Spectrofluorometric determination of orphenadrine, dimenhydrinate, and cinnarizine using direct and synchronous techniques with greenness assessment

Abstract Orphenadrine (ORP), dimenhydrinate (DMN), and cinnarizine (CNN) were investigated using green-sensitive spectrofluorometric methods. Method, I used for determination of DMN in 0.1 M hydrochloric acid (HCl) and 1.0% sodium dodecyl sulphate (SDS) at 286 nm after λex 222 nm, while for determination of ORP in 1.0% w/v SDS involves measuring the fluorescence at 285 nm after λex 220 nm. For DMN and ORP, the detection and quantitation limits were 2.99 and 4.71 and 9.08 and 14.29 ng/mL, respectively. The ranges of DMN and ORP were 0.10–1.0 and 0.04–0.5 µg/mL, respectively, in micellar aqueous solution. Method II, the derivative intensities of DMN and CNN were measured at a fixed of different wavelength between the excitation and the emission wavelengths (Δλ) = 60 nm at 282 and 322 nm, at the zero crossing of each other, respectively. The detection and quantitation limits for DMN and CNN were 1.77 and 0.88 ng/mL and 5.36 and 2.65 ng/mL, correspondingly, through the entire range of 0.1–1.0 µg/mL for DMN and CNN. The linearity was perfectly determined through the higher values of the correlation coefficient ranging from 0.9997 to 0.9999 for both direct and synchronous methods. The precision of the proposed methods was also confirmed via the lower values of the standard deviation which ranged from 0.39 to 1.11. The technique was expanded to analyze this mixture in combined tablets and laboratory-prepared mixtures. The method validation was done depending on the international conference on harmonization (ICH) recommendations. An analysis of the statistical data revealed a high agreement between the proposed data and the comparison methodology. Three different assessment methods demonstrated the greenness of the technique

Transcriptomic marker screening for evaluating the mortality rate of pediatric sepsis based on Henry gas solubility optimization

Sepsis is a potentially life-threatening medical condition that increases mortality in pediatric populations admitted in the intensive care unit (ICU). Due to the unpredictable nature of the disease course, it was challenging to find the informative genetic biomarkers at the earliest stages. Consequently, a considerable attention has been paid for the early prediction of pediatric sepsis based on genetic biomarkers analysis that would promote the early medical intervention. Therefore, the proposed study attempted to demonstrate the feasibility of Henry Gas Solubility Optimization (HGSO) in differential gene selection to train supervised machine learning algorithms for the early prediction of pediatric sepsis and survival rate evaluation. 26 nonoverlapping informative genes have been nominated using the gene expression profile of peripheral blood cells. After 20 runs of 5-fold cross-validation, the selected genes revealed its effectiveness in the early identification of sepsis subtypes with an estimated average accuracy of 98.03 ± 0.30 % evaluated using 20 runs of fivefold cross-validation and an average accuracy of 98.83 ± 0.57 % for evaluating the survival rate. Based on the experimental results, the present study using the novel metaheuristic algorithm HGSO determined the highest accuracy, the most predictive and informative genes for pediatric sepsis, thus allowing determination of the appropriate treatment plan

HPV tests for cervical cancer screening in low resource settings

Human papillomavirus tests for cervical cancer screening in low resource setting