method development

Nanomaterials are widely applied across diverse scientific and industrial sectors; however, their emergence has introduced a new generation of occupational hazards for workers. Concurrent with discussions on the adverse effects of nanomaterials on human health, researchers have sought to develop methods for assessing occupational risks associated with these materials. Accordingly, this study aims to propose a general framework for the development of such methods.

This is a critical analysis study designed to evaluate existing methods for assessing occupational risks related to nanomaterials and ultimately propose a modified framework for refining these methods. By examining current approaches and identifying their strengths and weaknesses, the authors have proposed an improved framework for occupational risk assessment of nanomaterials.

The proposed framework is based on two key dimensions: “Severity/Hazard” and “Probability/Exposure.” The first dimension determines the potential risk level arising from exposure to nanomaterials, with the most critical factors being the intrinsic properties and toxicology of the nanomaterial itself, parent materials, and similar substances. The second dimension describes the likelihood and nature of exposure to nanomaterials during work activities, with the most influential factors being worker, job tasks, and workplace environment characteristics.

The lack of sufficient data and numerous uncertainties regarding bio-nano interactions make quantitative risk assessment (the traditional occupational health approach) difficult, less reliable, and in some cases unfeasible for nanomaterials—given current knowledge. Qualitative and semi-quantitative approaches, such as Control Banding, despite demonstrating positive aspects, have faced significant criticism. The framework-based method proposed herein appears capable of partially overcoming these challenges.

In this study, a rapid, simple, and advanced reverse phase high-performance liquid chromatography (RP-HPLC) method was developed for the quantification of sulfasalazine in human plasma.

Sulfasalazine was extracted from plasma matrices using a simple protein precipitation method by acetonitrile. Chromatographic conditions were optimized (mobile phase compositon, flow rate, injection volume and temperature of the oven). The method was validated in protein precipitated human plasma for linearity, selectivity, accuracy, precision, limit of detection, limit of quantification.

The chromatographic separation was conducted on C18 brisa LC2 column (250 mm × 4.6 mm, 5μ) using isocratic elusion with mobile phase consisting of the mixture of acetonitrile: 10mM Ammonium acetate pH adjusted to 4.6 (30:70 v/v) with a flow rate of 1.0 ml/min at ambient temperature. Detection was carried out by UV detector at 361 nm. Calibration curves made in the human plasma were linear in the range of 3.125-50 μg/ml with the value of r2 > 0.9999. The LOD and LOQ was 0.5 and 2.5 µg/ml, repectively.

The developed and validated HPLC-UV method is suitable for accurately determining sulfasalazine levels in pharmacokinetic studies of new formulations.

Different types of antibiotics are used indiscriminately in animal husbandry for the growth and treatment of livestock, which cause accumulation of antibiotic residues in livestock products such as milk. Long-term consumption of milk contaminated with antibiotic residues above the permissible limits is harmful to human health, causing antibiotic resistance problems. Therefore, monitoring of antibiotic residues in milk and dairy products is particularly important.

In this study, temperature-controlled ionic liquid-assisted dispersive liquid-liquid microextraction  method was developed for the extraction of amoxicillin, cloxacillin and erythromycin residues from cow milk and their assessment were carried out using high-performance liquid chromatography equipped with a diode array detector. Furthermore, effects of various parameters on the extraction efficiency such as volume of precipitating agent and vortexing time, type and quantity of extracting solvent, effects of salt addition and temperature of aqueous solution were optimized.

Analysis of milk samples showed presence of amoxicillin in eight milk samples, all of which included values greater the permissible limits. No other antibiotics were detected in the milk samples. The suggested method included high efficiencies in extracting the target antibiotic residues from milk samples. Therefore, repeatability of the method based on relative standard deviation proportion included 3–4.5. Under optimal conditions, extraction recoveries were in range of 78–87% and the limits of detection and quantification less than 1.1 and 3.7 ng/ml were reported, respectively.

The developed method is appropriate for the determination of selected analytics in cow milk and its detection limit is lower than the maximum residue limits for the residues of these antibiotics.