrmS5bFIWX4pOn/7rqw8FLEsa6tsAP/lvfx5y+czWdfVyfPy+ull7z11pV4e6tSrYRhu7ZdedmS94Pt+7fP1Xcj6qOx+KJWVjziOS+sz/b5YlHcOzr/+tem/Rf5SexRFtZbZti2CIMikC4JA2LatXstfe9eXA9j6nL6dJEmEEEJ4nicO6PQ3Rl4r/Trk6ddJXiP92spzX5QX9Pf19URRVLpNz/My6fTrmiRJaX7YdYxVPM8rfB+AymOmaar/B0EgTNN81378a6Zpqmth23bh9RLiv3JXdM7K8ov+2jTNd1+j70ieozrHKsuIzD+SaZqF+Sif7/X603Gcym3atr117d5TV0ZRpMpD3XwOQDiOk3mvrEzZtl267LvxPE8ddxAEW+dAKisf+XueaZrq3ERRpM59kiTfrr75DAdzh3ccRziOU3iTbCrAEiKb4XYpqsBoNz2gKZKv0OW1F2L7RlD1uXwe0G/4+f2R68/nCXnzqNpfnbxZVN1QZH7NV2D5/Knvl3z9XW4UemUtxPaxFaUvO2dF+UUvd/rNfN/VCSaTJCkMsPRyVLRemTZfBvLXUrerLEtVdaX+fpIkpfsoOY6zVTZk/il78NKP7zvL12Fl57WsfOSDMj2gledVKrqHHpqD6CKUbm5usFgsMk3IYRji6uqqkfX7vo8kSfDjx4/an2m1Wo1sm/6Tpik2m416fXp6qv4/mUzgOE5hN9psNkO/31dpF4sFjo+P1WvLsvDy8rK1vR8/fmA2myGOY9zf3yMIArVsOp1CCKE+v8t8Pt+Zf25ubiCEQLfbzXSLrVarTH46OTnJNNMPh0OMRqOd+/AVPD09Zc7X8fEx1ut1Y+uX5ymOY3Q6HZZDze3tLYbDYeY9WT6A/7qOpDAMYVmWOoe/f/+GaZpq+dHREZIkKdzWaDRCq9Wq1a1Udo30929vbzGdTkvXIa933tHREYQQiKIIFxcXW92Ck8kEt7e3pev9DtI0RZIkODo6Uu+ZponX19ettGXlY7PZZK6RXj8ul0ucnJxk1qHXw4fooAKsVqsFx3Ewn8/Ve6vVCu12uzD9YDDI9MkPBoPCdHL58/MzhBC1Kus4juF53scOhCpdXl7i169fhcsWiwVOT08hhEAQBBgMBkjTFL9//4Zt21vp9cqoTKvVUhUzgEyQJvPCw8MDLMuqHM/h+z5ubm5qbQ94C94cx8mMT9KDySK2bX+bsX+fHfS4rouLiwsMBoNvc04+W9kDpywfnU4HQgjYtq3G5qxWK1xeXmbS13mYiOMYpmmqdTqOg263W5huV10px0/NZrPKMXXz+TxTPiWZ19rtNqIowng8ziw/OzvDcrnceUzfQd1yVVQ+zs/PsVgsSgNhPeCiAwuwAODq6gqz2QxpmpY+zUhBEEC8daOqG3IRIQQ8z3vXoL75fL71lEjNkOdVBr6j0QidTkddH7m83++rJ7jNZvNHN/T5fA4hBEajUemg3vF4XPpEJ/ftvftwc3OD5+fnd32mqBXuEMnWRdM0MZlM/vXufAllD5yyfMjgZDweqxbFNE0zLRd1vby8wLIstc6bm5vClq46dWWr1VJ1dL6XQqr7ANNutwsDxLJWuH1VVD7a7TYcx4FpmjAMAxcXF4VBMb05uACr3W7Dtm3c3t7i6emp8GnmI2QFsGu2E/A246xOQaePW6/XqsnfNE30+30VvNQNhG3bxu/fvzPrPDs720rnuq5qORJCqO7CIvKOSCgAAAAgAElEQVRGpM8s7PV6uL+/x2g0gmEYqnul7owoue1Op4PHx0f1/maz2WpZ+C7Oz88zLQYvLy+fVpHf3d19ynq/mzAMMZvNMl2ApmkiDMOt7uYqZ2dnWCwW6vXr62th63Cdbt+iujI/m1vX7/cLtwUAv379UoHBaDTCbDYrbWnbxy7jVqsF0zQzdUqSJKU9OFK+fMjASw59kNen2+3i6elJpUvTFOfn503t/vf0l8d8/TNFAxr1AXloaJA7dszScRwns51dAzJpW92BsXJ2mS4/4F0O0CwaRK2/VzXIOj95AiUDR+vOqinKV2Vs285sS9920X5Uzcb7avQBuWUTDKT3DnLX7Zrltk/eM2Myn3/0OlIf0Jwf3CxEtv6sGuys709+4PlH6sq6s9fy29IFQbB1PPsyK04/7qpzoCsqH0Wzs/X6tGpiwyE5iADLNM2tGSJyVog+e0QGXfprmQHlzC35p08N19erv5+/Iej7UZaGqunXR79p2ratXu/6Kgf9OuiKbtD6dc+n1SthfZ35Kc3yr+4spKJZWHpeqco/en4uuql9p2cq/fzp51qWMXk+82VYV5Rf8tflkIKrsjxallf0PKuf5/wDa1FZK0qbv3b6tch/NUDdulIvo/mbetmM3KJgrmh/9f3el4dhObNZP1d6PqgqH1VfiSJEtrztw6zLP2UI8b92PqID57ouOp1OY93GX00YhthsNhz7R42zLAvL5XIvu9aAt67K6XS6t8dHn4MBFpHGsizc3d3tHJfw3cRxjOvr60a/6oBIStMUpmliH28ncowlH0zovRhgEeVYlrVXgUiapnBdFw8PD/96V2iP7WM+830fJycne9uqTZ+LARYRERFRww7uaxqIiIiIPhsDLCIiIqKGMcAiIiIiahgDLCIiIqKGMcAiIiIiahgDLCIiIqKGMcAiIiIiahgDLCIiIqKGHUSAZVkWDMOAZVmFy9M0hWEYMAwDvu+r/xuGAdd1Abz9XIL+fhiGmdf656vo60nTtPFjPRS+79c6171eb+t9mR8Mw0Acx1vLe71e5tpYlrVzW71er/S66tdc5qcyel6qyh8yTT5Px3G8ta00TQvPw3egl80wDD+UTi+r+XMqP1eUD/aNfh7q5gfDMErXUVQm4jjO5HGZvu6+5enlqu7+VpVtAJk6Pp9X9PIjt+m67t7lD3led+WDsnT6dcmfQ70sHvx97p/9zPRfJn8tvegX2YuW2ba99QvzQRBkfvFd/uq4vhwlv0wvl8tlnucV/gI97SbPu+d5pWnkNc2fY8dxhOM4Qoi366H/orwQQpimmbl+pmnW2pZcLvOAvq9l+SHPtm21nqr8YZqmyquO46h0+fxommZmv/LH+lXp5xOASJJECPF2PPL/eWXpoihSx50kSeYcyPNVVi/skyRJVL4XQuzM00L8V4Yk/VzKdeh5O5/HZFnYdZspS+d5Xiaf6/tfZNfxyG3ly4t+7cvWka8XvjO9fHmeV3rMZen06yKvnV4u61yHQ3FQAZZ+M8ovayLAEiJ7U8vTM2EURcyIHySv5a7zVxSk6J/L3zDKKhs98CmSv/HrlbFt24WBXhE9z+XzmlQWJMhATr8J5W94VZXpVyBvsmXnwPO8wptsVbp8Pikq14cQYOWVnUtJ1k96/ZbPP/nyVRRIRVG0M8AqS6eXKf3htIgeLFelcxwnszz/wCXXURTI70t7hH58Rfew96bTH/jkdTzEMlXkILoIpZubGywWi0xzbxiGuLq6amT9vu8jSRL8+PGjcHmr1VL/n8/n/HX2D/jT63V1dYXRaIQ0TTGfz3F3d6eWjUaj0mtXJUkSvL6+qtd6t910OoX43899lnVRS9fX1xgMBgCAu7s7TKfTrTSvr69IkkS91vPUZrPJNMcfHx9nPjscDjEajeoc0l+jd8mItwc+9cO6q9Uqc3wnJyeF3Q1V6ZbLJU5OTtSyVquFzWbzWYfzrZyenpYum8/nW2Xh+fkZz8/P6rV+Xn3fh+M4je6ffk1Xq9XOH1wWQiBJEgwGg9Iu/TRNM9dfPwdnZ2cQQiAIApimudUt6HnezqECX508Jnlu5b/5clU3nXR0dKT+FUIgiiJcXFxUdusfgoMKsFqtFhzHwXw+V++tViu02+3C9IPBINOXLG9+eXL58/MzhBCZiiFP3lBms