## 3 Results  
The averages of the values obtained with the two different methods are reported in Figure 1. The t-test performed on the samples indicated a higher number of copies detected with the _165 rRNA_ method (1,568,482.9 \(\pm\) 53,725.1 se, SD = 939,805.8) compared to the _Hsp70_ ones (152,579.8 \(\pm\) 5,853.1 se, SD = 102,388.2) (t (305) = + 28.991, \(p\) = 0.000). A positive correlation was calculated between the two analytical data series (r (306) = 0.851, \(p\) = 0.000). In a linear regression model (Table 2), the abundance obtained with _165 rRNA_ was considered as the dependent variable versus the abundance obtained with Hsp70 considered as the independent predictor variable (Figure 2). \begin{table}
\begin{tabular}{c c c c} \hline
**Gene** & **Primer** & **Sequence (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Reference** \\ \hline \multirow{4}{*}{**165 rRNA**} & Forward & AAGAGTGAGACCTATCAGCTAGCTTG & \multirow{4}{*}{[70]} \\  & Reverse & CCGTCTCAGGCTCCTTCTC & \\  & Taqman Probe & ACCGTTACCCGTCACAGCCTTGTT & \\  & Forward & GGGGATACAAGTGCTTAGAGTGATT & \\ \multirow{2}{*}{_Hsp70_} & Reverse & TGTCAAGCCATAAGCCAAGTG & \multirow{2}{*}{[50]} \\  & Taqman Probe & TGAGCCTACTGCG & \\ \hline \end{tabular}
\end{table} 
Table 1: Primer sequences used for the qPCR analysis with the _Hsp70_ and _16S rRNA_ genes and TaqMan Probes. \begin{table}
\begin{tabular}{c c} \hline
**Model** & F (1.304) = 800.403, \(p\) = 0.000, Adj. R\({}^{2}\) = 0.724 \\
**Intercept** & 376,209.4 \(\pm\) 50,726.0 s.e. (95\% CI = 276,390.9, 476,028.0), t (304) = 7.417, \(p\) = 0.000 \\
**Slope** & 7.8 \(\pm\) 0.3 s.e. (95\% CI = 7.3, 8.4), t (304) = 28.291, \(p\) = 0.000 \\ \hline \end{tabular}
\end{table} 
Table 2: Characterization of the regression model relating the _16S rRNA_ abundance (dependent variable) to the _Hsp70_ abundance (independent variable). The regression model is significant (\(p\) = 0.000), indicating the existence of a relationship between the two variables. Similarly, the intercept and slope values are also significant  
Figure 1: Boxplot showing average, standard error and standard deviation of the individual \(N\). _ceranue_ abundance values measured with the two methods examined here (\(16\)_rRNA_ and _Hsp70_). The number of copies is reported against a logarithmic vertical axis. Figure 2: Linear regression analysis between the abundance measured with \(16\)_rRNA_ (dependent variable) and with _Hsp70_ (independent variable) methods. Legend-blue circles: experimental data, red cross: average values, red dashed line: inoculated dose, thick solid line: regression line, dashed thin lines: confidence interval of regression, solid thin lines: prediction interval. (\(p=0.000\)). The obtained regression index (R\({}^{2}\)) is high (0.724), therefore in this model, the independent variable (_Hsp70_) has a strong influence in the determination of the dependent one (_16S rRNA_). However, the forecast interval is very wide, and this is not compatible with an effective forecast of the value obtained with _16S rRNA_, starting from that obtained with _Hsp70_ and vice versa. In addition, the graph highlights an anomaly in the distribution of the values, which tends to concentrate around the number of copies relating to the dose of inoculum (187500). This is related to the fact that most of the bees analyzed were subjected to treatments with substances that produced efficacy against _N. ceranae_. Concerning the _16S rRNA/Hsp70_ ratio, the minimum and maximum values were 1.01 and 18.68, respectively. The distribution of values reached maximum frequency in the range 10.00 to 12.00 (50.3% of all values), with most of the remaining ones positioned in the ranges 8.00 to 10.00 and 12.00 to 14.00 (total 41.2% of all values). Only a few other observations fell outside the ranges mentioned (8.5% of all values).