In this study, a new green microwell spectrofluorimetric assay (MW-SFA) with high throughput was developed and validated, for the first time, for the determination of three selective serotonin reuptake inhibitors (SSRIs) in pharmaceutical dosage forms and plasma. These SSRIs were fluoxetine (FLX), fluvoxamine (FXM), and paroxetine (PXT), which are commonly prescribed drugs for depression treatment. The MW-SFA is based on the condensation reaction of SSRIs with 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole (NBD-Cl) in alkaline media to form highly fluorescent derivatives. The MW-SFA procedures were conducted in 96-microwell white opaque assay plates with a flat bottom and the fluorescence signals were measured using a microplate reader at their maximum excitation and emission wavelengths. The calibration curves were generated with good correlation coefficients (0.9992-0.9995) between the relative fluorescence intensity (RFI) and the SSRI concentrations in the range of 35-800 ng/mL. The limits of detection were in the range of 11-25 ng/mL, and the precision and accuracy were satisfactory. The proposed MW-SFA was successfully applied to the analysis of the SSRIs in their pharmaceutical dosage forms. The statistical analysis for the comparison between the MW-SFA assay results and those of pharmacopeial assays showed no significant differences between the assays in terms of their accuracy and precision. The application of the proposed MW-SFA was extended to successfully analyze SSRIs in plasma samples. The greenness of the assay was confirmed using three different metric tools. The assay was characterized with high throughput properties, enabling the sensitive simultaneous analysis of many samples in a short time. This assay is valuable for rapid routine applications in pharmaceutical quality control units and clinical laboratories for the determination of SSRIs.
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http://dx.doi.org/10.3390/molecules28135221 | DOI Listing |
J Wound Care
January 2025
Jobst Vascular Institute, ProMedica Health Network, Wound Care Program, Toledo, Ohio, US.
Objective: The presence of microorganisms in a wound may lead to the development of pathologically extensive inflammation, and either delay or prevent the healing of hard-to-heal (chronic) wounds. The aim of this case series is to explore the use of topical gentamicin ointment, an aminoglycoside with activity against aerobic Gram-negative bacteria, as an option to address hard-to-heal wounds.
Method: We present a retrospective case series of patients with hard-to-heal wounds of varying pathophysiologies treated with topical gentamicin.
BMJ
December 2024
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02120, USA.
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Design: New user cohort study.
Setting: Longitudinal commercial US claims data.
J Vet Intern Med
January 2025
School of Veterinary Science, Massey University, Palmerston North, New Zealand.
Background: Most veterinary literature examining medication compliance has described the phenomenon in dogs. The evidence available regarding factors affecting cat owner medication compliance is limited.
Objectives: Identify and describe factors associated with cat owners' noncompliance with veterinary recommendations for pet medications, as well as client-reported barriers and aids to administering medications prescribed by primary care veterinarians.
J Cosmet Dermatol
January 2025
CGH Compagnie Generale des Hopitaux, Rome, Italy.
Introduction: In recent years, the field of aesthetic dermatology has witnessed a surge in demand for minimally invasive procedures aimed at rejuvenating aging skin. This study aims to address this demand by evaluating the effectiveness of the injectable gel in rejuvenating aging skin, particularly by targeting collagen regeneration and lifting effect.
Materials And Methods: The study involved 43 participants who underwent three monthly injection sessions targeting retaining ligaments.
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
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