In this study, a novel technique was developed and validated for the simultaneous estimation of Remogliflozin (REMO) and Metformin (MET) in bulk and formulation. The separation was achieved using ODS, C18 (250mm × 4.6mm, i.d.2.5µm) column with isocratic elution. A mixture of acetonitrile: 20 mM ammonium formate buffer (pH 3.5) at the ratio of (60:40) as mobile phase with a flow rate of 1.0ml min-1 was used for the separation of analytes. The separated effluents were monitored with diode array detector at 243nm. The results showed good linearity over a range of 2.5–25μg ml-1 and 12.5–125μg ml-1 for REMO and MET respectively. Furthermore, the average percentage assay of marketed formulations of REMO and MET was found to be 100.30% and 100.52%, respectively. REMO limit of detection (LOD) and limit of quantitation (LOQ) were 0.42 and 1.28μg ml-1 while LOD and LOQ for MET were 1.97 and 5.96μg ml-1 respectively. The proposed method was simple, rapid and precise for the study of new formulations which has been approved for the cure of diabetes mellitus and applicable for the analysis of both drugs in pharmaceutical industries effectively.

In this work, a novel method for the simultaneous estimate of Montelukast (MONT) and Ebastine (EBAS) in bulk drugs was developed using Central Composite Design (CCD).A novel reverse-phase high-performance liquid chromatography (RP-HPLC) approach for the simultaneous measurement of Montelukast and Ebastine in bulk and formulation is what this work aims to create and test.A novel reverse-phase high-performance liquid chromatography (RP-HPLC) approach for the simultaneous measurement of Montelukast and Ebastine in bulk and formulation is what this work aims to create and test.With isocratic elution at a flow rate of 0.8 ml min-1 and a diode array detector operating at 241 nm, the separation was accomplished using methanol and 0.02M ammonium acetate buffer (pH 5.5 adjusted with diluted acetic acid) in the ratio of 80:20 v/v. Methanol composition (percent) (A), pH (B), and flow rate (C) were three independent variables that were examined.This method showed good linearity over a range of 2.5–25 μg/ml for both drugs. MONT limit of detection (LOD) and limit of quantitation (LOQ) were 0.298 and 0.905μg/ml while LOD and LOQ for EBAS were 0.594 and 1.80For both drugs, this technique demonstrated high linearity throughout a range of 2.5–25 μg/ml. Limits of detection (LOD) and quantitation (LOQ) for MONT were 0.298 and 0.905 μg/ml and 0.594 and 1.80 μg/ml for EBAS, respectively.The statistical data analysis determined that the Methanol composition and pH were the two independent variables that were most significant, with P 0.001. The suggested technique worked well and was optimised for the CCD model-based simultaneous estimate of both drugs.

Chemical usage has grown quickly in many industries across the globe, posing a threat to human health. In terms of sustainability and process efficiency, enzymes as industrial biocatalysts provide benefits over out-of-date chemical processes. This item covers the enzyme, including its many forms, as well as its uses in the detergent industry. Enzymes may assist decrease the amount of chemicals needed in conventional detergents, reducing their environmental effect since they're recyclable, non-toxic, and leave no hazardous residues behind. Additional enzymes, in addition to lipases, are widely utilized in household cleaning goods, laundry, agriculture, medicine, and other fields. The use of enzymes as detergents, particularly lipases, is discussed in this article. Lipases are a fat-based enzyme used in detergents to remove stains such as salad oils, fried fat, butter, fat-based items, lipstick soup, biological sebum, and soup. Lipases have a wide range of specificity. The primary benefit of using enzymatic detergent is that it is both cost efficient and environmentally friendly. Although biological implementations for industrial purposes, as well as certain advancements for increasing the stability and performance of detergent lipases enzyme, have previously been completed, there are still important opportunities for future study.

In this work, we present a novel method for determining the prescribed dosages of Dapagliflozin (DAPA), Vildagliptin (VIL), and Metformin (MET) all at once. The goal of this research is to create and test a new RP-HPLC technique that can simultaneously measure DAPA, VIL, and MET in formulation and bulk materials.The goal of this research is to create and test a new RP-HPLC technique that can simultaneously measure DAPA, VIL, and MET in formulation and bulk materials. An isocratic elution method was used with a flow rate of 1.0 ml min-1 and a diode array detector operating at 261nm to perform the chromatographic separation on a kromasil-C18 column(4.5 x 250mm; 5µm). Using orthophosphoric acid to get the pH down to 3.5, the mobile phase consisted of a combination of 0.05 mmol potassium dihydrogen phosphate buffer and acetonitrile in an 80:20 v/v ratio. With concentrations ranging from 0.1-1.0µg/ml and 2-25µg/ml, as well as DAPA, VIL, and MET values from 10 to 120µg/ml, the calibration curve displayed linearity. The research found that DAPA had a limit of detection and quantification of 0.0122µg/ml while VIL had a limit of 0.0323µg/ml. The upper limits for MET were 0.232µg/ml and 0.635µg/ml, while the lower limits were 1.124µg/ml and 3.124µg/ml, respectively. We have developed and validated a new reversed-phase high-performance liquid chromatography (RP-HPLC) method for the quantitative determination of vildagliptin and metformin. This method is very sensitive, easy to use, and stable. The suggested technique could be used to routinely measure DAPA, VIL, and MET.

Recent advances in artificial intelligence (AI) for the diagnosis, treatment, and management of neurological disorders are revolutionizing the world of neurology. This review discusses the current state of AI in neurology in terms of enhanced diagnostic accuracy and disease monitoring, as well as new personalized strategies for diagnosis and treatment. AI-driven models analyze vast amounts of healthcare data, including genetic information and brain scans, to identify patterns and biomarkers that may be overlooked using traditional methods. Machine learning and deep learning algorithms have shown promise in predicting the onset and progression of conditions, such as epilepsy, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease. AI-based neuroimaging analysis also enables more precise characterization of brain structure, facilitating early detection and intervention. Furthermore, AI-powered tools improve patient care by enabling remote monitoring and supporting rehabilitation, thereby enhancing the quality of life of individuals with chronic neurological conditions. Despite these advancements, challenges remain, including concerns about algorithmic bias, data security, and the need for rigorous clinical validation. This paper provides a comprehensive overview of AI-driven innovations in neurology, addressing both their practical implications and ethical considerations while highlighting future research opportunities. The ultimate goal is to illustrate AI’s transformative impact on neurology, contributing to better patient outcomes and a deeper understanding of complex neurological disorders.

The pharmaceutical business has a significant challenge in the worldwide need to adapt processes to green analytical chemistry (GAC) standards.One of the most common procedures employed at different points in the pharmaceutical manufacturing process is high-performance liquidchromatography (HPLC), which results in massive amounts of organic hazardous waste. Therefore, it is important to incorporate GAC conceptsinto pharmaceutical research and analysis. Although there has been a rise in recent years in the number of papers detailing green chromatographytechniques, they have not yet been widely adopted by the pharmaceutical industry. Reasons for this include analysts’ doubts about whetheror not they have met the method’s requirements, a lack of time to make the necessary adaptations to their standard HPLC instruments, anda lack of relevant experience. This chapter provides a high-level review of green strategies for liquid chromatography (LC) that can be simplyapplied to common instruments for creating environmentally friendly HPLC approaches in pharmaceutical analysis. The goal is to motivatethe analytical community within the pharmaceutical sector business to not only develop new green methods but also to transition the wellestablishedtraditional HPLC methods into environmentally green sustainable alternatives.