Rapid and sensitive method for simultaneous determination of citalopram with antihistamines by liquid chromatography.

A highly sensitive liquid chromatographic method with UV detection has been developed for simultaneous determination of citalopram, levocetirizine and loratadine in bulk drug, pharmaceutical formulation and human serum at 230nm employing 80:20 v/v methanol-water as mobile phase with pH3.5, adjusting flow rate of 1.0mL.min-1. Separation was achieved on Shimadzu Shim-pack CLC-ODS (M) 25M column within the linear range of 0.4-12.5, 0.8-25 and 0.8-25µg.mL-1 with R2 >0.998 and detection limit 7.75, 3.35 and 10.26ng.mL-1respectively. ICH guidelines were followed for validation showing 0.22-1.76, 0.06-1.83 and 0.22-2.11% RSD. The recovery of analytes in tablets and serum was found to be in acceptable range. The method was fruitfully employed for the determination of studied analyte in pharmaceutical formulation and human serum.

Liquid chromatographic method for simultaneous determination of alprazolam with NSAIDs in bulk drug, pharmaceutical formulation and human serum.

High performance liquid chromatography with UV/vis detection was optimized and validated for simultaneous quantification of alprazolam with celecoxib and diclofenac sodium in pharmaceutical formulation and human serum. Chromatographic separation was achieved at detection wavelength of 230 nm on Shimadzu Shim-pack CLC-ODS (M) 25M column employing 80:20 (v/v) methanol: water (pH 3.5) as mobile phase with elution rate 1.0mL min-1. Analytes were quantified in the ranges 0.2-15, 0.3-20 and 0.6-40 µg mL-1 with detection limits 19.76, 17.29 and 11.83ng mL-1 respectively. Recoveries were in the range 98.15-101.15, 99.24-99.90 and 98.87-101.19% in pharmaceutical formulation and 98.05-101.01, 98.72-99.49 and 98.25-99.47% in human serum respectively and precision ranged from 0.19-1.84%. The analytes were successfully detected without any observable interference commonly present in pharmaceutical formulation and human serum demonstrating applicability of method.

Skin Cancer Detection: A Review Using Deep Learning Techniques.

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.