Inflammatory Bowel Disease ( IBD ) is a chronic non-specific intestinal inflammatory disease, and its pathogenesis has not been fully elucidated. Interleukin-18 ( IL-18 ) is a potent pro-inflammatory cytokine belonging to the IL-1 family, which can effectively stimulate the production of interferon ( IFN ) -γ. IL-18 binds to its receptor on target cells and ultimately activates the nuclear factor ( NF ) -κB signaling pathway ( Figure 1), up-regulating the expression of various inflammatory cytokines and causing a variety of inflammatory diseases. The purpose of this study is to prepare a high-yield and active recombinant IL-18BP-Fc protein by using a low-cost, short-cycle and high-yield prokaryotic expression system, and to use the recombinant IL-18BP-Fc protein to neutralize IL-18 to inhibit its mediated IBD disease.

1.1Raw data：
The ability of IL-18BPa-Fc protein to inhibit the production of IFN-γ in vitro was detected by cell experiments. The recombinant IL-18BPa-Fc protein was placed at-80 °C, -20 °C, 4 °C and 37 °C for 24 h, and the concentration of IFN-γ was detected at 0,0.5,1,1.5 and 2 μg / mL of recombinant IL-18BPa-Fc protein. The specific data are shown in the following table 1.
1.2 Data statistical analysis and output results :
Analysis steps :
A.Test the normality test of the variables separately to see whether the overall distribution of the data presents a normal distribution. If the test is passed, it is recommended to use analysis of variance.
B. View the Friedman test table, if it is significant, you can view the median to analyze the difference, otherwise it shows no difference.
C. If the Friedman test is significant, the difference can also be quantitatively analyzed by effect quantitative analysis.
Output result 1 : Normality test result
Table 2 shows the results of descriptive statistics and normality test of quantitative variables-80, -20,4,37, including mean, standard deviation, etc., to test the normality of the data.
There are two test methods for normal distribution. One is Shapiro-Wilk test, which is suitable for small sample data ( sample size ≤ 5000 ). The other is Kolmogorov-Smirnov test, which is suitable for large sample data ( sample size > 5000 ).
If it is significant ( P < 0.05 ), it indicates that the original hypothesis is rejected ( the data conforms to the normal distribution ), and the data does not satisfy the normal distribution.
PS : If the distribution is normal, analysis of variance is generally used. Analysis items : -80 samples N < 5000, using S-W test, the significant P value is 0.938, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis item : -20 sample N < 5000, using S-W test, the significant P value is 0.767, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis items : 4 samples N < 5000, using S-W test, the significant P value is 0.361, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis items : 37 samples N < 5000, using S-W test, the significant P value is 0.941, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Output result 2 : Normality test histogram
Figure 2 A, B, C and D show the results of data normality test. If the normal diagram basically presents a bell shape ( high in the middle and low at both ends ), it shows that the data is not absolutely normal, but it can be basically accepted as normal distribution.
1.3 Modeling process
Using the least squares method, the above data are used to establish a double exponential model.

