Does anyone have any experience with the Motic EasyScan Pro Slide Scanner?

Example link: https://www.ncbi.nlm.nih.gov/protein?term=txid562%5Borganism%3Aexp%5D+AND+((%2210%22%5BSLEN%5D+%3A+%2220%22%5BSLEN%5D)&cmd=DetailsSearch

I am trying to extract DNA from serum. I found an article that claimed simple extraction can be done in a single tube -

Here they used a solution containing 6M NaI/13mM EDTA/0.5% sodium N-lauroylsarcosine/10 µg glycogen as a carrier/26mM Tris-HCl, pH 8.

But currently, I don't have NaI and glycogen. So, I am thinking of making a solution with KI + EDTA + sodium lauroyl sulfate + Tris-HCl, pH 8. And finally, use Na-acetate and absolute ethanol for the precipitation of DNA.

What consideration should I take into account to use alternative reagents?

And, in their protocol does it mean the final solution containing all reagent should have a pH 8 or it just means the use of Tris-HCL with pH 8?

> summary(model) Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod'] Family: binomial ( logit ) Formula: Infestate ~ Trattamento + (1 | ID_pianta) + (1 | Blocco) Data: data AIC BIC logLik deviance df.resid 3835.8 3871.7 -1911.9 3823.8 2931 Scaled residuals: Min 1Q Median 3Q Max -2.1969 -1.0611 0.6139 0.8091 1.5079 Random effects: Groups Name Variance Std.Dev. ID_pianta (Intercept) 0.16880 0.4108 Blocco (Intercept) 0.09686 0.3112 Number of obs: 2937, groups: ID_pianta, 40; Blocco, 3 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.59808 0.20650 2.896 0.003776 ** TrattamentoLavanda -0.16521 0.11116 -1.486 0.137218 TrattamentoRosmarino -0.02389 0.11000 -0.217 0.828075 TrattamentoTimo -0.37733 0.11017 -3.425 0.000615 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) TrttmL TrttmR TrttmntLvnd -0.266 TrttmntRsmr -0.269 0.502 TrattamntTm -0.269 0.499 0.504

I wanted also to check the predictive abilities. I used this code

library(caret) data$Infestate <- factor(data$Infestate, levels = c(0, 1)) # Convert predicted probabilities to binary predictions using a threshold binary_predictions <- ifelse(predicted_probabilities > 0.5, 1, 0) # Convert binary_predictions to a factor with levels 0 and 1 binary_predictions <- factor(binary_predictions, levels = c(0, 1)) # Create a confusion matrix conf_matrix <- confusionMatrix(data$Infestate, binary_predictions) print(conf_matrix)

And these are the results:

Confusion Matrix and Statistics Reference Prediction 0 1 0 1811 28 1 751 55 Accuracy : 0.7055 95% CI : (0.6877, 0.7228) No Information Rate : 0.9686 P-Value [Acc > NIR] : 1 Kappa : 0.0709 Mcnemar's Test P-Value : <2e-16 Sensitivity : 0.70687 Specificity : 0.66265 Pos Pred Value : 0.98477 Neg Pred Value : 0.06824 Prevalence : 0.96862 Detection Rate : 0.68469 Detection Prevalence : 0.69527 Balanced Accuracy : 0.68476 'Positive' Class : 0

It seems te model is good in predicting negative but it predicts 751 false positive. How to deal this aspect? Can the model be considered a good predictor? How can I increase predictive abilities?