16 research outputs found
DEVELOPMENT AND OPTIMIZATION OF NANOCOCHLEATE-BASED DIOSMIN CARRIERS FOR CANCER TREATMENT
No full textObjective: This study aimed to develop and optimize diosmin-loaded nanocochleates as a potential targeted delivery system for cancer therapy.
Methods: A three-factor, three-level Box–Behnken design was employed to evaluate the effects of phospholipid choline (Factor A), cholesterol (Factor B), and stirring speed (Factor C) on particle size, entrapment efficiency, and polydispersity index (PDI). Diagnostic plots confirmed the model\u27s robustness, with normal distribution of residuals and strong correlation between predicted and actual values.
Results: Interaction and 3D surface plots revealed that higher phospholipid and cholesterol concentrations increased particle size and entrapment efficiency, while optimal stirring speed improved uniformity. FTIR spectroscopy confirmed diosmin encapsulation by showing shifts in O–H and C=O stretching peaks, suggesting hydrogen bonding and lipid interactions. Differential Scanning Calorimetry (DSC) further supported the drug\u27s successful encapsulation by revealing the disappearance of diosmin’s endothermic peak, indicating conversion from crystalline to amorphous form. In vitro drug release studies showed sustained release of diosmin from the nanocochleates compared to the pure drug, highlighting enhanced solubility and prolonged availability. Cell cycle analysis using flow cytometry demonstrated that the optimized formulation induced significant cell cycle arrest in MCF-7 breast cancer cells, confirming its potential antiproliferative activity.
Conclusion: These findings validate the nanocochleate system as a promising platform for targeted diosmin delivery, offering improved encapsulation efficiency, stability, sustained release, and therapeutic efficacy. The optimized formulation achieved a desirable balance among key parameters, supporting its application in cancer nanomedicine
A modified mayfly-SVM approach for early detection of type 2 diabetes mellitus
Get PDFDiabetes mellitus is a chronic disease that affects many people in the world badly. Early diagnosis of this disease is of paramount importance as physicians and patients can work towards prevention and mitigation of future complications. Hence, there is a necessity to develop a system that diagnoses type 2 diabetes mellitus (T2DM) at an early stage. Recently, large number of studies have emerged with prediction models to diagnose T2DM. Most importantly, published literature lacks the availability of multi-class studies. Therefore, the primary objective of the study is development of multi-class predictive model by taking advantage of routinely available clinical data in diagnosing T2DM using machine learning algorithms. In this work, modified mayfly-support vector machine is implemented to notice the prediabetic stage accurately. To assess the effectiveness of proposed model, a comparative study was undertaken and was contrasted with T2DM prediction models developed by other researchers from last five years. Proposed model was validated over data collected from local hospitals and the benchmark PIMA dataset available on UCI repository. The study reveals that modified Mayfly-SVM has a considerable edge over metaheuristic optimization algorithms in local as well as global searching capabilities and has attained maximum test accuracy of 94.5% over PIMA
IMPROVEMENT OF MICROMERITIC, COMPRESSIBILITY AND SOLUBILITY CHARACTERISTICS OF LINEZOLID BY CRYSTALLO-CO-AGGLOMERATION TECHNIQUE
Get PDFObjective: The purpose of current study was to improve physicochemical properties such as micrometric, compressibility and solubility of linezolid (LNZ) by preparing crystallo-co-agglomerates (CCA) in the presence of polymer for the enhancement of overall physicochemical performance.Methods: The process of agglomeration involves the use of dichloromethane (DCM) as a good solvent and chloroform as bridging liquid were used to prepare agglomerates. Agglomerates were characterised in the solid state using several techniques such as Scanning electronic microscopy(SEM), Fourier transformation infrared spectroscopy (FTIR), X-ray powder diffraction analysis (XRPD) The agglomerates obtained were evaluated for micrometric, mechanical, deformation, compressibility and drug release properties.Results: It was found that micrometric properties and dissolution characteristics of agglomerates were significantly improved than that of pure linezolid. Solubility was found to be increased than pure linezolid. The solubility of crystallo co-agglomerates was found an increase in 5 fold 3 fold and 3.7 fold for PVPK30 (0.5%), PVPK30 (0.25%) and PVPK30 (0.75%) respectively. The