Substitute the data fitting parameter values.
The code is as follows :
clear;clc;
IL18_Bpa0=[0 0.5 1 1.5 2];
INF_80=[290.8888967 234.4167 177.9444333 103.0555333 14.77778333];
INF_20=[279.7777933 244.2777667 208.7777667 141.8888667 57.44445333];
INF_4=[280.16665 263.5278 246.8889 175.9444667 104.7777667];
INF_37=[279.5555567 260.6389 241.7222333 237.3333 218.0000333];
%IL18-BpaThe protein concentration of -80 °C fitting curve
[xData1, yData1] = prepareCurveData( IL18_Bpa0, INF_80 );
ft1 = fittype( 'exp2' );
opts1 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts1.Display = 'Off';
opts1.StartPoint = [-1.85313921668156 2.09854544524619 287.544325859466 -0.386560461190012];
[fitresult1, gof] = fit( xData1, yData1, ft1, opts1 );
figure(1)
h1=plot( fitresult1);
h1.LineWidth = 1.2;
hold on
plot(xData1, yData1,'*','LineWidth',1.2)
hold off
%IL18-BpaThe protein concentration of -20 °C fitting curve
[xData2, yData2] = prepareCurveData( IL18_Bpa0, INF_20 );
ft2 = fittype( 'exp2' );
opts2 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts2.Display = 'Off';
opts2.StartPoint = [-0.0245240690225558 4.1820200732454 271.416423067439 -0.266277951220617];
[fitresult2, gof] = fit( xData2, yData2, ft2, opts2 );
figure(2)
h1=plot( fitresult2);
h1.LineWidth = 1.2;
hold on
plot(xData2, yData2,'*','LineWidth',1.2)
hold off
%IL18-BpaThe protein concentration of 4 °C fitting curve
[xData3, yData3] = prepareCurveData( IL18_Bpa0, INF_4 );
ft3 = fittype( 'exp2' );
opts3 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts3.Display = 'Off';
opts3.StartPoint = [-1.06229569819979e-05 8.05114419302709 266.363796361016 -0.12231529921971];
[fitresult3, gof] = fit( xData3, yData3, ft3, opts3 );
figure(3)
h3=plot( fitresult3);
h3.LineWidth = 1.2;
hold on
plot(xData3, yData3,'*','LineWidth',1.2)
hold off
%IL18-BpaThe protein concentration of -37°C fitting curve
[xData4, yData4] = prepareCurveData( IL18_Bpa0, INF_37 );
ft4 = fittype( 'exp2' );
opts4 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts4.Display = 'Off';
opts4.StartPoint = [281.680048926518 -0.141006355765894 0.000569038951961272 4.66066438932426];
[fitresult4, gof] = fit( xData4, yData4, ft4, opts4 );
figure(4)
h4=plot( fitresult4);
h4.LineWidth = 1.2;
hold on
plot(xData4, yData4,'*','LineWidth',1.2)
hold off
%The results of the four temperatures are compared.
figure(5)
h1=plot( fitresult1,'r');
h1.LineWidth = 1.2;
hold on
h2=plot( fitresult2,'b');
h2.LineWidth = 1.2;
h3=plot( fitresult3,'g');
h3.LineWidth = 1.2;
h4=plot( fitresult4,'k');
h4.LineWidth = 1.2;
grid on
hold off

1.4 Modeling results
According to the results of Figure.3, the data of IFN-γ showed a decreasing trend under four different temperature environments. It can be seen from the control diagram that the lower the temperature, the faster the rate of decline. Low temperature ( -80 °C and-20 °C ) did not affect the activity of protein IL18-Bpa, and did not affect the inhibition of IFN-γ, but 4 °C and 37 °C affected the activity of protein IIL18-Bpa, but also inhibited IFN-γ. It shows that after 24 h in the follow-up treatment process, the drug can be used again to improve the treatment effect.

2.1Raw data：
The ability of IL-18BPc-Fc protein to inhibit the production of IFN-γ in vitro was detected by cell experiments. The recombinant IL-18BPc-Fc protein was placed at-80 °C, -20 °C, 4 °C and 37 °C for 24 h, and the concentration of IFN-γ was detected at 0,0.5,1,1.5 and 2 μg / mL of recombinant IL-18BPc-Fc protein. The specific data are shown in Table 3 below.
2.2 Data statistical analysis and output results :
Analysis steps :
A.Test the normality test of the variables separately to see whether the overall distribution of the data presents a normal distribution. If the test is passed, it is recommended to use analysis of variance.
B. View the Friedman test table, if it is significant, you can view the median to analyze the difference, otherwise it shows no difference.
C. If the Friedman test is significant, the difference can also be quantitatively analyzed by effect quantitative analysis.
Output result 1 : Normality test result
Table 4 shows the results of descriptive statistics and normality test of quantitative variables-80, -20,4,37, including mean, standard deviation, etc., to test the normality of the data.
There are two test methods for normal distribution. One is Shapiro-Wilk test, which is suitable for small sample data ( sample size ≤ 5000 ). The other is Kolmogorov-Smirnov test, which is suitable for large sample data ( sample size > 5000 ).
If it is significant ( P < 0.05 ), it indicates that the original hypothesis is rejected ( the data conforms to the normal distribution ), and the data does not satisfy the normal distribution.
PS : If the distribution is normal, analysis of variance is generally used.
Output Result 1 : Normality Test Results
Analysis items : -80 samples N < 5000, using S-W test, the significant P value is 0.915, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis item : -20 sample N < 5000, using S-W test, the significant P value is 0.647, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis items : 4 samples N < 5000, using S-W test, the significant P value is 0.728, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis items : 37 samples N < 5000, using S-W test, the significant P value is 0.756, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Output result 2 : Normality test histogram
Figure 4A, B, C and D show the results of data normality test. If the normal diagram basically presents a bell shape ( high in the middle and low at both ends ), it shows that the data is not absolutely normal, but it can be basically accepted as normal distribution.
2.3 Modeling process
Still use the double exponential model using the least square method:

Substitute the data fitting parameter values.
clear;clc;
IL18_BPc_Fc0=[0 0.5 1 1.5 2];
INF_80=[281.4444667 229.2222333 177.0000333 106.8333667 10.722226];
INF_20=[273.4444667 243.2778 213.1111333 138.9444667 58.77778333];
INF_4=[290.7778 254.8889 219 142.2222333 57.55556667];
INF_37=[280 265.1389 250.2777667 218.2222333 188.6111333];
%IL18-BPc-FcThe protein concentration of -80 °C fitting curve
[xData1, yData1] = prepareCurveData( IL18_BPc_Fc0, INF_80 );
ft1 = fittype( 'exp2' );
opts1 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts1.Display = 'Off';
opts1.StartPoint = [-1.50501331209517 2.21173241411546 279.050534524858 -0.372547424477234];
[fitresult1, gof] = fit( xData1, yData1, ft1, opts1 );
figure(1)
h1=plot( fitresult1);
h1.LineWidth = 1.2;
hold on
plot(xData1, yData1,'*','LineWidth',1.2)
hold off
%IL18-BPc-FcThe protein concentration of -20 °C fitting curve
[xData2, yData2] = prepareCurveData(IL18_BPc_Fc0, INF_20 );
ft2 = fittype( 'exp2' );
opts2 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts2.Display = 'Off';
opts2.StartPoint = [-0.00257403271040611 5.32478062325234 261.795774758923 -0.231754568400623];
[fitresult2, gof] = fit( xData2, yData2, ft2, opts2 );
figure(2)
h1=plot( fitresult2);
h1.LineWidth = 1.2;
hold on
plot(xData2, yData2,'*','LineWidth',1.2)
hold off
%IL18-BPc-Fc The fitting curve of protein concentration of c at 4 °C was obtained.
[xData3, yData3] = prepareCurveData( IL18_BPc_Fc0, INF_4 );
ft3 = fittype( 'exp2' );
opts3 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts3.Display = 'Off';
opts3.StartPoint = [-0.00977436663134554 4.67450597545503 279.732289055241 -0.259798842211822];
[fitresult3, gof] = fit( xData3, yData3, ft3, opts3 );
figure(3)
h3=plot( fitresult3);
h3.LineWidth = 1.2;
hold on
plot(xData3, yData3,'*','LineWidth',1.2)
hold off
%IL18-BpaThe protein concentration of 37 °C fitting curve
[xData4, yData4] = prepareCurveData( IL18_BPc_Fc0, INF_37 );
ft4 = fittype( 'exp2' );
opts4 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts4.Display = 'Off';
opts4.StartPoint = [-0.000123117598347532 6.2580174622426 275.424944279632 -0.108799909414148];
[fitresult4, gof] = fit( xData4, yData4, ft4, opts4 );
figure(4)
h4=plot( fitresult4);
h4.LineWidth = 1.2;
hold on
plot(xData4, yData4,'*','LineWidth',1.2)
hold off
%The results at four temperatures are compared.
figure(5)
h1=plot( fitresult1,'r');
h1.LineWidth = 1.2;
hold on
h2=plot( fitresult2,'b');
h2.LineWidth = 1.2;
h3=plot( fitresult3,'g');
h3.LineWidth = 1.2;
h4=plot( fitresult4,'k');
h4.LineWidth = 1.2;
grid on
hold off