9nO8Ti0rep61dFut1UF2ul01LriOIZpmpXXroxt25k8pZPre3h4gGVZlRVOv9+H53kwDAPj8bhwX+T+Fq3n/Pwci8WicsyDbdtfZjxJGIaYTCYqsCpSFQTUTZcPNAl4fHwsfcDzfR83Nzdb73c6Hcxms9L1dTqdRvcR+G9s1mw2qwxu9EAgSZLSB4nLy0v8+vWrch39fh9BEGAymWSWn5+f4/Hx8SOH8aWYptlYujRN0e12twKxdruNKIowHo8/vqN74KACLOCtBWM2myFNU8RxXFkpBEGgKn/5ZFNECAHP82oN5mu32yq93A+qx3Xdwlad91qtVltPui8vLztbmMpMp1PMZjMVaC8WC5ydnW2lG4/Hla0nMi/sevoLgiAT/MvAsN1uw3EcmKYJwzBwcXGBbre79fmXl5cPHWfT+v0+xuMxB8L+ZWEYlt745HUoCu77/T5s2848cF5eXgIA1uv1pwSy/X4fQgg4jlO79VU+SBflKRlUymMYjUaF94Cy1rL1ev2Ovd9/t7e3pXVyu93+cJ26Lw4uwGq327BtG7e3t3h6etrZ7FyXLLh1m5CHw2HtJwl6owcxs9kMo9Ho3bPjer0eOp1O5kn3T2/urVZLBeGe58FxnNKWMNmtos8slMfQ7XZxfn6unv7KWkzlTUcIAdu2M0/asktStggVtUR8JfKBo9vtbs1I6nQ6mRaDzWajbui6qnTdbhdPT09qWZqmOD8//4xD+RbiOK5sBb6/v8doNFKBO4DMjLyHhwfVFQfgrw1z0G/i+VlqZS2yZWVwvV6rbizTNEvvAR9pzf7q2u02kiRRdV6apoUt93XSua67c7jGPp7D9zi4AAt4a0koa+r+Ew8PDxiNRrX6neM4hmVZB58B30NvTXQcB57n4eHh4d3rWa1WmdetVgvHx8d//HTqui4eHx9Ln+iur69VZa4HQvox6MHArgDcsixcXl5u3SDkDSiKosL89RW7zORNb7Vaqa8A6Pf7mS7PsjFyVemurq5Ul1Acx1iv13/UxfydpWmKyWSi8qfv+1sPF8PhUOVLGUQJITLnTHan62MBLcv61JbRMAzVGC/9gSa/b3L/6qzv4uKitMz7vl/YsrUPLTKO4+D+/h7AWwvUz58/351Onh957ouGu4RhWLuLf2997hj6r0FOtYc2s0HO/NJnPeB/U5f11/psJP19fcaJvl79/fwsCjmjDDVnlVG5otlh+ZlkZee66LoJUfw1APo107envy77Kgd9ZhNKZibVSS/fD4Kg8qsF6kyL/25FXi+f+uyv/Ay3snR62qJrUJYX9k2+vtLLhTx3+ePPzxyT57Ho6z7yM1j19ebzZNW109/X6+06X9FQVt4dx1H7XPb1LUKUl3V9G195Fu57yHOrn4eifFCUTj9P+TKn3yd3XbNDYAhRMrqU6MDI7t19nd0ZhiE2m83eHh/9W3Im6L6yLItjsOhd/u9f7wDRVzEcDtHr9XByctLY2LyvIo5jjMdj3iDo00RRtLdBSK/X25pVSLQLW7CIcnq9HqbT6d6Mj0vTFK7rfmi8GtF7xHGM+XzeyGzfr0IO5j7UsXv0cQywiIiIiBp2kLMIiYiIiD4TAywiIiKihjHAIiIiImoYAywiIiKihjHAIiIiImoYAywiIiKihjHAIiIiImoYAywiIiKihjHAIiIiImoYAywiIiKihjHAIiIiImoYAywiIiKihjHAIiIiImpY4wFWr9eDYRiwLKvpVQMA4jiGYRgwDANhGFamtSxLpU3TNLPMdV21rNfrNb6f8jwYhoE4jjPLfN9Xy3adp6q0+jEQERHR19FogOW6LhaLBYQQsCxrZwD0Xmma4uLiAp7nIYoijMfj0rS9Xg/L5RJJkqh909czm83U68vLy0b303VdjMdjCCEAANfX15nlo9FI/b/b7Vauqyyt7/uYzWZIkgSO48D3/SZ2nYiIiBrwf02tKI5jzGYzeJ4HAFiv11itVuj3+01tQgVJw+EQYRgiSRKkaYpWq7WV9uHhQf3fNM3Msvv7exX8fIbpdKr+b9s21uu1ei33u2if88rSpmmK0WgEx3HQarWQpinSNMVwOGzuIIiIiOjDGmvBms/nAIAfP34gTVMkSYLT09OtdK7rqkDJdV3VPSe71Hq9Xqa1SUrTFIvFAo7jAABWqxUA1ApUkiTJtCKNRqOdXWthGKouuTAMVVq9y873/a2ux7z1eo2fP3+q1+PxGKZpFnZb5pWlvb+/BwBcXV0BABaLReF58H1fnV/f99XxyK5Fy7IKzzURERH9IdEQAMK2bSGEEJ7nCQAiCIJMGtu2BQBhmqYQQoggCIRt2yKKIpU+iiLhOM7W+uU6oyhS25PrqeJ5XiZdkiRCCCEcxxEAKrcFQCRJIpIkEfJU6Z/Ztf0gCET+FCdJotYvz1eZsrSmaWbOIQDheV7ms/L45PajKBKmaapj8TxPJEmycx+IiIjo/RoJsPSbthBvAUBZ7CaDLCHebvoyCJPBgAyg8mTAIER5UJEng4oypmmWBhj5gE4GVXL/d21bD8qK2LZdK0AsSqsHeXJ/ZOCok+dMBon565MPgImIiKgZjXQRvr6+Ash2D8quvDzZlZWmKebzuRqjJYSAaZq4uLgo/Fyaplvdgz9+/MjM1tNn7IVhiMlkgvV6XdoNZllWaRfjyckJAODl5QWu66pxVQ8PD3AcB6PRaGt2oBTHMbrdLoQQpd2IrVZLddnpsx2Lug71tHKZ3j1o23bhccgu2tfXV9ze3qoxWuv1GrZtYzAY7OymJCIiog9oIkrTW2v0lqYi+e43If7rapNdhUUcx1HdWtjRgiRbuOSfbO2R64+iSK2nqOVHTwutpcdxnEwXZVFrm/45aF17cnt6i11Za92utHK/5bksOwb9PMjP27ad6fYs+ywRERF9XGNjsPJjfsoEQZDpmtIDharASQYE7w2uioKTOvuZJMnW+CzHcVSXXFHXYj640oMkIf7rmqsT2FSlLQpSi0RRVDg2S667aPwZERER/TlDiE/8vgIiIiKiA8SfyiEiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJq2F8LsNI0hWEY6i+OY7UsDMPC94vo6zAMA67rqmW9Xq/w/b9BP4aiY9HfD8Pwr+4bERER/V1/LcC6vb3NvG632wCAOI4xGAwQBAGCIMBkMildR1FgcnNzo9azWCzU+51Op4ndBgBYlrUz8Lu7u8u8tm1bHaPv+5llZ2dnje0bERERfT1/LcA6PT2FEEL9SdfX1zBNE/1+H5vNJhMk5a1WK/X5JEngeR5arRYA4OnpKbP+fr/f2L6v12tMJpPSlrE0TXF5eam27XkeptNpJo2+b3KfiYiIaD/9lQDL932MRiMYhpFpzYnjGEmS4OfPnwCAx8dHmKZZuh7ZWgW8tYgNh0MAbwGOXL9lWZX70ev11P9lWtd11WfLuhYfHh4ghECn0yncjr4vAFQQFYah2re/3W1JRERE/4j4C5IkEUmSCAACgEiSRAghhOM46rVcbtv2zvVFUSSCIMisXwghbNsWAITneVufkduShxxFkTBNU23X8zyRJEmt7ctt6seibyefTgihth1FUa31ExER0ff1V1qwWq0WWq0WgiAAALy+vgJ4a+0xTROtVgv39/cA3roMd5nP55kuQNla9PDwAAB4fn7e+sx0OoXjOGq7R0dH+PnzJ1qtFkzTxGg0wu/fv9U6ysjB7N1ud6u7L47jrbFfcnkURQCAl5eXncdHRERE39tf/ZqG4+NjAMDR0RGAt7FNevcgAPT7fViWlZl1J7vdgLeuPb2rMM80TZyenhYuk++/vr5muhjX6zVs28ZgMMhsSydnKMpxYOv1eitNPvDTyWOW54CIiIj216cHWL1eT417