angle of repose for all batches was found between 22 ° to 30 °Carrs index was between 12.27±0.6 to 18.73±0.4 and Hausners ratio Near to 1, indicated good flow ability of agglomerates. The time required for drug release over a period of 60 min, is as LA1>LA2>LA3. LA3 shows fast drug release than LA1 and LA2, due to solubilization of drug due to more concentration of PVPK30 and less concentration of talc.Conclusion: Based on the above results, it was revealed that CCA of linezolid prepared with DCM and HPMC (Hydroxypropyl methyl cellulose)/PEG (Polyethylene glycol)/PVP (Polyvinylpyrrolidone) K30 exhibited improved micrometric properties, compressibility and in addition to improving solubility and dissolution rate.</jats:p
An efficient stacking based NSGA-II approach for predicting type 2 diabetes
Get PDFDiabetes has been acknowledged as a well-known risk factor for renal and cardiovascular disorders, cardiac stroke and leads to a lot of morbidity in the society. Reducing the disease prevalence in the community will provide substantial benefits to the community and lessen the burden on the public health care system. So far, to detect the disease innumerable data mining approaches have been used. These days, incorporation of machine learning is conducive for the construction of a faster, accurate and reliable model. Several methods based on ensemble classifiers are being used by researchers for the prediction of diabetes. The proposed framework of prediction of diabetes mellitus employs an approach called stacking based ensemble using non-dominated sorting genetic algorithm (NSGA-II) scheme. The primary objective of the work is to develop a more accurate prediction model that reduces the lead time i.e., the time between the onset of diabetes and clinical diagnosis. Proposed NSGA-II stacking approach has been compared with Boosting, Bagging, Random Forest and Random Subspace method. The performance of Stacking approach has eclipsed the other conventional ensemble methods. It has been noted that k-nearest neighbors (KNN) gives a better performance over decision tree as a stacking combiner
Engineered atenolol-glycoconjugates to target H9c2 cardiomyocyte cell lines
No full textBackground: One of the most important fields of biomedical engineering study nowadays is targeted drug delivery to specific cells. A drug's therapeutic efficacy can be improved and optimised by tightly targeting it to a pathophysiologically essential tissue architecture. The goal of this research is to develop saccharide conjugates for the targeted delivery of Atenolol, a -blocker. Methods: Galactose (monosaccharide), pectin (polysaccharide), and chitosan were chosen as the saccharides (polysaccharide). By grafting Atenolol with the modified saccharides, the conjugates were created. Spectroscopic and thermal studies were used to describe the chemically changed saccharides conjugates. H9c2 cell lines were used to conduct drug release research and cellular uptake studies. To investigate cytotoxicity, a brine shrimp lethality test was done. Results: The outcomes exhibit that Atenolol-modified saccharide conjugates can productively convey the medication to the target. Conclusion: It can be inferred that the improvement of saccharide-drug conjugates can be a compelling methodology for targeting cardiovascular medication
Formulation and evaluation of prazosin hydrochloride loaded solid lipid nanoparticles: PRAZOSIN HYDROCHLORIDE LOADED SOLID LIPID NANOPARTICLES
No full textSolid Lipid Nanoparticles (SLN) are rapidly developing field of nanotechnology with several potential application in drug delivery and research. Drugs having low aqueous solubility not only give low oral bioavailability but provide high inter-and intra subject variability. The purpose of the present study was to investigate the bioavailability enhancement of Prazosin Hydrochloride drug by formulating solid lipid nanoparticle. Prazosin Hydrochloride Drug is an antihypertensive drug with limited bioavailability so that solid lipid nanoparticle (SLN) is one of the approaches to improve bioavailability. SLN were prepared using glyceryl monostearate by hot homogenization followed by Solvent emulsification-ultrasonication. Prazosin Hydrochloride loaded SLN were characterized and optimized by parameters like particle size, zeta potential, XRD, DSC. Proposing Hydrochloride loaded SLN having the particle size 263.8±1.88 and entrapment efficiency 89.29±0.65% shows better bioavailability and optimum stability in studies. The SLN studies prepared using glyceryl mono stearate as a lipid and Polaxamer 407 as a polymer leads to improve bioavailability of the drug.
Keywords: Prazosin Hydrochloride, Solid Lipid Nanoparticles, Entrapment efficiency, DS