According to the results of Figure.5, the data of IFN-γ showed a decreasing trend under four different temperature environments. It can be seen from the control diagram that the lower the temperature, the faster the rate of decline. Low temperature ( -80 °C and-20 °C ) did not affect the activity of protein IL18-BPc-Fc , and did not affect the inhibition of IFN-γ, but 4 °C and 37 °C affected the activity of protein IL18-BPc-Fc , but also inhibited IFN-γ. It shows that after 24 hh in the follow-up treatment process, the drug can be used again to improve the treatment effect.

3.1Raw data：
The ability of IL-10-Fc protein to inhibit the production of IFN-γ in vitro was detected by cell experiments. The recombinant IL-10-Fc protein was placed at-80 °C, -20 °C, 4 °C and 37 °C for 24 h, and the concentration of IFN-γ was detected at 0,0.5,1,1.5 and 2 μg / mL of recombinant IL-10-Fc protein. The specific data are shown in Table 5.
3.2 Data statistical analysis and output results :
Analysis steps :
A.Test the normality test of the variables separately to see whether the overall distribution of the data presents a normal distribution. If the test is passed, it is recommended to use analysis of variance.
B. View the Friedman test table, if it is significant, you can view the median to analyze the difference, otherwise it shows no difference.
C. If the Friedman test is significant, the difference can also be quantitatively analyzed by effect quantitative analysis.
Output result 1 : Normality test result
Table 6 shows the results of descriptive statistics and normality test of quantitative variables-80, -20,4,37, including mean, standard deviation, etc., to test the normality of the data.
There are two test methods for normal distribution. One is Shapiro-Wilk test, which is suitable for small sample data ( sample size ≤ 5000 ). The other is Kolmogorov-Smirnov test, which is suitable for large sample data ( sample size > 5000 ).
If it is significant ( P < 0.05 ), it indicates that the original hypothesis is rejected ( the data conforms to the normal distribution ), and the data does not satisfy the normal distribution.
PS : If the distribution is normal, analysis of variance is generally used.
Analysis items : -80 samples N < 5000, using S-W test, the significant P value is 0.915, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis item : -20 sample N < 5000, using S-W test, the significant P value is 0.782, the level is not significant, the original hypothesis can not be rejected, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis items : 4 samples N < 5000, using S-W test, the significant P value is 0.845, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Analysis items : 37 samples N < 5000, using S-W test, the significant P value is 0.946, the level does not show significant, can not reject the original hypothesis, so the data meet the normal distribution, it is recommended to use variance analysis.
Output result 2 : Normality test histogram
Figure 6 A.B.C.D shows the results of data normality test. If the normal diagram basically presents a bell shape ( high in the middle and low at both ends ), it shows that it basically conforms to the normal distribution.
3.3 Modeling process
Still use the double exponential model using the least square method:

Substitute the data fitting parameter values.
clear;clc;
IL_10_Fc=[0 0.5 1 1.5 2];
INF_80=[287.6362667 234.2651333 180.894 109.1837333 10.95808667];
INF_20=[279.4602667 238.8988 209.2751333 136.4434667 57.71978333];
INF_4=[297.1749333 250.3009 215.058 139.6622333 56.51746333];
INF_37=[286.16 260.3664 245.7727667 214.2942333 185.2161333];
%IL-10-FcThe protein concentration of -80 °C fitting curve
[xData1, yData1] = prepareCurveData( IL_10_Fc, INF_80 );
ft1 = fittype( 'exp2' );
opts1 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts1.Display = 'Off';
opts1.StartPoint = [-1.53816883562104 2.21171837164724 285.189775425131 -0.372546982107642];
[fitresult1, gof] = fit( xData1, yData1, ft1, opts1 );
figure(1)
h1=plot( fitresult1);
h1.LineWidth = 1.2;
hold on
plot(xData1, yData1,'*','LineWidth',1.2)
hold off
%IL-10-FcThe protein concentration of -20 °C fitting curve
[xData2, yData2] = prepareCurveData(IL_10_Fc, INF_20 );
ft2 = fittype( 'exp2' );
opts2 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts2.Display = 'Off';
opts2.StartPoint = [-0.0126031026239902 4.45204324401564 274.454178874356 -0.309465522307718];
[fitresult2, gof] = fit( xData2, yData2, ft2, opts2 );
figure(2)
h1=plot( fitresult2);
h1.LineWidth = 1.2;
hold on
plot(xData2, yData2,'*','LineWidth',1.2)
hold off
%IL-10-FcThe protein concentration of 4 °C fitting curve
[xData3, yData3] = prepareCurveData( IL_10_Fc, INF_4 );
ft3 = fittype( 'exp2' );
opts3 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts3.Display = 'Off';
opts3.StartPoint = [-0.00733279197850969 4.72749617080512 291.829620034807 -0.340365241611098];
[fitresult3, gof] = fit( xData3, yData3, ft3, opts3 );
figure(3)
h3=plot( fitresult3);
h3.LineWidth = 1.2;
hold on
plot(xData3, yData3,'*','LineWidth',1.2)
hold off
%IL-10-FcThe protein concentration of 37 °C fitting curve
[xData4, yData4] = prepareCurveData( IL_10_Fc, INF_37 );
ft4 = fittype( 'exp2' );
opts4 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts4.Display = 'Off';
opts4.StartPoint = [-1.42868997398366 1.35745345711138 289.152428728236 -0.165400155791915];
[fitresult4, gof] = fit( xData4, yData4, ft4, opts4 );
figure(4)
h4=plot( fitresult4);
h4.LineWidth = 1.2;
hold on
plot(xData4, yData4,'*','LineWidth',1.2)
hold off
%The results at four temperatures are compared.
figure(5)
h1=plot( fitresult1,'r');
h1.LineWidth = 1.2;
hold on
h2=plot( fitresult2,'b');
h2.LineWidth = 1.2;
h3=plot( fitresult3,'g');
h3.LineWidth = 1.2;
h4=plot( fitresult4,'k');
h4.LineWidth = 1.2;
grid on
hold off According to the results of Figure. 7, the data of IFN-γ showed a decreasing trend under four different temperature environments. It can be seen from the control diagram that the lower the temperature, the faster the rate of decline. Low temperature (-80 °C and-20 °C) did not affect the activity of protein IL-10-Fc , and did not affect the inhibition of IFN-γ, but 4 °C and 37 °C affected the activity of protein IL-10-Fc , but also inhibited IFN-γ. It shows that after 24 hh in the follow-up treatment process, the drug can be used again to improve the treatment effect.

We first performed a T-test on all data, and the results showed a normal distribution, indicating that the data was credible ; according to the fitting results, the data of IFN-γ showed a decreasing trend under four different temperature environments. It can be seen from the control diagram that the lower the temperature. The faster the rate of decline. Low temperature ( -80C and-20 degree) did not affect the activity of protein IL-18-BPa, IL-18-BPc, and IL-10-Fc, and did not affect the inhibition of IFN-γ, but 4 and 37 degree affected the activity of protein IL-18-BPa, IL-18-BPc, and IL-10-Fc, but also inhibited IFN-γ. It shows that after 24 in the follow-up treatment process. The drug can be used again to improve the treatment effect. The modeling results can provide data support for the treatment of IBD.

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[ 2 ] Cheng Xiaoliang. Nonparametric statistical analysis of economic data in Anshan area [ J ].Journal of Anshan Normal University, 2017,19 ( 04 ) : 6-8.