ms/ncBxHtepYloWTkxM1A1C2cK3X69JB4c/Pz5nXskUpTVM1pksGTnknJycAgIuLC1xdXan9S9N0a1C6zrIsjMdjCCFK04VhqNYpua6rxmrd399nZhYSERHR/vr0AOvy8hKLxQKGYeD09DQToIzHYwwGA1xcXCAIgp0z/1zX3Qpw5FcemKaJ6+vrzAzFvOPjY3ieByGECnRarRa63S5M08wEf7r1er0zMFqtVltpOp0OkiSBYRh4fn7e2f1IRERE+8EQVREJEREREb0bfyqHiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgaxgCLiIiIqGEMsIiIiIgadhABlmVZMAwDlmUVLk/TFIZhwDAM+L6v/m8YBlzXBQC4rpt5PwzDzGv987u4roter9foMVJz8nlAv+Z1xXG8lYeIaLsulX9xHNdeh17/1qlzif4JcSAcxxEARBRFtZbZti2CIMikC4JA2LatXidJIvRTGASBALD1uaJt6euhryeKImGapnrteV5p/ikCQCRJIqIoEgdUzIhqydelsl5MkmTnZ/V6NwiCTDkl+koOogVLchwHk8nk09bf7/dhmiY2m01pmul0Cs/zPm0f6HMMh0MAwMvLy860aZoCAFqtFtrtNoQQn7pvRN/dzc0NAOD19XVn2tfXV5imCeCtzl2v15+6b0QfdVAB1s3NDRaLRaYpOgxDXF1dNbJ+3/eRJAl+/PjRyPro65DdfP1+X72ndy3LLt84jlXlz+5Bonpc14Vpmmi32+q9ovIVhiEuLi6QJAm7B+nLO6gAq9VqwXEczOdz9d5qtcoUat1gMMiMERgMBoXp5PLn52cIIdBqtT5l/+nvkpW4YRg4PT3daokyTRNJkqj3fd9Hu91GkiQAACEEptPpX99vou9gsVio8nV9fb3VElVUvvr9PqIogmmaEEKolmWir+igAiwAuLq6wmw2Q5qmiOMYnU6nNG0QBBBCqL8gCArTCSHgeZ7qGqL9ICtxx3Hw/PycWSZbQU3ThGEYWCwWW2mIqJxt2xBCwLbtrWEVLF+0Dw4uwGq327BtG7e3t3h6esp0+fwJ+STFJuv9M51OsVwut2YRygBM/rG1iuj9Hh4eMBqNtmYRsnzRd3dwARYAjMdjzGazxtcrK4r3TOen72G5XGIwGKibgOwK1ANqjrci+pgoinBxcaF6AVi+aC/85VmL/4RpmgJAZpq9aZqZafTyT07Hl3+O4wgh/ptGLP/kVzLk16u/XzSlX18/v6rha8rnATl1XH8/SRI1XVx/L5+fgiBQnyOi7bq06H0hRGH5Kqp3HcfhVzXQl2QIwTnkRERERE06yC5CIiIios/EAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBrGAIuIiIioYQywiIiIiBp2EAGWZVkwDAOWZRUuT9MUhmHAMAz4vq/+bxiG+gV313Uz74dhmHmtf77OvhiGgTiOGz/WQ+C6Lnq9Xq20vV5v67rsugb5fGJZ1s7rquebImmaluY/Sc9TVceXz4v6NuXxyjwqt133fH01dctVWTr9XKVpWvhZy7JKl+0bPV9UpSnLgzKPFsnnM1nOZB1aRM+v+TpaL1O79rko35fxfX+rLMZxvFWmwjCE7/s71/fdyDJRVR/p56PqXOWvrV7edpXZg/Cvf236b5G/1B5FUa1ltm2LIAgy6YIgELZtq9fy19715QC2PqdvJ0kSIYQQnueJAzr9jZHXSr8OReS1cRxn6/PyvSAIhGmameX5fGCapgAgPM8r3ZaeL6IoKtw327a3tpXfX31fq7aZz19ye57nqX2XeVHmt6Jj/epM01THY9t2YdmtShcEgTpXnucVXhdZDuV52mcAKusnIUTmHJmmmUkr81RRvRVFUeZ9vf50HKdwm0mSZK6pXgb0MiXLctn1r8r3RWkBbJWF/P7pdbVt25Xl/zvxPC9zjvP1o5TPBzJd/p5nmqY6N3odI9MdQrmqcjB3eHljLapkmwqwhMhmuF2YAT+m7Gapy98cJMdx1PWJoihT0ZbdCHZVsPm8ot/whXjLN57nvSvA0StCXT6/RFFUesPM74fned/mRpG/NvmyVyedfq6iKNo69iRJDirAEqK8XEj6edCDDCkfSOnrlWnz9WL+GhVtS4i3ayfza74s6g9Gu+TzfV7Rw0Z+X/Lb0o/vO8ufmzp5Xy9T+aBMP5f5MnpI5arMQXQRSjc3N1gsFpluoTAMcXV11cj6fd9HkiT48eNH7c+0Wq1Gtk3/kU36d3d3W10dV1dXGI1GSNMU8/kcd3d3AN66N2azGfr9/ru3t1gscHx8rF5bloWXlxf1erVa4fz8/N3rPT093Xovn1/m8znOzs5K13F0dKT+PxwOMRqN3r0f/8LT01Oma+L4+Bjr9fpd6fRzNZ/PMRwOM5+9vb3deu/QyXMWxzE6nU6t+ikMQ1iWpdL+/v0bpmmq5UdHR0iSpHRb0t3dHdrtNoC38rjZbNSyorJQRc/3dej7Esfx1vYmkwlub2/ftc6vJk1TJEmSOTemaeL19XXnZ+X52Ww2me50vd7r9/tYLBYIwxBhGMLzvIO/vx1UgNVqteA4DubzuXpvtVqpQp03GAwyffKDwaAwnVz+/PwMIUStTBXHMTzP+9iBUKXVaoVut4vxeAwhBBaLhRoP0G63EQQBTNNEp9NR1/7379+wbfvD2yyr0F3XxXQ6fff6Hh8fa9/8i/Jbmqbodrtby2zb/jZj/+pWzlXp5HiR2WyWGS/S5IPVvnFdFxcXFxgMBrXyymq1wuXlZea9XeMNi+jX8fLyEr9+/Xr3Osry/Xs8PT1tPSSfnZ1huVx+eJ1fyXvPzWq1ws3NDQDg/Pwci8WidMxikiQYDAZYrVZ8eMGBBVjAWwvGbDZDmqbqKa1MEAQQb92oEEIgCILCdEIIeJ73roGyRU/U1Iw0TTPBk+d5eH5+VstXq5WqCGTgtdlsGn/a2pW/yoRhiPF4XGv9ZU/2t7e3pYGd3rq279rttiqfstwD1Q9Wh246nUIIAdM0MZlMdqZP0xQnJyd/tM0wDDNlRdaN8uF1NBrVKktV+b6ux8fHwrqgqBVu3+VbMtvtNhzHgWmaMAwDFxcX6Ha7Kv3t7S2EEFgul992Yk2TDi7AarfbsG0bt7e3eHp6+lCXUBFZIdSZOdHr9dQTATWv1Wpluhd0vV5PVRhJkqjuwj9h2zZ+//6tXq/Xa5ydnWE+n6tW0IuLCyRJomaz6bNt9IoojuPaN/+iJ23grQXiu7fOnJ+fZ1oMXl5eMhX5e9MNh0PVbRWGIWazWWYGpmmaezdb7E/J7vP3Ojs7w2KxUK9fX193tg6vVqutuni9XkMIgSiKYJqmWp6f6Sc1ke/TNN1qjdsXrVYLpmlmWiWTJCmta9I0xWQy2bouMgAXQgCAupfp9771eo31en3wZergAiwAGI/HmM1mja/34eEBo9GoMlO5rovxeKyeCKqmMNPHyHFW0q9fvzIV72q1yqRvtVo4OTn5cKB1fX2tbkb6eBS9IpI3CdmFrC97eHgA8F+FJp/Afd+v3KeiJ23f9zOtd0X5Sx838VXJ/Zc3g/F4XHjzrJsujmN1Xfr9fqZlGni70TT1sLUv5vM5rq+vd6bLP9C0Wi3Ytq3qwclksnM9Zfk8DENcXFxkxt/p105eszr5vo77+/vS8ZL6uLLv6ufPn2qIjO/7cBynNG2321V1UxiGmcBMfrVRFEWZOijfjfod6ppP9RcH1P8zcqo9tJmCclaInBUj/+SsIvknZ0zImYbQpjrrr/PThFEwrVjfj7I0VE2/PvqMFdu2M6/1dPlZU2Xnv6g42LadyRt6Wv21nj+KlM2kkvL5ST8+mUf1fS2aFafva