A highly significant cytotoxic effect ( p < 0.0001) was identified in MDA-MB-231 (100 to 700 μM) and in MDA-MB-468 (100 to 400 μM).
Excerpts
The serum IL1α levels were similar for both groups when the comparison was performed using a Tukey’s test ( p = 0.778); however, the linear test for trend indicated a significant trend ( p = 0.0001).
The statistical difference between the NaCl and NaOH groups was highly significant ( p < 0.0001), confirming the non-irritating nature of NaCl.
Table 2 shows that the model is highly significant with a low p -value ( p < 0.0001).
The post hoc Tukey’s test shown in Figure 5 provides strong statistical evidence for this cytotoxic effect, particularly at 100 μg/mL, which showed highly significant reductions in cell viability ( p < 0.0001).
After 14 days of treatment, the analysis revealed a highly significant ( p < 0.0001) superiority of TBG in the primary efficacy variable. 1.4.
From the table, it is easy to see that the regression model reached a highly significant level ( p < 0.0001), with a good fit (R 2 = 0.9848).
Worsening was highly significant when compared to basal values (BV; p < 0.0001 vs.
From the variance analysis, it is shown that the p -value of the regression model is extremely significant ( p < 0.0001).
The regression model is highly significant ( p < 0.0001), but the out-of-fit phase is not significant, with R 2 of 0.9741, indicating that the model fits well and can reasonably predict the taxanes yield within the ranges of the variables [ 33 ].
Statistical analysis confirmed that the reduction in viability at concentrations ≥ 2.50% was highly significant (**** p < 0.0001).
After statistical analysis, the F -value of the APS of the lignin nanoparticles model is 230.57, all of which are highly significant ( p -value < 0.0001), and the p -value of the lack-of-fit term is not significant ( p > 0.05), suggesting that the model is accurate and reliable.
The results indicated that the regression model was highly significant ( F = 116.64, p < 0.0001), while the lack-of-fit term was not significant ( p > 0.05) [ 34 ].
Accordingly, the second-order polynomial equation was established as follows: Y = − 9.16012 + 0.123691 × A + 0.143075 × B + 0.162185 × C − 0.0001375 × A × B − 7.5 × 10 − 5 × A × C + 3 × 10 − 5 × B × C − 0.0014058 × A 2 − 0.0013893 × B 2 − 0.0015433 × C 2 From Table 2 , it is evident that the model’s regression is highly significant ( p < 0.0001), with a lack of fit of 0.3369, exceeding the significance threshold of 0.05.
6.65 X 2 X 3 − 35.03 X 1 2 − 47.97 X 2 2 − 29.11 X 3 2 Y 2 (TFC) = 58.85 + 5.13 X 1 + 0.27 X 2 − 1.13 X 3 − 0.43 X 1 X 2 + 0.80 X 1 X 3 − 0.39 X 2 X 3 − 4.51 X 1 2 − 2.53 X 2 2 − 1.71 X 3 2 The results of the analysis of variance in Table 2 suggest that the fitted models for TPC and TFC were both highly significant ( p < 0.0001), while the lack of fit was not significant ( p -values: 0.2161 and 0.3343, respectively), indicating a good fitness for the models.
The regression model was highly significant ( F = 121.05, p < 0.0001), while the lack of fit ( p = 0.97 > 0.05) was insignificant relative to the pure error, indicating a good fit of the regression equation to the experiment.
The experimental conditions exerted a highly significant overall effect on the biomarker panel (Wilks’ λ test, F(77, 115.3) = 3.518, p < 0.0001).
Furthermore, all quadratic terms (A 2 , B 2 , C 2 , and D 2 ) were highly significant ( p < 0.0001), suggesting pronounced nonlinear relationships in the extraction process, which is essential for interpreting the experimental outcomes [ 59 ].
The results indicated that the model for 2, 6-DIPA hydrolysis efficiency is highly significant, with an F-value of 50.59 and a p -value < 0.0001.
The prevention is highly significant ( p < 0.0001) with BEN extract from the third to the fifth hour.
Highly significant differences ( p < 0.0001) were observed among all sample types across all parameters, indicating substantial textural modifications resulting from blend composition.
Multiple regression analysis was performed using the quadratic polynomial model described by Equation (1), linking the independent variables to EE response: (1) Y = 56.32 + 0.5366 X 1 + 1.73 X 2 − 0.2654 X 3 − 0.4980 X 1 X 2 + 0.5468 X 1 X 3 + 0.0390 X 2 X 3 − 8.95 X 1 2 - 6.52 X 2 2 − 5.14 X 3 2 Analysis of variance for the regression model ( Table 2 ) revealed that the model exhibited a highly significant F-value of 830.28 ( p = 0.0001).
The results show that the differences between strains, oils and antibiotics were very highly significant p < 0.0001 ( Table 3 ).
The second-order model obtained in terms of coded variables for extraction efficiency ( E (%)) of BB seed oil using UAE is given by Equation (1): (1) E % = 80.74 + 9.375 X 1 − 2.495 X 1 2 + 2.875 X 2 − 3.495 X 2 2 + 0.5 X 3 − 2.745 X 3 2 − 1.25 X 1 X 2 − X 1 X 3 + X 2 X 3 The ANOVA statistic for extraction efficiency (the response variable) proved that the regression model is highly significant, having a very high F-value of 46.67 and a low p -value of p < 0.0001.
Additionally, the model reached a highly significant correlation ( p < 0.0001), and the lack of fit of this model is insignificant ( p > 0.05).