Four mixed ligand complexes were prepared from 1,10-phenanthroline (Phen), 5-chlorosalicylic acid (CSA), and anthranilic acid (Anthra) dissolved in aqueous ethanol at a ratio of (1:1:1:1) M: Phen:CSA: Anthra, M(II)= Cu, Zn, Cd, and Hg. The prepared compounds were analyzed by flame atomic absorption, FT-IR, UV-Vis, and spectroscopic methods, as well as conductivity measurements and magnetic properties. After analyzing the prepared compounds using the acquired data, the complexes formed by mixing ligands were concluded to adopt an octahedral geometry. That study has been conducted to test the inhibitory effectiveness of the complexes (1,10-Phenanthroline (Phen), 5-Chlorosalicylic acid (CSA), Na[Cu(Phen)(CSA)(Anthra), Na[Zn(Phen)(CSA)(Anthra)], Na[Cd(Phen)(CSA)(Anthra)] and Na [Hg(Phen)(CSA)(Anthra)] at a concentration 10-3 mg /mL against some types of bacteria that cause urinary tract infections ( S. aureus, S. epidermidis, E.coli, K.pneumoniae and C. albicans) and test its sensitivity by Vitek -2 system to the most common antibiotics used in hospitals at the present time. The results showed that all complexes showed high inhibitory activity. All of these bacterial species and Candida albicans were resistant to antibiotics (meropenem, penicillin, gentamicin, imipenem, cefixime, ceftriaxone, amoxicillin, azithromycin, tobramycin, levofloxacin, and vancomycin). By making the prepared compounds resistant to antibiotics, they can be used as pharmaceutical compounds.
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
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