9WxfxdyNlr+XOen4pel089H1axTfV37TK9/ZL6Q5y4Igsx5LMo7+bpSf7/o/ObrUCGKv0ZB/zoNqe7XsQhRne/zX4ejl7Oi8li2vaqvNPhu5PnSj1/PB/nrrKet+qoOfd35snioDCH+9whHdOBc10Wn09nblowwDLHZbDj2jxpnWRaWy+Xezhrr9XqYTqd7e3z0ORhgEWksy8pMF98XcRzj+vq68KsOiP5UmqaqC3zfuK6L09NTPpjQuzHAIsqxLGuvAhE5qF6OpyD6DPuYz3zfx8nJyd62atPnYoBFRERE1LCDnEVIRERE9JkYYBERERE1jAEWERERUcMYYBERERE1jAEWERERUcMYYBERERE1jAEWERERUcMYYBERERE17CACLMuyYBgGLMsqXC5/GdwwDPi+r/5vGIb6VXbXdTPvh2GYea1/voq+nrJfkKfdfN+vPNdV51nmB8MwMr8Qry8HsvnCMIzSbRXlBbneuuvQxXG8M72+Tv349H3p9XpqH+T/vxv9/IVh+KF0epku+1xRPtg3RXmjjExXVGfq5Sd/rsMwzJRLuc0q+vry+6aX4111q7SrbtDzg6zfi/ZF34d9zB91z2tZOr2eKjo/u8rsQfhnPzP9l8lfZ4+iqNYy27a3fuE9CILMr63LXyDXl6PgV+j15fqvvNf5pXjaJs972a+1V51nx3GE4zgqnf6L8kKITD7QP2fbdun1yl9vPSG1PuEAAAhJSURBVJ1pmmq5vu0yMi8mSVKaxrZtdez68cnzIj+rb7voWL8qx3HU8eWPp+y8lKXTy2wURYXlt6xe2CdJkmTyXlX5MU1TnQ/HcTLnLAiC0muQL2uyPqy6zURRlFlfFEVqv/Q8m8/bVcdZdWxRFGX2J1/e9XpDLy96WfquPM9TeUC/xrZtl+b/snT5e1/+Gsvr/t3P2Z86iBYsyXEcTCaTT1t/v9+HaZrYbDaFy8/OztRvWp2fn+Py8vLT9mWf3d7ewnGc0uW7zvPp6SkA4Pj4OPO+67oIgkD90LP+m2rj8bj09wnPzs4yr1utVua13I7cbpkwDLFcLiGE2FpH3snJSebfou3rrQ/9fh8/f/6s3QrwL8in4U6ng+FwiDAMYdu2Op6fP3/i9vZ263NV6e7u7nB9fQ0AaLfbWK/X6mm71Wrt5Y8TF2m1WphOp+q153l4fn7eSidbQ2UZuLm5wWKxUO8PBgOYprmVj9I0xa9fvzJlpt/vI4qiyv06OjrK5PWnpyf8+PFDvZZ5eFd5kHbVDQBgmmbh/xeLhao3hsMhkiRReWW9XmMwGNTah69GtsoBwHQ6Vcckr/H19XXhfbEq3f39feY8O46TyRNCiMy5PVQHFWDJykJvzgzDEFdXV42s3/d9JEmSqSB0eiUxn8/56+wfUOd6VZ3nq6srjEYjpGmK+XyOu7s7AG83iNlsVvmjrmVdzPr2wjBEp9NRr3/+/Klu8L9+/crc5PLG4zG63W5lszvwVtHJyv7u7k6ts9VqwbZtuK6rboj68QyHQ4xGo9Lt/wt6N4MQAkIItc+r1Spzbk9OTgq71avSLRaLTCBtWRZeXl4+63C+laKA//X1FUmSqNf6eU3TVF2j0WiU6V67vb3Fz58/370P+cDp8fFRvdfv97FYLBCGIcIwhOd5lYFWnbpBBgu+7yOOY3S7XbTbbZVf9PyVDxA8z/vSDyg6vctcPrTJevDp6SlTlx0fHxc+PFale3x8zOSf09PTwoD90B1UgNVqteA4DubzuXpvtVqpQpc3GAwyYwPKnmDk8ufn552tD/KGMpvNtvr/abeq66UrO8/tdhtBEMA0TXQ6HbWu379/w7bt0vU9PT1hPB7X2r98UCODpuVyWfq5NE2RJAk6nQ6EEPA8DxcXF4Vp+/0+PM+DYRgYj8eZ/Pbw8IDlcolut5tpTZBs2/4y40nCMMRkMlE37SK7Wv3qpDs6OvrQ/u2zx8fHwgc8WR6Kxs7o+UwIgdlspgKS5XKJ8/PzP9qnNE23WpuTJMFgMMBqtdr5QFq3bliv1xiNRphMJpmHE9M0cX9/X/q58/NzPD4+1jiSf0sGjrJcFd2P6rYIVqUraj2nrIMKsIC3FgxZMcRxnGltyAuCQGVSIQSCIChMJ2+IdQatt9ttlV6voGg313UrW4B0Ved5tVqpils+kW42m9LKJN9tUiV/PWUek0FdWXDz+voK0zQzXRRF69Pfi6IIFxcXmZuh7/sqwCobXPxVWnD6/T7G4zEnfPxlYRhWPiwEQZB5uDRNs7BseJ6H19dXAG+B0J8Gsvf391tB2u3tLYQQWC6XlQPz31M3uK6rAnq9hebu7g6j0Ugdd5IkW2W+bJjAV9Jut7FcLg9m8sZXdnABVrvdhm3buL29xdPTU2WX0HvIG2LdJuThcMg+6neazWaq8pvNZhiNRjtnQ+XPc6/XQ6fTQavVQpIkqruwSrfbrdWdG8ex6g6ULi4u1JiwIAjUcn12VK/Xw9HRUaZrZtf+nJ+fo91uI4oi1bIaxzF+/fqlxts4jvPlW0llICwDQj1Y7HQ6mRaDzWZTOG6xKp1t2/j9+7datl6vt8bMHZI4jne29PT7ffVQadt25bjVJlsHn5+fM/ul16Xr9Rrr9RphGG7NzI3juHbdID8PvLX2WpaltiPzohACjuPA87zGju1vk+MLJ5PJ1gzA8/PzTGv6y8sLut3u1jqq0l1eXmK1Wqllz8/PlY0Vh+rgAizgbazLbDZrfL0PDw8YjUa1pqbGcQzLsmo31RIyrYmyAizqBtMVnWe9YgDeKqOy8T2WZamn1jiOK6/tfD4vvHnrN3j5xDydTtWxPDw8qPFTsiLMD9zOe3p6Uv/XA8gkSTLHUdR1lh/c/xWs12sIIbBardQNQY7BkcczGo0KxzdWpbu+vlbj7MIwPOgyl6ZpplvM9/3KhwvLsnB5eVn4EJqmaWa8lGmaqjXro/tWJN+tfnx8rIIH+acHRnXqhsVikXmd7+qSgVnRQ1XZOMyv6uHhQbXWya+lkEGsbN0aj8eFY9eq0v348UPdQ+uMXz1YnzU98SsxTXNrOracyi2n7co/z/Myr+W0Vjl9Htr0U/21XK/+fn7qq23bahm/ouHP6FP5hch+jcKu81x03eT7kj6FX//T0+anghdtK5+/dpHp9Cnich35KdLyT5+6ruffsmP/TvTzp0/5dhyn8Bzl08m0Zee/LC/sm3x9pecPPX9VfXWFvo78V354nrdVHsryvsyj+c8XnX+9LJd99UJeVd0gl+frdyF2f7VA0TF+V/p11o9JXmNZp5Sl09MW5RX9nnvIX9VgCHEg85SJdnBdF51OZ2+fxMIwxGaz4exValyapuh2u99ijNJH6a3ZRHUwwCLSWJaFu7u7WgPavxM5Pow3CPosYRji7u5uZ7f9d9Tr9XB9fb23D1/0ORhgEeXs25NqmqZwXXcvb3z0texjK6nruri6utq7hy76fAywiIiIiBp2kLMIiYiIiD7T/zWxkl2/lk5ERET03fxJJ18jARZ7GYmIiIj+wy5CIiIiooYxwCIiIiJqGAMsIiIiooYxwCIiIiJq2P8DWau/4WeFdogAAAAASUVORK5CYII=)
Gensini score = 0, non-CAD; 0 < Gensini score ≤ 25, mild coronary artery stenosis; 25 < Gensini score ≤ 50, moderate coronary artery stenosis; 50 < Gensini score ≤ 75, severe coronary artery stenosis; Gensini score > 75, very severe coronary artery stenosis. Adjusted: adjusted for gender, age, smoking.