Association between the Alvarado score and surgical and histopathological findings in acute appendicitis.

Objective: to compare the results of the Alvarado score with the surgical findings and the results of the histopathological examination of the appendix of patients operated on for acute appendicitis.

Methods: we conducted an observational study with a cross-sectional design of 101 patients aged 14 years and over undergoing emergency appendectomy. The evaluation comprised the Alvarado score, gender, age, ethnicity and time of evolution.

A study on the antipicornavirus activity of flavonoid compounds (flavones) by using quantum chemical and chemometric methods.

The AM1 semiempirical method is employed to calculate a set of molecular properties (variables) of 45 flavone compounds with antipicornavirus activity, and 9 new flavone molecules are used for an activity prediction study. Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Stepwise Discriminant Analysis (SDA), and K-Nearest Neighbor (KNN) are employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying the flavone compounds according to their degree of antipicornavirus activity. The PCA, HCA, SDA, and KNN methods showed that the variables MR (molar refractivity), B(9) (bond order between C(9) and C(10) atoms), and B(25) (bond order between C(11) and R(7) atoms) are important properties for the separation between active and inactive flavone compounds, and this fact reveals that electronic and steric effects are relevant when one is trying to understand the interaction between flavone compounds with antipicornavirus activity and the biological receptor.

A quantum chemical and statistical study of flavonoid compounds (flavones) with anti-HIV activity.

The molecular orbital semi-empirical method AM1 was employed to calculate a set of molecular properties (variables) of 22 flavonoid compounds (flavones) with anti-HIV-1 activity and nine new compounds were proposed for anti-HIV-1 activity prediction. Pattern recognition techniques, principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA) and K-nearest neighbor (KNN), were employed in order to reduce dimensionality and investigate which subset of variables could be more effective for classifying the flavones according to their degree of anti-HIV-1 activity. The PCA, HCA, SDA and KNN studies showed that the variables log P (partition coefficient), molecular volume (VOL) and electron affinity (EA) are responsible for the separation between anti-HIV-1 active and inactive compounds.